From 1eb1ac0f416abfdf66d15b18b375e8d12beabcb8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 26 Aug 2025 15:38:46 +0200 Subject: [PATCH 001/124] chore(ui-deps): bump @testing-library/jest-dom from 6.6.3 to 6.8.0 in /llama_stack/ui (#3243) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [@testing-library/jest-dom](https://github.com/testing-library/jest-dom) from 6.6.3 to 6.8.0.
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v6.8.0

6.8.0 (2025-08-20)

Features

v6.7.0

6.7.0 (2025-08-13)

Features

v6.6.4

6.6.4 (2025-07-26)

Performance Improvements

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 32 +++++--------------------------- llama_stack/ui/package.json | 2 +- 2 files changed, 6 insertions(+), 28 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 58888e586..98a1e4fe5 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -36,7 +36,7 @@ "@eslint/eslintrc": "^3", "@tailwindcss/postcss": "^4", "@testing-library/dom": "^10.4.1", - "@testing-library/jest-dom": "^6.6.3", + "@testing-library/jest-dom": "^6.8.0", "@testing-library/react": "^16.3.0", "@types/jest": "^29.5.14", "@types/node": "^20", @@ -3597,18 +3597,17 @@ } }, "node_modules/@testing-library/jest-dom": { - "version": "6.6.3", - "resolved": "https://registry.npmjs.org/@testing-library/jest-dom/-/jest-dom-6.6.3.tgz", - "integrity": "sha512-IteBhl4XqYNkM54f4ejhLRJiZNqcSCoXUOG2CPK7qbD322KjQozM4kHQOfkG2oln9b9HTYqs+Sae8vBATubxxA==", + "version": "6.8.0", + "resolved": "https://registry.npmjs.org/@testing-library/jest-dom/-/jest-dom-6.8.0.tgz", + "integrity": "sha512-WgXcWzVM6idy5JaftTVC8Vs83NKRmGJz4Hqs4oyOuO2J4r/y79vvKZsb+CaGyCSEbUPI6OsewfPd0G1A0/TUZQ==", "dev": true, "license": "MIT", "dependencies": { "@adobe/css-tools": "^4.4.0", "aria-query": "^5.0.0", - "chalk": "^3.0.0", "css.escape": "^1.5.1", "dom-accessibility-api": "^0.6.3", - "lodash": "^4.17.21", + "picocolors": "^1.1.1", "redent": "^3.0.0" }, "engines": { @@ -3617,20 +3616,6 @@ "yarn": ">=1" } }, - "node_modules/@testing-library/jest-dom/node_modules/chalk": { - "version": "3.0.0", - "resolved": "https://registry.npmjs.org/chalk/-/chalk-3.0.0.tgz", - "integrity": "sha512-4D3B6Wf41KOYRFdszmDqMCGq5VV/uMAB273JILmO+3jAlh8X4qDtdtgCR3fxtbLEMzSx22QdhnDcJvu2u1fVwg==", - "dev": true, - "license": "MIT", - "dependencies": { - "ansi-styles": "^4.1.0", - "supports-color": "^7.1.0" - }, - "engines": { - "node": ">=8" - } - }, "node_modules/@testing-library/jest-dom/node_modules/dom-accessibility-api": { "version": "0.6.3", "resolved": "https://registry.npmjs.org/dom-accessibility-api/-/dom-accessibility-api-0.6.3.tgz", @@ -10066,13 +10051,6 @@ "url": "https://github.com/sponsors/sindresorhus" } }, - "node_modules/lodash": { - "version": "4.17.21", - "resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.21.tgz", - "integrity": "sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==", - "dev": true, - "license": "MIT" - }, "node_modules/lodash.merge": { "version": "4.6.2", "resolved": "https://registry.npmjs.org/lodash.merge/-/lodash.merge-4.6.2.tgz", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 4e29e8a5c..7a17d93dd 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -41,7 +41,7 @@ "@eslint/eslintrc": "^3", "@tailwindcss/postcss": "^4", "@testing-library/dom": "^10.4.1", - "@testing-library/jest-dom": "^6.6.3", + "@testing-library/jest-dom": "^6.8.0", "@testing-library/react": "^16.3.0", "@types/jest": "^29.5.14", "@types/node": "^20", From 7ca82338890e3000659d0bd177339d8d3b822bf3 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Tue, 26 Aug 2025 17:17:00 +0100 Subject: [PATCH 002/124] feat(testing): remove SQLite dependency from inference recorder (#3254) Recording files use a predictable naming format, making the SQLite index redundant. The binary SQLite file was causing frequent git conflicts. Simplify by calculating file paths directly from request hashes. Signed-off-by: Derek Higgins --- llama_stack/testing/inference_recorder.py | 43 +----------------- tests/integration/recordings/index.sqlite | Bin 57344 -> 0 bytes .../distribution/test_inference_recordings.py | 16 +------ 3 files changed, 2 insertions(+), 57 deletions(-) delete mode 100644 tests/integration/recordings/index.sqlite diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 4a6958399..8fa5f5f2e 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -9,7 +9,6 @@ from __future__ import annotations # for forward references import hashlib import json import os -import sqlite3 from collections.abc import Generator from contextlib import contextmanager from enum import StrEnum @@ -125,28 +124,13 @@ class ResponseStorage: def __init__(self, test_dir: Path): self.test_dir = test_dir self.responses_dir = self.test_dir / "responses" - self.db_path = self.test_dir / "index.sqlite" self._ensure_directories() - self._init_database() def _ensure_directories(self): self.test_dir.mkdir(parents=True, exist_ok=True) self.responses_dir.mkdir(exist_ok=True) - def _init_database(self): - with sqlite3.connect(self.db_path) as conn: - conn.execute(""" - CREATE TABLE IF NOT EXISTS recordings ( - request_hash TEXT PRIMARY KEY, - response_file TEXT, - endpoint TEXT, - model TEXT, - timestamp TEXT, - is_streaming BOOLEAN - ) - """) - def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]): """Store a request/response pair.""" # Generate unique response filename @@ -169,34 +153,9 @@ class ResponseStorage: f.write("\n") f.flush() - # Update SQLite index - with sqlite3.connect(self.db_path) as conn: - conn.execute( - """ - INSERT OR REPLACE INTO recordings - (request_hash, response_file, endpoint, model, timestamp, is_streaming) - VALUES (?, ?, ?, ?, datetime('now'), ?) - """, - ( - request_hash, - response_file, - request.get("endpoint", ""), - request.get("model", ""), - response.get("is_streaming", False), - ), - ) - def find_recording(self, request_hash: str) -> dict[str, Any] | None: """Find a recorded response by request hash.""" - with sqlite3.connect(self.db_path) as conn: - result = conn.execute( - "SELECT response_file FROM recordings WHERE request_hash = ?", (request_hash,) - ).fetchone() - - if not result: - return None - - response_file = result[0] + response_file = f"{request_hash[:12]}.json" response_path = self.responses_dir / response_file if not response_path.exists(): diff --git a/tests/integration/recordings/index.sqlite b/tests/integration/recordings/index.sqlite deleted file mode 100644 index 0c88416f1e7c84196c1dd80877c3ff4bcd8322da..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 57344 zcmeI53y@_;dEd|6duQg(8!h4?z%UZ20+m?p>C>klXG#S~us8$(LMF05ZrSm6#&3jhzIUr}O&t 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zH$8Ivhms@iU6WJyUgmV*pbvgh#iki;DGSHXUJJZVMZ-`i;IWrHaB@+0pK9rxo7g*%JE?A3M2u;#9f0zd0_TNpLhp zc0X`HKr_@Wk}baM+#4o&tTeDeo3zpHc?t=T;3nwPn-mC ro;MLEv}=q%zLz*MJ9Tfnqb@o=@wSt Date: Tue, 26 Aug 2025 11:34:08 -0700 Subject: [PATCH 003/124] feat: Add example notebook for Langchain + LLAMAStack integration (#3228) # What does this PR do? Add LLAMAStack + Langchain integration example notebook ## Test Plan Ran in Jupyter notebook, works end to end. (Used Claude mainly for documentation and coding/debugging help) --- .../langchain/Llama_Stack_LangChain.ipynb | 946 ++++++++++++++++++ 1 file changed, 946 insertions(+) create mode 100644 docs/notebooks/langchain/Llama_Stack_LangChain.ipynb diff --git a/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb b/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb new file mode 100644 index 000000000..ed918ff50 --- /dev/null +++ b/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb @@ -0,0 +1,946 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ztegmwm4sp", + "metadata": {}, + "source": [ + "## LlamaStack + LangChain Integration Tutorial\n", + "\n", + "This notebook demonstrates how to integrate **LlamaStack** with **LangChain** to build a complete RAG (Retrieval-Augmented Generation) system.\n", + "\n", + "### Overview\n", + "\n", + "- **LlamaStack**: Provides the infrastructure for running LLMs and vector databases\n", + "- **LangChain**: Provides the framework for chaining operations and prompt templates\n", + "- **Integration**: Uses LlamaStack's OpenAI-compatible API with LangChain\n", + "\n", + "### What You'll See\n", + "\n", + "1. Setting up LlamaStack server with Together AI provider\n", + "2. Creating and managing vector databases\n", + "3. Building RAG chains with LangChain + LLAMAStack\n", + "4. Querying the chain for relevant information\n", + "\n", + "### Prerequisites\n", + "\n", + "- Together AI API key\n", + "\n", + "---\n", + "\n", + "### 1. Installation and Setup" + ] + }, + { + "cell_type": "markdown", + "id": "2ktr5ls2cas", + "metadata": {}, + "source": [ + "#### Install Required Dependencies\n", + "\n", + "First, we install all the necessary packages for LangChain and FastAPI integration." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5b6a6a17-b931-4bea-8273-0d6e5563637a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: fastapi in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.115.14)\n", + "Requirement already satisfied: uvicorn in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.29.0)\n", + "Requirement already satisfied: langchain>=0.2 in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.3.27)\n", + "Requirement already satisfied: langchain-openai in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.3.30)\n", + "Requirement already satisfied: 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install fastapi uvicorn \"langchain>=0.2\" langchain-openai \\\n", + " langchain-community langchain-text-splitters \\\n", + " faiss-cpu" + ] + }, + { + "cell_type": "markdown", + "id": "wmt9jvqzh7n", + "metadata": {}, + "source": [ + "### 2. LlamaStack Server Setup\n", + "\n", + "#### Build and Start LlamaStack Server\n", + "\n", + "This section sets up the LlamaStack server with:\n", + "- **Together AI** as the inference provider\n", + "- **FAISS** as the vector database\n", + "- **Sentence Transformers** for embeddings\n", + "\n", + "The server runs on `localhost:8321` and provides OpenAI-compatible endpoints." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd2dacf3-ec8b-4cc7-8ff4-b5b6ea4a6e9e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uv in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.7.20)\n", + "Environment '/Users/swapna942/llama-stack/.venv' already exists, re-using it.\n", + "Virtual environment /Users/swapna942/llama-stack/.venv is already active\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 86ms\u001b[0m\u001b[0m\n", + "Installing pip dependencies\n", + "\u001b[2K\u001b[2mResolved \u001b[1m178 packages\u001b[0m \u001b[2min 462ms\u001b[0m\u001b[0m \u001b[0m\n", + "\u001b[2mUninstalled \u001b[1m2 packages\u001b[0m \u001b[2min 28ms\u001b[0m\u001b[0m\n", + "\u001b[2K\u001b[2mInstalled \u001b[1m2 packages\u001b[0m \u001b[2min 5ms\u001b[0m\u001b[0m \u001b[0m\n", + " \u001b[31m-\u001b[39m \u001b[1mprotobuf\u001b[0m\u001b[2m==5.29.5\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mprotobuf\u001b[0m\u001b[2m==5.29.4\u001b[0m\n", + " \u001b[31m-\u001b[39m \u001b[1mruff\u001b[0m\u001b[2m==0.12.5\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mruff\u001b[0m\u001b[2m==0.9.10\u001b[0m\n", + "Installing special provider module: torch torchvision --index-url https://download.pytorch.org/whl/cpu\n", + "\u001b[2mAudited \u001b[1m2 packages\u001b[0m \u001b[2min 5ms\u001b[0m\u001b[0m\n", + "Installing special provider module: sentence-transformers --no-deps\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 9ms\u001b[0m\u001b[0m\n", + "\u001b[32mBuild Successful!\u001b[0m\n", + "\u001b[34mYou can find the newly-built distribution here: /Users/swapna942/.llama/distributions/starter/starter-run.yaml\u001b[0m\n", + "\u001b[32mYou can run the new Llama Stack distro via: \u001b[34mllama stack run /Users/swapna942/.llama/distributions/starter/starter-run.yaml --image-type venv\u001b[0m\u001b[0m\n" + ] + } + ], + "source": [ + "import os\n", + "import subprocess\n", + "import time\n", + "\n", + "!pip install uv\n", + "\n", + "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", + " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", + "\n", + "# this command installs all the dependencies needed for the llama stack server with the together inference provider\n", + "!uv run --with llama-stack llama stack build --distro starter --image-type venv\n", + "\n", + "\n", + "def run_llama_stack_server_background():\n", + " log_file = open(\"llama_stack_server.log\", \"w\")\n", + " process = subprocess.Popen(\n", + " \"uv run --with llama-stack llama stack run /Users/swapna942/.llama/distributions/starter/starter-run.yaml --image-type venv\",\n", + " shell=True,\n", + " stdout=log_file,\n", + " stderr=log_file,\n", + " text=True,\n", + " )\n", + "\n", + " print(f\"Starting Llama Stack server with PID: {process.pid}\")\n", + " return process\n", + "\n", + "\n", + "def wait_for_server_to_start():\n", + " import requests\n", + " from requests.exceptions import ConnectionError\n", + "\n", + " url = \"http://0.0.0.0:8321/v1/health\"\n", + " max_retries = 30\n", + " retry_interval = 1\n", + "\n", + " print(\"Waiting for server to start\", end=\"\")\n", + " for _ in range(max_retries):\n", + " try:\n", + " response = requests.get(url)\n", + " if response.status_code == 200:\n", + " print(\"\\nServer is ready!\")\n", + " return True\n", + " except ConnectionError:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(retry_interval)\n", + "\n", + " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", + " return False\n", + "\n", + "\n", + "# use this helper if needed to kill the server\n", + "def kill_llama_stack_server():\n", + " # Kill any existing llama stack server processes\n", + " os.system(\"ps aux | grep -v grep | grep llama_stack.core.server.server | awk '{print $2}' | xargs kill -9\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "28bd8dbd-4576-4e76-813f-21ab94db44a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting Llama Stack server with PID: 99016\n", + "Waiting for server to start....\n", + "Server is ready!\n" + ] + } + ], + "source": [ + "server_process = run_llama_stack_server_background()\n", + "assert wait_for_server_to_start()" + ] + }, + { + "cell_type": "markdown", + "id": "gr9cdcg4r7n", + "metadata": {}, + "source": [ + "#### Install LlamaStack Client\n", + "\n", + "Install the client library to interact with the LlamaStack server." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "487d2dbc-d071-400e-b4f0-dcee58f8dc95", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: llama_stack_client in /opt/homebrew/Cellar/jupyterlab/4.4.5/libexec/lib/python3.13/site-packages (0.2.17)\n", + "Requirement already satisfied: anyio<5,>=3.5.0 in /opt/homebrew/Cellar/jupyterlab/4.4.5/libexec/lib/python3.13/site-packages (from llama_stack_client) (4.9.0)\n", + "Requirement already satisfied: click in /opt/homebrew/Cellar/jupyterlab/4.4.5/libexec/lib/python3.13/site-packages (from llama_stack_client) (8.2.1)\n", + "Requirement already satisfied: distro<2,>=1.7.0 in /opt/homebrew/Cellar/jupyterlab/4.4.5/libexec/lib/python3.13/site-packages (from llama_stack_client) (1.9.0)\n", + "Requirement already satisfied: fire in /opt/homebrew/Cellar/jupyterlab/4.4.5/libexec/lib/python3.13/site-packages (from llama_stack_client) (0.7.0)\n", 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Initialize LlamaStack Client\n", + "\n", + "Create a client connection to the LlamaStack server with API keys for different providers:\n", + "\n", + "- **OpenAI API Key**: For OpenAI models\n", + "- **Gemini API Key**: For Google's Gemini models \n", + "- **Together API Key**: For Together AI models\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ab4eff97-4565-4c73-b1b3-0020a4c7e2a5", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_stack_client import LlamaStackClient\n", + "\n", + "client = LlamaStackClient(\n", + " base_url=\"http://0.0.0.0:8321\",\n", + " provider_data={\"openai_api_key\": \"****\", \"gemini_api_key\": \"****\", \"together_api_key\": \"****\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "vwhexjy1e8o", + "metadata": {}, + "source": [ + "#### Explore Available Models and Safety Features\n", + "\n", + "Check what models and safety shields are available through your LlamaStack instance." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "880443ef-ac3c-48b1-a80a-7dab5b25ac61", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/models \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/shields \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available models:\n", + "- all-minilm\n", + "- ollama/all-minilm:l6-v2\n", + "- ollama/llama-guard3:1b\n", + "- ollama/llama-guard3:8b\n", + "- ollama/llama3.2:3b-instruct-fp16\n", + "- ollama/nomic-embed-text\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-405b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-3b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-11b-vision-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-90b-vision-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p3-70b-instruct\n", + "- fireworks/accounts/fireworks/models/llama4-scout-instruct-basic\n", + "- fireworks/accounts/fireworks/models/llama4-maverick-instruct-basic\n", + "- fireworks/nomic-ai/nomic-embed-text-v1.5\n", + "- fireworks/accounts/fireworks/models/llama-guard-3-8b\n", + "- fireworks/accounts/fireworks/models/llama-guard-3-11b-vision\n", + "- together/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\n", + "- together/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\n", + "- together/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\n", + "- together/meta-llama/Llama-3.2-3B-Instruct-Turbo\n", + "- together/meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo\n", + "- together/meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo\n", + "- together/meta-llama/Llama-3.3-70B-Instruct-Turbo\n", + "- together/togethercomputer/m2-bert-80M-8k-retrieval\n", + "- together/togethercomputer/m2-bert-80M-32k-retrieval\n", + "- together/meta-llama/Llama-4-Scout-17B-16E-Instruct\n", + "- together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\n", + "- together/meta-llama/Llama-Guard-3-8B\n", + "- together/meta-llama/Llama-Guard-3-11B-Vision-Turbo\n", + "- bedrock/meta.llama3-1-8b-instruct-v1:0\n", + "- bedrock/meta.llama3-1-70b-instruct-v1:0\n", + "- bedrock/meta.llama3-1-405b-instruct-v1:0\n", + "- openai/gpt-3.5-turbo-0125\n", + "- openai/gpt-3.5-turbo\n", + "- openai/gpt-3.5-turbo-instruct\n", + "- openai/gpt-4\n", + "- openai/gpt-4-turbo\n", + "- openai/gpt-4o\n", + "- openai/gpt-4o-2024-08-06\n", + "- openai/gpt-4o-mini\n", + "- openai/gpt-4o-audio-preview\n", + "- openai/chatgpt-4o-latest\n", + "- openai/o1\n", + "- openai/o1-mini\n", + "- openai/o3-mini\n", + "- openai/o4-mini\n", + "- openai/text-embedding-3-small\n", + "- openai/text-embedding-3-large\n", + "- anthropic/claude-3-5-sonnet-latest\n", + "- anthropic/claude-3-7-sonnet-latest\n", + "- anthropic/claude-3-5-haiku-latest\n", + "- anthropic/voyage-3\n", + "- anthropic/voyage-3-lite\n", + "- anthropic/voyage-code-3\n", + "- gemini/gemini-1.5-flash\n", + "- gemini/gemini-1.5-pro\n", + "- gemini/gemini-2.0-flash\n", + "- gemini/gemini-2.0-flash-lite\n", + "- gemini/gemini-2.5-flash\n", + "- gemini/gemini-2.5-flash-lite\n", + "- gemini/gemini-2.5-pro\n", + "- gemini/text-embedding-004\n", + "- groq/llama3-8b-8192\n", + "- groq/llama-3.1-8b-instant\n", + "- groq/llama3-70b-8192\n", + "- groq/llama-3.3-70b-versatile\n", + "- groq/llama-3.2-3b-preview\n", + "- groq/meta-llama/llama-4-scout-17b-16e-instruct\n", + "- groq/meta-llama/llama-4-maverick-17b-128e-instruct\n", + "- sambanova/Meta-Llama-3.1-8B-Instruct\n", + "- sambanova/Meta-Llama-3.3-70B-Instruct\n", + "- sambanova/Llama-4-Maverick-17B-128E-Instruct\n", + "- sentence-transformers/all-MiniLM-L6-v2\n", + "----\n", + "Available shields (safety models):\n", + "code-scanner\n", + "llama-guard\n", + "----\n" + ] + } + ], + "source": [ + "print(\"Available models:\")\n", + "for m in client.models.list():\n", + " print(f\"- {m.identifier}\")\n", + "\n", + "print(\"----\")\n", + "print(\"Available shields (safety models):\")\n", + "for s in client.shields.list():\n", + " print(s.identifier)\n", + "print(\"----\")" + ] + }, + { + "cell_type": "markdown", + "id": "gojp7at31ht", + "metadata": {}, + "source": [ + "### 4. Vector Database Setup\n", + "\n", + "#### Register a Vector Database\n", + "\n", + "Create a FAISS vector database for storing document embeddings:\n", + "\n", + "- **Vector DB ID**: Unique identifier for the database\n", + "- **Provider**: FAISS (Facebook AI Similarity Search)\n", + "- **Embedding Model**: Sentence Transformers model for text embeddings\n", + "- **Dimensions**: 384-dimensional embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a16e2885-ae70-4fa6-9778-2433fa4dbfff", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-dbs \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/vector-dbs \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Registered new vector DB: VectorDBRegisterResponse(embedding_dimension=384, embedding_model='sentence-transformers/all-MiniLM-L6-v2', identifier='acme_docs', provider_id='faiss', type='vector_db', provider_resource_id='acme_docs_v2', owner=None, source='via_register_api', vector_db_name=None)\n", + "Existing vector DBs: [VectorDBListResponseItem(embedding_dimension=384, embedding_model='sentence-transformers/all-MiniLM-L6-v2', identifier='acme_docs', provider_id='faiss', type='vector_db', provider_resource_id='acme_docs_v2', vector_db_name=None)]\n" + ] + } + ], + "source": [ + "# Register a new clean vector database\n", + "vector_db = client.vector_dbs.register(\n", + " vector_db_id=\"acme_docs\", # Use a new unique name\n", + " provider_id=\"faiss\",\n", + " provider_vector_db_id=\"acme_docs_v2\",\n", + " embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n", + " embedding_dimension=384,\n", + ")\n", + "print(\"Registered new vector DB:\", vector_db)\n", + "\n", + "# List all registered vector databases\n", + "dbs = client.vector_dbs.list()\n", + "print(\"Existing vector DBs:\", dbs)" + ] + }, + { + "cell_type": "markdown", + "id": "pcgjqzfr3eo", + "metadata": {}, + "source": [ + "#### Prepare Sample Documents\n", + "\n", + "Create LLAMA Stack Chunks for FAISS vector store" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a0a6619-c9fb-4938-8ff3-f84304eed91e", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_stack_client.types.vector_io_insert_params import Chunk\n", + "\n", + "docs = [\n", + " (\"Acme ships globally in 3-5 business days.\", {\"title\": \"Shipping Policy\"}),\n", + " (\"Returns are accepted within 30 days of purchase.\", {\"title\": \"Returns Policy\"}),\n", + " (\"Support is available 24/7 via chat and email.\", {\"title\": \"Support\"}),\n", + "]\n", + "\n", + "# Convert to Chunk objects\n", + "chunks = []\n", + "for _, (content, metadata) in enumerate(docs):\n", + " # Transform metadata to required format with document_id from title\n", + " metadata = {\"document_id\": metadata[\"title\"]}\n", + " chunk = Chunk(\n", + " content=content, # Required[InterleavedContent]\n", + " metadata=metadata, # Required[Dict]\n", + " )\n", + " chunks.append(chunk)" + ] + }, + { + "cell_type": "markdown", + "id": "6bg3sm2ko5g", + "metadata": {}, + "source": [ + "#### Insert Documents into Vector Database\n", + "\n", + "Store the prepared documents in the FAISS vector database. This process:\n", + "1. Generates embeddings for each document\n", + "2. Stores embeddings with metadata\n", + "3. Enables semantic search capabilities" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0e8740d8-b809-44b9-915f-1e0200e3c3f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/insert \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Documents inserted: None\n" + ] + } + ], + "source": [ + "# Insert chunks into FAISS vector store\n", + "\n", + "response = client.vector_io.insert(vector_db_id=\"acme_docs\", chunks=chunks)\n", + "print(\"Documents inserted:\", response)" + ] + }, + { + "cell_type": "markdown", + "id": "9061tmi1zpq", + "metadata": {}, + "source": [ + "#### Test Vector Search\n", + "\n", + "Query the vector database to verify it's working correctly. This performs semantic search to find relevant documents based on the query." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4a5e010c-eeeb-4020-a957-74d6d1cba342", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "metadata : {'document_id': 'Shipping Policy'}\n", + "content : Acme ships globally in 3–5 business days.\n", + "metadata : {'document_id': 'Shipping Policy'}\n", + "content : Acme ships globally in 3–5 business days.\n", + "metadata : {'document_id': 'Returns Policy'}\n", + "content : Returns are accepted within 30 days of purchase.\n" + ] + } + ], + "source": [ + "# Query chunks from FAISS vector store\n", + "\n", + "query_chunk_response = client.vector_io.query(\n", + " vector_db_id=\"acme_docs\",\n", + " query=\"How long does Acme take to ship orders?\",\n", + ")\n", + "for chunk in query_chunk_response.chunks:\n", + " print(\"metadata\", \":\", chunk.metadata)\n", + " print(\"content\", \":\", chunk.content)" + ] + }, + { + "cell_type": "markdown", + "id": "usne6mbspms", + "metadata": {}, + "source": [ + "### 5. LangChain Integration\n", + "\n", + "#### Configure LangChain with LlamaStack\n", + "\n", + "Set up LangChain to use LlamaStack's OpenAI-compatible API:\n", + "\n", + "- **Base URL**: Points to LlamaStack's OpenAI endpoint\n", + "- **Headers**: Include Together AI API key for model access\n", + "- **Model**: Use Meta Llama 3.1 8B model via Together AI" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c378bd10-09c2-417c-bdfc-1e0a2dd19084", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# Point LangChain to Llamastack Server\n", + "os.environ[\"OPENAI_API_KEY\"] = \"dummy\"\n", + "os.environ[\"OPENAI_BASE_URL\"] = \"http://0.0.0.0:8321/v1/openai/v1\"\n", + "\n", + "# LLM from Llamastack together model\n", + "llm = ChatOpenAI(\n", + " model=\"together/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\",\n", + " default_headers={\"X-LlamaStack-Provider-Data\": '{\"together_api_key\": \"***\"}'},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5a4ddpcuk3l", + "metadata": {}, + "source": [ + "#### Test LLM Connection\n", + "\n", + "Verify that LangChain can successfully communicate with the LlamaStack server." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f88ffb5a-657b-4916-9375-c6ddc156c25e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "data": { + "text/plain": [ + "AIMessage(content=\"In the Andes, a gentle soul resides, \\nA llama's soft eyes, with kindness abide.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 50, 'total_tokens': 72, 'completion_tokens_details': None, 'prompt_tokens_details': None, 'cached_tokens': 0}, 'model_name': 'meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo', 'system_fingerprint': None, 'id': 'o86Jy3i-2j9zxn-972d7b27f8f22aaa', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--4797f8b9-a5f6-4730-aece-80c1fd88ac55-0', usage_metadata={'input_tokens': 50, 'output_tokens': 22, 'total_tokens': 72, 'input_token_details': {}, 'output_token_details': {}})" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test llm with simple message\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n", + "]\n", + "llm.invoke(messages)" + ] + }, + { + "cell_type": "markdown", + "id": "0xh0jg6a0l4a", + "metadata": {}, + "source": [ + "### 6. Building the RAG Chain\n", + "\n", + "#### Create a Complete RAG Pipeline\n", + "\n", + "Build a LangChain pipeline that combines:\n", + "\n", + "1. **Vector Search**: Query LlamaStack's vector database\n", + "2. **Context Assembly**: Format retrieved documents\n", + "3. **Prompt Template**: Structure the input for the LLM\n", + "4. **LLM Generation**: Generate answers using context\n", + "5. **Output Parsing**: Extract the final response\n", + "\n", + "**Chain Flow**: `Query → Vector Search → Context + Question → LLM → Response`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9684427d-dcc7-4544-9af5-8b110d014c42", + "metadata": {}, + "outputs": [], + "source": [ + "# LangChain for prompt template and chaining + LLAMA Stack Client Vector DB and LLM chat completion\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n", + "\n", + "\n", + "def join_docs(docs):\n", + " return \"\\n\\n\".join([f\"[{d.metadata.get('document_id')}] {d.content}\" for d in docs.chunks])\n", + "\n", + "\n", + "PROMPT = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\"system\", \"You are a helpful assistant. Use the following context to answer.\"),\n", + " (\"user\", \"Question: {question}\\n\\nContext:\\n{context}\"),\n", + " ]\n", + ")\n", + "\n", + "vector_step = RunnableLambda(\n", + " lambda x: client.vector_io.query(\n", + " vector_db_id=\"acme_docs\",\n", + " query=x,\n", + " )\n", + ")\n", + "\n", + "chain = (\n", + " {\"context\": vector_step | RunnableLambda(join_docs), \"question\": RunnablePassthrough()}\n", + " | PROMPT\n", + " | llm\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0onu6rhphlra", + "metadata": {}, + "source": [ + "### 7. Testing the RAG System\n", + "\n", + "#### Example 1: Shipping Query" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "03322188-9509-446a-a4a8-ce3bb83ec87c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "❓ How long does shipping take?\n", + "💡 According to the Shipping Policy, shipping from Acme takes 3-5 business days.\n" + ] + } + ], + "source": [ + "query = \"How long does shipping take?\"\n", + "response = chain.invoke(query)\n", + "print(\"❓\", query)\n", + "print(\"💡\", response)" + ] + }, + { + "cell_type": "markdown", + "id": "b7krhqj88ku", + "metadata": {}, + "source": [ + "#### Example 2: Returns Policy Query" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "61995550-bb0b-46a8-a5d0-023207475d60", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "❓ Can I return a product after 40 days?\n", + "💡 Based on the provided returns policy, it appears that returns are only accepted within 30 days of purchase. Since you're asking about returning a product after 40 days, it would not be within the specified 30-day return window.\n", + "\n", + "Unfortunately, it seems that you would not be eligible for a return in this case. However, I would recommend reaching out to the support team via chat or email to confirm their policy and see if there are any exceptions or alternative solutions available.\n" + ] + } + ], + "source": [ + "query = \"Can I return a product after 40 days?\"\n", + "response = chain.invoke(query)\n", + "print(\"❓\", query)\n", + "print(\"💡\", response)" + ] + }, + { + "cell_type": "markdown", + "id": "h4w24fadvjs", + "metadata": {}, + "source": [ + "---\n", + "We have successfully built a RAG system that combines:\n", + "\n", + "- **LlamaStack** for infrastructure (LLM serving + vector database)\n", + "- **LangChain** for orchestration (prompts + chains)\n", + "- **Together AI** for high-quality language models\n", + "\n", + "### Key Benefits\n", + "\n", + "1. **Unified Infrastructure**: Single server for LLMs and vector databases\n", + "2. **OpenAI Compatibility**: Easy integration with existing LangChain code\n", + "3. **Multi-Provider Support**: Switch between different LLM providers\n", + "4. **Production Ready**: Built-in safety shields and monitoring\n", + "\n", + "### Next Steps\n", + "\n", + "- Add more sophisticated document processing\n", + "- Implement conversation memory\n", + "- Add safety filtering and monitoring\n", + "- Scale to larger document collections\n", + "- Integrate with web frameworks like FastAPI or Streamlit\n", + "\n", + "---\n", + "\n", + "##### 🔧 Cleanup\n", + "\n", + "Don't forget to stop the LlamaStack server when you're done:\n", + "\n", + "```python\n", + "kill_llama_stack_server()\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 00bd9a61ed6d67c728dfe9cfcdf9b592ec1be7fb Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Tue, 26 Aug 2025 15:58:44 -0400 Subject: [PATCH 004/124] chore: Add example notebook for Langchain + LLAMAStack integration (#3228) (#3259) --- .../langchain/Llama_Stack_LangChain.ipynb | 946 ------------------ 1 file changed, 946 deletions(-) delete mode 100644 docs/notebooks/langchain/Llama_Stack_LangChain.ipynb diff --git a/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb b/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb deleted file mode 100644 index ed918ff50..000000000 --- a/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb +++ /dev/null @@ -1,946 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1ztegmwm4sp", - "metadata": {}, - "source": [ - "## LlamaStack + LangChain Integration Tutorial\n", - "\n", - "This notebook demonstrates how to integrate **LlamaStack** with **LangChain** to build a complete RAG (Retrieval-Augmented Generation) system.\n", - "\n", - "### Overview\n", - "\n", - "- **LlamaStack**: Provides the infrastructure for running LLMs and vector databases\n", - "- **LangChain**: Provides the framework for chaining operations and prompt templates\n", - "- **Integration**: Uses LlamaStack's OpenAI-compatible API with LangChain\n", - "\n", - "### What You'll See\n", - "\n", - "1. Setting up LlamaStack server with Together AI provider\n", - "2. Creating and managing vector databases\n", - "3. Building RAG chains with LangChain + LLAMAStack\n", - "4. Querying the chain for relevant information\n", - "\n", - "### Prerequisites\n", - "\n", - "- Together AI API key\n", - "\n", - "---\n", - "\n", - "### 1. Installation and Setup" - ] - }, - { - "cell_type": "markdown", - "id": "2ktr5ls2cas", - "metadata": {}, - "source": [ - "#### Install Required Dependencies\n", - "\n", - "First, we install all the necessary packages for LangChain and FastAPI integration." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5b6a6a17-b931-4bea-8273-0d6e5563637a", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: fastapi in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.115.14)\n", - "Requirement already satisfied: uvicorn in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.29.0)\n", - "Requirement already satisfied: langchain>=0.2 in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.3.27)\n", - "Requirement already satisfied: langchain-openai in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.3.30)\n", - "Requirement already satisfied: langchain-community in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.3.27)\n", - 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" langchain-community langchain-text-splitters \\\n", - " faiss-cpu" - ] - }, - { - "cell_type": "markdown", - "id": "wmt9jvqzh7n", - "metadata": {}, - "source": [ - "### 2. LlamaStack Server Setup\n", - "\n", - "#### Build and Start LlamaStack Server\n", - "\n", - "This section sets up the LlamaStack server with:\n", - "- **Together AI** as the inference provider\n", - "- **FAISS** as the vector database\n", - "- **Sentence Transformers** for embeddings\n", - "\n", - "The server runs on `localhost:8321` and provides OpenAI-compatible endpoints." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "dd2dacf3-ec8b-4cc7-8ff4-b5b6ea4a6e9e", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: uv in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.7.20)\n", - "Environment '/Users/swapna942/llama-stack/.venv' already exists, re-using it.\n", - "Virtual environment /Users/swapna942/llama-stack/.venv is already active\n", - "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 86ms\u001b[0m\u001b[0m\n", - "Installing pip dependencies\n", - "\u001b[2K\u001b[2mResolved \u001b[1m178 packages\u001b[0m \u001b[2min 462ms\u001b[0m\u001b[0m \u001b[0m\n", - "\u001b[2mUninstalled \u001b[1m2 packages\u001b[0m \u001b[2min 28ms\u001b[0m\u001b[0m\n", - "\u001b[2K\u001b[2mInstalled \u001b[1m2 packages\u001b[0m \u001b[2min 5ms\u001b[0m\u001b[0m \u001b[0m\n", - " \u001b[31m-\u001b[39m \u001b[1mprotobuf\u001b[0m\u001b[2m==5.29.5\u001b[0m\n", - " \u001b[32m+\u001b[39m \u001b[1mprotobuf\u001b[0m\u001b[2m==5.29.4\u001b[0m\n", - " \u001b[31m-\u001b[39m \u001b[1mruff\u001b[0m\u001b[2m==0.12.5\u001b[0m\n", - " \u001b[32m+\u001b[39m \u001b[1mruff\u001b[0m\u001b[2m==0.9.10\u001b[0m\n", - "Installing special provider module: torch torchvision --index-url https://download.pytorch.org/whl/cpu\n", - "\u001b[2mAudited \u001b[1m2 packages\u001b[0m \u001b[2min 5ms\u001b[0m\u001b[0m\n", - "Installing special provider module: sentence-transformers --no-deps\n", - "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 9ms\u001b[0m\u001b[0m\n", - "\u001b[32mBuild Successful!\u001b[0m\n", - "\u001b[34mYou can find the newly-built distribution here: /Users/swapna942/.llama/distributions/starter/starter-run.yaml\u001b[0m\n", - "\u001b[32mYou can run the new Llama Stack distro via: \u001b[34mllama stack run /Users/swapna942/.llama/distributions/starter/starter-run.yaml --image-type venv\u001b[0m\u001b[0m\n" - ] - } - ], - "source": [ - "import os\n", - "import subprocess\n", - "import time\n", - "\n", - "!pip install uv\n", - "\n", - "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", - " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", - "\n", - "# this command installs all the dependencies needed for the llama stack server with the together inference provider\n", - "!uv run --with llama-stack llama stack build --distro starter --image-type venv\n", - "\n", - "\n", - "def run_llama_stack_server_background():\n", - " log_file = open(\"llama_stack_server.log\", \"w\")\n", - " process = subprocess.Popen(\n", - " \"uv run --with llama-stack llama stack run /Users/swapna942/.llama/distributions/starter/starter-run.yaml --image-type venv\",\n", - " shell=True,\n", - " stdout=log_file,\n", - " stderr=log_file,\n", - " text=True,\n", - " )\n", - "\n", - " print(f\"Starting Llama Stack server with PID: {process.pid}\")\n", - " return process\n", - "\n", - "\n", - "def wait_for_server_to_start():\n", - " import requests\n", - " from requests.exceptions import ConnectionError\n", - "\n", - " url = \"http://0.0.0.0:8321/v1/health\"\n", - " max_retries = 30\n", - " retry_interval = 1\n", - "\n", - " print(\"Waiting for server to start\", end=\"\")\n", - " for _ in range(max_retries):\n", - " try:\n", - " response = requests.get(url)\n", - " if response.status_code == 200:\n", - " print(\"\\nServer is ready!\")\n", - " return True\n", - " except ConnectionError:\n", - " print(\".\", end=\"\", flush=True)\n", - " time.sleep(retry_interval)\n", - "\n", - " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", - " return False\n", - "\n", - "\n", - "# use this helper if needed to kill the server\n", - "def kill_llama_stack_server():\n", - " # Kill any existing llama stack server processes\n", - " os.system(\"ps aux | grep -v grep | grep llama_stack.core.server.server | awk '{print $2}' | xargs kill -9\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "28bd8dbd-4576-4e76-813f-21ab94db44a2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting Llama Stack server with PID: 99016\n", - "Waiting for server to start....\n", - "Server is ready!\n" - ] - } - ], - "source": [ - "server_process = run_llama_stack_server_background()\n", - "assert wait_for_server_to_start()" - ] - }, - { - "cell_type": "markdown", - "id": "gr9cdcg4r7n", - "metadata": {}, - "source": [ - "#### Install LlamaStack Client\n", - "\n", - "Install the client library to interact with the LlamaStack server." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "487d2dbc-d071-400e-b4f0-dcee58f8dc95", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - 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Initialize LlamaStack Client\n", - "\n", - "Create a client connection to the LlamaStack server with API keys for different providers:\n", - "\n", - "- **OpenAI API Key**: For OpenAI models\n", - "- **Gemini API Key**: For Google's Gemini models \n", - "- **Together API Key**: For Together AI models\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ab4eff97-4565-4c73-b1b3-0020a4c7e2a5", - "metadata": {}, - "outputs": [], - "source": [ - "from llama_stack_client import LlamaStackClient\n", - "\n", - "client = LlamaStackClient(\n", - " base_url=\"http://0.0.0.0:8321\",\n", - " provider_data={\"openai_api_key\": \"****\", \"gemini_api_key\": \"****\", \"together_api_key\": \"****\"},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "vwhexjy1e8o", - "metadata": {}, - "source": [ - "#### Explore Available Models and Safety Features\n", - "\n", - "Check what models and safety shields are available through your LlamaStack instance." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "880443ef-ac3c-48b1-a80a-7dab5b25ac61", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/models \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/shields \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available models:\n", - "- all-minilm\n", - "- ollama/all-minilm:l6-v2\n", - "- ollama/llama-guard3:1b\n", - "- ollama/llama-guard3:8b\n", - "- ollama/llama3.2:3b-instruct-fp16\n", - "- ollama/nomic-embed-text\n", - "- fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\n", - "- fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\n", - "- fireworks/accounts/fireworks/models/llama-v3p1-405b-instruct\n", - "- fireworks/accounts/fireworks/models/llama-v3p2-3b-instruct\n", - "- fireworks/accounts/fireworks/models/llama-v3p2-11b-vision-instruct\n", - "- fireworks/accounts/fireworks/models/llama-v3p2-90b-vision-instruct\n", - "- fireworks/accounts/fireworks/models/llama-v3p3-70b-instruct\n", - "- fireworks/accounts/fireworks/models/llama4-scout-instruct-basic\n", - "- fireworks/accounts/fireworks/models/llama4-maverick-instruct-basic\n", - "- fireworks/nomic-ai/nomic-embed-text-v1.5\n", - "- fireworks/accounts/fireworks/models/llama-guard-3-8b\n", - "- fireworks/accounts/fireworks/models/llama-guard-3-11b-vision\n", - "- together/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\n", - "- together/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\n", - "- together/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\n", - "- together/meta-llama/Llama-3.2-3B-Instruct-Turbo\n", - "- together/meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo\n", - "- together/meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo\n", - "- together/meta-llama/Llama-3.3-70B-Instruct-Turbo\n", - "- together/togethercomputer/m2-bert-80M-8k-retrieval\n", - "- together/togethercomputer/m2-bert-80M-32k-retrieval\n", - "- together/meta-llama/Llama-4-Scout-17B-16E-Instruct\n", - "- together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\n", - "- together/meta-llama/Llama-Guard-3-8B\n", - "- together/meta-llama/Llama-Guard-3-11B-Vision-Turbo\n", - "- bedrock/meta.llama3-1-8b-instruct-v1:0\n", - "- bedrock/meta.llama3-1-70b-instruct-v1:0\n", - "- bedrock/meta.llama3-1-405b-instruct-v1:0\n", - "- openai/gpt-3.5-turbo-0125\n", - "- openai/gpt-3.5-turbo\n", - "- openai/gpt-3.5-turbo-instruct\n", - "- openai/gpt-4\n", - "- openai/gpt-4-turbo\n", - "- openai/gpt-4o\n", - "- openai/gpt-4o-2024-08-06\n", - "- openai/gpt-4o-mini\n", - "- openai/gpt-4o-audio-preview\n", - "- openai/chatgpt-4o-latest\n", - "- openai/o1\n", - "- openai/o1-mini\n", - "- openai/o3-mini\n", - "- openai/o4-mini\n", - "- openai/text-embedding-3-small\n", - "- openai/text-embedding-3-large\n", - "- anthropic/claude-3-5-sonnet-latest\n", - "- anthropic/claude-3-7-sonnet-latest\n", - "- anthropic/claude-3-5-haiku-latest\n", - "- anthropic/voyage-3\n", - "- anthropic/voyage-3-lite\n", - "- anthropic/voyage-code-3\n", - "- gemini/gemini-1.5-flash\n", - "- gemini/gemini-1.5-pro\n", - "- gemini/gemini-2.0-flash\n", - "- gemini/gemini-2.0-flash-lite\n", - "- gemini/gemini-2.5-flash\n", - "- gemini/gemini-2.5-flash-lite\n", - "- gemini/gemini-2.5-pro\n", - "- gemini/text-embedding-004\n", - "- groq/llama3-8b-8192\n", - "- groq/llama-3.1-8b-instant\n", - "- groq/llama3-70b-8192\n", - "- groq/llama-3.3-70b-versatile\n", - "- groq/llama-3.2-3b-preview\n", - "- groq/meta-llama/llama-4-scout-17b-16e-instruct\n", - "- groq/meta-llama/llama-4-maverick-17b-128e-instruct\n", - "- sambanova/Meta-Llama-3.1-8B-Instruct\n", - "- sambanova/Meta-Llama-3.3-70B-Instruct\n", - "- sambanova/Llama-4-Maverick-17B-128E-Instruct\n", - "- sentence-transformers/all-MiniLM-L6-v2\n", - "----\n", - "Available shields (safety models):\n", - "code-scanner\n", - "llama-guard\n", - "----\n" - ] - } - ], - "source": [ - "print(\"Available models:\")\n", - "for m in client.models.list():\n", - " print(f\"- {m.identifier}\")\n", - "\n", - "print(\"----\")\n", - "print(\"Available shields (safety models):\")\n", - "for s in client.shields.list():\n", - " print(s.identifier)\n", - "print(\"----\")" - ] - }, - { - "cell_type": "markdown", - "id": "gojp7at31ht", - "metadata": {}, - "source": [ - "### 4. Vector Database Setup\n", - "\n", - "#### Register a Vector Database\n", - "\n", - "Create a FAISS vector database for storing document embeddings:\n", - "\n", - "- **Vector DB ID**: Unique identifier for the database\n", - "- **Provider**: FAISS (Facebook AI Similarity Search)\n", - "- **Embedding Model**: Sentence Transformers model for text embeddings\n", - "- **Dimensions**: 384-dimensional embeddings" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a16e2885-ae70-4fa6-9778-2433fa4dbfff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-dbs \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/vector-dbs \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Registered new vector DB: VectorDBRegisterResponse(embedding_dimension=384, embedding_model='sentence-transformers/all-MiniLM-L6-v2', identifier='acme_docs', provider_id='faiss', type='vector_db', provider_resource_id='acme_docs_v2', owner=None, source='via_register_api', vector_db_name=None)\n", - "Existing vector DBs: [VectorDBListResponseItem(embedding_dimension=384, embedding_model='sentence-transformers/all-MiniLM-L6-v2', identifier='acme_docs', provider_id='faiss', type='vector_db', provider_resource_id='acme_docs_v2', vector_db_name=None)]\n" - ] - } - ], - "source": [ - "# Register a new clean vector database\n", - "vector_db = client.vector_dbs.register(\n", - " vector_db_id=\"acme_docs\", # Use a new unique name\n", - " provider_id=\"faiss\",\n", - " provider_vector_db_id=\"acme_docs_v2\",\n", - " embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n", - " embedding_dimension=384,\n", - ")\n", - "print(\"Registered new vector DB:\", vector_db)\n", - "\n", - "# List all registered vector databases\n", - "dbs = client.vector_dbs.list()\n", - "print(\"Existing vector DBs:\", dbs)" - ] - }, - { - "cell_type": "markdown", - "id": "pcgjqzfr3eo", - "metadata": {}, - "source": [ - "#### Prepare Sample Documents\n", - "\n", - "Create LLAMA Stack Chunks for FAISS vector store" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a0a6619-c9fb-4938-8ff3-f84304eed91e", - "metadata": {}, - "outputs": [], - "source": [ - "from llama_stack_client.types.vector_io_insert_params import Chunk\n", - "\n", - "docs = [\n", - " (\"Acme ships globally in 3-5 business days.\", {\"title\": \"Shipping Policy\"}),\n", - " (\"Returns are accepted within 30 days of purchase.\", {\"title\": \"Returns Policy\"}),\n", - " (\"Support is available 24/7 via chat and email.\", {\"title\": \"Support\"}),\n", - "]\n", - "\n", - "# Convert to Chunk objects\n", - "chunks = []\n", - "for _, (content, metadata) in enumerate(docs):\n", - " # Transform metadata to required format with document_id from title\n", - " metadata = {\"document_id\": metadata[\"title\"]}\n", - " chunk = Chunk(\n", - " content=content, # Required[InterleavedContent]\n", - " metadata=metadata, # Required[Dict]\n", - " )\n", - " chunks.append(chunk)" - ] - }, - { - "cell_type": "markdown", - "id": "6bg3sm2ko5g", - "metadata": {}, - "source": [ - "#### Insert Documents into Vector Database\n", - "\n", - "Store the prepared documents in the FAISS vector database. This process:\n", - "1. Generates embeddings for each document\n", - "2. Stores embeddings with metadata\n", - "3. Enables semantic search capabilities" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "0e8740d8-b809-44b9-915f-1e0200e3c3f1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/insert \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Documents inserted: None\n" - ] - } - ], - "source": [ - "# Insert chunks into FAISS vector store\n", - "\n", - "response = client.vector_io.insert(vector_db_id=\"acme_docs\", chunks=chunks)\n", - "print(\"Documents inserted:\", response)" - ] - }, - { - "cell_type": "markdown", - "id": "9061tmi1zpq", - "metadata": {}, - "source": [ - "#### Test Vector Search\n", - "\n", - "Query the vector database to verify it's working correctly. This performs semantic search to find relevant documents based on the query." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4a5e010c-eeeb-4020-a957-74d6d1cba342", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "metadata : {'document_id': 'Shipping Policy'}\n", - "content : Acme ships globally in 3–5 business days.\n", - "metadata : {'document_id': 'Shipping Policy'}\n", - "content : Acme ships globally in 3–5 business days.\n", - "metadata : {'document_id': 'Returns Policy'}\n", - "content : Returns are accepted within 30 days of purchase.\n" - ] - } - ], - "source": [ - "# Query chunks from FAISS vector store\n", - "\n", - "query_chunk_response = client.vector_io.query(\n", - " vector_db_id=\"acme_docs\",\n", - " query=\"How long does Acme take to ship orders?\",\n", - ")\n", - "for chunk in query_chunk_response.chunks:\n", - " print(\"metadata\", \":\", chunk.metadata)\n", - " print(\"content\", \":\", chunk.content)" - ] - }, - { - "cell_type": "markdown", - "id": "usne6mbspms", - "metadata": {}, - "source": [ - "### 5. LangChain Integration\n", - "\n", - "#### Configure LangChain with LlamaStack\n", - "\n", - "Set up LangChain to use LlamaStack's OpenAI-compatible API:\n", - "\n", - "- **Base URL**: Points to LlamaStack's OpenAI endpoint\n", - "- **Headers**: Include Together AI API key for model access\n", - "- **Model**: Use Meta Llama 3.1 8B model via Together AI" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c378bd10-09c2-417c-bdfc-1e0a2dd19084", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "# Point LangChain to Llamastack Server\n", - "os.environ[\"OPENAI_API_KEY\"] = \"dummy\"\n", - "os.environ[\"OPENAI_BASE_URL\"] = \"http://0.0.0.0:8321/v1/openai/v1\"\n", - "\n", - "# LLM from Llamastack together model\n", - "llm = ChatOpenAI(\n", - " model=\"together/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\",\n", - " default_headers={\"X-LlamaStack-Provider-Data\": '{\"together_api_key\": \"***\"}'},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5a4ddpcuk3l", - "metadata": {}, - "source": [ - "#### Test LLM Connection\n", - "\n", - "Verify that LangChain can successfully communicate with the LlamaStack server." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f88ffb5a-657b-4916-9375-c6ddc156c25e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "data": { - "text/plain": [ - "AIMessage(content=\"In the Andes, a gentle soul resides, \\nA llama's soft eyes, with kindness abide.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 50, 'total_tokens': 72, 'completion_tokens_details': None, 'prompt_tokens_details': None, 'cached_tokens': 0}, 'model_name': 'meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo', 'system_fingerprint': None, 'id': 'o86Jy3i-2j9zxn-972d7b27f8f22aaa', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--4797f8b9-a5f6-4730-aece-80c1fd88ac55-0', usage_metadata={'input_tokens': 50, 'output_tokens': 22, 'total_tokens': 72, 'input_token_details': {}, 'output_token_details': {}})" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Test llm with simple message\n", - "messages = [\n", - " {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n", - " {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n", - "]\n", - "llm.invoke(messages)" - ] - }, - { - "cell_type": "markdown", - "id": "0xh0jg6a0l4a", - "metadata": {}, - "source": [ - "### 6. Building the RAG Chain\n", - "\n", - "#### Create a Complete RAG Pipeline\n", - "\n", - "Build a LangChain pipeline that combines:\n", - "\n", - "1. **Vector Search**: Query LlamaStack's vector database\n", - "2. **Context Assembly**: Format retrieved documents\n", - "3. **Prompt Template**: Structure the input for the LLM\n", - "4. **LLM Generation**: Generate answers using context\n", - "5. **Output Parsing**: Extract the final response\n", - "\n", - "**Chain Flow**: `Query → Vector Search → Context + Question → LLM → Response`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9684427d-dcc7-4544-9af5-8b110d014c42", - "metadata": {}, - "outputs": [], - "source": [ - "# LangChain for prompt template and chaining + LLAMA Stack Client Vector DB and LLM chat completion\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "from langchain_core.prompts import ChatPromptTemplate\n", - "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n", - "\n", - "\n", - "def join_docs(docs):\n", - " return \"\\n\\n\".join([f\"[{d.metadata.get('document_id')}] {d.content}\" for d in docs.chunks])\n", - "\n", - "\n", - "PROMPT = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", \"You are a helpful assistant. Use the following context to answer.\"),\n", - " (\"user\", \"Question: {question}\\n\\nContext:\\n{context}\"),\n", - " ]\n", - ")\n", - "\n", - "vector_step = RunnableLambda(\n", - " lambda x: client.vector_io.query(\n", - " vector_db_id=\"acme_docs\",\n", - " query=x,\n", - " )\n", - ")\n", - "\n", - "chain = (\n", - " {\"context\": vector_step | RunnableLambda(join_docs), \"question\": RunnablePassthrough()}\n", - " | PROMPT\n", - " | llm\n", - " | StrOutputParser()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0onu6rhphlra", - "metadata": {}, - "source": [ - "### 7. Testing the RAG System\n", - "\n", - "#### Example 1: Shipping Query" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "03322188-9509-446a-a4a8-ce3bb83ec87c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "❓ How long does shipping take?\n", - "💡 According to the Shipping Policy, shipping from Acme takes 3-5 business days.\n" - ] - } - ], - "source": [ - "query = \"How long does shipping take?\"\n", - "response = chain.invoke(query)\n", - "print(\"❓\", query)\n", - "print(\"💡\", response)" - ] - }, - { - "cell_type": "markdown", - "id": "b7krhqj88ku", - "metadata": {}, - "source": [ - "#### Example 2: Returns Policy Query" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "61995550-bb0b-46a8-a5d0-023207475d60", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "❓ Can I return a product after 40 days?\n", - "💡 Based on the provided returns policy, it appears that returns are only accepted within 30 days of purchase. Since you're asking about returning a product after 40 days, it would not be within the specified 30-day return window.\n", - "\n", - "Unfortunately, it seems that you would not be eligible for a return in this case. However, I would recommend reaching out to the support team via chat or email to confirm their policy and see if there are any exceptions or alternative solutions available.\n" - ] - } - ], - "source": [ - "query = \"Can I return a product after 40 days?\"\n", - "response = chain.invoke(query)\n", - "print(\"❓\", query)\n", - "print(\"💡\", response)" - ] - }, - { - "cell_type": "markdown", - "id": "h4w24fadvjs", - "metadata": {}, - "source": [ - "---\n", - "We have successfully built a RAG system that combines:\n", - "\n", - "- **LlamaStack** for infrastructure (LLM serving + vector database)\n", - "- **LangChain** for orchestration (prompts + chains)\n", - "- **Together AI** for high-quality language models\n", - "\n", - "### Key Benefits\n", - "\n", - "1. **Unified Infrastructure**: Single server for LLMs and vector databases\n", - "2. **OpenAI Compatibility**: Easy integration with existing LangChain code\n", - "3. **Multi-Provider Support**: Switch between different LLM providers\n", - "4. **Production Ready**: Built-in safety shields and monitoring\n", - "\n", - "### Next Steps\n", - "\n", - "- Add more sophisticated document processing\n", - "- Implement conversation memory\n", - "- Add safety filtering and monitoring\n", - "- Scale to larger document collections\n", - "- Integrate with web frameworks like FastAPI or Streamlit\n", - "\n", - "---\n", - "\n", - "##### 🔧 Cleanup\n", - "\n", - "Don't forget to stop the LlamaStack server when you're done:\n", - "\n", - "```python\n", - "kill_llama_stack_server()\n", - "```" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 9fa69b0337b8a88d2d3324092ffacf454d383188 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Tue, 26 Aug 2025 14:06:36 -0700 Subject: [PATCH 005/124] feat(distro): no huggingface provider for starter (#3258) The `trl` dependency brings in `accelerate` which brings in nvidia dependencies for torch. We cannot have that in the starter distro. As such, no CPU-only post-training for the huggingface provider. --- docs/source/providers/post_training/index.md | 1 - llama_stack/core/build.py | 2 +- llama_stack/distributions/ci-tests/build.yaml | 2 +- llama_stack/distributions/ci-tests/run.yaml | 9 ++-- .../distributions/starter-gpu/build.yaml | 2 +- .../distributions/starter-gpu/run.yaml | 9 ++-- .../distributions/starter-gpu/starter_gpu.py | 2 +- llama_stack/distributions/starter/build.yaml | 2 +- llama_stack/distributions/starter/run.yaml | 9 ++-- llama_stack/distributions/starter/starter.py | 2 +- llama_stack/providers/registry/inference.py | 3 +- .../providers/registry/post_training.py | 47 ++++++------------- 12 files changed, 35 insertions(+), 55 deletions(-) diff --git a/docs/source/providers/post_training/index.md b/docs/source/providers/post_training/index.md index 5ada6f9aa..e69f2a45a 100644 --- a/docs/source/providers/post_training/index.md +++ b/docs/source/providers/post_training/index.md @@ -9,7 +9,6 @@ This section contains documentation for all available providers for the **post_t ```{toctree} :maxdepth: 1 -inline_huggingface-cpu inline_huggingface-gpu inline_torchtune-cpu inline_torchtune-gpu diff --git a/llama_stack/core/build.py b/llama_stack/core/build.py index fa1fe632b..2ceb9e9be 100644 --- a/llama_stack/core/build.py +++ b/llama_stack/core/build.py @@ -80,7 +80,7 @@ def get_provider_dependencies( normal_deps = [] special_deps = [] for package in deps: - if "--no-deps" in package or "--index-url" in package: + if any(f in package for f in ["--no-deps", "--index-url", "--extra-index-url"]): special_deps.append(package) else: normal_deps.append(package) diff --git a/llama_stack/distributions/ci-tests/build.yaml b/llama_stack/distributions/ci-tests/build.yaml index b4701cb81..8e6c0bf67 100644 --- a/llama_stack/distributions/ci-tests/build.yaml +++ b/llama_stack/distributions/ci-tests/build.yaml @@ -34,7 +34,7 @@ distribution_spec: telemetry: - provider_type: inline::meta-reference post_training: - - provider_type: inline::huggingface-cpu + - provider_type: inline::torchtune-cpu eval: - provider_type: inline::meta-reference datasetio: diff --git a/llama_stack/distributions/ci-tests/run.yaml b/llama_stack/distributions/ci-tests/run.yaml index 3acdd20f9..7523df581 100644 --- a/llama_stack/distributions/ci-tests/run.yaml +++ b/llama_stack/distributions/ci-tests/run.yaml @@ -156,13 +156,10 @@ providers: sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/trace_store.db otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=} post_training: - - provider_id: huggingface-cpu - provider_type: inline::huggingface-cpu + - provider_id: torchtune-cpu + provider_type: inline::torchtune-cpu config: - checkpoint_format: huggingface - distributed_backend: null - device: cpu - dpo_output_dir: ~/.llama/distributions/ci-tests/dpo_output + checkpoint_format: meta eval: - provider_id: meta-reference provider_type: inline::meta-reference diff --git a/llama_stack/distributions/starter-gpu/build.yaml b/llama_stack/distributions/starter-gpu/build.yaml index ae0680cdc..ff7c58e6f 100644 --- a/llama_stack/distributions/starter-gpu/build.yaml +++ b/llama_stack/distributions/starter-gpu/build.yaml @@ -35,7 +35,7 @@ distribution_spec: telemetry: - provider_type: inline::meta-reference post_training: - - provider_type: inline::torchtune-gpu + - provider_type: inline::huggingface-gpu eval: - provider_type: inline::meta-reference datasetio: diff --git a/llama_stack/distributions/starter-gpu/run.yaml b/llama_stack/distributions/starter-gpu/run.yaml index 81c802317..8aed61519 100644 --- a/llama_stack/distributions/starter-gpu/run.yaml +++ b/llama_stack/distributions/starter-gpu/run.yaml @@ -156,10 +156,13 @@ providers: sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/trace_store.db otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=} post_training: - - provider_id: torchtune-gpu - provider_type: inline::torchtune-gpu + - provider_id: huggingface-gpu + provider_type: inline::huggingface-gpu config: - checkpoint_format: meta + checkpoint_format: huggingface + distributed_backend: null + device: cpu + dpo_output_dir: ~/.llama/distributions/starter-gpu/dpo_output eval: - provider_id: meta-reference provider_type: inline::meta-reference diff --git a/llama_stack/distributions/starter-gpu/starter_gpu.py b/llama_stack/distributions/starter-gpu/starter_gpu.py index 893df6c17..245334749 100644 --- a/llama_stack/distributions/starter-gpu/starter_gpu.py +++ b/llama_stack/distributions/starter-gpu/starter_gpu.py @@ -17,6 +17,6 @@ def get_distribution_template() -> DistributionTemplate: template.description = "Quick start template for running Llama Stack with several popular providers. This distribution is intended for GPU-enabled environments." template.providers["post_training"] = [ - BuildProvider(provider_type="inline::torchtune-gpu"), + BuildProvider(provider_type="inline::huggingface-gpu"), ] return template diff --git a/llama_stack/distributions/starter/build.yaml b/llama_stack/distributions/starter/build.yaml index 3df0eb129..e84e528da 100644 --- a/llama_stack/distributions/starter/build.yaml +++ b/llama_stack/distributions/starter/build.yaml @@ -35,7 +35,7 @@ distribution_spec: telemetry: - provider_type: inline::meta-reference post_training: - - provider_type: inline::huggingface-cpu + - provider_type: inline::torchtune-cpu eval: - provider_type: inline::meta-reference datasetio: diff --git a/llama_stack/distributions/starter/run.yaml b/llama_stack/distributions/starter/run.yaml index 7e1d46a61..a3962b8aa 100644 --- a/llama_stack/distributions/starter/run.yaml +++ b/llama_stack/distributions/starter/run.yaml @@ -156,13 +156,10 @@ providers: sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/trace_store.db otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=} post_training: - - provider_id: huggingface-cpu - provider_type: inline::huggingface-cpu + - provider_id: torchtune-cpu + provider_type: inline::torchtune-cpu config: - checkpoint_format: huggingface - distributed_backend: null - device: cpu - dpo_output_dir: ~/.llama/distributions/starter/dpo_output + checkpoint_format: meta eval: - provider_id: meta-reference provider_type: inline::meta-reference diff --git a/llama_stack/distributions/starter/starter.py b/llama_stack/distributions/starter/starter.py index f49da0bb7..a4bbc6371 100644 --- a/llama_stack/distributions/starter/starter.py +++ b/llama_stack/distributions/starter/starter.py @@ -120,7 +120,7 @@ def get_distribution_template() -> DistributionTemplate: ], "agents": [BuildProvider(provider_type="inline::meta-reference")], "telemetry": [BuildProvider(provider_type="inline::meta-reference")], - "post_training": [BuildProvider(provider_type="inline::huggingface-cpu")], + "post_training": [BuildProvider(provider_type="inline::torchtune-cpu")], "eval": [BuildProvider(provider_type="inline::meta-reference")], "datasetio": [ BuildProvider(provider_type="remote::huggingface"), diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 1801cdcad..82b771a28 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -40,8 +40,9 @@ def available_providers() -> list[ProviderSpec]: InlineProviderSpec( api=Api.inference, provider_type="inline::sentence-transformers", + # CrossEncoder depends on torchao.quantization pip_packages=[ - "torch torchvision --index-url https://download.pytorch.org/whl/cpu", + "torch torchvision torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu", "sentence-transformers --no-deps", ], module="llama_stack.providers.inline.inference.sentence_transformers", diff --git a/llama_stack/providers/registry/post_training.py b/llama_stack/providers/registry/post_training.py index 4443f4df1..67238e3fc 100644 --- a/llama_stack/providers/registry/post_training.py +++ b/llama_stack/providers/registry/post_training.py @@ -13,7 +13,7 @@ from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec # The CPU version is used for distributions that don't have GPU support -- they result in smaller container images. torchtune_def = dict( api=Api.post_training, - pip_packages=["torchtune==0.5.0", "torchao==0.8.0", "numpy"], + pip_packages=["numpy"], module="llama_stack.providers.inline.post_training.torchtune", config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig", api_dependencies=[ @@ -23,56 +23,39 @@ torchtune_def = dict( description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.", ) -huggingface_def = dict( - api=Api.post_training, - pip_packages=["trl", "transformers", "peft", "datasets"], - module="llama_stack.providers.inline.post_training.huggingface", - config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig", - api_dependencies=[ - Api.datasetio, - Api.datasets, - ], - description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.", -) - def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( - **{ + **{ # type: ignore **torchtune_def, "provider_type": "inline::torchtune-cpu", "pip_packages": ( cast(list[str], torchtune_def["pip_packages"]) - + ["torch torchtune==0.5.0 torchao==0.8.0 --index-url https://download.pytorch.org/whl/cpu"] + + ["torch torchtune>=0.5.0 torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu"] ), }, ), InlineProviderSpec( - **{ - **huggingface_def, - "provider_type": "inline::huggingface-cpu", - "pip_packages": ( - cast(list[str], huggingface_def["pip_packages"]) - + ["torch --index-url https://download.pytorch.org/whl/cpu"] - ), - }, - ), - InlineProviderSpec( - **{ + **{ # type: ignore **torchtune_def, "provider_type": "inline::torchtune-gpu", "pip_packages": ( - cast(list[str], torchtune_def["pip_packages"]) + ["torch torchtune==0.5.0 torchao==0.8.0"] + cast(list[str], torchtune_def["pip_packages"]) + ["torch torchtune>=0.5.0 torchao>=0.12.0"] ), }, ), InlineProviderSpec( - **{ - **huggingface_def, - "provider_type": "inline::huggingface-gpu", - "pip_packages": (cast(list[str], huggingface_def["pip_packages"]) + ["torch"]), - }, + api=Api.post_training, + provider_type="inline::huggingface-gpu", + pip_packages=["trl", "transformers", "peft", "datasets", "torch"], + module="llama_stack.providers.inline.post_training.huggingface", + config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig", + api_dependencies=[ + Api.datasetio, + Api.datasets, + ], + description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.", ), remote_provider_spec( api=Api.post_training, From 963305c84da587124937c71d0d7727d46525e7ec Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 26 Aug 2025 22:02:47 +0000 Subject: [PATCH 006/124] build: Bump version to 0.2.19 --- llama_stack/ui/package-lock.json | 8 ++-- llama_stack/ui/package.json | 2 +- pyproject.toml | 6 +-- uv.lock | 68 +++++++++++++++++++++----------- 4 files changed, 54 insertions(+), 30 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 98a1e4fe5..2da25615c 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -18,7 +18,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^11.18.2", - "llama-stack-client": "^0.2.18", + "llama-stack-client": "^0.2.19", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", @@ -10006,9 +10006,9 @@ "license": "MIT" }, "node_modules/llama-stack-client": { - "version": "0.2.18", - "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.18.tgz", - "integrity": "sha512-k+xQOz/TIU0cINP4Aih8q6xs7f/6qs0fLDMXTTKQr5C0F1jtCjRiwsas7bTsDfpKfYhg/7Xy/wPw/uZgi6aIVg==", + "version": "0.2.19", + "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.19.tgz", + "integrity": "sha512-sDuAhUdEGlERZ3jlMUzPXcQTgMv/pGbDrPX0ifbE5S+gr7Q+7ohuQYrIXe+hXgIipFjq+y4b2c5laZ76tmAyEA==", "license": "MIT", "dependencies": { "@types/node": "^18.11.18", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 7a17d93dd..31c836057 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -23,7 +23,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^11.18.2", - "llama-stack-client": "^0.2.18", + "llama-stack-client": "^0.2.19", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", diff --git a/pyproject.toml b/pyproject.toml index 6c76da895..dd8529546 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ required-version = ">=0.7.0" [project] name = "llama_stack" -version = "0.2.18" +version = "0.2.19" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -31,7 +31,7 @@ dependencies = [ "huggingface-hub>=0.34.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.18", + "llama-stack-client>=0.2.19", "llama-api-client>=0.1.2", "openai>=1.99.6,<1.100.0", "prompt-toolkit", @@ -56,7 +56,7 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.18", + "llama-stack-client>=0.2.19", "streamlit-option-menu", ] diff --git a/uv.lock b/uv.lock index 385c75bea..0626caba6 100644 --- a/uv.lock +++ b/uv.lock @@ -1128,6 +1128,9 @@ wheels = [ { url = 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"https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-manylinux_2_28_aarch64.whl" }, + { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl" }, + { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-win_amd64.whl" }, ] [[package]] From cec00c54762565f7ac09a826ae88c0c0d714894f Mon Sep 17 00:00:00 2001 From: Charlie Doern Date: Tue, 26 Aug 2025 21:21:15 -0400 Subject: [PATCH 007/124] docs: fix post_training docs (#3262) # What does this PR do? the post training docs are missing references to the more indepth `huggingface.md` and `torchtune.md` which explain how to actually use the providers. These files show up in search though. Add references to these files into the `inline_..md` files currently pointed to by `index.md` Signed-off-by: Charlie Doern --- docs/source/advanced_apis/post_training/inline_huggingface.md | 3 +++ docs/source/advanced_apis/post_training/inline_torchtune.md | 1 + 2 files changed, 4 insertions(+) diff --git a/docs/source/advanced_apis/post_training/inline_huggingface.md b/docs/source/advanced_apis/post_training/inline_huggingface.md index 4d2201c99..6536b4f8c 100644 --- a/docs/source/advanced_apis/post_training/inline_huggingface.md +++ b/docs/source/advanced_apis/post_training/inline_huggingface.md @@ -35,3 +35,6 @@ device: cpu ``` +[Find more detailed information here!](huggingface.md) + + diff --git a/docs/source/advanced_apis/post_training/inline_torchtune.md b/docs/source/advanced_apis/post_training/inline_torchtune.md index 6684c99ac..617975b0d 100644 --- a/docs/source/advanced_apis/post_training/inline_torchtune.md +++ b/docs/source/advanced_apis/post_training/inline_torchtune.md @@ -22,3 +22,4 @@ checkpoint_format: meta ``` +[Find more detailed information here!](torchtune.md) From d73955a41e246d4d394ad31454d7c54599d2f812 Mon Sep 17 00:00:00 2001 From: raghotham Date: Wed, 27 Aug 2025 12:04:25 -0700 Subject: [PATCH 008/124] chore: remove absolute paths (#3263) # What does this PR do? Finding these issues while moving to github pages. ## Test Plan uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all --- docs/source/advanced_apis/evaluation_concepts.md | 2 +- docs/source/building_applications/playground/index.md | 2 +- docs/source/building_applications/responses_vs_agents.md | 8 ++++---- docs/source/concepts/distributions.md | 2 +- docs/source/distributions/importing_as_library.md | 2 +- docs/source/distributions/k8s/apply.sh | 6 +++--- docs/source/distributions/ondevice_distro/android_sdk.md | 2 +- .../self_hosted_distro/meta-reference-gpu.md | 4 ++-- docs/source/references/evals_reference/index.md | 2 +- .../distributions/meta-reference-gpu/doc_template.md | 4 ++-- 10 files changed, 17 insertions(+), 17 deletions(-) diff --git a/docs/source/advanced_apis/evaluation_concepts.md b/docs/source/advanced_apis/evaluation_concepts.md index c26ec8f5e..52ad53ece 100644 --- a/docs/source/advanced_apis/evaluation_concepts.md +++ b/docs/source/advanced_apis/evaluation_concepts.md @@ -33,7 +33,7 @@ The list of open-benchmarks we currently support: - [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)]: Benchmark designed to evaluate multimodal models. -You can follow this [contributing guide](https://llama-stack.readthedocs.io/en/latest/references/evals_reference/index.html#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack +You can follow this [contributing guide](../references/evals_reference/index.md#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack #### Run evaluation on open-benchmarks via CLI diff --git a/docs/source/building_applications/playground/index.md b/docs/source/building_applications/playground/index.md index fd2b92434..2390c422f 100644 --- a/docs/source/building_applications/playground/index.md +++ b/docs/source/building_applications/playground/index.md @@ -88,7 +88,7 @@ Interactive pages for users to play with and explore Llama Stack API capabilitie - **API Resources**: Inspect Llama Stack API resources - This page allows you to inspect Llama Stack API resources (`models`, `datasets`, `memory_banks`, `benchmarks`, `shields`). - Under the hood, it uses Llama Stack's `//list` API to get information about each resources. - - Please visit [Core Concepts](https://llama-stack.readthedocs.io/en/latest/concepts/index.html) for more details about the resources. + - Please visit [Core Concepts](../../concepts/index.md) for more details about the resources. ### Starting the Llama Stack Playground diff --git a/docs/source/building_applications/responses_vs_agents.md b/docs/source/building_applications/responses_vs_agents.md index 5abe951d6..63ff69e4f 100644 --- a/docs/source/building_applications/responses_vs_agents.md +++ b/docs/source/building_applications/responses_vs_agents.md @@ -3,7 +3,7 @@ Llama Stack (LLS) provides two different APIs for building AI applications with tool calling capabilities: the **Agents API** and the **OpenAI Responses API**. While both enable AI systems to use tools, and maintain full conversation history, they serve different use cases and have distinct characteristics. ```{note} -For simple and basic inferencing, you may want to use the [Chat Completions API](https://llama-stack.readthedocs.io/en/latest/providers/index.html#chat-completions) directly, before progressing to Agents or Responses API. + **Note:** For simple and basic inferencing, you may want to use the [Chat Completions API](../providers/openai.md#chat-completions) directly, before progressing to Agents or Responses API. ``` ## Overview @@ -173,7 +173,7 @@ Both APIs demonstrate distinct strengths that make them valuable on their own fo ## For More Information -- **LLS Agents API**: For detailed information on creating and managing agents, see the [Agents documentation](https://llama-stack.readthedocs.io/en/latest/building_applications/agent.html) +- **LLS Agents API**: For detailed information on creating and managing agents, see the [Agents documentation](agent.md) - **OpenAI Responses API**: For information on using the OpenAI-compatible responses API, see the [OpenAI API documentation](https://platform.openai.com/docs/api-reference/responses) -- **Chat Completions API**: For the default backend API used by Agents, see the [Chat Completions providers documentation](https://llama-stack.readthedocs.io/en/latest/providers/index.html#chat-completions) -- **Agent Execution Loop**: For understanding how agents process turns and steps in their execution, see the [Agent Execution Loop documentation](https://llama-stack.readthedocs.io/en/latest/building_applications/agent_execution_loop.html) +- **Chat Completions API**: For the default backend API used by Agents, see the [Chat Completions providers documentation](../providers/openai.md#chat-completions) +- **Agent Execution Loop**: For understanding how agents process turns and steps in their execution, see the [Agent Execution Loop documentation](agent_execution_loop.md) diff --git a/docs/source/concepts/distributions.md b/docs/source/concepts/distributions.md index c3be12d93..8c63914d1 100644 --- a/docs/source/concepts/distributions.md +++ b/docs/source/concepts/distributions.md @@ -6,4 +6,4 @@ While there is a lot of flexibility to mix-and-match providers, often users will **Locally Hosted Distro**: You may want to run Llama Stack on your own hardware. Typically though, you still need to use Inference via an external service. You can use providers like HuggingFace TGI, Fireworks, Together, etc. for this purpose. Or you may have access to GPUs and can run a [vLLM](https://github.com/vllm-project/vllm) or [NVIDIA NIM](https://build.nvidia.com/nim?filters=nimType%3Anim_type_run_anywhere&q=llama) instance. If you "just" have a regular desktop machine, you can use [Ollama](https://ollama.com/) for inference. To provide convenient quick access to these options, we provide a number of such pre-configured locally-hosted Distros. -**On-device Distro**: To run Llama Stack directly on an edge device (mobile phone or a tablet), we provide Distros for [iOS](https://llama-stack.readthedocs.io/en/latest/distributions/ondevice_distro/ios_sdk.html) and [Android](https://llama-stack.readthedocs.io/en/latest/distributions/ondevice_distro/android_sdk.html) +**On-device Distro**: To run Llama Stack directly on an edge device (mobile phone or a tablet), we provide Distros for [iOS](../distributions/ondevice_distro/ios_sdk.md) and [Android](../distributions/ondevice_distro/android_sdk.md) diff --git a/docs/source/distributions/importing_as_library.md b/docs/source/distributions/importing_as_library.md index b9b4b065a..9993be227 100644 --- a/docs/source/distributions/importing_as_library.md +++ b/docs/source/distributions/importing_as_library.md @@ -27,7 +27,7 @@ Then, you can access the APIs like `models` and `inference` on the client and ca response = client.models.list() ``` -If you've created a [custom distribution](https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html), you can also use the run.yaml configuration file directly: +If you've created a [custom distribution](building_distro.md), you can also use the run.yaml configuration file directly: ```python client = LlamaStackAsLibraryClient(config_path) diff --git a/docs/source/distributions/k8s/apply.sh b/docs/source/distributions/k8s/apply.sh index 3356da53e..1b5b26863 100755 --- a/docs/source/distributions/k8s/apply.sh +++ b/docs/source/distributions/k8s/apply.sh @@ -22,17 +22,17 @@ else fi if [ -z "${GITHUB_CLIENT_ID:-}" ]; then - echo "ERROR: GITHUB_CLIENT_ID not set. You need it for Github login to work. Refer to https://llama-stack.readthedocs.io/en/latest/deploying/index.html#kubernetes-deployment-guide" + echo "ERROR: GITHUB_CLIENT_ID not set. You need it for Github login to work. See the Kubernetes Deployment Guide in the Llama Stack documentation." exit 1 fi if [ -z "${GITHUB_CLIENT_SECRET:-}" ]; then - echo "ERROR: GITHUB_CLIENT_SECRET not set. You need it for Github login to work. Refer to https://llama-stack.readthedocs.io/en/latest/deploying/index.html#kubernetes-deployment-guide" + echo "ERROR: GITHUB_CLIENT_SECRET not set. You need it for Github login to work. See the Kubernetes Deployment Guide in the Llama Stack documentation." exit 1 fi if [ -z "${LLAMA_STACK_UI_URL:-}" ]; then - echo "ERROR: LLAMA_STACK_UI_URL not set. Should be set to the external URL of the UI (excluding port). You need it for Github login to work. Refer to https://llama-stack.readthedocs.io/en/latest/deploying/index.html#kubernetes-deployment-guide" + echo "ERROR: LLAMA_STACK_UI_URL not set. Should be set to the external URL of the UI (excluding port). You need it for Github login to work. See the Kubernetes Deployment Guide in the Llama Stack documentation." exit 1 fi diff --git a/docs/source/distributions/ondevice_distro/android_sdk.md b/docs/source/distributions/ondevice_distro/android_sdk.md index 9d16d07d7..ad86fa5f3 100644 --- a/docs/source/distributions/ondevice_distro/android_sdk.md +++ b/docs/source/distributions/ondevice_distro/android_sdk.md @@ -66,7 +66,7 @@ llama stack run starter --port 5050 Ensure the Llama Stack server version is the same as the Kotlin SDK Library for maximum compatibility. -Other inference providers: [Table](https://llama-stack.readthedocs.io/en/latest/index.html#supported-llama-stack-implementations) +Other inference providers: [Table](../../index.md#supported-llama-stack-implementations) How to set remote localhost in Demo App: [Settings](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/examples/android_app#settings) diff --git a/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md b/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md index 7e50a4161..84b85b91c 100644 --- a/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md +++ b/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md @@ -2,7 +2,7 @@ orphan: true --- -# Meta Reference Distribution +# Meta Reference GPU Distribution ```{toctree} :maxdepth: 2 @@ -41,7 +41,7 @@ The following environment variables can be configured: ## Prerequisite: Downloading Models -Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. +Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](../../references/llama_cli_reference/download_models.md) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. ``` $ llama model list --downloaded diff --git a/docs/source/references/evals_reference/index.md b/docs/source/references/evals_reference/index.md index 054a0b809..9a5ed2f1b 100644 --- a/docs/source/references/evals_reference/index.md +++ b/docs/source/references/evals_reference/index.md @@ -202,7 +202,7 @@ pprint(response) Llama Stack offers a library of scoring functions and the `/scoring` API, allowing you to run evaluations on your pre-annotated AI application datasets. -In this example, we will work with an example RAG dataset you have built previously, label with an annotation, and use LLM-As-Judge with custom judge prompt for scoring. Please checkout our [Llama Stack Playground](https://llama-stack.readthedocs.io/en/latest/playground/index.html) for an interactive interface to upload datasets and run scorings. +In this example, we will work with an example RAG dataset you have built previously, label with an annotation, and use LLM-As-Judge with custom judge prompt for scoring. Please checkout our [Llama Stack Playground](../../building_applications/playground/index.md) for an interactive interface to upload datasets and run scorings. ```python judge_model_id = "meta-llama/Llama-3.1-405B-Instruct-FP8" diff --git a/llama_stack/distributions/meta-reference-gpu/doc_template.md b/llama_stack/distributions/meta-reference-gpu/doc_template.md index ff45c3826..602d053c4 100644 --- a/llama_stack/distributions/meta-reference-gpu/doc_template.md +++ b/llama_stack/distributions/meta-reference-gpu/doc_template.md @@ -1,7 +1,7 @@ --- orphan: true --- -# Meta Reference Distribution +# Meta Reference GPU Distribution ```{toctree} :maxdepth: 2 @@ -29,7 +29,7 @@ The following environment variables can be configured: ## Prerequisite: Downloading Models -Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. +Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](../../references/llama_cli_reference/download_models.md) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. ``` $ llama model list --downloaded From 1a9fa3c0b88a60aece2cbbcaa9c98dc635becc48 Mon Sep 17 00:00:00 2001 From: Kelly Brown <86735520+kelbrown20@users.noreply.github.com> Date: Thu, 28 Aug 2025 06:26:47 -0400 Subject: [PATCH 009/124] docs: Contributor guidelines for creating Internal or External providers (#3111) **Description:** Adding information and guidelines on when contributors should create an in-tree vs out-of-tree provider. Im still learning a bit about this subject so Im very open to feedback on this PR Will also add this section to the API Providers section of the docs --- docs/source/contributing/new_api_provider.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/contributing/new_api_provider.md b/docs/source/contributing/new_api_provider.md index 6f8f59a47..9a7a62a38 100644 --- a/docs/source/contributing/new_api_provider.md +++ b/docs/source/contributing/new_api_provider.md @@ -14,6 +14,13 @@ Here are some example PRs to help you get started: - [Nvidia Inference Implementation](https://github.com/meta-llama/llama-stack/pull/355) - [Model context protocol Tool Runtime](https://github.com/meta-llama/llama-stack/pull/665) +## Guidelines for creating Internal or External Providers + +|**Type** |Internal (In-tree) |External (out-of-tree) +|---------|-------------------|---------------------| +|**Description** |A provider that is directly in the Llama Stack code|A provider that is outside of the Llama stack core codebase but is still accessible and usable by Llama Stack. +|**Benefits** |Ability to interact with the provider with minimal additional configurations or installations| Contributors do not have to add directly to the code to create providers accessible on Llama Stack. Keep provider-specific code separate from the core Llama Stack code. + ## Inference Provider Patterns When implementing Inference providers for OpenAI-compatible APIs, Llama Stack provides several mixin classes to simplify development and ensure consistent behavior across providers. From 75fad445a6c62808779da08d9a374c5dccf9ee72 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Thu, 28 Aug 2025 05:03:31 -0600 Subject: [PATCH 010/124] feat(UI): Implementing File Upload and VectorDB Creation/Configuration in Playground (#3266) --- .../chat-playground/chunk-processor.test.tsx | 610 +++++++++++ .../ui/app/chat-playground/page.test.tsx | 217 +++- llama_stack/ui/app/chat-playground/page.tsx | 963 +++++++++++++++--- .../ui/components/chat-playground/chat.tsx | 11 +- .../chat-playground/conversations.tsx | 11 +- .../chat-playground/message-input.tsx | 48 +- .../chat-playground/vector-db-creator.tsx | 243 +++++ llama_stack/ui/lib/message-content-utils.ts | 51 + 8 files changed, 1953 insertions(+), 201 deletions(-) create mode 100644 llama_stack/ui/app/chat-playground/chunk-processor.test.tsx create mode 100644 llama_stack/ui/components/chat-playground/vector-db-creator.tsx create mode 100644 llama_stack/ui/lib/message-content-utils.ts diff --git a/llama_stack/ui/app/chat-playground/chunk-processor.test.tsx b/llama_stack/ui/app/chat-playground/chunk-processor.test.tsx new file mode 100644 index 000000000..70e8b3afa --- /dev/null +++ b/llama_stack/ui/app/chat-playground/chunk-processor.test.tsx @@ -0,0 +1,610 @@ +import { describe, test, expect } from "@jest/globals"; + +// Extract the exact processChunk function implementation for testing +function createProcessChunk() { + return (chunk: unknown): { text: string | null; isToolCall: boolean } => { + const chunkObj = chunk as Record; + + // Helper function to check if content contains function call JSON + const containsToolCall = (content: string): boolean => { + return ( + content.includes('"type": "function"') || + content.includes('"name": "knowledge_search"') || + content.includes('"parameters":') || + !!content.match(/\{"type":\s*"function".*?\}/) + ); + }; + + // Check if this chunk contains a tool call (function call) + let isToolCall = false; + + // Check direct chunk content if it's a string + if (typeof chunk === "string") { + isToolCall = containsToolCall(chunk); + } + + // Check delta structures + if ( + chunkObj?.delta && + typeof chunkObj.delta === "object" && + chunkObj.delta !== null + ) { + const delta = chunkObj.delta as Record; + if ("tool_calls" in delta) { + isToolCall = true; + } + if (typeof delta.text === "string") { + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + } + + // Check event structures + if ( + chunkObj?.event && + typeof chunkObj.event === "object" && + chunkObj.event !== null + ) { + const event = chunkObj.event as Record; + + // Check event payload + if ( + event?.payload && + typeof event.payload === "object" && + event.payload !== null + ) { + const payload = event.payload as Record; + if (typeof payload.content === "string") { + if (containsToolCall(payload.content)) { + isToolCall = true; + } + } + + // Check payload delta + if ( + payload?.delta && + typeof payload.delta === "object" && + payload.delta !== null + ) { + const delta = payload.delta as Record; + if (typeof delta.text === "string") { + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + } + } + + // Check event delta + if ( + event?.delta && + typeof event.delta === "object" && + event.delta !== null + ) { + const delta = event.delta as Record; + if (typeof delta.text === "string") { + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + if (typeof delta.content === "string") { + if (containsToolCall(delta.content)) { + isToolCall = true; + } + } + } + } + + // if it's a tool call, skip it (don't display in chat) + if (isToolCall) { + return { text: null, isToolCall: true }; + } + + // Extract text content from various chunk formats + let text: string | null = null; + + // Helper function to extract clean text content, filtering out function calls + const extractCleanText = (content: string): string | null => { + if (containsToolCall(content)) { + try { + // Try to parse and extract non-function call parts + const jsonMatch = content.match( + /\{"type":\s*"function"[^}]*\}[^}]*\}/ + ); + if (jsonMatch) { + const jsonPart = jsonMatch[0]; + const parsedJson = JSON.parse(jsonPart); + + // If it's a function call, extract text after JSON + if (parsedJson.type === "function") { + const textAfterJson = content + .substring(content.indexOf(jsonPart) + jsonPart.length) + .trim(); + return textAfterJson || null; + } + } + // If we can't parse it properly, skip the whole thing + return null; + } catch { + return null; + } + } + return content; + }; + + // Try direct delta text + if ( + chunkObj?.delta && + typeof chunkObj.delta === "object" && + chunkObj.delta !== null + ) { + const delta = chunkObj.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + } + + // Try event structures + if ( + !text && + chunkObj?.event && + typeof chunkObj.event === "object" && + chunkObj.event !== null + ) { + const event = chunkObj.event as Record; + + // Try event payload content + if ( + event?.payload && + typeof event.payload === "object" && + event.payload !== null + ) { + const payload = event.payload as Record; + + // Try direct payload content + if (typeof payload.content === "string") { + text = extractCleanText(payload.content); + } + + // Try turn_complete event structure: payload.turn.output_message.content + if ( + !text && + payload?.turn && + typeof payload.turn === "object" && + payload.turn !== null + ) { + const turn = payload.turn as Record; + if ( + turn?.output_message && + typeof turn.output_message === "object" && + turn.output_message !== null + ) { + const outputMessage = turn.output_message as Record< + string, + unknown + >; + if (typeof outputMessage.content === "string") { + text = extractCleanText(outputMessage.content); + } + } + + // Fallback to model_response in steps if no output_message + if ( + !text && + turn?.steps && + Array.isArray(turn.steps) && + turn.steps.length > 0 + ) { + for (const step of turn.steps) { + if (step && typeof step === "object" && step !== null) { + const stepObj = step as Record; + if ( + stepObj?.model_response && + typeof stepObj.model_response === "object" && + stepObj.model_response !== null + ) { + const modelResponse = stepObj.model_response as Record< + string, + unknown + >; + if (typeof modelResponse.content === "string") { + text = extractCleanText(modelResponse.content); + break; + } + } + } + } + } + } + + // Try payload delta + if ( + !text && + payload?.delta && + typeof payload.delta === "object" && + payload.delta !== null + ) { + const delta = payload.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + } + } + + // Try event delta + if ( + !text && + event?.delta && + typeof event.delta === "object" && + event.delta !== null + ) { + const delta = event.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + if (!text && typeof delta.content === "string") { + text = extractCleanText(delta.content); + } + } + } + + // Try choices structure (ChatML format) + if ( + !text && + chunkObj?.choices && + Array.isArray(chunkObj.choices) && + chunkObj.choices.length > 0 + ) { + const choice = chunkObj.choices[0] as Record; + if ( + choice?.delta && + typeof choice.delta === "object" && + choice.delta !== null + ) { + const delta = choice.delta as Record; + if (typeof delta.content === "string") { + text = extractCleanText(delta.content); + } + } + } + + // Try direct string content + if (!text && typeof chunk === "string") { + text = extractCleanText(chunk); + } + + return { text, isToolCall: false }; + }; +} + +describe("Chunk Processor", () => { + const processChunk = createProcessChunk(); + + describe("Real Event Structures", () => { + test("handles turn_complete event with cancellation policy response", () => { + const chunk = { + event: { + payload: { + event_type: "turn_complete", + turn: { + turn_id: "50a2d6b7-49ed-4d1e-b1c2-6d68b3f726db", + session_id: "e7f62b8e-518c-4450-82df-e65fe49f27a3", + input_messages: [ + { + role: "user", + content: "nice, what's the cancellation policy?", + context: null, + }, + ], + steps: [ + { + turn_id: "50a2d6b7-49ed-4d1e-b1c2-6d68b3f726db", + step_id: "54074310-af42-414c-9ffe-fba5b2ead0ad", + started_at: "2025-08-27T18:15:25.870703Z", + completed_at: "2025-08-27T18:15:51.288993Z", + step_type: "inference", + model_response: { + role: "assistant", + content: + "According to the search results, the cancellation policy for Red Hat Summit is as follows:\n\n* Cancellations must be received by 5 PM EDT on April 18, 2025 for a 50% refund of the registration fee.\n* No refunds will be given for cancellations received after 5 PM EDT on April 18, 2025.\n* Cancellation of travel reservations and hotel reservations are the responsibility of the registrant.", + stop_reason: "end_of_turn", + tool_calls: [], + }, + }, + ], + output_message: { + role: "assistant", + content: + "According to the search results, the cancellation policy for Red Hat Summit is as follows:\n\n* Cancellations must be received by 5 PM EDT on April 18, 2025 for a 50% refund of the registration fee.\n* No refunds will be given for cancellations received after 5 PM EDT on April 18, 2025.\n* Cancellation of travel reservations and hotel reservations are the responsibility of the registrant.", + stop_reason: "end_of_turn", + tool_calls: [], + }, + output_attachments: [], + started_at: "2025-08-27T18:15:25.868548Z", + completed_at: "2025-08-27T18:15:51.289262Z", + }, + }, + }, + }; + + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toContain( + "According to the search results, the cancellation policy for Red Hat Summit is as follows:" + ); + expect(result.text).toContain("5 PM EDT on April 18, 2025"); + }); + + test("handles turn_complete event with address response", () => { + const chunk = { + event: { + payload: { + event_type: "turn_complete", + turn: { + turn_id: "2f4a1520-8ecc-4cb7-bb7b-886939e042b0", + session_id: "e7f62b8e-518c-4450-82df-e65fe49f27a3", + input_messages: [ + { + role: "user", + content: "what's francisco's address", + context: null, + }, + ], + steps: [ + { + turn_id: "2f4a1520-8ecc-4cb7-bb7b-886939e042b0", + step_id: "c13dd277-1acb-4419-8fbf-d5e2f45392ea", + started_at: "2025-08-27T18:14:52.558761Z", + completed_at: "2025-08-27T18:15:11.306032Z", + step_type: "inference", + model_response: { + role: "assistant", + content: + "Francisco Arceo's address is:\n\nRed Hat\nUnited States\n17 Primrose Ln \nBasking Ridge New Jersey 07920", + stop_reason: "end_of_turn", + tool_calls: [], + }, + }, + ], + output_message: { + role: "assistant", + content: + "Francisco Arceo's address is:\n\nRed Hat\nUnited States\n17 Primrose Ln \nBasking Ridge New Jersey 07920", + stop_reason: "end_of_turn", + tool_calls: [], + }, + output_attachments: [], + started_at: "2025-08-27T18:14:52.553707Z", + completed_at: "2025-08-27T18:15:11.306729Z", + }, + }, + }, + }; + + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toContain("Francisco Arceo's address is:"); + expect(result.text).toContain("17 Primrose Ln"); + expect(result.text).toContain("Basking Ridge New Jersey 07920"); + }); + + test("handles turn_complete event with ticket cost response", () => { + const chunk = { + event: { + payload: { + event_type: "turn_complete", + turn: { + turn_id: "7ef244a3-efee-42ca-a9c8-942865251002", + session_id: "e7f62b8e-518c-4450-82df-e65fe49f27a3", + input_messages: [ + { + role: "user", + content: "what was the ticket cost for summit?", + context: null, + }, + ], + steps: [ + { + turn_id: "7ef244a3-efee-42ca-a9c8-942865251002", + step_id: "7651dda0-315a-472d-b1c1-3c2725f55bc5", + started_at: "2025-08-27T18:14:21.710611Z", + completed_at: "2025-08-27T18:14:39.706452Z", + step_type: "inference", + model_response: { + role: "assistant", + content: + "The ticket cost for the Red Hat Summit was $999.00 for a conference pass.", + stop_reason: "end_of_turn", + tool_calls: [], + }, + }, + ], + output_message: { + role: "assistant", + content: + "The ticket cost for the Red Hat Summit was $999.00 for a conference pass.", + stop_reason: "end_of_turn", + tool_calls: [], + }, + output_attachments: [], + started_at: "2025-08-27T18:14:21.705289Z", + completed_at: "2025-08-27T18:14:39.706752Z", + }, + }, + }, + }; + + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe( + "The ticket cost for the Red Hat Summit was $999.00 for a conference pass." + ); + }); + }); + + describe("Function Call Detection", () => { + test("detects function calls in direct string chunks", () => { + const chunk = + '{"type": "function", "name": "knowledge_search", "parameters": {"query": "test"}}'; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + + test("detects function calls in event payload content", () => { + const chunk = { + event: { + payload: { + content: + '{"type": "function", "name": "knowledge_search", "parameters": {"query": "test"}}', + }, + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + + test("detects tool_calls in delta structure", () => { + const chunk = { + delta: { + tool_calls: [{ function: { name: "knowledge_search" } }], + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + + test("detects function call in mixed content but skips it", () => { + const chunk = + '{"type": "function", "name": "knowledge_search", "parameters": {"query": "test"}} Based on the search results, here is your answer.'; + const result = processChunk(chunk); + // This is detected as a tool call and skipped entirely - the implementation prioritizes safety + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + }); + + describe("Text Extraction", () => { + test("extracts text from direct string chunks", () => { + const chunk = "Hello, this is a normal response."; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("Hello, this is a normal response."); + }); + + test("extracts text from delta structure", () => { + const chunk = { + delta: { + text: "Hello, this is a normal response.", + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("Hello, this is a normal response."); + }); + + test("extracts text from choices structure", () => { + const chunk = { + choices: [ + { + delta: { + content: "Hello, this is a normal response.", + }, + }, + ], + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("Hello, this is a normal response."); + }); + + test("prioritizes output_message over model_response in turn structure", () => { + const chunk = { + event: { + payload: { + turn: { + steps: [ + { + model_response: { + content: "Model response content.", + }, + }, + ], + output_message: { + content: "Final output message content.", + }, + }, + }, + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("Final output message content."); + }); + + test("falls back to model_response when no output_message", () => { + const chunk = { + event: { + payload: { + turn: { + steps: [ + { + model_response: { + content: "This is from the model response.", + }, + }, + ], + }, + }, + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("This is from the model response."); + }); + }); + + describe("Edge Cases", () => { + test("handles empty chunks", () => { + const result = processChunk(""); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe(""); + }); + + test("handles null chunks", () => { + const result = processChunk(null); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe(null); + }); + + test("handles undefined chunks", () => { + const result = processChunk(undefined); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe(null); + }); + + test("handles chunks with no text content", () => { + const chunk = { + event: { + metadata: { + timestamp: "2024-01-01", + }, + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe(null); + }); + + test("handles malformed JSON in function calls gracefully", () => { + const chunk = + '{"type": "function", "name": "knowledge_search"} incomplete json'; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + }); +}); diff --git a/llama_stack/ui/app/chat-playground/page.test.tsx b/llama_stack/ui/app/chat-playground/page.test.tsx index 54c15f95a..d9025e523 100644 --- a/llama_stack/ui/app/chat-playground/page.test.tsx +++ b/llama_stack/ui/app/chat-playground/page.test.tsx @@ -31,6 +31,9 @@ const mockClient = { toolgroups: { list: jest.fn(), }, + vectorDBs: { + list: jest.fn(), + }, }; jest.mock("@/hooks/use-auth-client", () => ({ @@ -164,7 +167,7 @@ describe("ChatPlaygroundPage", () => { session_name: "Test Session", started_at: new Date().toISOString(), turns: [], - }); // No turns by default + }); mockClient.agents.retrieve.mockResolvedValue({ agent_id: "test-agent", agent_config: { @@ -417,7 +420,6 @@ describe("ChatPlaygroundPage", () => { }); await waitFor(() => { - // first agent should be auto-selected expect(mockClient.agents.session.create).toHaveBeenCalledWith( "agent_123", { session_name: "Default Session" } @@ -464,7 +466,7 @@ describe("ChatPlaygroundPage", () => { }); }); - test("hides delete button when only one agent exists", async () => { + test("shows delete button even when only one agent exists", async () => { mockClient.agents.list.mockResolvedValue({ data: [mockAgents[0]], }); @@ -474,9 +476,7 @@ describe("ChatPlaygroundPage", () => { }); await waitFor(() => { - expect( - screen.queryByTitle("Delete current agent") - ).not.toBeInTheDocument(); + expect(screen.getByTitle("Delete current agent")).toBeInTheDocument(); }); }); @@ -505,7 +505,7 @@ describe("ChatPlaygroundPage", () => { await waitFor(() => { expect(mockClient.agents.delete).toHaveBeenCalledWith("agent_123"); expect(global.confirm).toHaveBeenCalledWith( - "Are you sure you want to delete this agent? This action cannot be undone and will delete all associated sessions." + "Are you sure you want to delete this agent? This action cannot be undone and will delete the agent and all its sessions." ); }); @@ -584,4 +584,207 @@ describe("ChatPlaygroundPage", () => { consoleSpy.mockRestore(); }); }); + + describe("RAG File Upload", () => { + let mockFileReader: { + readAsDataURL: jest.Mock; + readAsText: jest.Mock; + result: string | null; + onload: (() => void) | null; + onerror: (() => void) | null; + }; + let mockRAGTool: { + insert: jest.Mock; + }; + + beforeEach(() => { + mockFileReader = { + readAsDataURL: jest.fn(), + readAsText: jest.fn(), + result: null, + onload: null, + onerror: null, + }; + global.FileReader = jest.fn(() => mockFileReader); + + mockRAGTool = { + insert: jest.fn().mockResolvedValue({}), + }; + mockClient.toolRuntime = { + ragTool: mockRAGTool, + }; + }); + + afterEach(() => { + jest.clearAllMocks(); + }); + + test("handles text file upload", async () => { + new File(["Hello, world!"], "test.txt", { + type: "text/plain", + }); + + mockClient.agents.retrieve.mockResolvedValue({ + agent_id: "test-agent", + agent_config: { + toolgroups: [ + { + name: "builtin::rag/knowledge_search", + args: { vector_db_ids: ["test-vector-db"] }, + }, + ], + }, + }); + + await act(async () => { + render(); + }); + + await waitFor(() => { + expect(screen.getByTestId("chat-component")).toBeInTheDocument(); + }); + + const chatComponent = screen.getByTestId("chat-component"); + chatComponent.getAttribute("data-onragfileupload"); + + // this is a simplified test + expect(mockRAGTool.insert).not.toHaveBeenCalled(); + }); + + test("handles PDF file upload with FileReader", async () => { + new File([new ArrayBuffer(1000)], "test.pdf", { + type: "application/pdf", + }); + + const mockDataURL = "data:application/pdf;base64,JVBERi0xLjQK"; + mockFileReader.result = mockDataURL; + + mockClient.agents.retrieve.mockResolvedValue({ + agent_id: "test-agent", + agent_config: { + toolgroups: [ + { + name: "builtin::rag/knowledge_search", + args: { vector_db_ids: ["test-vector-db"] }, + }, + ], + }, + }); + + await act(async () => { + render(); + }); + + await waitFor(() => { + expect(screen.getByTestId("chat-component")).toBeInTheDocument(); + }); + + expect(global.FileReader).toBeDefined(); + }); + + test("handles different file types correctly", () => { + const getContentType = (filename: string): string => { + const ext = filename.toLowerCase().split(".").pop(); + switch (ext) { + case "pdf": + return "application/pdf"; + case "txt": + return "text/plain"; + case "md": + return "text/markdown"; + case "html": + return "text/html"; + case "csv": + return "text/csv"; + case "json": + return "application/json"; + case "docx": + return "application/vnd.openxmlformats-officedocument.wordprocessingml.document"; + case "doc": + return "application/msword"; + default: + return "application/octet-stream"; + } + }; + + expect(getContentType("test.pdf")).toBe("application/pdf"); + expect(getContentType("test.txt")).toBe("text/plain"); + expect(getContentType("test.md")).toBe("text/markdown"); + expect(getContentType("test.html")).toBe("text/html"); + expect(getContentType("test.csv")).toBe("text/csv"); + expect(getContentType("test.json")).toBe("application/json"); + expect(getContentType("test.docx")).toBe( + "application/vnd.openxmlformats-officedocument.wordprocessingml.document" + ); + expect(getContentType("test.doc")).toBe("application/msword"); + expect(getContentType("test.unknown")).toBe("application/octet-stream"); + }); + + test("determines text vs binary file types correctly", () => { + const isTextFile = (mimeType: string): boolean => { + return ( + mimeType.startsWith("text/") || + mimeType === "application/json" || + mimeType === "text/markdown" || + mimeType === "text/html" || + mimeType === "text/csv" + ); + }; + + expect(isTextFile("text/plain")).toBe(true); + expect(isTextFile("text/markdown")).toBe(true); + expect(isTextFile("text/html")).toBe(true); + expect(isTextFile("text/csv")).toBe(true); + expect(isTextFile("application/json")).toBe(true); + + expect(isTextFile("application/pdf")).toBe(false); + expect(isTextFile("application/msword")).toBe(false); + expect( + isTextFile( + "application/vnd.openxmlformats-officedocument.wordprocessingml.document" + ) + ).toBe(false); + expect(isTextFile("application/octet-stream")).toBe(false); + }); + + test("handles FileReader error gracefully", async () => { + const pdfFile = new File([new ArrayBuffer(1000)], "test.pdf", { + type: "application/pdf", + }); + + mockFileReader.onerror = jest.fn(); + const mockError = new Error("FileReader failed"); + + const fileReaderPromise = new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onload = () => resolve(reader.result as string); + reader.onerror = () => reject(reader.error || mockError); + reader.readAsDataURL(pdfFile); + + setTimeout(() => { + reader.onerror?.(new ProgressEvent("error")); + }, 0); + }); + + await expect(fileReaderPromise).rejects.toBeDefined(); + }); + + test("handles large file upload with FileReader approach", () => { + // create a large file + const largeFile = new File( + [new ArrayBuffer(10 * 1024 * 1024)], + "large.pdf", + { + type: "application/pdf", + } + ); + + expect(largeFile.size).toBe(10 * 1024 * 1024); // 10MB + + expect(global.FileReader).toBeDefined(); + + const reader = new FileReader(); + expect(reader.readAsDataURL).toBeDefined(); + }); + }); }); diff --git a/llama_stack/ui/app/chat-playground/page.tsx b/llama_stack/ui/app/chat-playground/page.tsx index f26791a41..0417f7083 100644 --- a/llama_stack/ui/app/chat-playground/page.tsx +++ b/llama_stack/ui/app/chat-playground/page.tsx @@ -15,6 +15,7 @@ import { Input } from "@/components/ui/input"; import { Trash2 } from "lucide-react"; import { Chat } from "@/components/chat-playground/chat"; import { type Message } from "@/components/chat-playground/chat-message"; +import { VectorDBCreator } from "@/components/chat-playground/vector-db-creator"; import { useAuthClient } from "@/hooks/use-auth-client"; import type { Model } from "llama-stack-client/resources/models"; import type { TurnCreateParams } from "llama-stack-client/resources/agents/turn"; @@ -22,6 +23,10 @@ import { SessionUtils, type ChatSession, } from "@/components/chat-playground/conversations"; +import { + cleanMessageContent, + extractCleanText, +} from "@/lib/message-content-utils"; export default function ChatPlaygroundPage() { const [currentSession, setCurrentSession] = useState( null @@ -65,6 +70,20 @@ export default function ChatPlaygroundPage() { provider_resource_id?: string; }> >([]); + const [showCreateVectorDB, setShowCreateVectorDB] = useState(false); + const [availableVectorDBs, setAvailableVectorDBs] = useState< + Array<{ + identifier: string; + vector_db_name?: string; + embedding_model: string; + }> + >([]); + const [uploadNotification, setUploadNotification] = useState<{ + show: boolean; + message: string; + type: "success" | "error" | "loading"; + }>({ show: false, message: "", type: "success" }); + const [selectedVectorDBs, setSelectedVectorDBs] = useState([]); const client = useAuthClient(); const abortControllerRef = useRef(null); @@ -73,26 +92,22 @@ export default function ChatPlaygroundPage() { const loadAgentConfig = useCallback( async (agentId: string) => { try { - console.log("Loading agent config for:", agentId); - // try to load from cache first const cachedConfig = SessionUtils.loadAgentConfig(agentId); if (cachedConfig) { - console.log("✅ Loaded agent config from cache:", cachedConfig); setSelectedAgentConfig({ toolgroups: cachedConfig.toolgroups, }); return; } - console.log("📡 Fetching agent config from API..."); const agentDetails = await client.agents.retrieve(agentId); - console.log("Agent details retrieved:", agentDetails); - console.log("Agent config:", agentDetails.agent_config); - console.log("Agent toolgroups:", agentDetails.agent_config?.toolgroups); - // cache the config - SessionUtils.saveAgentConfig(agentId, agentDetails.agent_config); + // cache config + SessionUtils.saveAgentConfig(agentId, { + ...agentDetails.agent_config, + toolgroups: agentDetails.agent_config?.toolgroups, + }); setSelectedAgentConfig({ toolgroups: agentDetails.agent_config?.toolgroups, @@ -116,7 +131,7 @@ export default function ChatPlaygroundPage() { id: response.session_id, name: "Default Session", messages: [], - selectedModel: selectedModel, // Use current selected model + selectedModel: selectedModel, // use current selected model systemMessage: "You are a helpful assistant.", agentId, createdAt: Date.now(), @@ -124,10 +139,6 @@ export default function ChatPlaygroundPage() { }; setCurrentSession(defaultSession); - console.log( - `💾 Saving default session ID for agent ${agentId}:`, - defaultSession.id - ); SessionUtils.saveCurrentSessionId(defaultSession.id, agentId); // cache entire session data SessionUtils.saveSessionData(agentId, defaultSession); @@ -152,7 +163,6 @@ export default function ChatPlaygroundPage() { const messages: Message[] = []; for (const turn of session.turns) { - // add user messages if (turn.input_messages && Array.isArray(turn.input_messages)) { for (const input of turn.input_messages) { if (input.role === "user" && input.content) { @@ -169,15 +179,18 @@ export default function ChatPlaygroundPage() { } } - // add assistant message from output_message if (turn.output_message && turn.output_message.content) { + console.log("Raw message content:", turn.output_message.content); + console.log("Content type:", typeof turn.output_message.content); + + const cleanContent = cleanMessageContent( + turn.output_message.content + ); + messages.push({ id: `${turn.turn_id}-assistant-${messages.length}`, role: "assistant", - content: - typeof turn.output_message.content === "string" - ? turn.output_message.content - : JSON.stringify(turn.output_message.content), + content: cleanContent, createdAt: new Date( turn.completed_at || turn.started_at || Date.now() ), @@ -197,27 +210,22 @@ export default function ChatPlaygroundPage() { const loadAgentSessions = useCallback( async (agentId: string) => { try { - console.log("Loading sessions for agent:", agentId); const response = await client.agents.session.list(agentId); - console.log("Available sessions:", response.data); if ( response.data && Array.isArray(response.data) && response.data.length > 0 ) { - // check for a previously saved session ID for this specific agent + // check for saved session ID for this agent const savedSessionId = SessionUtils.loadCurrentSessionId(agentId); - console.log(`Saved session ID for agent ${agentId}:`, savedSessionId); - - // try to load cached session data first + // try to load cached agent session data first if (savedSessionId) { const cachedSession = SessionUtils.loadSessionData( agentId, savedSessionId ); if (cachedSession) { - console.log("✅ Loaded session from cache:", cachedSession.id); setCurrentSession(cachedSession); SessionUtils.saveCurrentSessionId(cachedSession.id, agentId); return; @@ -238,7 +246,8 @@ export default function ChatPlaygroundPage() { // try to find saved session id in available sessions if (savedSessionId) { const foundSession = response.data.find( - (s: { session_id: string }) => s.session_id === savedSessionId + (s: { [key: string]: unknown }) => + (s as { session_id: string }).session_id === savedSessionId ); console.log("Found saved session in list:", foundSession); if (foundSession) { @@ -269,7 +278,7 @@ export default function ChatPlaygroundPage() { id: sessionToLoad.session_id, name: sessionToLoad.session_name || "Session", messages, - selectedModel: selectedModel || "", // Preserve current model or use empty + selectedModel: selectedModel || "", systemMessage: "You are a helpful assistant.", agentId, createdAt: sessionToLoad.started_at @@ -330,7 +339,8 @@ export default function ChatPlaygroundPage() { // if we have a saved agent ID, find it in the available agents if (savedAgentId) { const foundAgent = agentList.data.find( - (a: { agent_id: string }) => a.agent_id === savedAgentId + (a: { [key: string]: unknown }) => + (a as { agent_id: string }).agent_id === savedAgentId ); if (foundAgent) { agentToSelect = foundAgent as typeof agentToSelect; @@ -353,14 +363,10 @@ export default function ChatPlaygroundPage() { fetchAgents(); - // fetch available toolgroups const fetchToolgroups = async () => { try { - console.log("Fetching toolgroups..."); const toolgroups = await client.toolgroups.list(); - console.log("Toolgroups response:", toolgroups); - // The client returns data directly, not wrapped in .data const toolGroupsArray = Array.isArray(toolgroups) ? toolgroups : toolgroups && @@ -381,7 +387,6 @@ export default function ChatPlaygroundPage() { if (toolGroupsArray && Array.isArray(toolGroupsArray)) { setAvailableToolgroups(toolGroupsArray); - console.log("Set toolgroups:", toolGroupsArray); } else { console.error("Invalid toolgroups data format:", toolgroups); } @@ -398,6 +403,24 @@ export default function ChatPlaygroundPage() { }; fetchToolgroups(); + + const fetchVectorDBs = async () => { + try { + const vectorDBs = await client.vectorDBs.list(); + + const vectorDBsArray = Array.isArray(vectorDBs) ? vectorDBs : []; + + if (vectorDBsArray && Array.isArray(vectorDBsArray)) { + setAvailableVectorDBs(vectorDBsArray); + } else { + console.error("Invalid vector DBs data format:", vectorDBs); + } + } catch (error) { + console.error("Error fetching vector DBs:", error); + } + }; + + fetchVectorDBs(); }, [client, loadAgentSessions, loadAgentConfig]); const createNewAgent = useCallback( @@ -405,24 +428,35 @@ export default function ChatPlaygroundPage() { name: string, instructions: string, model: string, - toolgroups: string[] = [] + toolgroups: string[] = [], + vectorDBs: string[] = [] ) => { try { - console.log("Creating agent with toolgroups:", toolgroups); + const processedToolgroups = toolgroups.map(toolgroup => { + if (toolgroup === "builtin::rag" && vectorDBs.length > 0) { + return { + name: "builtin::rag/knowledge_search", + args: { + vector_db_ids: vectorDBs, + }, + }; + } + return toolgroup; + }); + const agentConfig = { model, instructions, name: name || undefined, enable_session_persistence: true, - toolgroups: toolgroups.length > 0 ? toolgroups : undefined, + toolgroups: + processedToolgroups.length > 0 ? processedToolgroups : undefined, }; - console.log("Agent config being sent:", agentConfig); const response = await client.agents.create({ agent_config: agentConfig, }); - // refresh agents list const agentList = await client.agents.list(); setAgents( (agentList.data as Array<{ @@ -436,7 +470,6 @@ export default function ChatPlaygroundPage() { }>) || [] ); - // set the new agent as selected setSelectedAgentId(response.agent_id); await loadAgentConfig(response.agent_id); await loadAgentSessions(response.agent_id); @@ -450,24 +483,47 @@ export default function ChatPlaygroundPage() { [client, loadAgentSessions, loadAgentConfig] ); + const handleVectorDBCreated = useCallback( + // eslint-disable-next-line @typescript-eslint/no-unused-vars + async (_vectorDbId: string) => { + setShowCreateVectorDB(false); + + try { + const vectorDBs = await client.vectorDBs.list(); + const vectorDBsArray = Array.isArray(vectorDBs) ? vectorDBs : []; + + if (vectorDBsArray && Array.isArray(vectorDBsArray)) { + setAvailableVectorDBs(vectorDBsArray); + } + } catch (error) { + console.error("Error refreshing vector DBs:", error); + } + }, + [client] + ); + const deleteAgent = useCallback( async (agentId: string) => { - if (agents.length <= 1) { - return; - } - if ( confirm( - "Are you sure you want to delete this agent? This action cannot be undone and will delete all associated sessions." + "Are you sure you want to delete this agent? This action cannot be undone and will delete the agent and all its sessions." ) ) { try { - await client.agents.delete(agentId); + // there's a known error where the delete API returns 500 even on success + try { + await client.agents.delete(agentId); + console.log("Agent deleted successfully"); + } catch (deleteError) { + // log the error but don't re-throw - we know deletion succeeded + console.log( + "Agent delete API returned error (but deletion likely succeeded):", + deleteError + ); + } - // clear cached data for agent SessionUtils.clearAgentCache(agentId); - // Refresh agents list const agentList = await client.agents.list(); setAgents( (agentList.data as Array<{ @@ -481,10 +537,11 @@ export default function ChatPlaygroundPage() { }>) || [] ); - // if we deleted the current agent, switch to another one + // if we delete current agent, switch to another if (selectedAgentId === agentId) { const remainingAgents = agentList.data?.filter( - (a: { agent_id: string }) => a.agent_id !== agentId + (a: { [key: string]: unknown }) => + (a as { agent_id: string }).agent_id !== agentId ); if (remainingAgents && remainingAgents.length > 0) { const newAgent = remainingAgents[0] as { @@ -501,7 +558,7 @@ export default function ChatPlaygroundPage() { await loadAgentConfig(newAgent.agent_id); await loadAgentSessions(newAgent.agent_id); } else { - // No agents left + // no agents left setSelectedAgentId(""); setCurrentSession(null); setSelectedAgentConfig(null); @@ -509,10 +566,76 @@ export default function ChatPlaygroundPage() { } } catch (error) { console.error("Error deleting agent:", error); + + // check if this is known server bug where deletion succeeds but returns 500 + // The error message will typically contain status codes or "Could not find agent" + const errorMessage = + error instanceof Error ? error.message : String(error); + const isKnownServerBug = + errorMessage.includes("500") || + errorMessage.includes("Internal Server Error") || + errorMessage.includes("Could not find agent") || + errorMessage.includes("400"); + + if (isKnownServerBug) { + console.log( + "Agent deletion succeeded despite error, cleaning up UI" + ); + SessionUtils.clearAgentCache(agentId); + try { + const agentList = await client.agents.list(); + setAgents( + (agentList.data as Array<{ + agent_id: string; + agent_config?: { + agent_name?: string; + name?: string; + instructions?: string; + }; + [key: string]: unknown; + }>) || [] + ); + + if (selectedAgentId === agentId) { + const remainingAgents = agentList.data?.filter( + (a: { [key: string]: unknown }) => + (a as { agent_id: string }).agent_id !== agentId + ); + if (remainingAgents && remainingAgents.length > 0) { + const newAgent = remainingAgents[0] as { + agent_id: string; + agent_config?: { + agent_name?: string; + name?: string; + instructions?: string; + }; + [key: string]: unknown; + }; + setSelectedAgentId(newAgent.agent_id); + SessionUtils.saveCurrentAgentId(newAgent.agent_id); + await loadAgentConfig(newAgent.agent_id); + await loadAgentSessions(newAgent.agent_id); + } else { + // no agents left + setSelectedAgentId(""); + setCurrentSession(null); + setSelectedAgentConfig(null); + } + } + } catch (refreshError) { + console.error("Error refreshing agents list:", refreshError); + } + } else { + // show error that we don't know about to user + console.error("Unexpected error during agent deletion:", error); + if (error instanceof Error) { + alert(`Failed to delete agent: ${error.message}`); + } + } } } }, - [agents.length, client, selectedAgentId, loadAgentConfig, loadAgentSessions] + [client, selectedAgentId, loadAgentConfig, loadAgentSessions] ); const handleModelChange = useCallback((newModel: string) => { @@ -530,10 +653,6 @@ export default function ChatPlaygroundPage() { useEffect(() => { if (currentSession) { - console.log( - `💾 Auto-saving session ID for agent ${currentSession.agentId}:`, - currentSession.id - ); SessionUtils.saveCurrentSessionId( currentSession.id, currentSession.agentId @@ -556,8 +675,12 @@ export default function ChatPlaygroundPage() { setModelsLoading(true); setModelsError(null); const modelList = await client.models.list(); + + // store all models (including embedding models for vector DB creation) + setModels(modelList); + + // set default LLM model for chat const llmModels = modelList.filter(model => model.model_type === "llm"); - setModels(llmModels); if (llmModels.length > 0) { handleModelChange(llmModels[0].identifier); } @@ -614,7 +737,7 @@ export default function ChatPlaygroundPage() { messages: [...prev.messages, userMessage], updatedAt: Date.now(), }; - // Update cache with new message + // update cache with new message SessionUtils.saveSessionData(prev.agentId, updatedSession); return updatedSession; }); @@ -653,7 +776,8 @@ export default function ChatPlaygroundPage() { turnParams, { signal: abortController.signal, - } as { signal: AbortSignal } + timeout: 300000, // 5 minutes timeout for RAG queries + } as { signal: AbortSignal; timeout: number } ); const assistantMessage: Message = { @@ -663,42 +787,242 @@ export default function ChatPlaygroundPage() { createdAt: new Date(), }; - const extractDeltaText = (chunk: unknown): string | null => { - // this is an awful way to handle different chunk formats, but i'm not sure if there's much of a better way - if (chunk?.delta?.text && typeof chunk.delta.text === "string") { - return chunk.delta.text; - } + const processChunk = ( + chunk: unknown + ): { text: string | null; isToolCall: boolean } => { + const chunkObj = chunk as Record; - if ( - chunk?.event?.delta?.text && - typeof chunk.event.delta.text === "string" - ) { - return chunk.event.delta.text; - } + // helper to check if content contains function call JSON + const containsToolCall = (content: string): boolean => { + return ( + content.includes('"type": "function"') || + content.includes('"name": "knowledge_search"') || + content.includes('"parameters":') || + !!content.match(/\{"type":\s*"function".*?\}/) + ); + }; - if ( - chunk?.choices?.[0]?.delta?.content && - typeof chunk.choices[0].delta.content === "string" - ) { - return chunk.choices[0].delta.content; - } + let isToolCall = false; + let potentialContent = ""; if (typeof chunk === "string") { - return chunk; + potentialContent = chunk; + isToolCall = containsToolCall(chunk); } if ( - chunk?.event?.payload?.delta?.text && - typeof chunk.event.payload.delta.text === "string" + chunkObj?.delta && + typeof chunkObj.delta === "object" && + chunkObj.delta !== null ) { - return chunk.event.payload.delta.text; + const delta = chunkObj.delta as Record; + if ("tool_calls" in delta) { + isToolCall = true; + } + if (typeof delta.text === "string") { + potentialContent = delta.text; + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } } - if (process.env.NODE_ENV !== "production") { - console.debug("Unrecognized chunk format:", chunk); + if ( + chunkObj?.event && + typeof chunkObj.event === "object" && + chunkObj.event !== null + ) { + const event = chunkObj.event as Record; + + if ( + event?.payload && + typeof event.payload === "object" && + event.payload !== null + ) { + const payload = event.payload as Record; + if (typeof payload.content === "string") { + potentialContent = payload.content; + if (containsToolCall(payload.content)) { + isToolCall = true; + } + } + + if ( + payload?.delta && + typeof payload.delta === "object" && + payload.delta !== null + ) { + const delta = payload.delta as Record; + if (typeof delta.text === "string") { + potentialContent = delta.text; + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + } + } + + if ( + event?.delta && + typeof event.delta === "object" && + event.delta !== null + ) { + const delta = event.delta as Record; + if (typeof delta.text === "string") { + potentialContent = delta.text; + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + if (typeof delta.content === "string") { + // eslint-disable-next-line @typescript-eslint/no-unused-vars + potentialContent = delta.content; + if (containsToolCall(delta.content)) { + isToolCall = true; + } + } + } } - return null; + // if it's a tool call, skip it (don't display in chat) + if (isToolCall) { + return { text: null, isToolCall: true }; + } + + let text: string | null = null; + + if ( + chunkObj?.delta && + typeof chunkObj.delta === "object" && + chunkObj.delta !== null + ) { + const delta = chunkObj.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + } + + if ( + !text && + chunkObj?.event && + typeof chunkObj.event === "object" && + chunkObj.event !== null + ) { + const event = chunkObj.event as Record; + + if ( + event?.payload && + typeof event.payload === "object" && + event.payload !== null + ) { + const payload = event.payload as Record; + + if (typeof payload.content === "string") { + text = extractCleanText(payload.content); + } + + if ( + !text && + payload?.turn && + typeof payload.turn === "object" && + payload.turn !== null + ) { + const turn = payload.turn as Record; + if ( + turn?.output_message && + typeof turn.output_message === "object" && + turn.output_message !== null + ) { + const outputMessage = turn.output_message as Record< + string, + unknown + >; + if (typeof outputMessage.content === "string") { + text = extractCleanText(outputMessage.content); + } + } + + if ( + !text && + turn?.steps && + Array.isArray(turn.steps) && + turn.steps.length > 0 + ) { + for (const step of turn.steps) { + if (step && typeof step === "object" && step !== null) { + const stepObj = step as Record; + if ( + stepObj?.model_response && + typeof stepObj.model_response === "object" && + stepObj.model_response !== null + ) { + const modelResponse = stepObj.model_response as Record< + string, + unknown + >; + if (typeof modelResponse.content === "string") { + text = extractCleanText(modelResponse.content); + break; + } + } + } + } + } + } + + if ( + !text && + payload?.delta && + typeof payload.delta === "object" && + payload.delta !== null + ) { + const delta = payload.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + } + } + + if ( + !text && + event?.delta && + typeof event.delta === "object" && + event.delta !== null + ) { + const delta = event.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + if (!text && typeof delta.content === "string") { + text = extractCleanText(delta.content); + } + } + } + + if ( + !text && + chunkObj?.choices && + Array.isArray(chunkObj.choices) && + chunkObj.choices.length > 0 + ) { + const choice = chunkObj.choices[0] as Record; + if ( + choice?.delta && + typeof choice.delta === "object" && + choice.delta !== null + ) { + const delta = choice.delta as Record; + if (typeof delta.content === "string") { + text = extractCleanText(delta.content); + } + } + } + + if (!text && typeof chunk === "string") { + text = extractCleanText(chunk); + } + + return { text, isToolCall: false }; }; setCurrentSession(prev => { if (!prev) return null; @@ -713,8 +1037,34 @@ export default function ChatPlaygroundPage() { }); let fullContent = ""; + for await (const chunk of response) { - const deltaText = extractDeltaText(chunk); + const { text: deltaText } = processChunk(chunk); + + // logging for debugging function calls + // if (deltaText && deltaText.includes("knowledge_search")) { + // console.log("🔍 Function call detected in text output:", deltaText); + // console.log("🔍 Original chunk:", JSON.stringify(chunk, null, 2)); + // } + + if (chunk && typeof chunk === "object" && "event" in chunk) { + const event = ( + chunk as { + event: { + payload?: { + event_type?: string; + turn?: { output_message?: { content?: string } }; + }; + }; + } + ).event; + if (event?.payload?.event_type === "turn_complete") { + const content = event?.payload?.turn?.output_message?.content; + if (content && content.includes("knowledge_search")) { + console.log("🔍 Function call found in turn_complete:", content); + } + } + } if (deltaText) { fullContent += deltaText; @@ -732,9 +1082,9 @@ export default function ChatPlaygroundPage() { messages: newMessages, updatedAt: Date.now(), }; - // update cache with streaming content (throttled) + // update cache with streaming content if (fullContent.length % 100 === 0) { - // Only cache every 100 characters to avoid spam + // Only cache every 100 characters SessionUtils.saveSessionData(prev.agentId, updatedSession); } return updatedSession; @@ -809,8 +1159,180 @@ export default function ChatPlaygroundPage() { setError(null); }; + const handleRAGFileUpload = async (file: File) => { + if (!selectedAgentConfig?.toolgroups || !selectedAgentId) { + setError("No agent selected or agent has no RAG tools configured"); + return; + } + + // find RAG toolgroups that have vector_db_ids configured + const ragToolgroups = selectedAgentConfig.toolgroups.filter(toolgroup => { + if (typeof toolgroup === "object" && toolgroup.name?.includes("rag")) { + return toolgroup.args && "vector_db_ids" in toolgroup.args; + } + return false; + }); + + if (ragToolgroups.length === 0) { + setError("Current agent has no vector databases configured for RAG"); + return; + } + + try { + setError(null); + console.log("Uploading file using RAG tool..."); + + setUploadNotification({ + show: true, + message: `📄 Uploading and indexing "${file.name}"...`, + type: "loading", + }); + + const vectorDbIds = ragToolgroups.flatMap(toolgroup => { + if ( + typeof toolgroup === "object" && + toolgroup.args && + "vector_db_ids" in toolgroup.args + ) { + return toolgroup.args.vector_db_ids as string[]; + } + return []; + }); + + // determine mime type from file extension - this should be in the Llama Stack Client IMO + const getContentType = (filename: string): string => { + const ext = filename.toLowerCase().split(".").pop(); + switch (ext) { + case "pdf": + return "application/pdf"; + case "txt": + return "text/plain"; + case "md": + return "text/markdown"; + case "html": + return "text/html"; + case "csv": + return "text/csv"; + case "json": + return "application/json"; + case "docx": + return "application/vnd.openxmlformats-officedocument.wordprocessingml.document"; + case "doc": + return "application/msword"; + default: + return "application/octet-stream"; + } + }; + + const mimeType = getContentType(file.name); + let fileContent: string; + + // handle text files vs binary files differently + const isTextFile = + mimeType.startsWith("text/") || + mimeType === "application/json" || + mimeType === "text/markdown" || + mimeType === "text/html" || + mimeType === "text/csv"; + + if (isTextFile) { + fileContent = await file.text(); + } else { + // for PDFs and other binary files, create a data URL + // use FileReader for efficient base64 conversion + fileContent = await new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onload = () => resolve(reader.result as string); + reader.onerror = () => reject(reader.error); + reader.readAsDataURL(file); + }); + } + + for (const vectorDbId of vectorDbIds) { + await client.toolRuntime.ragTool.insert({ + documents: [ + { + content: fileContent, + document_id: `${file.name}-${Date.now()}`, + metadata: { + filename: file.name, + file_size: file.size, + uploaded_at: new Date().toISOString(), + agent_id: selectedAgentId, + }, + mime_type: mimeType, + }, + ], + vector_db_id: vectorDbId, + // TODO: parameterize this somewhere, probably in settings + chunk_size_in_tokens: 512, + }); + } + + console.log("✅ File successfully uploaded using RAG tool"); + + setUploadNotification({ + show: true, + message: `📄 File "${file.name}" uploaded and indexed successfully!`, + type: "success", + }); + + setTimeout(() => { + setUploadNotification(prev => ({ ...prev, show: false })); + }, 4000); + } catch (err) { + console.error("Error uploading file using RAG tool:", err); + const errorMessage = + err instanceof Error + ? `Failed to upload file: ${err.message}` + : "Failed to upload file using RAG tool"; + + setUploadNotification({ + show: true, + message: errorMessage, + type: "error", + }); + + setTimeout(() => { + setUploadNotification(prev => ({ ...prev, show: false })); + }, 6000); + } + }; + return (
+ {/* Upload Notification */} + {uploadNotification.show && ( +
+
+ {uploadNotification.type === "loading" && ( +
+ )} + + {uploadNotification.message} + + {uploadNotification.type !== "loading" && ( + + )} +
+
+ )} + {/* Header */}
@@ -822,7 +1344,6 @@ export default function ChatPlaygroundPage() { - {selectedAgentId && agents.length > 1 && ( + {selectedAgentId && (
)} - - setCurrentSession(prev => - prev ? { ...prev, messages, updatedAt: Date.now() } : prev - ) - } - /> + {!agentsLoading && agents.length === 0 ? ( +
+
+
🦙
+

+ Create an Agent with Llama Stack +

+

+ To get started, create your first agent. Each agent is + configured with specific instructions, models, and tools to + help you with different tasks. +

+ +
+
+ ) : ( + + setCurrentSession(prev => + prev ? { ...prev, messages, updatedAt: Date.now() } : prev + ) + } + onRAGFileUpload={handleRAGFileUpload} + /> + )}
@@ -1086,14 +1662,16 @@ export default function ChatPlaygroundPage() { - {models.map(model => ( - - {model.identifier} - - ))} + {models + .filter(model => model.model_type === "llm") + .map(model => ( + + {model.identifier} + + ))} @@ -1137,21 +1715,12 @@ export default function ChatPlaygroundPage() { toolgroup.identifier )} onChange={e => { - console.log( - "Tool selection changed:", - toolgroup.identifier, - e.target.checked - ); if (e.target.checked) { setSelectedToolgroups(prev => { const newSelection = [ ...prev, toolgroup.identifier, ]; - console.log( - "New selected toolgroups:", - newSelection - ); return newSelection; }); } else { @@ -1159,10 +1728,6 @@ export default function ChatPlaygroundPage() { const newSelection = prev.filter( id => id !== toolgroup.identifier ); - console.log( - "New selected toolgroups:", - newSelection - ); return newSelection; }); } @@ -1194,6 +1759,80 @@ export default function ChatPlaygroundPage() { text generation agents work without tools.

+ + {/* Vector DB Configuration for RAG */} + {selectedToolgroups.includes("builtin::rag") && ( +
+ +
+ + + {availableVectorDBs.length} available + +
+
+ {availableVectorDBs.length === 0 ? ( +

+ No vector databases available. Create one to use RAG + tools. +

+ ) : ( + availableVectorDBs.map(vectorDB => ( + + )) + )} +
+ {selectedVectorDBs.length === 0 && + selectedToolgroups.includes("builtin::rag") && ( +

+ ⚠️ RAG tool selected but no vector databases chosen. + Create or select a vector database. +

+ )} +
+ )}
@@ -1204,12 +1843,14 @@ export default function ChatPlaygroundPage() { newAgentName, newAgentInstructions, selectedModel, - selectedToolgroups + selectedToolgroups, + selectedVectorDBs ); setShowCreateAgent(false); setNewAgentName(""); setNewAgentInstructions("You are a helpful assistant."); setSelectedToolgroups([]); + setSelectedVectorDBs([]); } catch (error) { console.error("Failed to create agent:", error); } @@ -1226,6 +1867,7 @@ export default function ChatPlaygroundPage() { setNewAgentName(""); setNewAgentInstructions("You are a helpful assistant."); setSelectedToolgroups([]); + setSelectedVectorDBs([]); }} className="flex-1" > @@ -1235,6 +1877,17 @@ export default function ChatPlaygroundPage() {
)} + + {/* Create Vector DB Modal */} + {showCreateVectorDB && ( +
+ setShowCreateVectorDB(false)} + /> +
+ )} ); } diff --git a/llama_stack/ui/components/chat-playground/chat.tsx b/llama_stack/ui/components/chat-playground/chat.tsx index 023bf0728..3b37c4dfe 100644 --- a/llama_stack/ui/components/chat-playground/chat.tsx +++ b/llama_stack/ui/components/chat-playground/chat.tsx @@ -35,6 +35,7 @@ interface ChatPropsBase { ) => void; setMessages?: (messages: Message[]) => void; transcribeAudio?: (blob: Blob) => Promise; + onRAGFileUpload?: (file: File) => Promise; } interface ChatPropsWithoutSuggestions extends ChatPropsBase { @@ -62,6 +63,7 @@ export function Chat({ onRateResponse, setMessages, transcribeAudio, + onRAGFileUpload, }: ChatProps) { const lastMessage = messages.at(-1); const isEmpty = messages.length === 0; @@ -226,16 +228,17 @@ export function Chat({ isPending={isGenerating || isTyping} handleSubmit={handleSubmit} > - {({ files, setFiles }) => ( + {() => ( {}} stop={handleStop} isGenerating={isGenerating} transcribeAudio={transcribeAudio} + onRAGFileUpload={onRAGFileUpload} /> )} diff --git a/llama_stack/ui/components/chat-playground/conversations.tsx b/llama_stack/ui/components/chat-playground/conversations.tsx index 1a9c960fe..40045b9fe 100644 --- a/llama_stack/ui/components/chat-playground/conversations.tsx +++ b/llama_stack/ui/components/chat-playground/conversations.tsx @@ -14,6 +14,7 @@ import { Card } from "@/components/ui/card"; import { Trash2 } from "lucide-react"; import type { Message } from "@/components/chat-playground/chat-message"; import { useAuthClient } from "@/hooks/use-auth-client"; +import { cleanMessageContent } from "@/lib/message-content-utils"; import type { Session, SessionCreateParams, @@ -219,10 +220,7 @@ export function Conversations({ messages.push({ id: `${turn.turn_id}-assistant-${messages.length}`, role: "assistant", - content: - typeof turn.output_message.content === "string" - ? turn.output_message.content - : JSON.stringify(turn.output_message.content), + content: cleanMessageContent(turn.output_message.content), createdAt: new Date( turn.completed_at || turn.started_at || Date.now() ), @@ -271,7 +269,7 @@ export function Conversations({ ); const deleteSession = async (sessionId: string) => { - if (sessions.length <= 1 || !selectedAgentId) { + if (!selectedAgentId) { return; } @@ -324,7 +322,6 @@ export function Conversations({ } }, [currentSession]); - // Don't render if no agent is selected if (!selectedAgentId) { return null; } @@ -357,7 +354,7 @@ export function Conversations({ + New - {currentSession && sessions.length > 1 && ( + {currentSession && ( + {onCancel && ( + + )} + + + +
+ Note: This will create a new vector database that can + be used with RAG tools. After creation, you'll be able to upload + documents and use it for knowledge search in your agent conversations. +
+ + ); +} diff --git a/llama_stack/ui/lib/message-content-utils.ts b/llama_stack/ui/lib/message-content-utils.ts new file mode 100644 index 000000000..378f8d669 --- /dev/null +++ b/llama_stack/ui/lib/message-content-utils.ts @@ -0,0 +1,51 @@ +// check if content contains function call JSON +export const containsToolCall = (content: string): boolean => { + return ( + content.includes('"type": "function"') || + content.includes('"name": "knowledge_search"') || + content.includes('"parameters":') || + !!content.match(/\{"type":\s*"function".*?\}/) + ); +}; + +export const extractCleanText = (content: string): string | null => { + if (containsToolCall(content)) { + try { + // parse and extract non-function call parts + const jsonMatch = content.match(/\{"type":\s*"function"[^}]*\}[^}]*\}/); + if (jsonMatch) { + const jsonPart = jsonMatch[0]; + const parsedJson = JSON.parse(jsonPart); + + // if function call, extract text after JSON + if (parsedJson.type === "function") { + const textAfterJson = content + .substring(content.indexOf(jsonPart) + jsonPart.length) + .trim(); + return textAfterJson || null; + } + } + return null; + } catch { + return null; + } + } + return content; +}; + +// removes function call JSON handling different content types +export const cleanMessageContent = ( + content: string | unknown[] | unknown +): string => { + if (typeof content === "string") { + const cleaned = extractCleanText(content); + return cleaned || ""; + } else if (Array.isArray(content)) { + return content + .filter((item: { type: string }) => item.type === "text") + .map((item: { text: string }) => item.text) + .join(""); + } else { + return JSON.stringify(content); + } +}; From 52106d95d3a7cb089917693c51d3f58867b5a0c5 Mon Sep 17 00:00:00 2001 From: Omer Tuchfeld Date: Thu, 28 Aug 2025 17:07:18 +0200 Subject: [PATCH 011/124] fix(env): env var replacement preserve types (#3270) # What does this PR do? During env var replacement, we're implicitly converting all config types to their apparent types (e.g., "true" to True, "123" to 123). This may be arguably useful for when doing an env var substitution, as those are always strings, but we should definitely avoid touching config values that have explicit types and are uninvolved in env var substitution. ## Test Plan Unit --- llama_stack/core/stack.py | 5 ++++- tests/unit/server/test_replace_env_vars.py | 7 +++++++ 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/llama_stack/core/stack.py b/llama_stack/core/stack.py index 87a3978c1..f734d0285 100644 --- a/llama_stack/core/stack.py +++ b/llama_stack/core/stack.py @@ -225,7 +225,10 @@ def replace_env_vars(config: Any, path: str = "") -> Any: try: result = re.sub(pattern, get_env_var, config) - return _convert_string_to_proper_type(result) + # Only apply type conversion if substitution actually happened + if result != config: + return _convert_string_to_proper_type(result) + return result except EnvVarError as e: raise EnvVarError(e.var_name, e.path) from None diff --git a/tests/unit/server/test_replace_env_vars.py b/tests/unit/server/test_replace_env_vars.py index 0dda682c0..14b3b7231 100644 --- a/tests/unit/server/test_replace_env_vars.py +++ b/tests/unit/server/test_replace_env_vars.py @@ -88,3 +88,10 @@ def test_nested_structures(setup_env_vars): } expected = {"key1": "test_value", "key2": ["default", "conditional"], "key3": {"nested": None}} assert replace_env_vars(data) == expected + + +def test_explicit_strings_preserved(setup_env_vars): + # Explicit strings that look like numbers/booleans should remain strings + data = {"port": "8080", "enabled": "true", "count": "123", "ratio": "3.14"} + expected = {"port": "8080", "enabled": "true", "count": "123", "ratio": "3.14"} + assert replace_env_vars(data) == expected From 30117dea227cee088d3748769455a75928dc9dd5 Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Thu, 28 Aug 2025 13:20:36 -0700 Subject: [PATCH 012/124] fix: docker failing to start container [fireworks-ai] (#3267) # What does this PR do? https://github.com/llamastack/llama-stack-ops/actions/runs/17253649880 Fixes the issue with open ai package incompatibilty introduced through new dependency of fireworks-ai==0.19.18->reward-kit by pinning to fireworks older version that doesnt pull in reward-kit ## Test Plan Tested locally with the following commands to start a container 1. Build container `llama stack build --distro starter --image-type container` 2. start container `docker run -d -p 8321:8321 --name llama-stack-test distribution-starter:0.2.19` 3. check health http://localhost:8321/v1/health Above steps fails without the fix --- llama_stack/providers/registry/inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 82b771a28..6264de7c7 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -116,7 +116,7 @@ def available_providers() -> list[ProviderSpec]: adapter=AdapterSpec( adapter_type="fireworks", pip_packages=[ - "fireworks-ai", + "fireworks-ai<=0.18.0", ], module="llama_stack.providers.remote.inference.fireworks", config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig", From ed418653ec95997c3de024b00db4ddb553209ec7 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Thu, 28 Aug 2025 17:49:36 -0400 Subject: [PATCH 013/124] chore(dev): add inequality support to sqlstore where clause (#3272) # What does this PR do? add the ability to use inequalities in the where clause of the sqlstore. this is infrastructure for files expiration. ## Test Plan unit tests --- .../utils/sqlstore/sqlalchemy_sqlstore.py | 31 +++++++++- tests/unit/utils/sqlstore/test_sqlstore.py | 57 +++++++++++++++++++ 2 files changed, 85 insertions(+), 3 deletions(-) diff --git a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py index f75c35314..46ed8c1d1 100644 --- a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py +++ b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py @@ -23,6 +23,7 @@ from sqlalchemy import ( ) from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine from sqlalchemy.ext.asyncio.engine import AsyncEngine +from sqlalchemy.sql.elements import ColumnElement from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.log import get_logger @@ -43,6 +44,30 @@ TYPE_MAPPING: dict[ColumnType, Any] = { } +def _build_where_expr(column: ColumnElement, value: Any) -> ColumnElement: + """Return a SQLAlchemy expression for a where condition. + + `value` may be a simple scalar (equality) or a mapping like {">": 123}. + The returned expression is a SQLAlchemy ColumnElement usable in query.where(...). + """ + if isinstance(value, Mapping): + if len(value) != 1: + raise ValueError(f"Operator mapping must have a single operator, got: {value}") + op, operand = next(iter(value.items())) + if op == "==" or op == "=": + return column == operand + if op == ">": + return column > operand + if op == "<": + return column < operand + if op == ">=": + return column >= operand + if op == "<=": + return column <= operand + raise ValueError(f"Unsupported operator '{op}' in where mapping") + return column == value + + class SqlAlchemySqlStoreImpl(SqlStore): def __init__(self, config: SqlAlchemySqlStoreConfig): self.config = config @@ -111,7 +136,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): if where: for key, value in where.items(): - query = query.where(table_obj.c[key] == value) + query = query.where(_build_where_expr(table_obj.c[key], value)) if where_sql: query = query.where(text(where_sql)) @@ -222,7 +247,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): async with self.async_session() as session: stmt = self.metadata.tables[table].update() for key, value in where.items(): - stmt = stmt.where(self.metadata.tables[table].c[key] == value) + stmt = stmt.where(_build_where_expr(self.metadata.tables[table].c[key], value)) await session.execute(stmt, data) await session.commit() @@ -233,7 +258,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): async with self.async_session() as session: stmt = self.metadata.tables[table].delete() for key, value in where.items(): - stmt = stmt.where(self.metadata.tables[table].c[key] == value) + stmt = stmt.where(_build_where_expr(self.metadata.tables[table].c[key], value)) await session.execute(stmt) await session.commit() diff --git a/tests/unit/utils/sqlstore/test_sqlstore.py b/tests/unit/utils/sqlstore/test_sqlstore.py index 778f0b658..ba59ec7ec 100644 --- a/tests/unit/utils/sqlstore/test_sqlstore.py +++ b/tests/unit/utils/sqlstore/test_sqlstore.py @@ -332,6 +332,63 @@ async def test_sqlstore_pagination_error_handling(): ) +async def test_where_operator_gt_and_update_delete(): + with TemporaryDirectory() as tmp_dir: + db_path = tmp_dir + "/test.db" + store = SqlAlchemySqlStoreImpl(SqliteSqlStoreConfig(db_path=db_path)) + + await store.create_table( + "items", + { + "id": ColumnType.INTEGER, + "value": ColumnType.INTEGER, + "name": ColumnType.STRING, + }, + ) + + await store.insert("items", {"id": 1, "value": 10, "name": "one"}) + await store.insert("items", {"id": 2, "value": 20, "name": "two"}) + await store.insert("items", {"id": 3, "value": 30, "name": "three"}) + + result = await store.fetch_all("items", where={"value": {">": 15}}) + assert {r["id"] for r in result.data} == {2, 3} + + row = await store.fetch_one("items", where={"value": {">=": 30}}) + assert row["id"] == 3 + + await store.update("items", {"name": "small"}, {"value": {"<": 25}}) + rows = (await store.fetch_all("items")).data + names = {r["id"]: r["name"] for r in rows} + assert names[1] == "small" + assert names[2] == "small" + assert names[3] == "three" + + await store.delete("items", {"id": {"==": 2}}) + rows_after = (await store.fetch_all("items")).data + assert {r["id"] for r in rows_after} == {1, 3} + + +async def test_where_operator_edge_cases(): + with TemporaryDirectory() as tmp_dir: + db_path = tmp_dir + "/test.db" + store = SqlAlchemySqlStoreImpl(SqliteSqlStoreConfig(db_path=db_path)) + + await store.create_table( + "events", + {"id": ColumnType.STRING, "ts": ColumnType.INTEGER}, + ) + + base = 1024 + await store.insert("events", {"id": "a", "ts": base - 10}) + await store.insert("events", {"id": "b", "ts": base + 10}) + + row = await store.fetch_one("events", where={"id": "a"}) + assert row["id"] == "a" + + with pytest.raises(ValueError, match="Unsupported operator"): + await store.fetch_all("events", where={"ts": {"!=": base}}) + + async def test_sqlstore_pagination_custom_key_column(): """Test pagination with custom primary key column (not 'id').""" with TemporaryDirectory() as tmp_dir: From e96e3c4da430903dbf4e410f05909b99f47f358c Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 29 Aug 2025 10:14:00 -0400 Subject: [PATCH 014/124] feat(s3 auth): add authorization support for s3 files provider (#3265) # What does this PR do? adds support for authorized users to the s3 files provider ## Test Plan existing and new unit tests --- .../providers/remote/files/s3/__init__.py | 7 +- .../providers/remote/files/s3/files.py | 20 +++-- tests/unit/providers/files/conftest.py | 62 +++++++++++++ tests/unit/providers/files/test_s3_files.py | 64 ++----------- .../providers/files/test_s3_files_auth.py | 89 +++++++++++++++++++ 5 files changed, 172 insertions(+), 70 deletions(-) create mode 100644 tests/unit/providers/files/conftest.py create mode 100644 tests/unit/providers/files/test_s3_files_auth.py diff --git a/llama_stack/providers/remote/files/s3/__init__.py b/llama_stack/providers/remote/files/s3/__init__.py index 3f5dfc88a..7027f1db3 100644 --- a/llama_stack/providers/remote/files/s3/__init__.py +++ b/llama_stack/providers/remote/files/s3/__init__.py @@ -6,15 +6,14 @@ from typing import Any -from llama_stack.core.datatypes import Api +from llama_stack.core.datatypes import AccessRule, Api from .config import S3FilesImplConfig -async def get_adapter_impl(config: S3FilesImplConfig, deps: dict[Api, Any]): +async def get_adapter_impl(config: S3FilesImplConfig, deps: dict[Api, Any], policy: list[AccessRule] | None = None): from .files import S3FilesImpl - # TODO: authorization policies and user separation - impl = S3FilesImpl(config) + impl = S3FilesImpl(config, policy or []) await impl.initialize() return impl diff --git a/llama_stack/providers/remote/files/s3/files.py b/llama_stack/providers/remote/files/s3/files.py index 52e0cbbf4..0451f74ea 100644 --- a/llama_stack/providers/remote/files/s3/files.py +++ b/llama_stack/providers/remote/files/s3/files.py @@ -21,8 +21,10 @@ from llama_stack.apis.files import ( OpenAIFileObject, OpenAIFilePurpose, ) +from llama_stack.core.datatypes import AccessRule from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType -from llama_stack.providers.utils.sqlstore.sqlstore import SqlStore, sqlstore_impl +from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore +from llama_stack.providers.utils.sqlstore.sqlstore import sqlstore_impl from .config import S3FilesImplConfig @@ -89,16 +91,17 @@ class S3FilesImpl(Files): # TODO: implement expiration, for now a silly offset _SILLY_EXPIRATION_OFFSET = 100 * 365 * 24 * 60 * 60 - def __init__(self, config: S3FilesImplConfig) -> None: + def __init__(self, config: S3FilesImplConfig, policy: list[AccessRule]) -> None: self._config = config + self.policy = policy self._client: boto3.client | None = None - self._sql_store: SqlStore | None = None + self._sql_store: AuthorizedSqlStore | None = None async def initialize(self) -> None: self._client = _create_s3_client(self._config) await _create_bucket_if_not_exists(self._client, self._config) - self._sql_store = sqlstore_impl(self._config.metadata_store) + self._sql_store = AuthorizedSqlStore(sqlstore_impl(self._config.metadata_store)) await self._sql_store.create_table( "openai_files", { @@ -121,7 +124,7 @@ class S3FilesImpl(Files): return self._client @property - def sql_store(self) -> SqlStore: + def sql_store(self) -> AuthorizedSqlStore: assert self._sql_store is not None, "Provider not initialized" return self._sql_store @@ -189,6 +192,7 @@ class S3FilesImpl(Files): paginated_result = await self.sql_store.fetch_all( table="openai_files", + policy=self.policy, where=where_conditions if where_conditions else None, order_by=[("created_at", order.value)], cursor=("id", after) if after else None, @@ -216,7 +220,7 @@ class S3FilesImpl(Files): ) async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) + row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) if not row: raise ResourceNotFoundError(file_id, "File", "files.list()") @@ -230,7 +234,7 @@ class S3FilesImpl(Files): ) async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) + row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) if not row: raise ResourceNotFoundError(file_id, "File", "files.list()") @@ -248,7 +252,7 @@ class S3FilesImpl(Files): return OpenAIFileDeleteResponse(id=file_id, deleted=True) async def openai_retrieve_file_content(self, file_id: str) -> Response: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) + row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) if not row: raise ResourceNotFoundError(file_id, "File", "files.list()") diff --git a/tests/unit/providers/files/conftest.py b/tests/unit/providers/files/conftest.py new file mode 100644 index 000000000..46282e3dc --- /dev/null +++ b/tests/unit/providers/files/conftest.py @@ -0,0 +1,62 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import boto3 +import pytest +from moto import mock_aws + +from llama_stack.providers.remote.files.s3 import S3FilesImplConfig, get_adapter_impl +from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig + + +class MockUploadFile: + def __init__(self, content: bytes, filename: str, content_type: str = "text/plain"): + self.content = content + self.filename = filename + self.content_type = content_type + + async def read(self): + return self.content + + +@pytest.fixture +def sample_text_file(): + content = b"Hello, this is a test file for the S3 Files API!" + return MockUploadFile(content, "sample_text_file-0.txt") + + +@pytest.fixture +def sample_text_file2(): + content = b"Hello, this is a second test file for the S3 Files API!" + return MockUploadFile(content, "sample_text_file-1.txt") + + +@pytest.fixture +def s3_config(tmp_path): + db_path = tmp_path / "s3_files_metadata.db" + + return S3FilesImplConfig( + bucket_name=f"test-bucket-{tmp_path.name}", + region="not-a-region", + auto_create_bucket=True, + metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()), + ) + + +@pytest.fixture +def s3_client(): + # we use `with mock_aws()` because @mock_aws decorator does not support + # being a generator + with mock_aws(): + # must yield or the mock will be reset before it is used + yield boto3.client("s3") + + +@pytest.fixture +async def s3_provider(s3_config, s3_client): # s3_client provides the moto mock, don't remove it + provider = await get_adapter_impl(s3_config, {}) + yield provider + await provider.shutdown() diff --git a/tests/unit/providers/files/test_s3_files.py b/tests/unit/providers/files/test_s3_files.py index daa250f10..3bd4836df 100644 --- a/tests/unit/providers/files/test_s3_files.py +++ b/tests/unit/providers/files/test_s3_files.py @@ -6,63 +6,11 @@ from unittest.mock import patch -import boto3 import pytest from botocore.exceptions import ClientError -from moto import mock_aws from llama_stack.apis.common.errors import ResourceNotFoundError from llama_stack.apis.files import OpenAIFilePurpose -from llama_stack.providers.remote.files.s3 import ( - S3FilesImplConfig, - get_adapter_impl, -) -from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig - - -class MockUploadFile: - def __init__(self, content: bytes, filename: str, content_type: str = "text/plain"): - self.content = content - self.filename = filename - self.content_type = content_type - - async def read(self): - return self.content - - -@pytest.fixture -def s3_config(tmp_path): - db_path = tmp_path / "s3_files_metadata.db" - - return S3FilesImplConfig( - bucket_name="test-bucket", - region="not-a-region", - auto_create_bucket=True, - metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()), - ) - - -@pytest.fixture -def s3_client(): - """Create a mocked S3 client for testing.""" - # we use `with mock_aws()` because @mock_aws decorator does not support being a generator - with mock_aws(): - # must yield or the mock will be reset before it is used - yield boto3.client("s3") - - -@pytest.fixture -async def s3_provider(s3_config, s3_client): - """Create an S3 files provider with mocked S3 for testing.""" - provider = await get_adapter_impl(s3_config, {}) - yield provider - await provider.shutdown() - - -@pytest.fixture -def sample_text_file(): - content = b"Hello, this is a test file for the S3 Files API!" - return MockUploadFile(content, "sample_text_file.txt") class TestS3FilesImpl: @@ -143,7 +91,7 @@ class TestS3FilesImpl: s3_client.head_object(Bucket=s3_config.bucket_name, Key=uploaded.id) assert exc_info.value.response["Error"]["Code"] == "404" - async def test_list_files(self, s3_provider, sample_text_file): + async def test_list_files(self, s3_provider, sample_text_file, sample_text_file2): """Test listing files after uploading some.""" sample_text_file.filename = "test_list_files_with_content_file1" file1 = await s3_provider.openai_upload_file( @@ -151,9 +99,9 @@ class TestS3FilesImpl: purpose=OpenAIFilePurpose.ASSISTANTS, ) - file2_content = MockUploadFile(b"Second file content", "test_list_files_with_content_file2") + sample_text_file2.filename = "test_list_files_with_content_file2" file2 = await s3_provider.openai_upload_file( - file=file2_content, + file=sample_text_file2, purpose=OpenAIFilePurpose.BATCH, ) @@ -164,7 +112,7 @@ class TestS3FilesImpl: assert file1.id in file_ids assert file2.id in file_ids - async def test_list_files_with_purpose_filter(self, s3_provider, sample_text_file): + async def test_list_files_with_purpose_filter(self, s3_provider, sample_text_file, sample_text_file2): """Test listing files with purpose filter.""" sample_text_file.filename = "test_list_files_with_purpose_filter_file1" file1 = await s3_provider.openai_upload_file( @@ -172,9 +120,9 @@ class TestS3FilesImpl: purpose=OpenAIFilePurpose.ASSISTANTS, ) - file2_content = MockUploadFile(b"Batch file content", "test_list_files_with_purpose_filter_file2") + sample_text_file2.filename = "test_list_files_with_purpose_filter_file2" await s3_provider.openai_upload_file( - file=file2_content, + file=sample_text_file2, purpose=OpenAIFilePurpose.BATCH, ) diff --git a/tests/unit/providers/files/test_s3_files_auth.py b/tests/unit/providers/files/test_s3_files_auth.py new file mode 100644 index 000000000..6097f2808 --- /dev/null +++ b/tests/unit/providers/files/test_s3_files_auth.py @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from unittest.mock import patch + +import pytest + +from llama_stack.apis.common.errors import ResourceNotFoundError +from llama_stack.apis.files import OpenAIFilePurpose +from llama_stack.core.datatypes import User +from llama_stack.providers.remote.files.s3.files import S3FilesImpl + + +async def test_listing_hides_other_users_file(s3_provider, sample_text_file): + """Listing should not show files uploaded by other users.""" + user_a = User("user-a", {"roles": ["team-a"]}) + user_b = User("user-b", {"roles": ["team-b"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + listed = await s3_provider.openai_list_files() + assert all(f.id != uploaded.id for f in listed.data) + + +@pytest.mark.parametrize( + "op", + [S3FilesImpl.openai_retrieve_file, S3FilesImpl.openai_retrieve_file_content, S3FilesImpl.openai_delete_file], + ids=["retrieve", "content", "delete"], +) +async def test_cannot_access_other_user_file(s3_provider, sample_text_file, op): + """Operations (metadata/content/delete) on another user's file should raise ResourceNotFoundError. + + `op` is an async callable (provider, file_id) -> awaits the requested operation. + """ + user_a = User("user-a", {"roles": ["team-a"]}) + user_b = User("user-b", {"roles": ["team-b"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + with pytest.raises(ResourceNotFoundError): + await op(s3_provider, uploaded.id) + + +async def test_shared_role_allows_listing(s3_provider, sample_text_file): + """Listing should show files uploaded by other users when roles are shared.""" + user_a = User("user-a", {"roles": ["shared-role"]}) + user_b = User("user-b", {"roles": ["shared-role"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + listed = await s3_provider.openai_list_files() + assert any(f.id == uploaded.id for f in listed.data) + + +@pytest.mark.parametrize( + "op", + [S3FilesImpl.openai_retrieve_file, S3FilesImpl.openai_retrieve_file_content, S3FilesImpl.openai_delete_file], + ids=["retrieve", "content", "delete"], +) +async def test_shared_role_allows_access(s3_provider, sample_text_file, op): + """Operations (metadata/content/delete) on another user's file should succeed when users share a role. + + `op` is an async callable (provider, file_id) -> awaits the requested operation. + """ + user_x = User("user-x", {"roles": ["shared-role"]}) + user_y = User("user-y", {"roles": ["shared-role"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_x + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_y + await op(s3_provider, uploaded.id) From 3130ca0a787bf9d6bf936229fcf4c334d58b8a70 Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Fri, 29 Aug 2025 15:30:12 +0100 Subject: [PATCH 015/124] feat: implement keyword, vector and hybrid search inside vector stores for PGVector provider (#3064) # What does this PR do? The purpose of this task is to implement `openai/v1/vector_stores/{vector_store_id}/search` for PGVector provider. It involves implementing vector similarity search, keyword search and hybrid search for `PGVectorIndex`. Closes #3006 ## Test Plan Run unit tests: ` ./scripts/unit-tests.sh ` Run integration tests for openai vector stores: 1. Export env vars: ``` export ENABLE_PGVECTOR=true export PGVECTOR_HOST=localhost export PGVECTOR_PORT=5432 export PGVECTOR_DB=llamastack export PGVECTOR_USER=llamastack export PGVECTOR_PASSWORD=llamastack ``` 2. Create DB: ``` psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" ``` 3. Install sentence-transformers: ` uv pip install sentence-transformers ` 4. Run: ``` uv run --group test pytest -s -v --stack-config="inference=inline::sentence-transformers,vector_io=remote::pgvector" --embedding-model sentence-transformers/all-MiniLM-L6-v2 tests/integration/vector_io/test_openai_vector_stores.py ``` Inspect PGVector vector stores (optional): ``` psql llamastack psql (14.18 (Homebrew)) Type "help" for help. llamastack=# \z Access privileges Schema | Name | Type | Access privileges | Column privileges | Policies --------+------------------------------------------------------+-------+-------------------+-------------------+---------- public | llamastack_kvstore | table | | | public | metadata_store | table | | | public | vector_store_pgvector_main | table | | | public | vector_store_vs_1dfbc061_1f4d_4497_9165_ecba2622ba3a | table | | | public | vector_store_vs_2085a9fb_1822_4e42_a277_c6a685843fa7 | table | | | public | vector_store_vs_2b3dae46_38be_462a_afd6_37ee5fe661b1 | table | | | public | vector_store_vs_2f438de6_f606_4561_9d50_ef9160eb9060 | table | | | public | vector_store_vs_3eeca564_2580_4c68_bfea_83dc57e31214 | table | | | public | vector_store_vs_53942163_05f3_40e0_83c0_0997c64613da | table | | | public | vector_store_vs_545bac75_8950_4ff1_b084_e221192d4709 | table | | | public | vector_store_vs_688a37d8_35b2_4298_a035_bfedf5b21f86 | table | | | public | vector_store_vs_70624d9a_f6ac_4c42_b8ab_0649473c6600 | table | | | public | vector_store_vs_73fc1dd2_e942_4972_afb1_1e177b591ac2 | table | | | public | vector_store_vs_9d464949_d51f_49db_9f87_e033b8b84ac9 | table | | | public | vector_store_vs_a1e4d724_5162_4d6d_a6c0_bdafaf6b76ec | table | | | public | vector_store_vs_a328fb1b_1a21_480f_9624_ffaa60fb6672 | table | | | public | vector_store_vs_a8981bf0_2e66_4445_a267_a8fff442db53 | table | | | public | vector_store_vs_ccd4b6a4_1efd_4984_ad03_e7ff8eadb296 | table | | | public | vector_store_vs_cd6420a4_a1fc_4cec_948c_1413a26281c9 | table | | | public | vector_store_vs_cd709284_e5cf_4a88_aba5_dc76a35364bd | table | | | public | vector_store_vs_d7a4548e_fbc1_44d7_b2ec_b664417f2a46 | table | | | public | vector_store_vs_e7f73231_414c_4523_886c_d1174eee836e | table | | | public | vector_store_vs_ffd53588_819f_47e8_bb9d_954af6f7833d | table | | | (23 rows) llamastack=# ``` Co-authored-by: Francisco Arceo --- .../providers/vector_io/remote_pgvector.md | 73 ++++++ .../providers/vector_io/remote_weaviate.md | 1 + llama_stack/providers/registry/vector_io.py | 74 ++++++ .../remote/vector_io/pgvector/pgvector.py | 230 ++++++++++++++-- .../providers/utils/vector_io/vector_utils.py | 119 +++++++++ pyproject.toml | 2 + .../vector_io/test_openai_vector_stores.py | 2 + .../providers/utils/memory/test_reranking.py | 248 ++++++++++++++++++ tests/unit/providers/vector_io/conftest.py | 121 ++++++++- .../vector_io/remote/test_pgvector.py | 138 ++++++++++ uv.lock | 35 +++ 11 files changed, 1014 insertions(+), 29 deletions(-) create mode 100644 tests/unit/providers/utils/memory/test_reranking.py create mode 100644 tests/unit/providers/vector_io/remote/test_pgvector.py diff --git a/docs/source/providers/vector_io/remote_pgvector.md b/docs/source/providers/vector_io/remote_pgvector.md index 74f588a13..6312edabc 100644 --- a/docs/source/providers/vector_io/remote_pgvector.md +++ b/docs/source/providers/vector_io/remote_pgvector.md @@ -12,6 +12,60 @@ That means you'll get fast and efficient vector retrieval. - Easy to use - Fully integrated with Llama Stack +There are three implementations of search for PGVectoIndex available: + +1. Vector Search: +- How it works: + - Uses PostgreSQL's vector extension (pgvector) to perform similarity search + - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics + - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance + +-Characteristics: + - Semantic understanding - finds documents similar in meaning even if they don't share keywords + - Works with high-dimensional vector embeddings (typically 768, 1024, or higher dimensions) + - Best for: Finding conceptually related content, handling synonyms, cross-language search + +2. Keyword Search +- How it works: + - Uses PostgreSQL's full-text search capabilities with tsvector and ts_rank + - Converts text to searchable tokens using to_tsvector('english', text). Default language is English. + - Eg. SQL query: SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + +- Characteristics: + - Lexical matching - finds exact keyword matches and variations + - Uses GIN (Generalized Inverted Index) for fast text search performance + - Scoring: Uses PostgreSQL's ts_rank function for relevance scoring + - Best for: Exact term matching, proper names, technical terms, Boolean-style queries + +3. Hybrid Search +- How it works: + - Combines both vector and keyword search results + - Runs both searches independently, then merges results using configurable reranking + +- Two reranking strategies available: + - Reciprocal Rank Fusion (RRF) - (default: 60.0) + - Weighted Average - (default: 0.5) + +- Characteristics: + - Best of both worlds: semantic understanding + exact matching + - Documents appearing in both searches get boosted scores + - Configurable balance between semantic and lexical matching + - Best for: General-purpose search where you want both precision and recall + +4. Database Schema +The PGVector implementation stores data optimized for all three search types: +CREATE TABLE vector_store_xxx ( + id TEXT PRIMARY KEY, + document JSONB, -- Original document + embedding vector(dimension), -- For vector search + content_text TEXT, -- Raw text content + tokenized_content TSVECTOR -- For keyword search +); + +-- Indexes for performance +CREATE INDEX content_gin_idx ON table USING GIN(tokenized_content); -- Keyword search +-- Vector index created automatically by pgvector + ## Usage To use PGVector in your Llama Stack project, follow these steps: @@ -20,6 +74,25 @@ To use PGVector in your Llama Stack project, follow these steps: 2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector). 3. Start storing and querying vectors. +## This is an example how you can set up your environment for using PGVector + +1. Export env vars: +```bash +export ENABLE_PGVECTOR=true +export PGVECTOR_HOST=localhost +export PGVECTOR_PORT=5432 +export PGVECTOR_DB=llamastack +export PGVECTOR_USER=llamastack +export PGVECTOR_PASSWORD=llamastack +``` + +2. Create DB: +```bash +psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" +psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" +psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" +``` + ## Installation You can install PGVector using docker: diff --git a/docs/source/providers/vector_io/remote_weaviate.md b/docs/source/providers/vector_io/remote_weaviate.md index c59487cf6..8fb0f7c11 100644 --- a/docs/source/providers/vector_io/remote_weaviate.md +++ b/docs/source/providers/vector_io/remote_weaviate.md @@ -17,6 +17,7 @@ Weaviate supports: - Metadata filtering - Multi-modal retrieval + ## Usage To use Weaviate in your Llama Stack project, follow these steps: diff --git a/llama_stack/providers/registry/vector_io.py b/llama_stack/providers/registry/vector_io.py index 70148eb15..511734d57 100644 --- a/llama_stack/providers/registry/vector_io.py +++ b/llama_stack/providers/registry/vector_io.py @@ -404,6 +404,60 @@ That means you'll get fast and efficient vector retrieval. - Easy to use - Fully integrated with Llama Stack +There are three implementations of search for PGVectoIndex available: + +1. Vector Search: +- How it works: + - Uses PostgreSQL's vector extension (pgvector) to perform similarity search + - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics + - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance + +-Characteristics: + - Semantic understanding - finds documents similar in meaning even if they don't share keywords + - Works with high-dimensional vector embeddings (typically 768, 1024, or higher dimensions) + - Best for: Finding conceptually related content, handling synonyms, cross-language search + +2. Keyword Search +- How it works: + - Uses PostgreSQL's full-text search capabilities with tsvector and ts_rank + - Converts text to searchable tokens using to_tsvector('english', text). Default language is English. + - Eg. SQL query: SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + +- Characteristics: + - Lexical matching - finds exact keyword matches and variations + - Uses GIN (Generalized Inverted Index) for fast text search performance + - Scoring: Uses PostgreSQL's ts_rank function for relevance scoring + - Best for: Exact term matching, proper names, technical terms, Boolean-style queries + +3. Hybrid Search +- How it works: + - Combines both vector and keyword search results + - Runs both searches independently, then merges results using configurable reranking + +- Two reranking strategies available: + - Reciprocal Rank Fusion (RRF) - (default: 60.0) + - Weighted Average - (default: 0.5) + +- Characteristics: + - Best of both worlds: semantic understanding + exact matching + - Documents appearing in both searches get boosted scores + - Configurable balance between semantic and lexical matching + - Best for: General-purpose search where you want both precision and recall + +4. Database Schema +The PGVector implementation stores data optimized for all three search types: +CREATE TABLE vector_store_xxx ( + id TEXT PRIMARY KEY, + document JSONB, -- Original document + embedding vector(dimension), -- For vector search + content_text TEXT, -- Raw text content + tokenized_content TSVECTOR -- For keyword search +); + +-- Indexes for performance +CREATE INDEX content_gin_idx ON table USING GIN(tokenized_content); -- Keyword search +-- Vector index created automatically by pgvector + ## Usage To use PGVector in your Llama Stack project, follow these steps: @@ -412,6 +466,25 @@ To use PGVector in your Llama Stack project, follow these steps: 2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector). 3. Start storing and querying vectors. +## This is an example how you can set up your environment for using PGVector + +1. Export env vars: +```bash +export ENABLE_PGVECTOR=true +export PGVECTOR_HOST=localhost +export PGVECTOR_PORT=5432 +export PGVECTOR_DB=llamastack +export PGVECTOR_USER=llamastack +export PGVECTOR_PASSWORD=llamastack +``` + +2. Create DB: +```bash +psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" +psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" +psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" +``` + ## Installation You can install PGVector using docker: @@ -449,6 +522,7 @@ Weaviate supports: - Metadata filtering - Multi-modal retrieval + ## Usage To use Weaviate in your Llama Stack project, follow these steps: diff --git a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py index 1c8d361c2..1c140e782 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py +++ b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py @@ -4,6 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import heapq from typing import Any import psycopg2 @@ -23,6 +24,9 @@ from llama_stack.apis.vector_io import ( ) from llama_stack.log import get_logger from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate +from llama_stack.providers.utils.inference.prompt_adapter import ( + interleaved_content_as_str, +) from llama_stack.providers.utils.kvstore import kvstore_impl from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin @@ -31,6 +35,7 @@ from llama_stack.providers.utils.memory.vector_store import ( EmbeddingIndex, VectorDBWithIndex, ) +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator, sanitize_collection_name from .config import PGVectorVectorIOConfig @@ -72,25 +77,63 @@ def load_models(cur, cls): class PGVectorIndex(EmbeddingIndex): - def __init__(self, vector_db: VectorDB, dimension: int, conn, kvstore: KVStore | None = None): - self.conn = conn - with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: - # Sanitize the table name by replacing hyphens with underscores - # SQL doesn't allow hyphens in table names, and vector_db.identifier may contain hyphens - # when created with patterns like "test-vector-db-{uuid4()}" - sanitized_identifier = vector_db.identifier.replace("-", "_") - self.table_name = f"vector_store_{sanitized_identifier}" - self.kvstore = kvstore + # reference: https://github.com/pgvector/pgvector?tab=readme-ov-file#querying + PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION: dict[str, str] = { + "L2": "<->", + "L1": "<+>", + "COSINE": "<=>", + "INNER_PRODUCT": "<#>", + "HAMMING": "<~>", + "JACCARD": "<%>", + } - cur.execute( - f""" - CREATE TABLE IF NOT EXISTS {self.table_name} ( - id TEXT PRIMARY KEY, - document JSONB, - embedding vector({dimension}) + def __init__( + self, + vector_db: VectorDB, + dimension: int, + conn: psycopg2.extensions.connection, + kvstore: KVStore | None = None, + distance_metric: str = "COSINE", + ): + self.vector_db = vector_db + self.dimension = dimension + self.conn = conn + self.kvstore = kvstore + self.check_distance_metric_availability(distance_metric) + self.distance_metric = distance_metric + self.table_name = None + + async def initialize(self) -> None: + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + # Sanitize the table name by replacing hyphens with underscores + # SQL doesn't allow hyphens in table names, and vector_db.identifier may contain hyphens + # when created with patterns like "test-vector-db-{uuid4()}" + sanitized_identifier = sanitize_collection_name(self.vector_db.identifier) + self.table_name = f"vs_{sanitized_identifier}" + + cur.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.table_name} ( + id TEXT PRIMARY KEY, + document JSONB, + embedding vector({self.dimension}), + content_text TEXT, + tokenized_content TSVECTOR + ) + """ ) - """ - ) + + # Create GIN index for full-text search performance + cur.execute( + f""" + CREATE INDEX IF NOT EXISTS {self.table_name}_content_gin_idx + ON {self.table_name} USING GIN(tokenized_content) + """ + ) + except Exception as e: + log.exception(f"Error creating PGVectorIndex for vector_db: {self.vector_db.identifier}") + raise RuntimeError(f"Error creating PGVectorIndex for vector_db: {self.vector_db.identifier}") from e async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( @@ -99,29 +142,49 @@ class PGVectorIndex(EmbeddingIndex): values = [] for i, chunk in enumerate(chunks): + content_text = interleaved_content_as_str(chunk.content) values.append( ( f"{chunk.chunk_id}", Json(chunk.model_dump()), embeddings[i].tolist(), + content_text, + content_text, # Pass content_text twice - once for content_text column, once for to_tsvector function. Eg. to_tsvector(content_text) = tokenized_content ) ) query = sql.SQL( f""" - INSERT INTO {self.table_name} (id, document, embedding) + INSERT INTO {self.table_name} (id, document, embedding, content_text, tokenized_content) VALUES %s - ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding, document = EXCLUDED.document + ON CONFLICT (id) DO UPDATE SET + embedding = EXCLUDED.embedding, + document = EXCLUDED.document, + content_text = EXCLUDED.content_text, + tokenized_content = EXCLUDED.tokenized_content """ ) with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: - execute_values(cur, query, values, template="(%s, %s, %s::vector)") + execute_values(cur, query, values, template="(%s, %s, %s::vector, %s, to_tsvector('english', %s))") async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + """ + Performs vector similarity search using PostgreSQL's search function. Default distance metric is COSINE. + + Args: + embedding: The query embedding vector + k: Number of results to return + score_threshold: Minimum similarity score threshold + + Returns: + QueryChunksResponse with combined results + """ + pgvector_search_function = self.get_pgvector_search_function() + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: cur.execute( f""" - SELECT document, embedding <-> %s::vector AS distance + SELECT document, embedding {pgvector_search_function} %s::vector AS distance FROM {self.table_name} ORDER BY distance LIMIT %s @@ -147,7 +210,40 @@ class PGVectorIndex(EmbeddingIndex): k: int, score_threshold: float, ) -> QueryChunksResponse: - raise NotImplementedError("Keyword search is not supported in PGVector") + """ + Performs keyword-based search using PostgreSQL's full-text search with ts_rank scoring. + + Args: + query_string: The text query for keyword search + k: Number of results to return + score_threshold: Minimum similarity score threshold + + Returns: + QueryChunksResponse with combined results + """ + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + # Use plainto_tsquery to handle user input safely and ts_rank for relevance scoring + cur.execute( + f""" + SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + FROM {self.table_name} + WHERE tokenized_content @@ plainto_tsquery('english', %s) + ORDER BY score DESC + LIMIT %s + """, + (query_string, query_string, k), + ) + results = cur.fetchall() + + chunks = [] + scores = [] + for doc, score in results: + if score < score_threshold: + continue + chunks.append(Chunk(**doc)) + scores.append(float(score)) + + return QueryChunksResponse(chunks=chunks, scores=scores) async def query_hybrid( self, @@ -158,7 +254,59 @@ class PGVectorIndex(EmbeddingIndex): reranker_type: str, reranker_params: dict[str, Any] | None = None, ) -> QueryChunksResponse: - raise NotImplementedError("Hybrid search is not supported in PGVector") + """ + Hybrid search combining vector similarity and keyword search using configurable reranking. + + Args: + embedding: The query embedding vector + query_string: The text query for keyword search + k: Number of results to return + score_threshold: Minimum similarity score threshold + reranker_type: Type of reranker to use ("rrf" or "weighted") + reranker_params: Parameters for the reranker + + Returns: + QueryChunksResponse with combined results + """ + if reranker_params is None: + reranker_params = {} + + # Get results from both search methods + vector_response = await self.query_vector(embedding, k, score_threshold) + keyword_response = await self.query_keyword(query_string, k, score_threshold) + + # Convert responses to score dictionaries using chunk_id + vector_scores = { + chunk.chunk_id: score for chunk, score in zip(vector_response.chunks, vector_response.scores, strict=False) + } + keyword_scores = { + chunk.chunk_id: score + for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False) + } + + # Combine scores using the reranking utility + combined_scores = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type, reranker_params + ) + + # Efficient top-k selection because it only tracks the k best candidates it's seen so far + top_k_items = heapq.nlargest(k, combined_scores.items(), key=lambda x: x[1]) + + # Filter by score threshold + filtered_items = [(doc_id, score) for doc_id, score in top_k_items if score >= score_threshold] + + # Create a map of chunk_id to chunk for both responses + chunk_map = {c.chunk_id: c for c in vector_response.chunks + keyword_response.chunks} + + # Use the map to look up chunks by their IDs + chunks = [] + scores = [] + for doc_id, score in filtered_items: + if doc_id in chunk_map: + chunks.append(chunk_map[doc_id]) + scores.append(score) + + return QueryChunksResponse(chunks=chunks, scores=scores) async def delete(self): with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: @@ -170,6 +318,25 @@ class PGVectorIndex(EmbeddingIndex): with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: cur.execute(f"DELETE FROM {self.table_name} WHERE id = ANY(%s)", (chunk_ids,)) + def get_pgvector_search_function(self) -> str: + return self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION[self.distance_metric] + + def check_distance_metric_availability(self, distance_metric: str) -> None: + """Check if the distance metric is supported by PGVector. + + Args: + distance_metric: The distance metric to check + + Raises: + ValueError: If the distance metric is not supported + """ + if distance_metric not in self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION: + supported_metrics = list(self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION.keys()) + raise ValueError( + f"Distance metric '{distance_metric}' is not supported by PGVector. " + f"Supported metrics are: {', '.join(supported_metrics)}" + ) + class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate): def __init__( @@ -185,8 +352,8 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco self.files_api = files_api self.kvstore: KVStore | None = None self.vector_db_store = None - self.openai_vector_store: dict[str, dict[str, Any]] = {} - self.metadatadata_collection_name = "openai_vector_stores_metadata" + self.openai_vector_stores: dict[str, dict[str, Any]] = {} + self.metadata_collection_name = "openai_vector_stores_metadata" async def initialize(self) -> None: log.info(f"Initializing PGVector memory adapter with config: {self.config}") @@ -233,9 +400,13 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco upsert_models(self.conn, [(vector_db.identifier, vector_db)]) # Create and cache the PGVector index table for the vector DB + pgvector_index = PGVectorIndex( + vector_db=vector_db, dimension=vector_db.embedding_dimension, conn=self.conn, kvstore=self.kvstore + ) + await pgvector_index.initialize() index = VectorDBWithIndex( vector_db, - index=PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn, kvstore=self.kvstore), + index=pgvector_index, inference_api=self.inference_api, ) self.cache[vector_db.identifier] = index @@ -272,8 +443,15 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco if vector_db_id in self.cache: return self.cache[vector_db_id] + if self.vector_db_store is None: + raise VectorStoreNotFoundError(vector_db_id) + vector_db = await self.vector_db_store.get_vector_db(vector_db_id) + if not vector_db: + raise VectorStoreNotFoundError(vector_db_id) + index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn) + await index.initialize() self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api) return self.cache[vector_db_id] diff --git a/llama_stack/providers/utils/vector_io/vector_utils.py b/llama_stack/providers/utils/vector_io/vector_utils.py index f2888043e..e55ac75ae 100644 --- a/llama_stack/providers/utils/vector_io/vector_utils.py +++ b/llama_stack/providers/utils/vector_io/vector_utils.py @@ -37,3 +37,122 @@ def sanitize_collection_name(name: str, weaviate_format=False) -> str: else: s = proper_case(re.sub(r"[^a-zA-Z0-9]", "", name)) return s + + +class WeightedInMemoryAggregator: + @staticmethod + def _normalize_scores(scores: dict[str, float]) -> dict[str, float]: + """ + Normalize scores to 0-1 range using min-max normalization. + + Args: + scores: dictionary of scores with document IDs as keys and scores as values + + Returns: + Normalized scores with document IDs as keys and normalized scores as values + """ + if not scores: + return {} + min_score, max_score = min(scores.values()), max(scores.values()) + score_range = max_score - min_score + if score_range > 0: + return {doc_id: (score - min_score) / score_range for doc_id, score in scores.items()} + return dict.fromkeys(scores, 1.0) + + @staticmethod + def weighted_rerank( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + alpha: float = 0.5, + ) -> dict[str, float]: + """ + Rerank via weighted average of scores. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + alpha: weight factor between 0 and 1 (default: 0.5) + 0 = keyword only, 1 = vector only, 0.5 = equal weight + + Returns: + All unique document IDs with weighted combined scores + """ + all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) + normalized_vector_scores = WeightedInMemoryAggregator._normalize_scores(vector_scores) + normalized_keyword_scores = WeightedInMemoryAggregator._normalize_scores(keyword_scores) + + # Weighted formula: score = (1-alpha) * keyword_score + alpha * vector_score + # alpha=0 means keyword only, alpha=1 means vector only + return { + doc_id: ((1 - alpha) * normalized_keyword_scores.get(doc_id, 0.0)) + + (alpha * normalized_vector_scores.get(doc_id, 0.0)) + for doc_id in all_ids + } + + @staticmethod + def rrf_rerank( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + impact_factor: float = 60.0, + ) -> dict[str, float]: + """ + Rerank via Reciprocal Rank Fusion. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + impact_factor: impact factor for RRF (default: 60.0) + + Returns: + All unique document IDs with RRF combined scores + """ + + # Convert scores to ranks + vector_ranks = { + doc_id: i + 1 + for i, (doc_id, _) in enumerate(sorted(vector_scores.items(), key=lambda x: x[1], reverse=True)) + } + keyword_ranks = { + doc_id: i + 1 + for i, (doc_id, _) in enumerate(sorted(keyword_scores.items(), key=lambda x: x[1], reverse=True)) + } + + all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) + rrf_scores = {} + for doc_id in all_ids: + vector_rank = vector_ranks.get(doc_id, float("inf")) + keyword_rank = keyword_ranks.get(doc_id, float("inf")) + + # RRF formula: score = 1/(k + r) where k is impact_factor (default: 60.0) and r is the rank + rrf_scores[doc_id] = (1.0 / (impact_factor + vector_rank)) + (1.0 / (impact_factor + keyword_rank)) + return rrf_scores + + @staticmethod + def combine_search_results( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + reranker_type: str = "rrf", + reranker_params: dict[str, float] | None = None, + ) -> dict[str, float]: + """ + Combine vector and keyword search results using specified reranking strategy. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + reranker_type: type of reranker to use (default: RERANKER_TYPE_RRF) + reranker_params: parameters for the reranker + + Returns: + All unique document IDs with combined scores + """ + if reranker_params is None: + reranker_params = {} + + if reranker_type == "weighted": + alpha = reranker_params.get("alpha", 0.5) + return WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha) + else: + # Default to RRF for None, RRF, or any unknown types + impact_factor = reranker_params.get("impact_factor", 60.0) + return WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor) diff --git a/pyproject.toml b/pyproject.toml index dd8529546..3ab042a8e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -84,6 +84,7 @@ unit = [ "openai", "aiosqlite", "aiohttp", + "psycopg2-binary>=2.9.0", "pypdf", "mcp", "chardet", @@ -111,6 +112,7 @@ test = [ "torch>=2.6.0", "torchvision>=0.21.0", "chardet", + "psycopg2-binary>=2.9.0", "pypdf", "mcp", "datasets", diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index 82868164f..c67036eab 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -57,11 +57,13 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode "inline::sqlite-vec", "remote::milvus", "inline::milvus", + "remote::pgvector", ], "hybrid": [ "inline::sqlite-vec", "inline::milvus", "remote::milvus", + "remote::pgvector", ], } supported_providers = search_mode_support.get(search_mode, []) diff --git a/tests/unit/providers/utils/memory/test_reranking.py b/tests/unit/providers/utils/memory/test_reranking.py new file mode 100644 index 000000000..02d7a1b6a --- /dev/null +++ b/tests/unit/providers/utils/memory/test_reranking.py @@ -0,0 +1,248 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +from llama_stack.providers.utils.memory.vector_store import RERANKER_TYPE_RRF, RERANKER_TYPE_WEIGHTED +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator + + +class TestNormalizeScores: + """Test cases for score normalization.""" + + def test_normalize_scores_basic(self): + """Test basic score normalization.""" + scores = {"doc1": 10.0, "doc2": 5.0, "doc3": 0.0} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized["doc1"] == 1.0 # Max score + assert normalized["doc3"] == 0.0 # Min score + assert normalized["doc2"] == 0.5 # Middle score + assert all(0 <= score <= 1 for score in normalized.values()) + + def test_normalize_scores_identical(self): + """Test normalization when all scores are identical.""" + scores = {"doc1": 5.0, "doc2": 5.0, "doc3": 5.0} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + # All scores should be 1.0 when identical + assert all(score == 1.0 for score in normalized.values()) + + def test_normalize_scores_empty(self): + """Test normalization with empty scores.""" + scores = {} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized == {} + + def test_normalize_scores_single(self): + """Test normalization with single score.""" + scores = {"doc1": 7.5} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized["doc1"] == 1.0 + + +class TestWeightedRerank: + """Test cases for weighted reranking.""" + + def test_weighted_rerank_basic(self): + """Test basic weighted reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} + keyword_scores = {"doc1": 0.6, "doc2": 0.8, "doc4": 0.9} + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.5) + + # Should include all documents + expected_docs = {"doc1", "doc2", "doc3", "doc4"} + assert set(combined.keys()) == expected_docs + + # All scores should be between 0 and 1 + assert all(0 <= score <= 1 for score in combined.values()) + + # doc1 appears in both searches, should have higher combined score + assert combined["doc1"] > 0 + + def test_weighted_rerank_alpha_zero(self): + """Test weighted reranking with alpha=0 (keyword only).""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} # All docs present in vector + keyword_scores = {"doc1": 0.1, "doc2": 0.3, "doc3": 0.9} # All docs present in keyword + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.0) + + # Alpha=0 means vector scores are ignored, keyword scores dominate + # doc3 should score highest since it has highest keyword score + assert combined["doc3"] > combined["doc2"] > combined["doc1"] + + def test_weighted_rerank_alpha_one(self): + """Test weighted reranking with alpha=1 (vector only).""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} # All docs present in vector + keyword_scores = {"doc1": 0.1, "doc2": 0.3, "doc3": 0.9} # All docs present in keyword + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=1.0) + + # Alpha=1 means keyword scores are ignored, vector scores dominate + # doc1 should score highest since it has highest vector score + assert combined["doc1"] > combined["doc2"] > combined["doc3"] + + def test_weighted_rerank_no_overlap(self): + """Test weighted reranking with no overlapping documents.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc3": 0.8, "doc4": 0.6} + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.5) + + assert len(combined) == 4 + # With min-max normalization, lowest scoring docs in each group get 0.0 + # but highest scoring docs should get positive scores + assert all(score >= 0 for score in combined.values()) + assert combined["doc1"] > 0 # highest vector score + assert combined["doc3"] > 0 # highest keyword score + + +class TestRRFRerank: + """Test cases for RRF (Reciprocal Rank Fusion) reranking.""" + + def test_rrf_rerank_basic(self): + """Test basic RRF reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} + keyword_scores = {"doc1": 0.6, "doc2": 0.8, "doc4": 0.9} + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # Should include all documents + expected_docs = {"doc1", "doc2", "doc3", "doc4"} + assert set(combined.keys()) == expected_docs + + # All scores should be positive + assert all(score > 0 for score in combined.values()) + + # Documents appearing in both searches should have higher scores + # doc1 and doc2 appear in both, doc3 and doc4 appear in only one + assert combined["doc1"] > combined["doc3"] + assert combined["doc2"] > combined["doc4"] + + def test_rrf_rerank_rank_calculation(self): + """Test that RRF correctly calculates ranks.""" + # Create clear ranking order + vector_scores = {"doc1": 1.0, "doc2": 0.8, "doc3": 0.6} # Ranks: 1, 2, 3 + keyword_scores = {"doc1": 0.5, "doc2": 1.0, "doc3": 0.7} # Ranks: 3, 1, 2 + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # doc1: rank 1 in vector, rank 3 in keyword + # doc2: rank 2 in vector, rank 1 in keyword + # doc3: rank 3 in vector, rank 2 in keyword + + # doc2 should have the highest combined score (ranks 2+1=3) + # followed by doc1 (ranks 1+3=4) and doc3 (ranks 3+2=5) + # Remember: lower rank sum = higher RRF score + assert combined["doc2"] > combined["doc1"] > combined["doc3"] + + def test_rrf_rerank_impact_factor(self): + """Test that impact factor affects RRF scores.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.8, "doc2": 0.6} + + combined_low = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=10.0) + combined_high = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=100.0) + + # Higher impact factor should generally result in lower scores + # (because 1/(k+r) decreases as k increases) + assert combined_low["doc1"] > combined_high["doc1"] + assert combined_low["doc2"] > combined_high["doc2"] + + def test_rrf_rerank_missing_documents(self): + """Test RRF handling of documents missing from one search.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.8, "doc3": 0.6} + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # Should include all documents + assert len(combined) == 3 + + # doc1 appears in both searches, should have highest score + assert combined["doc1"] > combined["doc2"] + assert combined["doc1"] > combined["doc3"] + + +class TestCombineSearchResults: + """Test cases for the main combine_search_results function.""" + + def test_combine_search_results_rrf_default(self): + """Test combining with RRF as default.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results(vector_scores, keyword_scores) + + # Should default to RRF + assert len(combined) == 3 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_rrf_explicit(self): + """Test combining with explicit RRF.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type=RERANKER_TYPE_RRF, reranker_params={"impact_factor": 50.0} + ) + + assert len(combined) == 3 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_weighted(self): + """Test combining with weighted reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type=RERANKER_TYPE_WEIGHTED, reranker_params={"alpha": 0.3} + ) + + assert len(combined) == 3 + assert all(0 <= score <= 1 for score in combined.values()) + + def test_combine_search_results_unknown_type(self): + """Test combining with unknown reranker type defaults to RRF.""" + vector_scores = {"doc1": 0.9} + keyword_scores = {"doc2": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type="unknown_type" + ) + + # Should fall back to RRF + assert len(combined) == 2 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_empty_params(self): + """Test combining with empty parameters.""" + vector_scores = {"doc1": 0.9} + keyword_scores = {"doc2": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results(vector_scores, keyword_scores, reranker_params={}) + + # Should use default parameters + assert len(combined) == 2 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_empty_scores(self): + """Test combining with empty score dictionaries.""" + # Test with empty vector scores + combined = WeightedInMemoryAggregator.combine_search_results({}, {"doc1": 0.8}) + assert len(combined) == 1 + assert combined["doc1"] > 0 + + # Test with empty keyword scores + combined = WeightedInMemoryAggregator.combine_search_results({"doc1": 0.9}, {}) + assert len(combined) == 1 + assert combined["doc1"] > 0 + + # Test with both empty + combined = WeightedInMemoryAggregator.combine_search_results({}, {}) + assert len(combined) == 0 diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py index f71073651..91bddd037 100644 --- a/tests/unit/providers/vector_io/conftest.py +++ b/tests/unit/providers/vector_io/conftest.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import random +from unittest.mock import AsyncMock, MagicMock, patch import numpy as np import pytest @@ -12,7 +13,7 @@ from chromadb import PersistentClient from pymilvus import MilvusClient, connections from llama_stack.apis.vector_dbs import VectorDB -from llama_stack.apis.vector_io import Chunk, ChunkMetadata +from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse from llama_stack.providers.inline.vector_io.chroma.config import ChromaVectorIOConfig from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter @@ -22,6 +23,8 @@ from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConf from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaIndex, ChromaVectorIOAdapter, maybe_await from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex, MilvusVectorIOAdapter +from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig +from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter EMBEDDING_DIMENSION = 384 @@ -29,7 +32,7 @@ COLLECTION_PREFIX = "test_collection" MILVUS_ALIAS = "test_milvus" -@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma"]) +@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma", "pgvector"]) def vector_provider(request): return request.param @@ -333,15 +336,127 @@ async def qdrant_vec_index(qdrant_vec_db_path, embedding_dimension): await index.delete() +@pytest.fixture +def mock_psycopg2_connection(): + connection = MagicMock() + cursor = MagicMock() + + cursor.__enter__ = MagicMock(return_value=cursor) + cursor.__exit__ = MagicMock() + + connection.cursor.return_value = cursor + + return connection, cursor + + +@pytest.fixture +async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id="pgvector", + provider_resource_id="pgvector:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.execute_values"): + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + index._test_chunks = [] + original_add_chunks = index.add_chunks + + async def mock_add_chunks(chunks, embeddings): + index._test_chunks = list(chunks) + await original_add_chunks(chunks, embeddings) + + index.add_chunks = mock_add_chunks + + async def mock_query_vector(embedding, k, score_threshold): + chunks = index._test_chunks[:k] if hasattr(index, "_test_chunks") else [] + scores = [1.0] * len(chunks) + return QueryChunksResponse(chunks=chunks, scores=scores) + + index.query_vector = mock_query_vector + + yield index + + +@pytest.fixture +async def pgvector_vec_adapter(mock_inference_api, embedding_dimension): + config = PGVectorVectorIOConfig( + host="localhost", + port=5432, + db="test_db", + user="test_user", + password="test_password", + kvstore=SqliteKVStoreConfig(), + ) + + adapter = PGVectorVectorIOAdapter(config, mock_inference_api, None) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2.connect") as mock_connect: + mock_conn = MagicMock() + mock_cursor = MagicMock() + mock_cursor.__enter__ = MagicMock(return_value=mock_cursor) + mock_cursor.__exit__ = MagicMock() + mock_conn.cursor.return_value = mock_cursor + mock_conn.autocommit = True + mock_connect.return_value = mock_conn + + with patch( + "llama_stack.providers.remote.vector_io.pgvector.pgvector.check_extension_version" + ) as mock_check_version: + mock_check_version.return_value = "0.5.1" + + with patch("llama_stack.providers.utils.kvstore.kvstore_impl") as mock_kvstore_impl: + mock_kvstore = AsyncMock() + mock_kvstore_impl.return_value = mock_kvstore + + with patch.object(adapter, "initialize_openai_vector_stores", new_callable=AsyncMock): + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.upsert_models"): + await adapter.initialize() + adapter.conn = mock_conn + + async def mock_insert_chunks(vector_db_id, chunks, ttl_seconds=None): + index = await adapter._get_and_cache_vector_db_index(vector_db_id) + if not index: + raise ValueError(f"Vector DB {vector_db_id} not found") + await index.insert_chunks(chunks) + + adapter.insert_chunks = mock_insert_chunks + + async def mock_query_chunks(vector_db_id, query, params=None): + index = await adapter._get_and_cache_vector_db_index(vector_db_id) + if not index: + raise ValueError(f"Vector DB {vector_db_id} not found") + return await index.query_chunks(query, params) + + adapter.query_chunks = mock_query_chunks + + test_vector_db = VectorDB( + identifier=f"pgvector_test_collection_{random.randint(1, 1_000_000)}", + provider_id="test_provider", + embedding_model="test_model", + embedding_dimension=embedding_dimension, + ) + await adapter.register_vector_db(test_vector_db) + adapter.test_collection_id = test_vector_db.identifier + + yield adapter + await adapter.shutdown() + + @pytest.fixture def vector_io_adapter(vector_provider, request): - """Returns the appropriate vector IO adapter based on the provider parameter.""" vector_provider_dict = { "milvus": "milvus_vec_adapter", "faiss": "faiss_vec_adapter", "sqlite_vec": "sqlite_vec_adapter", "chroma": "chroma_vec_adapter", "qdrant": "qdrant_vec_adapter", + "pgvector": "pgvector_vec_adapter", } return request.getfixturevalue(vector_provider_dict[vector_provider]) diff --git a/tests/unit/providers/vector_io/remote/test_pgvector.py b/tests/unit/providers/vector_io/remote/test_pgvector.py new file mode 100644 index 000000000..6f498bf46 --- /dev/null +++ b/tests/unit/providers/vector_io/remote/test_pgvector.py @@ -0,0 +1,138 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import asyncio +from unittest.mock import patch + +import pytest + +from llama_stack.apis.vector_dbs import VectorDB +from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex + +PGVECTOR_PROVIDER = "pgvector" + + +@pytest.fixture(scope="session") +def loop(): + return asyncio.new_event_loop() + + +@pytest.fixture +def embedding_dimension(): + """Default embedding dimension for tests.""" + return 384 + + +@pytest.fixture +async def pgvector_index(embedding_dimension, mock_psycopg2_connection): + """Create a PGVectorIndex instance with mocked database connection.""" + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + # Use explicit COSINE distance metric for consistent testing + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + + return index, cursor + + +class TestPGVectorIndex: + def test_distance_metric_validation(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="L2") + assert index.distance_metric == "L2" + with pytest.raises(ValueError, match="Distance metric 'INVALID' is not supported"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="INVALID") + + def test_get_pgvector_search_function(self, pgvector_index): + index, cursor = pgvector_index + supported_metrics = index.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION + + for metric, function in supported_metrics.items(): + index.distance_metric = metric + assert index.get_pgvector_search_function() == function + + def test_check_distance_metric_availability(self, pgvector_index): + index, cursor = pgvector_index + supported_metrics = index.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION + + for metric in supported_metrics: + index.check_distance_metric_availability(metric) + + with pytest.raises(ValueError, match="Distance metric 'INVALID' is not supported"): + index.check_distance_metric_availability("INVALID") + + def test_constructor_invalid_distance_metric(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + with pytest.raises(ValueError, match="Distance metric 'INVALID_METRIC' is not supported by PGVector"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="INVALID_METRIC") + + with pytest.raises(ValueError, match="Supported metrics are:"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="UNKNOWN") + + try: + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + assert index.distance_metric == "COSINE" + except ValueError: + pytest.fail("Valid distance metric 'COSINE' should not raise ValueError") + + def test_constructor_all_supported_distance_metrics(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + supported_metrics = ["L2", "L1", "COSINE", "INNER_PRODUCT", "HAMMING", "JACCARD"] + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + for metric in supported_metrics: + try: + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric=metric) + assert index.distance_metric == metric + + expected_operators = { + "L2": "<->", + "L1": "<+>", + "COSINE": "<=>", + "INNER_PRODUCT": "<#>", + "HAMMING": "<~>", + "JACCARD": "<%>", + } + assert index.get_pgvector_search_function() == expected_operators[metric] + except Exception as e: + pytest.fail(f"Valid distance metric '{metric}' should not raise exception: {e}") diff --git 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url = "https://files.pythonhosted.org/packages/b2/d1/323581e9273ad2c0dbd1902f3fb50c441da86e894b6e25a73c3fda32c57e/psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f8157bed2f51db683f31306aa497311b560f2265998122abe1dce6428bd86567", size = 2959356, upload-time = "2024-10-16T11:22:30.562Z" }, + { url = "https://files.pythonhosted.org/packages/08/50/d13ea0a054189ae1bc21af1d85b6f8bb9bbc5572991055d70ad9006fe2d6/psycopg2_binary-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:27422aa5f11fbcd9b18da48373eb67081243662f9b46e6fd07c3eb46e4535142", size = 2569224, upload-time = "2025-01-04T20:09:19.234Z" }, +] + [[package]] name = "ptyprocess" version = "0.7.0" From efdb5558b8dcab4d141678bfed0a405e2f312b6f Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Fri, 29 Aug 2025 11:03:52 -0700 Subject: [PATCH 016/124] fix: Remove bfcl scoring function as not supported (#3281) # What does this PR do? BFCL scoring function is not supported, removing it. Also minor fixes as the llama stack run is broken for open-benchmark for test plan verification 1. Correct the model paths for supported models 2. Fix another issue as there is no `provider_id` for DatasetInput but logger assumes it exists. ``` File "/Users/swapna942/llama-stack/llama_stack/core/stack.py", line 332, in construct_stack await register_resources(run_config, impls) File "/Users/swapna942/llama-stack/llama_stack/core/stack.py", line 108, in register_resources logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}") ^^^^^^^^^^^^^^^ File "/Users/swapna942/llama-stack/.venv/lib/python3.13/site-packages/pydantic/main.py", line 991, in __getattr__ raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') AttributeError: 'DatasetInput' object has no attribute 'provider_id' ``` ## Test Plan ```llama stack build --distro open-benchmark --image-type venv``` and run the server succeeds Issue Link: https://github.com/llamastack/llama-stack/issues/3282 --- .../llama_stack_client_cli_reference.md | 1 - llama_stack/core/stack.py | 12 +- .../open-benchmark/open_benchmark.py | 16 +- .../distributions/open-benchmark/run.yaml | 19 +- .../providers/inline/scoring/basic/scoring.py | 2 - .../basic/scoring_fn/bfcl_scoring_fn.py | 93 -- .../scoring/basic/scoring_fn/fn_defs/bfcl.py | 21 - .../scoring/basic/utils/bfcl/__init__.py | 5 - .../scoring/basic/utils/bfcl/ast_parser.py | 296 ------ .../scoring/basic/utils/bfcl/checker.py | 989 ------------------ .../scoring/basic/utils/bfcl/tree_sitter.py | 40 - 11 files changed, 12 insertions(+), 1482 deletions(-) delete mode 100644 llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py delete mode 100644 llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py delete mode 100644 llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py delete mode 100644 llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py delete mode 100644 llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py delete mode 100644 llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py diff --git a/docs/source/references/llama_stack_client_cli_reference.md b/docs/source/references/llama_stack_client_cli_reference.md index 2d386dbfa..d4d79cea1 100644 --- a/docs/source/references/llama_stack_client_cli_reference.md +++ b/docs/source/references/llama_stack_client_cli_reference.md @@ -478,7 +478,6 @@ llama-stack-client scoring_functions list ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓ ┃ identifier ┃ provider_id ┃ description ┃ type ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩ -│ basic::bfcl │ basic │ BFCL complex scoring │ scoring_function │ │ basic::docvqa │ basic │ DocVQA Visual Question & Answer scoring function │ scoring_function │ │ basic::equality │ basic │ Returns 1.0 if the input is equal to the target, 0.0 │ scoring_function │ │ │ │ otherwise. │ │ diff --git a/llama_stack/core/stack.py b/llama_stack/core/stack.py index f734d0285..bccea48d3 100644 --- a/llama_stack/core/stack.py +++ b/llama_stack/core/stack.py @@ -105,12 +105,12 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]): method = getattr(impls[api], register_method) for obj in objects: - logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}") - - # Do not register models on disabled providers - if hasattr(obj, "provider_id") and (not obj.provider_id or obj.provider_id == "__disabled__"): - logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.") - continue + if hasattr(obj, "provider_id"): + # Do not register models on disabled providers + if not obj.provider_id or obj.provider_id == "__disabled__": + logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.") + continue + logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}") # we want to maintain the type information in arguments to method. # instead of method(**obj.model_dump()), which may convert a typed attr to a dict, diff --git a/llama_stack/distributions/open-benchmark/open_benchmark.py b/llama_stack/distributions/open-benchmark/open_benchmark.py index af08ac7ba..1d84512cd 100644 --- a/llama_stack/distributions/open-benchmark/open_benchmark.py +++ b/llama_stack/distributions/open-benchmark/open_benchmark.py @@ -43,7 +43,7 @@ def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderMo "openai", [ ProviderModelEntry( - provider_model_id="openai/gpt-4o", + provider_model_id="gpt-4o", model_type=ModelType.llm, ) ], @@ -53,7 +53,7 @@ def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderMo "anthropic", [ ProviderModelEntry( - provider_model_id="anthropic/claude-3-5-sonnet-latest", + provider_model_id="claude-3-5-sonnet-latest", model_type=ModelType.llm, ) ], @@ -206,13 +206,6 @@ def get_distribution_template() -> DistributionTemplate: uri="huggingface://datasets/llamastack/math_500?split=test", ), ), - DatasetInput( - dataset_id="bfcl", - purpose=DatasetPurpose.eval_messages_answer, - source=URIDataSource( - uri="huggingface://datasets/llamastack/bfcl_v3?split=train", - ), - ), DatasetInput( dataset_id="ifeval", purpose=DatasetPurpose.eval_messages_answer, @@ -250,11 +243,6 @@ def get_distribution_template() -> DistributionTemplate: dataset_id="math_500", scoring_functions=["basic::regex_parser_math_response"], ), - BenchmarkInput( - benchmark_id="meta-reference-bfcl", - dataset_id="bfcl", - scoring_functions=["basic::bfcl"], - ), BenchmarkInput( benchmark_id="meta-reference-ifeval", dataset_id="ifeval", diff --git a/llama_stack/distributions/open-benchmark/run.yaml b/llama_stack/distributions/open-benchmark/run.yaml index 779bca47e..d068a0b5a 100644 --- a/llama_stack/distributions/open-benchmark/run.yaml +++ b/llama_stack/distributions/open-benchmark/run.yaml @@ -136,14 +136,14 @@ inference_store: db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/inference_store.db models: - metadata: {} - model_id: openai/gpt-4o + model_id: gpt-4o provider_id: openai - provider_model_id: openai/gpt-4o + provider_model_id: gpt-4o model_type: llm - metadata: {} - model_id: anthropic/claude-3-5-sonnet-latest + model_id: claude-3-5-sonnet-latest provider_id: anthropic - provider_model_id: anthropic/claude-3-5-sonnet-latest + provider_model_id: claude-3-5-sonnet-latest model_type: llm - metadata: {} model_id: gemini/gemini-1.5-flash @@ -188,12 +188,6 @@ datasets: uri: huggingface://datasets/llamastack/math_500?split=test metadata: {} dataset_id: math_500 -- purpose: eval/messages-answer - source: - type: uri - uri: huggingface://datasets/llamastack/bfcl_v3?split=train - metadata: {} - dataset_id: bfcl - purpose: eval/messages-answer source: type: uri @@ -228,11 +222,6 @@ benchmarks: - basic::regex_parser_math_response metadata: {} benchmark_id: meta-reference-math-500 -- dataset_id: bfcl - scoring_functions: - - basic::bfcl - metadata: {} - benchmark_id: meta-reference-bfcl - dataset_id: ifeval scoring_functions: - basic::ifeval diff --git a/llama_stack/providers/inline/scoring/basic/scoring.py b/llama_stack/providers/inline/scoring/basic/scoring.py index 91b10daae..b19b68039 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring.py +++ b/llama_stack/providers/inline/scoring/basic/scoring.py @@ -22,7 +22,6 @@ from llama_stack.providers.utils.common.data_schema_validator import ( ) from .config import BasicScoringConfig -from .scoring_fn.bfcl_scoring_fn import BFCLScoringFn from .scoring_fn.docvqa_scoring_fn import DocVQAScoringFn from .scoring_fn.equality_scoring_fn import EqualityScoringFn from .scoring_fn.ifeval_scoring_fn import IfEvalScoringFn @@ -37,7 +36,6 @@ FIXED_FNS = [ SubsetOfScoringFn, RegexParserScoringFn, RegexParserMathResponseScoringFn, - BFCLScoringFn, IfEvalScoringFn, DocVQAScoringFn, ] diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py deleted file mode 100644 index b29620be2..000000000 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -import json -import re -from typing import Any - -from llama_stack.apis.scoring import ScoringResultRow -from llama_stack.apis.scoring_functions import ScoringFnParams -from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn - -from ..utils.bfcl.ast_parser import decode_ast -from ..utils.bfcl.checker import ast_checker, is_empty_output -from .fn_defs.bfcl import bfcl - - -def postprocess(x: dict[str, Any], test_category: str) -> dict[str, Any]: - contain_func_call = False - error = None - error_type = None - checker_result = {} - try: - prediction = decode_ast(x["generated_answer"], x["language"]) or "" - contain_func_call = True - # if not is_function_calling_format_output(prediction): - if is_empty_output(prediction): - contain_func_call = False - error = "Did not output in the specified format. Note: the model_result is wrapped in a string to ensure json serializability." - error_type = "ast_decoder:decoder_wrong_output_format" - else: - checker_result = ast_checker( - json.loads(x["function"]), - prediction, - json.loads(x["ground_truth"]), - x["language"], - test_category=test_category, - model_name="", - ) - except Exception as e: - prediction = "" - error = f"Invalid syntax. Failed to decode AST. {str(e)}" - error_type = "ast_decoder:decoder_failed" - return { - "prediction": prediction, - "contain_func_call": contain_func_call, - "valid": checker_result.get("valid", False), - "error": error or checker_result.get("error", ""), - "error_type": error_type or checker_result.get("error_type", ""), - } - - -def gen_valid(x: dict[str, Any]) -> dict[str, float]: - return {"valid": x["valid"]} - - -def gen_relevance_acc(x: dict[str, Any]) -> dict[str, float]: - # This function serves for both relevance and irrelevance tests, which share the exact opposite logic. - # If `test_category` is "irrelevance", the model is expected to output no function call. - # No function call means either the AST decoding fails (a error message is generated) or the decoded AST does not contain any function call (such as a empty list, `[]`). - # If `test_category` is "relevance", the model is expected to output to a function call, and empty list doesn't count as a function call. - acc = not x["contain_func_call"] if "irrelevance" in x["id"] else x["contain_func_call"] - return {"valid": float(acc)} - - -class BFCLScoringFn(RegisteredBaseScoringFn): - """ - A scoring_fn for BFCL - """ - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.supported_fn_defs_registry = { - bfcl.identifier: bfcl, - } - - async def score_row( - self, - input_row: dict[str, Any], - scoring_fn_identifier: str | None = "bfcl", - scoring_params: ScoringFnParams | None = None, - ) -> ScoringResultRow: - test_category = re.sub(r"_[0-9_-]+$", "", input_row["id"]) - score_result = postprocess(input_row, test_category) - if test_category in {"irrelevance", "live_relevance", "live_irrelevance"}: - score = gen_relevance_acc(score_result)["valid"] - else: - score = gen_valid(score_result)["valid"] - return { - "score": float(score), - } diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py deleted file mode 100644 index 392d92c86..000000000 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.common.type_system import NumberType -from llama_stack.apis.scoring_functions import ( - AggregationFunctionType, - BasicScoringFnParams, - ScoringFn, -) - -bfcl = ScoringFn( - identifier="basic::bfcl", - description="BFCL complex scoring", - return_type=NumberType(), - provider_id="basic", - provider_resource_id="bfcl", - params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.accuracy]), -) diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py deleted file mode 100644 index 756f351d8..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py deleted file mode 100644 index 445cdfc77..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py +++ /dev/null @@ -1,296 +0,0 @@ -# ruff: noqa -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. -import ast - -from .tree_sitter import get_parser - - -def parse_java_function_call(source_code): - if not source_code.endswith(";"): - source_code += ";" # Necessary for the parser not to register an error - parser = get_parser("java") - tree = parser.parse(bytes(source_code, "utf8")) - root_node = tree.root_node - - if root_node.has_error: - raise Exception("Error parsing java the source code.") - - def get_text(node): - """Returns the text represented by the node.""" - return source_code[node.start_byte : node.end_byte] - - def traverse_node(node, nested=False): - if node.type == "string_literal": - if nested: - return get_text(node) - # Strip surrounding quotes from string literals - return get_text(node)[1:-1] - elif node.type == "character_literal": - if nested: - return get_text(node) - # Strip surrounding single quotes from character literals - return get_text(node)[1:-1] - """Traverse the node to collect texts for complex structures.""" - if node.type in [ - "identifier", - "class_literal", - "type_identifier", - "method_invocation", - ]: - return get_text(node) - elif node.type == "array_creation_expression": - # Handle array creation expression specifically - type_node = node.child_by_field_name("type") - value_node = node.child_by_field_name("value") - type_text = traverse_node(type_node, True) - value_text = traverse_node(value_node, True) - return f"new {type_text}[]{value_text}" - elif node.type == "object_creation_expression": - # Handle object creation expression specifically - type_node = node.child_by_field_name("type") - arguments_node = node.child_by_field_name("arguments") - type_text = traverse_node(type_node, True) - if arguments_node: - # Process each argument carefully, avoiding unnecessary punctuation - argument_texts = [] - for child in arguments_node.children: - if child.type not in [ - ",", - "(", - ")", - ]: # Exclude commas and parentheses - argument_text = traverse_node(child, True) - argument_texts.append(argument_text) - arguments_text = ", ".join(argument_texts) - return f"new {type_text}({arguments_text})" - else: - return f"new {type_text}()" - elif node.type == "set": - # Handling sets specifically - items = [traverse_node(n, True) for n in node.children if n.type not in [",", "set"]] - return "{" + ", ".join(items) + "}" - - elif node.child_count > 0: - return "".join(traverse_node(child, True) for child in node.children) - else: - return get_text(node) - - def extract_arguments(args_node): - arguments = {} - for child in args_node.children: - if child.type == "assignment_expression": - # For named parameters - name_node, value_node = child.children[0], child.children[2] - name = get_text(name_node) - value = traverse_node(value_node) - if name in arguments: - if not isinstance(arguments[name], list): - arguments[name] = [arguments[name]] - arguments[name].append(value) - else: - arguments[name] = value - # arguments.append({'name': name, 'value': value}) - elif child.type in ["identifier", "class_literal", "set"]: - # For unnamed parameters and handling sets - value = traverse_node(child) - if None in arguments: - if not isinstance(arguments[None], list): - arguments[None] = [arguments[None]] - arguments[None].append(value) - else: - arguments[None] = value - return arguments - - def traverse(node): - if node.type == "method_invocation": - # Extract the function name and its arguments - method_name = get_text(node.child_by_field_name("name")) - class_name_node = node.child_by_field_name("object") - if class_name_node: - class_name = get_text(class_name_node) - function_name = f"{class_name}.{method_name}" - else: - function_name = method_name - arguments_node = node.child_by_field_name("arguments") - if arguments_node: - arguments = extract_arguments(arguments_node) - for key, value in arguments.items(): - if isinstance(value, list): - raise Exception("Error: Multiple arguments with the same name are not supported.") - return [{function_name: arguments}] - - else: - for child in node.children: - result = traverse(child) - if result: - return result - - result = traverse(root_node) - return result if result else {} - - -def parse_javascript_function_call(source_code): - if not source_code.endswith(";"): - source_code += ";" # Necessary for the parser not to register an error - parser = get_parser("javascript") - # Parse the source code - tree = parser.parse(bytes(source_code, "utf8")) - root_node = tree.root_node - if root_node.has_error: - raise Exception("Error js parsing the source code.") - - # Function to recursively extract argument details - def extract_arguments(node): - args = {} - for child in node.children: - if child.type == "assignment_expression": - # Extract left (name) and right (value) parts of the assignment - name = child.children[0].text.decode("utf-8") - value = child.children[2].text.decode("utf-8") - if (value.startswith('"') and value.endswith('"')) or (value.startswith("'") and value.endswith("'")): - value = value[1:-1] # Trim the quotation marks - if name in args: - if not isinstance(args[name], list): - args[name] = [args[name]] - args[name].append(value) - else: - args[name] = value - - elif child.type == "identifier" or child.type == "true": - # Handle non-named arguments and boolean values - value = child.text.decode("utf-8") - if None in args: - if not isinstance(args[None], list): - args[None] = [args[None]] - args[None].append(value) - else: - args[None] = value - return args - - # Find the function call and extract its name and arguments - if root_node.type == "program": - for child in root_node.children: - if child.type == "expression_statement": - for sub_child in child.children: - if sub_child.type == "call_expression": - function_name = sub_child.children[0].text.decode("utf8") - arguments_node = sub_child.children[1] - parameters = extract_arguments(arguments_node) - for key, value in parameters.items(): - if isinstance(value, list): - raise Exception("Error: Multiple arguments with the same name are not supported.") - result = [{function_name: parameters}] - return result - - -def ast_parse(input_str, language="Python"): - if language == "Python": - cleaned_input = input_str.strip("[]'") - parsed = ast.parse(cleaned_input, mode="eval") - extracted = [] - if isinstance(parsed.body, ast.Call): - extracted.append(resolve_ast_call(parsed.body)) - else: - for elem in parsed.body.elts: - extracted.append(resolve_ast_call(elem)) - return extracted - elif language == "Java": - return parse_java_function_call(input_str[1:-1]) # Remove the [ and ] from the string - elif language == "JavaScript": - return parse_javascript_function_call(input_str[1:-1]) - else: - raise NotImplementedError(f"Unsupported language: {language}") - - -def resolve_ast_call(elem): - # Handle nested attributes for deeply nested module paths - func_parts = [] - func_part = elem.func - while isinstance(func_part, ast.Attribute): - func_parts.append(func_part.attr) - func_part = func_part.value - if isinstance(func_part, ast.Name): - func_parts.append(func_part.id) - func_name = ".".join(reversed(func_parts)) - args_dict = {} - # Parse when args are simply passed as an unnamed dictionary arg - for arg in elem.args: - if isinstance(arg, ast.Dict): - for key, value in zip(arg.keys, arg.values): - if isinstance(key, ast.Constant): - arg_name = key.value - output = resolve_ast_by_type(value) - args_dict[arg_name] = output - for arg in elem.keywords: - output = resolve_ast_by_type(arg.value) - args_dict[arg.arg] = output - return {func_name: args_dict} - - -def resolve_ast_by_type(value): - if isinstance(value, ast.Constant): - if value.value is Ellipsis: - output = "..." - else: - output = value.value - elif isinstance(value, ast.UnaryOp): - output = -value.operand.value - elif isinstance(value, ast.List): - output = [resolve_ast_by_type(v) for v in value.elts] - elif isinstance(value, ast.Dict): - output = {resolve_ast_by_type(k): resolve_ast_by_type(v) for k, v in zip(value.keys, value.values)} - elif isinstance(value, ast.NameConstant): # Added this condition to handle boolean values - output = value.value - elif isinstance(value, ast.BinOp): # Added this condition to handle function calls as arguments - output = eval(ast.unparse(value)) - elif isinstance(value, ast.Name): - output = value.id - elif isinstance(value, ast.Call): - if len(value.keywords) == 0: - output = ast.unparse(value) - else: - output = resolve_ast_call(value) - elif isinstance(value, ast.Tuple): - output = tuple(resolve_ast_by_type(v) for v in value.elts) - elif isinstance(value, ast.Lambda): - output = eval(ast.unparse(value.body[0].value)) - elif isinstance(value, ast.Ellipsis): - output = "..." - elif isinstance(value, ast.Subscript): - try: - output = ast.unparse(value.body[0].value) - except: - output = ast.unparse(value.value) + "[" + ast.unparse(value.slice) + "]" - else: - raise Exception(f"Unsupported AST type: {type(value)}") - return output - - -def decode_ast(result, language="Python"): - func = result - func = func.replace("\n", "") # remove new line characters - if not func.startswith("["): - func = "[" + func - if not func.endswith("]"): - func = func + "]" - decoded_output = ast_parse(func, language) - return decoded_output - - -def decode_execute(result): - func = result - func = func.replace("\n", "") # remove new line characters - if not func.startswith("["): - func = "[" + func - if not func.endswith("]"): - func = func + "]" - decode_output = ast_parse(func) - execution_list = [] - for function_call in decode_output: - for key, value in function_call.items(): - execution_list.append(f"{key}({','.join([f'{k}={repr(v)}' for k, v in value.items()])})") - return execution_list diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py deleted file mode 100644 index f6aab123c..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py +++ /dev/null @@ -1,989 +0,0 @@ -# ruff: noqa -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. -import json -import re -import time -from typing import Any - -# Comment out for now until we actually use the rest checker in evals -# import requests # Do not remove this import even though it seems to be unused. It's used in the executable_checker_rest function. - - -class NoAPIKeyError(Exception): - def __init__(self): - self.message = "❗️Please fill in the API keys in the function_credential_config.json file. If you do not provide the API keys, the executable test category results will be inaccurate." - super().__init__(self.message) - - -REAL_TIME_MATCH_ALLOWED_DIFFERENCE = 0.2 - - -JAVA_TYPE_CONVERSION = { - "byte": int, - "short": int, - "integer": int, - "float": float, - "double": float, - "long": int, - "boolean": bool, - "char": str, - "Array": list, - "ArrayList": list, - "Set": set, - "HashMap": dict, - "Hashtable": dict, - "Queue": list, # this can be `queue.Queue` as well, for simplicity we check with list - "Stack": list, - "String": str, - "any": str, -} - -JS_TYPE_CONVERSION = { - "String": str, - "integer": int, - "float": float, - "Bigint": int, - "Boolean": bool, - "dict": dict, - "array": list, - "any": str, -} - -# We switch to conditional import for the following two imports to avoid unnecessary installations. -# User doesn't need to setup the tree-sitter packages if they are not running the test for that language. -# from js_type_converter import js_type_converter -# from java_type_converter import java_type_converter - -PYTHON_TYPE_MAPPING = { - "string": str, - "integer": int, - "float": float, - "boolean": bool, - "array": list, - "tuple": list, - "dict": dict, - "any": str, -} - -# This is the list of types that we need to recursively check its values -PYTHON_NESTED_TYPE_CHECK_LIST = ["array", "tuple"] - - -NESTED_CONVERSION_TYPE_LIST = ["Array", "ArrayList", "array"] - - -#### Helper functions for AST #### -def find_description(func_descriptions, name): - if type(func_descriptions) == list: - for func_description in func_descriptions: - if func_description["name"] == name: - return func_description - return None - else: - # it is a dict, there is only one function - return func_descriptions - - -def get_possible_answer_type(possible_answer: list): - for answer in possible_answer: - if answer != "": # Optional parameter - return type(answer) - return None - - -def type_checker( - param: str, - value, - possible_answer: list, - expected_type_description: str, - expected_type_converted, - nested_type_converted, -): - # NOTE: This type checker only supports nested type checking for one level deep. - # We didn't implement recursive type checking for nested types, as it's not needed for the current use case and it's very complex. - - result: Any = { - "valid": True, - "error": [], - "is_variable": False, - "error_type": "type_error:simple", - } - - is_variable = False - # check for the case where a variable is used instead of a actual value. - # use the type in possible_answer as the expected type - possible_answer_type = get_possible_answer_type(possible_answer) - # if possible_answer only contains optional parameters, we can't determine the type - if possible_answer_type != None: - # we are being precise here. - # in fact, possible_answer_type should always be string, as that's how we treat varibale in possible_answer - if possible_answer_type != expected_type_converted: - is_variable = True - - # value is the same type as in function description - if type(value) == expected_type_converted: - # We don't need to do recursive check for simple types - if nested_type_converted == None: - result["is_variable"] = is_variable - return result - else: - for possible_answer_item in possible_answer: - flag = True # Each parameter should match to at least one possible answer type. - # Here, we assume that each item should be the same type. We could also relax it. - if type(possible_answer_item) == list: - for value_item in value: - checker_result = type_checker( - param, - value_item, - possible_answer_item, - str(nested_type_converted), - nested_type_converted, - None, - ) - if not checker_result["valid"]: - flag = False - break - - if flag: - return {"valid": True, "error": [], "is_variable": is_variable} - - result["valid"] = False - result["error"] = [ - f"Nested type checking failed for parameter {repr(param)}. Expected outer type {expected_type_description} with inner type {str(nested_type_converted)}. Parameter value: {repr(value)}." - ] - result["error_type"] = "type_error:nested" - - # value is not as expected, check for the case where a variable is used instead of a actual value - # use the type in possible_answer as the expected type - possible_answer_type = get_possible_answer_type(possible_answer) - # if possible_answer only contains optional parameters, we can't determine the type - if possible_answer_type != None: - # we are being precise here. - # in fact, possible_answer_type should always be string, as that's how we treat varibale in possible_answer - if type(value) == possible_answer_type: - result["is_variable"] = True - return result - - result["valid"] = False - result["error"].append( - f"Incorrect type for parameter {repr(param)}. Expected type {expected_type_description}, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:simple" - return result - - -def standardize_string(input_string: str): - # This function standardizes the string by removing all the spaces, ",./-_*^" punctuation, and converting it to lowercase - # It will also convert all the single quotes to double quotes - # This is used to compare the model output with the possible answers - # We don't want to punish model for answer like April 1, 2024 vs April 1,2024, vs April 1 2024 - regex_string = r"[ \,\.\/\-\_\*\^]" - return re.sub(regex_string, "", input_string).lower().replace("'", '"') - - -def string_checker(param: str, model_output: str, possible_answer: list): - standardize_possible_answer = [] - standardize_model_output = standardize_string(model_output) - for i in range(len(possible_answer)): - if type(possible_answer[i]) == str: - standardize_possible_answer.append(standardize_string(possible_answer[i])) - - if standardize_model_output not in standardize_possible_answer: - return { - "valid": False, - "error": [ - f"Invalid value for parameter {repr(param)}: {repr(model_output)}. Expected one of {possible_answer}. Case insensitive." - ], - "error_type": "value_error:string", - } - - return {"valid": True, "error": []} - - -def list_checker(param: str, model_output: list, possible_answer: list): - # Convert the tuple to a list - - standardize_model_output = list(model_output) - - # If the element in the list is a string, we need to standardize it - for i in range(len(standardize_model_output)): - if type(standardize_model_output[i]) == str: - standardize_model_output[i] = standardize_string(model_output[i]) - - standardize_possible_answer: Any = [] - # We also need to standardize the possible answers - for i in range(len(possible_answer)): - standardize_possible_answer.append([]) - for j in range(len(possible_answer[i])): - if type(possible_answer[i][j]) == str: - standardize_possible_answer[i].append(standardize_string(possible_answer[i][j])) - else: - standardize_possible_answer[i].append(possible_answer[i][j]) - - if standardize_model_output not in standardize_possible_answer: - return { - "valid": False, - "error": [ - f"Invalid value for parameter {repr(param)}: {repr(model_output)}. Expected one of {possible_answer}." - ], - "error_type": "value_error:list/tuple", - } - - return {"valid": True, "error": []} - - -def dict_checker(param: str, model_output: dict, possible_answers: list): - # This function works for simple dictionaries, but not dictionaries with nested dictionaries. - # The current dataset only contains simple dictionaries, so this is sufficient. - - result = {"valid": False, "error": [], "error_type": "dict_checker:unclear"} - for i in range(len(possible_answers)): - if possible_answers[i] == "": - continue - - result = {"valid": False, "error": [], "error_type": "dict_checker:unclear"} - - flag = True - - possible_answer = possible_answers[i] - # possible_anwer is a single dictionary - - for key, value in model_output.items(): - if key not in possible_answer: - result["valid"] = False - result["error"].append(f"Unexpected dict key parameter: '{key}'.") # type: ignore[attr-defined] - result["error_type"] = "value_error:dict_key" - flag = False - break - - standardize_value = value - # If the value is a string, we need to standardize it - if type(value) == str: - standardize_value = standardize_string(value) - - # We also need to standardize the possible answers if they are string - standardize_possible_answer = [] - for i in range(len(possible_answer[key])): - if type(possible_answer[key][i]) == str: - standardize_possible_answer.append(standardize_string(possible_answer[key][i])) - else: - standardize_possible_answer.append(possible_answer[key][i]) - - if standardize_value not in standardize_possible_answer: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Invalid value for parameter {repr(key)}: {repr(value)}. Expected one of {standardize_possible_answer}." - ) - result["error_type"] = "value_error:dict_value" - flag = False - break - - for key, value in possible_answer.items(): - if key not in model_output and "" not in value: - result["valid"] = False - result["error"].append(f"Missing dict key parameter: '{key}'.") # type: ignore[attr-defined] - result["error_type"] = "value_error:dict_key" - flag = False - break - - if flag: - return {"valid": True, "error": []} - - return result - - -def list_dict_checker(param: str, model_output: list, possible_answers: list): - # This function takes in a list of dictionaries and checks if each dictionary is valid - # The order of the dictionaries in the list must match the order of the possible answers - - result = {"valid": False, "error": [], "error_type": "list_dict_checker:unclear"} - - for answer_index in range(len(possible_answers)): - flag = True # True means so far, all dictionaries are valid - - # Only proceed if the number of dictionaries in the list matches the number of dictionaries in the possible answers - if len(model_output) != len(possible_answers[answer_index]): - result["valid"] = False - result["error"] = ["Wrong number of dictionaries in the list."] - result["error_type"] = "value_error:list_dict_count" - flag = False - continue - - for dict_index in range(len(model_output)): - result = dict_checker( - param, - model_output[dict_index], - [possible_answers[answer_index][dict_index]], - ) - if not result["valid"]: - flag = False - break - if flag: - return {"valid": True, "error": []} - - return result - - -def simple_function_checker( - func_description: dict, - model_output: dict, - possible_answer: dict, - language: str, - model_name: str, -): - possible_answer = list(possible_answer.values())[0] - # Extract function name and parameters details - func_name = func_description["name"] - param_details = func_description["parameters"]["properties"] - required_params = func_description["parameters"]["required"] - - # Initialize a result dictionary - result = { - "valid": True, - "error": [], - "error_type": "simple_function_checker:unclear", - } - - # Check if function name matches - if func_name not in model_output: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Function name {repr(func_name)} not found in model output." - ) - result["error_type"] = "simple_function_checker:wrong_func_name" - return result - - model_params = model_output[func_name] - - # Check for required parameters in model output - for param in required_params: - if param not in model_params: - result["valid"] = False - result["error"].append(f"Missing required parameter: {repr(param)}.") # type: ignore[attr-defined] - result["error_type"] = "simple_function_checker:missing_required" - return result - - # Validate types and values for each parameter in model output - for param, value in model_params.items(): - if param not in param_details or param not in possible_answer: - result["valid"] = False - result["error"].append(f"Unexpected parameter: {repr(param)}.") # type: ignore[attr-defined] - result["error_type"] = "simple_function_checker:unexpected_param" - return result - - full_param_details = param_details[param] - expected_type_description = full_param_details["type"] # This is a string - is_variable = False - nested_type_converted = None - - if language == "Java": - from evals.utils.bfcl.java_type_converter import java_type_converter - - expected_type_converted = JAVA_TYPE_CONVERSION[expected_type_description] - - if expected_type_description in JAVA_TYPE_CONVERSION: - if type(value) != str: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Incorrect type for parameter {repr(param)}. Expected type String, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:java" - return result - - if expected_type_description in NESTED_CONVERSION_TYPE_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = JAVA_TYPE_CONVERSION[nested_type] - value = java_type_converter(value, expected_type_description, nested_type) - else: - value = java_type_converter(value, expected_type_description) - - elif language == "JavaScript": - from evals.utils.bfcl.js_type_converter import js_type_converter - - expected_type_converted = JS_TYPE_CONVERSION[expected_type_description] - - if expected_type_description in JS_TYPE_CONVERSION: - if type(value) != str: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Incorrect type for parameter {repr(param)}. Expected type String, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:js" - return result - - if expected_type_description in NESTED_CONVERSION_TYPE_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = JS_TYPE_CONVERSION[nested_type] - value = js_type_converter(value, expected_type_description, nested_type) - else: - value = js_type_converter(value, expected_type_description) - - elif language == "Python": - expected_type_converted = PYTHON_TYPE_MAPPING[expected_type_description] - if expected_type_description in PYTHON_NESTED_TYPE_CHECK_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = PYTHON_TYPE_MAPPING[nested_type] - - # We convert all tuple value to list when the expected type is tuple. - # The conversion is necessary because any tuple in the possible answer would become a list after being processed through json.dump() and json.load(). - # This does introduce some false positive (eg, when the model provides a list value instead of tuple). We hope to find a better solution in the future. - if expected_type_description == "tuple" and type(value) == tuple: - value = list(value) - - # Allow python auto conversion from int to float - if language == "Python" and expected_type_description == "float" and type(value) == int: - value = float(value) - - # Type checking - # In fact, we only check for Python here. - # Type check for other languages are handled by the type converter, and so their value (after conversion) is always correct. - type_check_result = type_checker( - param, - value, - possible_answer[param], - expected_type_description, - expected_type_converted, - nested_type_converted, - ) - is_variable = type_check_result["is_variable"] - if not type_check_result["valid"]: - return type_check_result - - # It doesn't make sense to special handle dictionaries and list of dictionaries if the value is a variable. - # We can just treat the variable as a string and use the normal flow. - if not is_variable: - # Special handle for dictionaries - if expected_type_converted == dict: - result = dict_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Special handle for list of dictionaries - elif expected_type_converted == list and nested_type_converted == dict: - result = list_dict_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Special handle for strings - elif expected_type_converted == str: - # We don't check for case sensitivity for string, as long as it's not a variable - result = string_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - elif expected_type_converted == list: - result = list_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Check if the value is within the possible answers - if value not in possible_answer[param]: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Invalid value for parameter {repr(param)}: {repr(value)}. Expected one of {possible_answer[param]}." - ) - result["error_type"] = "value_error:others" - return result - - # Check for optional parameters not provided but allowed - for param in possible_answer: - if param not in model_params and "" not in possible_answer[param]: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Optional parameter {repr(param)} not provided and not marked as optional." - ) - result["error_type"] = "simple_function_checker:missing_optional" - return result - - return result - - -def parallel_function_checker_enforce_order( - func_descriptions: list, - model_output: list, - possible_answers: dict, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "parallel_function_checker_enforce_order:wrong_count", - } - - func_name_list = list(possible_answers.keys()) - possible_answers_list = [] - - for key, value in possible_answers.items(): - possible_answers_list.append({key: value}) - - for i in range(len(possible_answers_list)): - func_description = find_description(func_descriptions, func_name_list[i]) - - result = simple_function_checker( - func_description, - model_output[i], - possible_answers_list[i], - language, - model_name, - ) - if not result["valid"]: - return result - - return {"valid": True, "error": []} - - -def parallel_function_checker_no_order( - func_descriptions: list, - model_output: list, - possible_answers: list, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "parallel_function_checker_no_order:wrong_count", - } - - matched_indices = [] - - # We go throught the possible answers one by one, and eliminate the model output that matches the possible answer - # It must be this way because we need ground truth to fetch the correct function description - for i in range(len(possible_answers)): - # possible_answers[i] is a dictionary with only one key - func_name_expected = list(possible_answers[i].keys())[0] - func_description = find_description(func_descriptions, func_name_expected) - - all_errors = [] - - for index in range(len(model_output)): - if index in matched_indices: - continue - - result = simple_function_checker( - func_description, - model_output[index], - possible_answers[i], - language, - model_name, - ) - - if result["valid"]: - matched_indices.append(index) - break - else: - all_errors.append( - { - f"Model Result Index {index}": { - "sub_error": result["error"], - "sub_error_type": result["error_type"], - "model_output_item": model_output[index], - "possible_answer_item": possible_answers[i], - } - } - ) - - if not result["valid"]: - considered_indices = [i for i in range(len(model_output)) if i not in matched_indices] - all_errors.insert( - 0, - f"Could not find a matching function among index {considered_indices} of model output for index {i} of possible answers.", # type: ignore[arg-type] - ) - return { - "valid": False, - "error": all_errors, - "error_type": "parallel_function_checker_no_order:cannot_find_match", - } - - return {"valid": True, "error": []} - - -def multiple_function_checker( - func_descriptions: list, - model_output: list, - possible_answers: list, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "multiple_function_checker:wrong_count", - } - - # possible_answers is a list of only one dictionary with only one key - func_name_expected = list(possible_answers[0].keys())[0] - func_description = find_description(func_descriptions, func_name_expected) - return simple_function_checker( - func_description, - model_output[0], - possible_answers[0], - language, - model_name, - ) - - -def patten_matcher(exec_output, expected_result, function_call, is_sanity_check): - result = {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - if type(exec_output) != type(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result type for {repr(function_call)}. Expected type: {type(expected_result)}, but got: {type(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type", - "model_executed_output": exec_output, - } - if type(exec_output) == dict: - # We loose the requirement for the sanity check as the expected result used in the sanity check might not be the most up-to-date one. - # This happens when the key is a timestamp or a random number. - if is_sanity_check: - if len(exec_output) != len(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but wrong number of elements in the output. Expected length: {len(expected_result)}, but got: {len(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type:dict_length", - "model_executed_output": exec_output, - } - else: - return result - - for key, value in expected_result.items(): - if key not in exec_output: - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but key {repr(key)} not found in the model output." - ], - "error_type": "executable_checker:wrong_result_type:dict_key_not_found", - "model_executed_output": exec_output, - } - for key, value in exec_output.items(): - if key not in expected_result: - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but key {repr(key)} not expected in the model output." - ], - "error_type": "executable_checker:wrong_result_type:dict_extra_key", - "model_executed_output": exec_output, - } - if type(exec_output) == list: - if len(exec_output) != len(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type list, but wrong number of elements in the output. Expected length: {len(expected_result)}, but got: {len(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type:list_length", - "model_executed_output": exec_output, - } - return result - - -#### Helper functions for Exec #### -def executable_checker_simple( - function_call: str, - expected_result, - expected_result_type: str, - is_sanity_check=False, -): - result = {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - exec_dict: Any = {} - - try: - exec( - "from executable_python_function import *" + "\nresult=" + function_call, - exec_dict, - ) - exec_output = exec_dict["result"] - except NoAPIKeyError as e: - raise e - except Exception as e: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Error in execution: {repr(function_call)}. Error: {str(e)}" - ) - result["error_type"] = "executable_checker:execution_error" - return result - - # We need to special handle the case where the execution result is a tuple and convert it to a list - # Because when json is stored, the tuple is converted to a list, and so the expected result is a list when loaded from json - if isinstance(exec_output, tuple): - exec_output = list(exec_output) - - if expected_result_type == "exact_match": - if exec_output != expected_result: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}." - ) - result["error_type"] = "executable_checker:wrong_result" - result["model_executed_output"] = exec_output - return result - - elif expected_result_type == "real_time_match": - # Allow for 5% difference - if (type(expected_result) == float or type(expected_result) == int) and ( - type(exec_output) == float or type(exec_output) == int - ): - if not ( - expected_result * (1 - REAL_TIME_MATCH_ALLOWED_DIFFERENCE) - <= exec_output - <= expected_result * (1 + REAL_TIME_MATCH_ALLOWED_DIFFERENCE) - ): - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}. {REAL_TIME_MATCH_ALLOWED_DIFFERENCE * 100}% difference allowed." - ) - result["error_type"] = "executable_checker:wrong_result_real_time" - result["model_executed_output"] = exec_output - return result - else: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}. Type needs to be float or int for real time match criteria." - ) - result["error_type"] = "executable_checker:wrong_result_real_time" - result["model_executed_output"] = exec_output - return result - - else: - # structural match - pattern_match_result = patten_matcher(exec_output, expected_result, function_call, is_sanity_check) - if not pattern_match_result["valid"]: - return pattern_match_result - - return result - - -def executable_checker_parallel_no_order( - decoded_result: list, expected_exec_result: list, expected_exec_result_type: list -): - if len(decoded_result) != len(expected_exec_result): - return { - "valid": False, - "error": [ - f"Wrong number of functions provided. Expected {len(expected_exec_result)}, but got {len(decoded_result)}." - ], - "error_type": "value_error:exec_result_count", - } - - matched_indices = [] - for i in range(len(expected_exec_result)): - all_errors = [] - for index in range(len(decoded_result)): - if index in matched_indices: - continue - - result = executable_checker_simple( - decoded_result[index], - expected_exec_result[i], - expected_exec_result_type[i], - False, - ) - - if result["valid"]: - matched_indices.append(index) - break - else: - all_errors.append( - { - f"Model Result Index {index}": { - "sub_error": result["error"], - "sub_error_type": result["error_type"], - "model_executed_output": ( - result["model_executed_output"] if "model_executed_output" in result else None - ), - } - } - ) - - if not result["valid"]: - considered_indices = [i for i in range(len(decoded_result)) if i not in matched_indices] - all_errors.insert( - 0, - f"Could not find a matching function among index {considered_indices} of model output for index {i} of possible answers.", # type: ignore[arg-type] - ) - return { - "valid": False, - "error": all_errors, - "error_type": "executable_checker:cannot_find_match", - } - - return {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - -#### Main function #### -def executable_checker_rest(func_call, idx): - # Move this here for now to avoid needing to read this file / fix paths to be relative to dataset_dir. Fix when it's actually needed / used. - EVAL_GROUND_TRUTH_PATH = "/mnt/wsfuse/fair_llm_v2/datasets/eval/bfcl/rest-eval-response_v5.jsonl" # Ground truth file for v5 for rest execution - with open(EVAL_GROUND_TRUTH_PATH, "r") as f: - EVAL_GROUND_TRUTH = f.readlines() - if "https://geocode.maps.co" in func_call: - time.sleep(2) - if "requests_get" in func_call: - func_call = func_call.replace("requests_get", "requests.get") - try: - response = eval(func_call) - except Exception as e: - return { - "valid": False, - "error": [f"Execution failed. {str(e)}"], - "error_type": "executable_checker_rest:execution_error", - } - - try: - if response.status_code == 200: - eval_GT_json = json.loads(EVAL_GROUND_TRUTH[idx]) - try: - if isinstance(eval_GT_json, dict): - if isinstance(response.json(), dict): - if set(eval_GT_json.keys()) == set(response.json().keys()): - return {"valid": True, "error": [], "error_type": ""} - return { - "valid": False, - "error": ["Key inconsistency"], - "error_type": "executable_checker_rest:wrong_key", - } - return { - "valid": False, - "error": [f"Expected dictionary, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - - elif isinstance(eval_GT_json, list): - if isinstance(response.json(), list): - if len(eval_GT_json) != len(response.json()): - return { - "valid": False, - "error": [f"Response list length inconsistency."], - "error_type": "value_error:exec_result_rest_count", - } - - else: - for i in range(len(eval_GT_json)): - if set(eval_GT_json[i].keys()) != set(response.json()[i].keys()): - return { - "valid": False, - "error": [f"Key inconsistency"], - "error_type": "executable_checker_rest:wrong_key", - } - - return {"valid": True, "error": []} - else: - return { - "valid": False, - "error": [f"Expected list, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - return { - "valid": False, - "error": [f"Expected dict or list, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - except Exception as e: - return { - "valid": False, - "error": [ - f"Error in execution and type checking. Status code: {response.status_code}. Error: {str(e)}" - ], - "error_type": "executable_checker_rest:response_format_error", - } - else: - return { - "valid": False, - "error": [f"Execution result status code is not 200, got {response.status_code}"], - "error_type": "executable_checker_rest:wrong_status_code", - } - except Exception as e: - return { - "valid": False, - "error": [f"Cannot get status code of the response. Error: {str(e)}"], - "error_type": "executable_checker_rest:cannot_get_status_code", - } - - -def ast_checker(func_description, model_output, possible_answer, language, test_category, model_name): - if "parallel" in test_category: - return parallel_function_checker_no_order(func_description, model_output, possible_answer, language, model_name) - - elif "multiple" in test_category: - return multiple_function_checker(func_description, model_output, possible_answer, language, model_name) - - else: - if len(model_output) != 1: - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "simple_function_checker:wrong_count", - } - - return simple_function_checker( - func_description[0], - model_output[0], - possible_answer[0], - language, - model_name, - ) - - -def exec_checker(decoded_result: list, func_description: dict, test_category: str): - if "multiple" in test_category or "parallel" in test_category: - return executable_checker_parallel_no_order( - decoded_result, - func_description["execution_result"], - func_description["execution_result_type"], - ) - - else: - if len(decoded_result) != 1: - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "simple_exec_checker:wrong_count", - } - return executable_checker_simple( - decoded_result[0], - func_description["execution_result"][0], - func_description["execution_result_type"][0], - False, - ) - - -def is_empty_output(decoded_output): - # This function is a patch to the ast decoder for relevance detection - # Sometimes the ast decoder will parse successfully, but the input doens't really have a function call - # [], [{}], and anything that is not in function calling format is considered empty (and thus should be marked as correct) - if not is_function_calling_format_output(decoded_output): - return True - if len(decoded_output) == 0: - return True - if len(decoded_output) == 1 and len(decoded_output[0]) == 0: - return True - - -def is_function_calling_format_output(decoded_output): - # Ensure the output is a list of dictionaries - if type(decoded_output) == list: - for item in decoded_output: - if type(item) != dict: - return False - return True - return False diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py deleted file mode 100644 index ed97ee360..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -""" -Tree-sitter changes its API with unfortunate frequency. Modules that need it should -import it from here so that we can centrally manage things as necessary. -""" - -# These currently work with tree-sitter 0.23.0 -# NOTE: Don't import tree-sitter or any of the language modules in the main module -# because not all environments have them. Import lazily inside functions where needed. - -import importlib -import typing - -if typing.TYPE_CHECKING: - import tree_sitter - - -def get_language(language: str) -> "tree_sitter.Language": - import tree_sitter - - language_module_name = f"tree_sitter_{language}" - try: - language_module = importlib.import_module(language_module_name) - except ModuleNotFoundError as exc: - raise ValueError( - f"Language {language} is not found. Please install the tree-sitter-{language} package." - ) from exc - return tree_sitter.Language(language_module.language()) - - -def get_parser(language: str, **kwargs) -> "tree_sitter.Parser": - import tree_sitter - - lang = get_language(language) - return tree_sitter.Parser(lang, **kwargs) From 78a78264a78db560051f0b352968f57fbbaf9198 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 29 Aug 2025 21:17:47 +0000 Subject: [PATCH 017/124] build: Bump version to 0.2.20 --- llama_stack/ui/package-lock.json | 8 ++++---- llama_stack/ui/package.json | 2 +- pyproject.toml | 6 +++--- uv.lock | 12 ++++++------ 4 files changed, 14 insertions(+), 14 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 2da25615c..8748d44ad 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -18,7 +18,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^11.18.2", - "llama-stack-client": "^0.2.19", + "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", @@ -10006,9 +10006,9 @@ "license": "MIT" }, "node_modules/llama-stack-client": { - "version": "0.2.19", - "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.19.tgz", - "integrity": "sha512-sDuAhUdEGlERZ3jlMUzPXcQTgMv/pGbDrPX0ifbE5S+gr7Q+7ohuQYrIXe+hXgIipFjq+y4b2c5laZ76tmAyEA==", + "version": "0.2.20", + "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.20.tgz", + "integrity": "sha512-1vD5nizTX5JEW8TADxKgy/P1W8YZoPSpdnmfxbdYbWgpQ3BWtbvLS6jmDk7VwVA5fRC4895VfHsRDfS1liHarw==", "license": "MIT", "dependencies": { "@types/node": "^18.11.18", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 31c836057..a9c56f98e 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -23,7 +23,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^11.18.2", - "llama-stack-client": "^0.2.19", + "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", diff --git a/pyproject.toml b/pyproject.toml index 3ab042a8e..aa1813e49 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ required-version = ">=0.7.0" [project] name = "llama_stack" -version = "0.2.19" +version = "0.2.20" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -31,7 +31,7 @@ dependencies = [ "huggingface-hub>=0.34.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.19", + "llama-stack-client>=0.2.20", "llama-api-client>=0.1.2", "openai>=1.99.6,<1.100.0", "prompt-toolkit", @@ -56,7 +56,7 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.19", + "llama-stack-client>=0.2.20", "streamlit-option-menu", ] diff --git a/uv.lock b/uv.lock index b47eeccc4..6eac1efb7 100644 --- a/uv.lock +++ b/uv.lock @@ -1767,7 +1767,7 @@ wheels = [ [[package]] name = "llama-stack" -version = "0.2.19" +version = "0.2.20" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -1907,8 +1907,8 @@ requires-dist = [ { name = "jinja2", specifier = ">=3.1.6" }, { name = "jsonschema" }, { name = "llama-api-client", specifier = ">=0.1.2" }, - { name = "llama-stack-client", specifier = ">=0.2.19" }, - { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.19" }, + { name = "llama-stack-client", specifier = ">=0.2.20" }, + { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.20" }, { name = "openai", specifier = ">=1.99.6,<1.100.0" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, @@ -2017,7 +2017,7 @@ unit = [ [[package]] name = "llama-stack-client" -version = "0.2.19" +version = "0.2.20" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -2036,9 +2036,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/14/e4/72683c10188ae93e97551ab6eeac725e46f13ec215618532505a7d91bf2b/llama_stack_client-0.2.19.tar.gz", hash = "sha256:6c857e528b83af7821120002ebe4d3db072fd9f7bf867a152a34c70fe606833f", size = 318325, upload-time = "2025-08-26T21:54:20.592Z" } +sdist = { url = "https://files.pythonhosted.org/packages/21/91/c5e32219a5192825dd601700e68205c815c5cfee60c64c22172e46a0c83e/llama_stack_client-0.2.20.tar.gz", hash = "sha256:356257f0a4bbb64205f89e113d715925853d5e34ec744e72466da72790ba415b", size = 318311, upload-time = "2025-08-29T21:10:12.854Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/51/51/c8dde9fae58193a539eac700502876d8edde8be354c2784ff7b707a47432/llama_stack_client-0.2.19-py3-none-any.whl", hash = "sha256:478565a54541ca03ca9f8fe2019f4136f93ab6afe9591bdd44bc6dde6ddddbd9", size = 369905, upload-time = "2025-08-26T21:54:18.929Z" }, + { url = "https://files.pythonhosted.org/packages/b0/ba/84914c4eead2fd9251c149fd6a7da28b78acd620793e3c4506116645cb60/llama_stack_client-0.2.20-py3-none-any.whl", hash = "sha256:6e178981d2ce971da2145c79d5b2b123fa50e063ed431494975c2ba01c5b8016", size = 369899, upload-time = "2025-08-29T21:10:11.113Z" }, ] [[package]] From 3370d8e557cdcf196fa1a324a32dbfb39c725ded Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 29 Aug 2025 19:17:24 -0400 Subject: [PATCH 018/124] feat(files, s3, expiration): add expires_after support to S3 files provider (#3283) --- docs/_static/llama-stack-spec.html | 26 ++- docs/_static/llama-stack-spec.yaml | 14 ++ llama_stack/apis/files/files.py | 25 ++- .../providers/inline/files/localfs/files.py | 5 + .../providers/remote/files/s3/files.py | 185 +++++++++++------- pyproject.toml | 4 +- tests/integration/files/test_files.py | 83 ++++++++ tests/unit/providers/files/test_s3_files.py | 101 ++++++++++ uv.lock | 10 +- 9 files changed, 372 insertions(+), 81 deletions(-) diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index a1f6a6f30..7cb2a73f3 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -4129,7 +4129,7 @@ "tags": [ "Files" ], - "description": "Upload a file that can be used across various endpoints.\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.", + "description": "Upload a file that can be used across various endpoints.\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file. Expected expires_after[anchor] = \"created_at\", expires_after[seconds] = . Seconds must be between 3600 and 2592000 (1 hour to 30 days).", "parameters": [], "requestBody": { "content": { @@ -4143,11 +4143,33 @@ }, "purpose": { "$ref": "#/components/schemas/OpenAIFilePurpose" + }, + "expires_after_anchor": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ] + }, + "expires_after_seconds": { + "oneOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ] } }, "required": [ "file", - "purpose" + "purpose", + "expires_after_anchor", + "expires_after_seconds" ] } } diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index 33142e3ff..25089868c 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -2933,6 +2933,10 @@ paths: - file: The File object (not file name) to be uploaded. - purpose: The intended purpose of the uploaded file. + + - expires_after: Optional form values describing expiration for the file. + Expected expires_after[anchor] = "created_at", expires_after[seconds] = . + Seconds must be between 3600 and 2592000 (1 hour to 30 days). parameters: [] requestBody: content: @@ -2945,9 +2949,19 @@ paths: format: binary purpose: $ref: '#/components/schemas/OpenAIFilePurpose' + expires_after_anchor: + oneOf: + - type: string + - type: 'null' + expires_after_seconds: + oneOf: + - type: integer + - type: 'null' required: - file - purpose + - expires_after_anchor + - expires_after_seconds required: true /v1/openai/v1/models: get: diff --git a/llama_stack/apis/files/files.py b/llama_stack/apis/files/files.py index a1b9dd4dc..d39e96e96 100644 --- a/llama_stack/apis/files/files.py +++ b/llama_stack/apis/files/files.py @@ -5,10 +5,10 @@ # the root directory of this source tree. from enum import StrEnum -from typing import Annotated, Literal, Protocol, runtime_checkable +from typing import Annotated, ClassVar, Literal, Protocol, runtime_checkable from fastapi import File, Form, Response, UploadFile -from pydantic import BaseModel +from pydantic import BaseModel, Field from llama_stack.apis.common.responses import Order from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol @@ -49,6 +49,23 @@ class OpenAIFileObject(BaseModel): purpose: OpenAIFilePurpose +@json_schema_type +class ExpiresAfter(BaseModel): + """ + Control expiration of uploaded files. + + Params: + - anchor, must be "created_at" + - seconds, must be int between 3600 and 2592000 (1 hour to 30 days) + """ + + MIN: ClassVar[int] = 3600 # 1 hour + MAX: ClassVar[int] = 2592000 # 30 days + + anchor: Literal["created_at"] + seconds: int = Field(..., ge=3600, le=2592000) + + @json_schema_type class ListOpenAIFileResponse(BaseModel): """ @@ -92,6 +109,9 @@ class Files(Protocol): self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, + # TODO: expires_after is producing strange openapi spec, params are showing up as a required w/ oneOf being null ) -> OpenAIFileObject: """ Upload a file that can be used across various endpoints. @@ -99,6 +119,7 @@ class Files(Protocol): The file upload should be a multipart form request with: - file: The File object (not file name) to be uploaded. - purpose: The intended purpose of the uploaded file. + - expires_after: Optional form values describing expiration for the file. Expected expires_after[anchor] = "created_at", expires_after[seconds] = . Seconds must be between 3600 and 2592000 (1 hour to 30 days). :param file: The uploaded file object containing content and metadata (filename, content_type, etc.). :param purpose: The intended purpose of the uploaded file (e.g., "assistants", "fine-tune"). diff --git a/llama_stack/providers/inline/files/localfs/files.py b/llama_stack/providers/inline/files/localfs/files.py index 4f6d571a4..9c610c1ba 100644 --- a/llama_stack/providers/inline/files/localfs/files.py +++ b/llama_stack/providers/inline/files/localfs/files.py @@ -86,11 +86,16 @@ class LocalfsFilesImpl(Files): self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, ) -> OpenAIFileObject: """Upload a file that can be used across various endpoints.""" if not self.sql_store: raise RuntimeError("Files provider not initialized") + if expires_after_anchor is not None or expires_after_seconds is not None: + raise NotImplementedError("File expiration is not supported by this provider") + file_id = self._generate_file_id() file_path = self._get_file_path(file_id) diff --git a/llama_stack/providers/remote/files/s3/files.py b/llama_stack/providers/remote/files/s3/files.py index 0451f74ea..54742d900 100644 --- a/llama_stack/providers/remote/files/s3/files.py +++ b/llama_stack/providers/remote/files/s3/files.py @@ -4,9 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import time import uuid -from typing import Annotated +from datetime import UTC, datetime +from typing import Annotated, Any import boto3 from botocore.exceptions import BotoCoreError, ClientError, NoCredentialsError @@ -15,6 +15,7 @@ from fastapi import File, Form, Response, UploadFile from llama_stack.apis.common.errors import ResourceNotFoundError from llama_stack.apis.common.responses import Order from llama_stack.apis.files import ( + ExpiresAfter, Files, ListOpenAIFileResponse, OpenAIFileDeleteResponse, @@ -85,18 +86,80 @@ async def _create_bucket_if_not_exists(client: boto3.client, config: S3FilesImpl raise RuntimeError(f"Failed to access S3 bucket '{config.bucket_name}': {e}") from e +def _make_file_object( + *, + id: str, + filename: str, + purpose: str, + bytes: int, + created_at: int, + expires_at: int, + **kwargs: Any, # here to ignore any additional fields, e.g. extra fields from AuthorizedSqlStore +) -> OpenAIFileObject: + """ + Construct an OpenAIFileObject and normalize expires_at. + + If expires_at is greater than the max we treat it as no-expiration and + return None for expires_at. + + The OpenAI spec says expires_at type is Integer, but the implementation + will return None for no expiration. + """ + obj = OpenAIFileObject( + id=id, + filename=filename, + purpose=OpenAIFilePurpose(purpose), + bytes=bytes, + created_at=created_at, + expires_at=expires_at, + ) + + if obj.expires_at is not None and obj.expires_at > (obj.created_at + ExpiresAfter.MAX): + obj.expires_at = None # type: ignore + + return obj + + class S3FilesImpl(Files): """S3-based implementation of the Files API.""" - # TODO: implement expiration, for now a silly offset - _SILLY_EXPIRATION_OFFSET = 100 * 365 * 24 * 60 * 60 - def __init__(self, config: S3FilesImplConfig, policy: list[AccessRule]) -> None: self._config = config self.policy = policy self._client: boto3.client | None = None self._sql_store: AuthorizedSqlStore | None = None + def _now(self) -> int: + """Return current UTC timestamp as int seconds.""" + return int(datetime.now(UTC).timestamp()) + + async def _get_file(self, file_id: str, return_expired: bool = False) -> dict[str, Any]: + where: dict[str, str | dict] = {"id": file_id} + if not return_expired: + where["expires_at"] = {">": self._now()} + if not (row := await self.sql_store.fetch_one("openai_files", policy=self.policy, where=where)): + raise ResourceNotFoundError(file_id, "File", "files.list()") + return row + + async def _delete_file(self, file_id: str) -> None: + """Delete a file from S3 and the database.""" + try: + self.client.delete_object( + Bucket=self._config.bucket_name, + Key=file_id, + ) + except ClientError as e: + if e.response["Error"]["Code"] != "NoSuchKey": + raise RuntimeError(f"Failed to delete file from S3: {e}") from e + + await self.sql_store.delete("openai_files", where={"id": file_id}) + + async def _delete_if_expired(self, file_id: str) -> None: + """If the file exists and is expired, delete it.""" + if row := await self._get_file(file_id, return_expired=True): + if (expires_at := row.get("expires_at")) and expires_at <= self._now(): + await self._delete_file(file_id) + async def initialize(self) -> None: self._client = _create_s3_client(self._config) await _create_bucket_if_not_exists(self._client, self._config) @@ -132,27 +195,47 @@ class S3FilesImpl(Files): self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, ) -> OpenAIFileObject: file_id = f"file-{uuid.uuid4().hex}" filename = getattr(file, "filename", None) or "uploaded_file" - created_at = int(time.time()) - expires_at = created_at + self._SILLY_EXPIRATION_OFFSET + created_at = self._now() + + expires_after = None + if expires_after_anchor is not None or expires_after_seconds is not None: + # we use ExpiresAfter to validate input + expires_after = ExpiresAfter( + anchor=expires_after_anchor, # type: ignore[arg-type] + seconds=expires_after_seconds, # type: ignore[arg-type] + ) + + # the default is no expiration. + # to implement no expiration we set an expiration beyond the max. + # we'll hide this fact from users when returning the file object. + expires_at = created_at + ExpiresAfter.MAX * 42 + # the default for BATCH files is 30 days, which happens to be the expiration max. + if purpose == OpenAIFilePurpose.BATCH: + expires_at = created_at + ExpiresAfter.MAX + + if expires_after is not None: + expires_at = created_at + expires_after.seconds + content = await file.read() file_size = len(content) - await self.sql_store.insert( - "openai_files", - { - "id": file_id, - "filename": filename, - "purpose": purpose.value, - "bytes": file_size, - "created_at": created_at, - "expires_at": expires_at, - }, - ) + entry: dict[str, Any] = { + "id": file_id, + "filename": filename, + "purpose": purpose.value, + "bytes": file_size, + "created_at": created_at, + "expires_at": expires_at, + } + + await self.sql_store.insert("openai_files", entry) try: self.client.put_object( @@ -166,14 +249,7 @@ class S3FilesImpl(Files): raise RuntimeError(f"Failed to upload file to S3: {e}") from e - return OpenAIFileObject( - id=file_id, - filename=filename, - purpose=purpose, - bytes=file_size, - created_at=created_at, - expires_at=expires_at, - ) + return _make_file_object(**entry) async def openai_list_files( self, @@ -186,30 +262,20 @@ class S3FilesImpl(Files): if not order: order = Order.desc - where_conditions = {} + where_conditions: dict[str, Any] = {"expires_at": {">": self._now()}} if purpose: where_conditions["purpose"] = purpose.value paginated_result = await self.sql_store.fetch_all( table="openai_files", policy=self.policy, - where=where_conditions if where_conditions else None, + where=where_conditions, order_by=[("created_at", order.value)], cursor=("id", after) if after else None, limit=limit, ) - files = [ - OpenAIFileObject( - id=row["id"], - filename=row["filename"], - purpose=OpenAIFilePurpose(row["purpose"]), - bytes=row["bytes"], - created_at=row["created_at"], - expires_at=row["expires_at"], - ) - for row in paginated_result.data - ] + files = [_make_file_object(**row) for row in paginated_result.data] return ListOpenAIFileResponse( data=files, @@ -220,41 +286,20 @@ class S3FilesImpl(Files): ) async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject: - row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") - - return OpenAIFileObject( - id=row["id"], - filename=row["filename"], - purpose=OpenAIFilePurpose(row["purpose"]), - bytes=row["bytes"], - created_at=row["created_at"], - expires_at=row["expires_at"], - ) + await self._delete_if_expired(file_id) + row = await self._get_file(file_id) + return _make_file_object(**row) async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse: - row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") - - try: - self.client.delete_object( - Bucket=self._config.bucket_name, - Key=row["id"], - ) - except ClientError as e: - if e.response["Error"]["Code"] != "NoSuchKey": - raise RuntimeError(f"Failed to delete file from S3: {e}") from e - - await self.sql_store.delete("openai_files", where={"id": file_id}) - + await self._delete_if_expired(file_id) + _ = await self._get_file(file_id) # raises if not found + await self._delete_file(file_id) return OpenAIFileDeleteResponse(id=file_id, deleted=True) async def openai_retrieve_file_content(self, file_id: str) -> Response: - row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") + await self._delete_if_expired(file_id) + + row = await self._get_file(file_id) try: response = self.client.get_object( @@ -265,7 +310,7 @@ class S3FilesImpl(Files): content = response["Body"].read() except ClientError as e: if e.response["Error"]["Code"] == "NoSuchKey": - await self.sql_store.delete("openai_files", where={"id": file_id}) + await self._delete_file(file_id) raise ResourceNotFoundError(file_id, "File", "files.list()") from e raise RuntimeError(f"Failed to download file from S3: {e}") from e diff --git a/pyproject.toml b/pyproject.toml index aa1813e49..1f87a3aaa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,7 +33,7 @@ dependencies = [ "jsonschema", "llama-stack-client>=0.2.20", "llama-api-client>=0.1.2", - "openai>=1.99.6,<1.100.0", + "openai>=1.99.6", "prompt-toolkit", "python-dotenv", "python-jose[cryptography]", @@ -106,7 +106,7 @@ unit = [ # separately. If you are using "uv" to execute your tests, you can use the "--group" flag to specify extra # dependencies. test = [ - "openai", + "openai>=1.100.0", # for expires_after support "aiosqlite", "aiohttp", "torch>=2.6.0", diff --git a/tests/integration/files/test_files.py b/tests/integration/files/test_files.py index 67351d4f7..516b0bd98 100644 --- a/tests/integration/files/test_files.py +++ b/tests/integration/files/test_files.py @@ -8,6 +8,7 @@ from io import BytesIO from unittest.mock import patch import pytest +import requests from llama_stack.core.datatypes import User @@ -79,6 +80,88 @@ def test_openai_client_basic_operations(openai_client): pass # ignore 404 +@pytest.mark.xfail(message="expires_after not available on all providers") +def test_expires_after(openai_client): + """Test uploading a file with expires_after parameter.""" + client = openai_client + + uploaded_file = None + try: + with BytesIO(b"expires_after test") as file_buffer: + file_buffer.name = "expires_after.txt" + uploaded_file = client.files.create( + file=file_buffer, + purpose="assistants", + expires_after={"anchor": "created_at", "seconds": 4545}, + ) + + assert uploaded_file.expires_at is not None + assert uploaded_file.expires_at == uploaded_file.created_at + 4545 + + listed = client.files.list() + ids = [f.id for f in listed.data] + assert uploaded_file.id in ids + + retrieved = client.files.retrieve(uploaded_file.id) + assert retrieved.id == uploaded_file.id + + finally: + if uploaded_file is not None: + try: + client.files.delete(uploaded_file.id) + except Exception: + pass + + +@pytest.mark.xfail(message="expires_after not available on all providers") +def test_expires_after_requests(openai_client): + """Upload a file using requests multipart/form-data and bracketed expires_after fields. + + This ensures clients that send form fields like `expires_after[anchor]` and + `expires_after[seconds]` are handled by the server. + """ + base_url = f"{openai_client.base_url}files" + + uploaded_id = None + try: + files = {"file": ("expires_after_with_requests.txt", BytesIO(b"expires_after via requests"))} + data = { + "purpose": "assistants", + "expires_after[anchor]": "created_at", + "expires_after[seconds]": "4545", + } + + session = requests.Session() + request = requests.Request("POST", base_url, files=files, data=data) + prepared = session.prepare_request(request) + resp = session.send(prepared, timeout=30) + resp.raise_for_status() + result = resp.json() + + assert result.get("id", "").startswith("file-") + uploaded_id = result["id"] + assert result.get("created_at") is not None + assert result.get("expires_at") == result["created_at"] + 4545 + + list_resp = requests.get(base_url, timeout=30) + list_resp.raise_for_status() + listed = list_resp.json() + ids = [f["id"] for f in listed.get("data", [])] + assert uploaded_id in ids + + retrieve_resp = requests.get(f"{base_url}/{uploaded_id}", timeout=30) + retrieve_resp.raise_for_status() + retrieved = retrieve_resp.json() + assert retrieved["id"] == uploaded_id + + finally: + if uploaded_id: + try: + requests.delete(f"{base_url}/{uploaded_id}", timeout=30) + except Exception: + pass + + @pytest.mark.xfail(message="User isolation broken for current providers, must be fixed.") @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") def test_files_authentication_isolation(mock_get_authenticated_user, llama_stack_client): diff --git a/tests/unit/providers/files/test_s3_files.py b/tests/unit/providers/files/test_s3_files.py index 3bd4836df..c665bf124 100644 --- a/tests/unit/providers/files/test_s3_files.py +++ b/tests/unit/providers/files/test_s3_files.py @@ -197,3 +197,104 @@ class TestS3FilesImpl: files_list = await s3_provider.openai_list_files() assert len(files_list.data) == 0, "No file metadata should remain after failed upload" + + @pytest.mark.parametrize("purpose", [p for p in OpenAIFilePurpose if p != OpenAIFilePurpose.BATCH]) + async def test_default_no_expiration(self, s3_provider, sample_text_file, purpose): + """Test that by default files have no expiration.""" + sample_text_file.filename = "test_default_no_expiration" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=purpose, + ) + assert uploaded.expires_at is None, "By default files should have no expiration" + + async def test_default_batch_expiration(self, s3_provider, sample_text_file): + """Test that by default batch files have an expiration.""" + sample_text_file.filename = "test_default_batch_an_expiration" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.BATCH, + ) + assert uploaded.expires_at is not None, "By default batch files should have an expiration" + thirty_days_seconds = 30 * 24 * 3600 + assert uploaded.expires_at == uploaded.created_at + thirty_days_seconds, ( + "Batch default expiration should be 30 days" + ) + + async def test_expired_file_is_unavailable(self, s3_provider, sample_text_file, s3_config, s3_client): + """Uploaded file that has expired should not be listed or retrievable/deletable.""" + with patch.object(s3_provider, "_now") as mock_now: # control time + two_hours = 2 * 60 * 60 + + mock_now.return_value = 0 + + sample_text_file.filename = "test_expired_file" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=two_hours, + ) + + mock_now.return_value = two_hours * 2 # fast forward 4 hours + + listed = await s3_provider.openai_list_files() + assert uploaded.id not in [f.id for f in listed.data] + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_retrieve_file(uploaded.id) + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_retrieve_file_content(uploaded.id) + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_delete_file(uploaded.id) + + with pytest.raises(ClientError) as exc_info: + s3_client.head_object(Bucket=s3_config.bucket_name, Key=uploaded.id) + assert exc_info.value.response["Error"]["Code"] == "404" + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider._get_file(uploaded.id, return_expired=True) + + async def test_unsupported_expires_after_anchor(self, s3_provider, sample_text_file): + """Unsupported anchor value should raise ValueError.""" + sample_text_file.filename = "test_unsupported_expires_after_anchor" + + with pytest.raises(ValueError, match="Input should be 'created_at'"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="now", + expires_after_seconds=3600, + ) + + async def test_nonint_expires_after_seconds(self, s3_provider, sample_text_file): + """Non-integer seconds in expires_after should raise ValueError.""" + sample_text_file.filename = "test_nonint_expires_after_seconds" + + with pytest.raises(ValueError, match="should be a valid integer"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds="many", + ) + + async def test_expires_after_seconds_out_of_bounds(self, s3_provider, sample_text_file): + """Seconds outside allowed range should raise ValueError.""" + with pytest.raises(ValueError, match="greater than or equal to 3600"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=3599, + ) + + with pytest.raises(ValueError, match="less than or equal to 2592000"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=2592001, + ) diff --git a/uv.lock b/uv.lock index 6eac1efb7..73b52a3e9 100644 --- a/uv.lock +++ b/uv.lock @@ -1909,7 +1909,7 @@ requires-dist = [ { name = "llama-api-client", specifier = ">=0.1.2" }, { name = "llama-stack-client", specifier = ">=0.2.20" }, { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.20" }, - { name = "openai", specifier = ">=1.99.6,<1.100.0" }, + { name = "openai", specifier = ">=1.99.6" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, { name = "pandas", marker = "extra == 'ui'" }, @@ -1979,7 +1979,7 @@ test = [ { name = "datasets" }, { name = "mcp" }, { name = "milvus-lite", specifier = ">=2.5.0" }, - { name = "openai" }, + { name = "openai", specifier = ">=1.100.0" }, { name = "psycopg2-binary", specifier = ">=2.9.0" }, { name = "pymilvus", specifier = ">=2.5.12" }, { name = "pypdf" }, @@ -2638,7 +2638,7 @@ wheels = [ [[package]] name = "openai" -version = "1.99.6" +version = "1.102.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -2650,9 +2650,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/11/45/38a87bd6949236db5ae3132f41d5861824702b149f86d2627d6900919103/openai-1.99.6.tar.gz", hash = "sha256:f48f4239b938ef187062f3d5199a05b69711d8b600b9a9b6a3853cd271799183", size = 505364, upload-time = "2025-08-09T15:20:54.438Z" } +sdist = { url = "https://files.pythonhosted.org/packages/07/55/da5598ed5c6bdd9939633854049cddc5cbac0da938dfcfcb3c6b119c16c0/openai-1.102.0.tar.gz", hash = "sha256:2e0153bcd64a6523071e90211cbfca1f2bbc5ceedd0993ba932a5869f93b7fc9", size = 519027, upload-time = "2025-08-26T20:50:29.397Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d6/dd/9aa956485c2856346b3181542fbb0aea4e5b457fa7a523944726746da8da/openai-1.99.6-py3-none-any.whl", hash = "sha256:e40d44b2989588c45ce13819598788b77b8fb80ba2f7ae95ce90d14e46f1bd26", size = 786296, upload-time = "2025-08-09T15:20:51.95Z" }, + { url = "https://files.pythonhosted.org/packages/bd/0d/c9e7016d82c53c5b5e23e2bad36daebb8921ed44f69c0a985c6529a35106/openai-1.102.0-py3-none-any.whl", hash = "sha256:d751a7e95e222b5325306362ad02a7aa96e1fab3ed05b5888ce1c7ca63451345", size = 812015, upload-time = "2025-08-26T20:50:27.219Z" }, ] [[package]] From b12cd528efbbf96b77a53910a196fb2854e32332 Mon Sep 17 00:00:00 2001 From: Jiayi Ni Date: Fri, 29 Aug 2025 16:23:52 -0700 Subject: [PATCH 019/124] docs: add VLM NIM example (#3277) --- .../self_hosted_distro/nvidia.md | 1 + llama_stack/distributions/nvidia/run.yaml | 5 ++ .../remote/inference/nvidia/NVIDIA.md | 60 +++++++++++++++++-- .../remote/inference/nvidia/models.py | 4 ++ 4 files changed, 64 insertions(+), 6 deletions(-) diff --git a/docs/source/distributions/self_hosted_distro/nvidia.md b/docs/source/distributions/self_hosted_distro/nvidia.md index e845c3c48..86d025ce7 100644 --- a/docs/source/distributions/self_hosted_distro/nvidia.md +++ b/docs/source/distributions/self_hosted_distro/nvidia.md @@ -50,6 +50,7 @@ The following models are available by default: - `meta/llama-3.2-11b-vision-instruct ` - `meta/llama-3.2-90b-vision-instruct ` - `meta/llama-3.3-70b-instruct ` +- `nvidia/vila ` - `nvidia/llama-3.2-nv-embedqa-1b-v2 ` - `nvidia/nv-embedqa-e5-v5 ` - `nvidia/nv-embedqa-mistral-7b-v2 ` diff --git a/llama_stack/distributions/nvidia/run.yaml b/llama_stack/distributions/nvidia/run.yaml index 8e915f586..9fd6b0404 100644 --- a/llama_stack/distributions/nvidia/run.yaml +++ b/llama_stack/distributions/nvidia/run.yaml @@ -134,6 +134,11 @@ models: provider_id: nvidia provider_model_id: meta/llama-3.3-70b-instruct model_type: llm +- metadata: {} + model_id: nvidia/vila + provider_id: nvidia + provider_model_id: nvidia/vila + model_type: llm - metadata: embedding_dimension: 2048 context_length: 8192 diff --git a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md index d96b29fef..d9c18533a 100644 --- a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md +++ b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md @@ -41,10 +41,10 @@ client.initialize() ### Create Completion -> Note on Completion API -> -> The hosted NVIDIA Llama NIMs (e.g., `meta-llama/Llama-3.1-8B-Instruct`) with ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` does not support the ```completion``` method, while the locally deployed NIM does. +The following example shows how to create a completion for an NVIDIA NIM. +> [!NOTE] +> The hosted NVIDIA Llama NIMs (for example ```meta-llama/Llama-3.1-8B-Instruct```) that have ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` do not support the ```completion``` method, while locally deployed NIMs do. ```python response = client.inference.completion( @@ -60,6 +60,8 @@ print(f"Response: {response.content}") ### Create Chat Completion +The following example shows how to create a chat completion for an NVIDIA NIM. + ```python response = client.inference.chat_completion( model_id="meta-llama/Llama-3.1-8B-Instruct", @@ -82,6 +84,9 @@ print(f"Response: {response.completion_message.content}") ``` ### Tool Calling Example ### + +The following example shows how to do tool calling for an NVIDIA NIM. + ```python from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition @@ -117,6 +122,9 @@ if tool_response.completion_message.tool_calls: ``` ### Structured Output Example + +The following example shows how to do structured output for an NVIDIA NIM. + ```python from llama_stack.apis.inference import JsonSchemaResponseFormat, ResponseFormatType @@ -149,8 +157,10 @@ print(f"Structured Response: {structured_response.completion_message.content}") ``` ### Create Embeddings -> Note on OpenAI embeddings compatibility -> + +The following example shows how to create embeddings for an NVIDIA NIM. + +> [!NOTE] > NVIDIA asymmetric embedding models (e.g., `nvidia/llama-3.2-nv-embedqa-1b-v2`) require an `input_type` parameter not present in the standard OpenAI embeddings API. The NVIDIA Inference Adapter automatically sets `input_type="query"` when using the OpenAI-compatible embeddings endpoint for NVIDIA. For passage embeddings, use the `embeddings` API with `task_type="document"`. ```python @@ -160,4 +170,42 @@ response = client.inference.embeddings( task_type="query", ) print(f"Embeddings: {response.embeddings}") -``` \ No newline at end of file +``` + +### Vision Language Models Example + +The following example shows how to run vision inference by using an NVIDIA NIM. + +```python +def load_image_as_base64(image_path): + with open(image_path, "rb") as image_file: + img_bytes = image_file.read() + return base64.b64encode(img_bytes).decode("utf-8") + + +image_path = {path_to_the_image} +demo_image_b64 = load_image_as_base64(image_path) + +vlm_response = client.inference.chat_completion( + model_id="nvidia/vila", + messages=[ + { + "role": "user", + "content": [ + { + "type": "image", + "image": { + "data": demo_image_b64, + }, + }, + { + "type": "text", + "text": "Please describe what you see in this image in detail.", + }, + ], + } + ], +) + +print(f"VLM Response: {vlm_response.completion_message.content}") +``` diff --git a/llama_stack/providers/remote/inference/nvidia/models.py b/llama_stack/providers/remote/inference/nvidia/models.py index 76e579da8..df07f46b6 100644 --- a/llama_stack/providers/remote/inference/nvidia/models.py +++ b/llama_stack/providers/remote/inference/nvidia/models.py @@ -55,6 +55,10 @@ MODEL_ENTRIES = [ "meta/llama-3.3-70b-instruct", CoreModelId.llama3_3_70b_instruct.value, ), + ProviderModelEntry( + provider_model_id="nvidia/vila", + model_type=ModelType.llm, + ), # NeMo Retriever Text Embedding models - # # https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html From 478b4ff1e648492ea90af2188107436b63b9b28c Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sun, 31 Aug 2025 17:48:35 -0400 Subject: [PATCH 020/124] chore(migrate apis): move VectorDBWithIndex from embeddings to openai_embeddings (#3294) # What does this PR do? migrates VectorDBWithIndex to use openai_embeddings part of #2365 ## Test Plan existing unit tests --- .../providers/utils/memory/vector_store.py | 10 +- .../recordings/responses/1e11c2b20ff8.json | 422 +++++ .../recordings/responses/23506e73bb9e.json | 422 +++++ .../recordings/responses/3dff18060ebc.json | 422 +++++ .../recordings/responses/417020320684.json | 422 +++++ .../recordings/responses/4420515208a8.json | 422 +++++ .../recordings/responses/5370751803dc.json | 422 +++++ .../recordings/responses/62aa454ea5f9.json | 422 +++++ .../recordings/responses/72c1126ff2f9.json | 422 +++++ .../recordings/responses/7b25b702ea18.json | 422 +++++ .../recordings/responses/802f60021837.json | 422 +++++ .../recordings/responses/9e651e5fcfe2.json | 1595 +++++++++++++++++ .../recordings/responses/b5e3ed420986.json | 422 +++++ .../recordings/responses/b612debbd3bf.json | 422 +++++ .../recordings/responses/c2199d6064db.json | 422 +++++ .../recordings/responses/d86d4fc1eaca.json | 422 +++++ .../recordings/responses/e0a6dce1d94b.json | 422 +++++ .../recordings/responses/f6d655e91ac3.json | 422 +++++ tests/unit/providers/vector_io/test_qdrant.py | 11 +- tests/unit/rag/test_vector_store.py | 21 +- 20 files changed, 8376 insertions(+), 13 deletions(-) create mode 100644 tests/integration/recordings/responses/1e11c2b20ff8.json create mode 100644 tests/integration/recordings/responses/23506e73bb9e.json create mode 100644 tests/integration/recordings/responses/3dff18060ebc.json create mode 100644 tests/integration/recordings/responses/417020320684.json create mode 100644 tests/integration/recordings/responses/4420515208a8.json create mode 100644 tests/integration/recordings/responses/5370751803dc.json create mode 100644 tests/integration/recordings/responses/62aa454ea5f9.json create mode 100644 tests/integration/recordings/responses/72c1126ff2f9.json create mode 100644 tests/integration/recordings/responses/7b25b702ea18.json create mode 100644 tests/integration/recordings/responses/802f60021837.json create mode 100644 tests/integration/recordings/responses/9e651e5fcfe2.json create mode 100644 tests/integration/recordings/responses/b5e3ed420986.json create mode 100644 tests/integration/recordings/responses/b612debbd3bf.json create mode 100644 tests/integration/recordings/responses/c2199d6064db.json create mode 100644 tests/integration/recordings/responses/d86d4fc1eaca.json create mode 100644 tests/integration/recordings/responses/e0a6dce1d94b.json create mode 100644 tests/integration/recordings/responses/f6d655e91ac3.json diff --git a/llama_stack/providers/utils/memory/vector_store.py b/llama_stack/providers/utils/memory/vector_store.py index b74080384..aaa470970 100644 --- a/llama_stack/providers/utils/memory/vector_store.py +++ b/llama_stack/providers/utils/memory/vector_store.py @@ -294,12 +294,12 @@ class VectorDBWithIndex: _validate_embedding(c.embedding, i, self.vector_db.embedding_dimension) if chunks_to_embed: - resp = await self.inference_api.embeddings( + resp = await self.inference_api.openai_embeddings( self.vector_db.embedding_model, [c.content for c in chunks_to_embed], ) - for c, embedding in zip(chunks_to_embed, resp.embeddings, strict=False): - c.embedding = embedding + for c, data in zip(chunks_to_embed, resp.data, strict=False): + c.embedding = data.embedding embeddings = np.array([c.embedding for c in chunks], dtype=np.float32) await self.index.add_chunks(chunks, embeddings) @@ -334,8 +334,8 @@ class VectorDBWithIndex: if mode == "keyword": return await self.index.query_keyword(query_string, k, score_threshold) - embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string]) - query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32) + embeddings_response = await self.inference_api.openai_embeddings(self.vector_db.embedding_model, [query_string]) + query_vector = np.array(embeddings_response.data[0].embedding, dtype=np.float32) if mode == "hybrid": return await self.index.query_hybrid( query_vector, query_string, k, score_threshold, reranker_type, reranker_params diff --git a/tests/integration/recordings/responses/1e11c2b20ff8.json b/tests/integration/recordings/responses/1e11c2b20ff8.json new file mode 100644 index 000000000..98e855fdf --- /dev/null +++ b/tests/integration/recordings/responses/1e11c2b20ff8.json @@ -0,0 +1,422 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/embeddings", + "headers": {}, + "body": { + "model": "all-minilm:l6-v2", + "input": [ + "How do systems learn automatically?" + ], + "encoding_format": "float" + }, + "endpoint": "/v1/embeddings", + "model": "all-minilm:l6-v2" + }, + "response": { + "body": { + "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse", + "__data__": { + "data": [ + { + "embedding": [ + 0.042499725, + -0.061890375, + -0.07846951, + 0.006408736, + 0.031287834, + 0.008066364, + 0.058032244, + 0.025457833, + 0.016401615, + 0.04601607, + 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b/tests/unit/providers/vector_io/test_qdrant.py @@ -11,7 +11,8 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest -from llama_stack.apis.inference import EmbeddingsResponse, Inference +from llama_stack.apis.inference import Inference +from llama_stack.apis.inference.inference import OpenAIEmbeddingData, OpenAIEmbeddingsResponse, OpenAIEmbeddingUsage from llama_stack.apis.vector_io import ( QueryChunksResponse, VectorDB, @@ -68,7 +69,13 @@ def mock_vector_db_store(mock_vector_db) -> MagicMock: @pytest.fixture def mock_api_service(sample_embeddings): mock_api_service = MagicMock(spec=Inference) - mock_api_service.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings)) + mock_api_service.openai_embeddings = AsyncMock( + return_value=OpenAIEmbeddingsResponse( + model="mock-embedding-model", + data=[OpenAIEmbeddingData(embedding=sample, index=i) for i, sample in enumerate(sample_embeddings)], + usage=OpenAIEmbeddingUsage(prompt_tokens=10, total_tokens=10), + ) + ) return mock_api_service diff --git a/tests/unit/rag/test_vector_store.py b/tests/unit/rag/test_vector_store.py index 919f97ba7..8c017a551 100644 --- a/tests/unit/rag/test_vector_store.py +++ b/tests/unit/rag/test_vector_store.py @@ -13,6 +13,7 @@ from unittest.mock import AsyncMock, MagicMock import numpy as np import pytest +from llama_stack.apis.inference.inference import OpenAIEmbeddingData from llama_stack.apis.tools import RAGDocument from llama_stack.apis.vector_io import Chunk from llama_stack.providers.utils.memory.vector_store import ( @@ -218,11 +219,16 @@ class TestVectorDBWithIndex: Chunk(content="Test 2", embedding=None, metadata={}), ] - mock_inference_api.embeddings.return_value.embeddings = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]] + mock_inference_api.openai_embeddings.return_value.data = [ + OpenAIEmbeddingData(embedding=[0.1, 0.2, 0.3], index=0), + OpenAIEmbeddingData(embedding=[0.4, 0.5, 0.6], index=1), + ] await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_called_once_with("test-model without embeddings", ["Test 1", "Test 2"]) + mock_inference_api.openai_embeddings.assert_called_once_with( + "test-model without embeddings", ["Test 1", "Test 2"] + ) mock_index.add_chunks.assert_called_once() args = mock_index.add_chunks.call_args[0] assert args[0] == chunks @@ -246,7 +252,7 @@ class TestVectorDBWithIndex: await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_not_called() + mock_inference_api.openai_embeddings.assert_not_called() mock_index.add_chunks.assert_called_once() args = mock_index.add_chunks.call_args[0] assert args[0] == chunks @@ -288,7 +294,7 @@ class TestVectorDBWithIndex: with pytest.raises(ValueError, match="has dimension 4, expected 3"): await vector_db_with_index.insert_chunks(chunks_wrong_dim) - mock_inference_api.embeddings.assert_not_called() + mock_inference_api.openai_embeddings.assert_not_called() mock_index.add_chunks.assert_not_called() async def test_insert_chunks_with_partially_precomputed_embeddings(self): @@ -308,11 +314,14 @@ class TestVectorDBWithIndex: Chunk(content="Test 3", embedding=None, metadata={}), ] - mock_inference_api.embeddings.return_value.embeddings = [[0.1, 0.1, 0.1], [0.3, 0.3, 0.3]] + mock_inference_api.openai_embeddings.return_value.data = [ + OpenAIEmbeddingData(embedding=[0.1, 0.1, 0.1], index=0), + OpenAIEmbeddingData(embedding=[0.3, 0.3, 0.3], index=1), + ] await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_called_once_with( + mock_inference_api.openai_embeddings.assert_called_once_with( "test-model with partial embeddings", ["Test 1", "Test 3"] ) mock_index.add_chunks.assert_called_once() From a4a89745b643b0052c2c1a6b826851edca2921a5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 31 Aug 2025 17:46:12 -0700 Subject: [PATCH 021/124] chore(ui-deps): bump framer-motion from 11.18.2 to 12.23.12 in /llama_stack/ui (#3291) Bumps [framer-motion](https://github.com/motiondivision/motion) from 11.18.2 to 12.23.12.
Changelog

Sourced from framer-motion's changelog.

[12.23.12] 2025-07-29

Added

  • Exporting internal APIs for use in view animations.

[12.23.11] 2025-07-28

Added

  • Children of variants with delayChildren: stagger() will now be staggered correctly alongside their newly-entering siblings.

[12.23.10] 2025-07-28

Fixed

  • Fixed shared layout animation in situations where no motion components have re-rendered between shared element switching.

[12.23.9] 2025-07-24

Changed

  • Removing redundant renderRequest MotionValue lifecycle.

[12.23.8] 2025-07-24

Fixed

  • Ensuring that when an animation is skipped via duration = 0 that we also set type = "keyframes" so that duration takes effect.

[12.23.7] 2025-07-23

Fixed

  • springValue cleanup.
  • Removed additional removeNode from AnimatePresence when using popLayout.

[12.23.6] 2025-07-11

Changed

  • Added explainer for reduced motion warning.
  • Refactored motion component creation to remove indirection.

[12.23.5] 2025-07-11

Fixed

  • Fix animation timings within dynamically-generated popups.

... (truncated)

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 26 +++++++++++++------------- llama_stack/ui/package.json | 2 +- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 8748d44ad..e4bbca085 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -17,7 +17,7 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "framer-motion": "^11.18.2", + "framer-motion": "^12.23.12", "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", @@ -7268,13 +7268,13 @@ } }, "node_modules/framer-motion": { - "version": "11.18.2", - "resolved": "https://registry.npmjs.org/framer-motion/-/framer-motion-11.18.2.tgz", - "integrity": "sha512-5F5Och7wrvtLVElIpclDT0CBzMVg3dL22B64aZwHtsIY8RB4mXICLrkajK4G9R+ieSAGcgrLeae2SeUTg2pr6w==", + "version": "12.23.12", + "resolved": "https://registry.npmjs.org/framer-motion/-/framer-motion-12.23.12.tgz", + "integrity": "sha512-6e78rdVtnBvlEVgu6eFEAgG9v3wLnYEboM8I5O5EXvfKC8gxGQB8wXJdhkMy10iVcn05jl6CNw7/HTsTCfwcWg==", "license": "MIT", "dependencies": { - "motion-dom": "^11.18.1", - "motion-utils": "^11.18.1", + "motion-dom": "^12.23.12", + "motion-utils": "^12.23.6", "tslib": "^2.4.0" }, "peerDependencies": { @@ -11184,18 +11184,18 @@ } }, "node_modules/motion-dom": { - "version": "11.18.1", - "resolved": "https://registry.npmjs.org/motion-dom/-/motion-dom-11.18.1.tgz", - "integrity": "sha512-g76KvA001z+atjfxczdRtw/RXOM3OMSdd1f4DL77qCTF/+avrRJiawSG4yDibEQ215sr9kpinSlX2pCTJ9zbhw==", + "version": "12.23.12", + "resolved": "https://registry.npmjs.org/motion-dom/-/motion-dom-12.23.12.tgz", + "integrity": "sha512-RcR4fvMCTESQBD/uKQe49D5RUeDOokkGRmz4ceaJKDBgHYtZtntC/s2vLvY38gqGaytinij/yi3hMcWVcEF5Kw==", "license": "MIT", "dependencies": { - "motion-utils": "^11.18.1" + "motion-utils": "^12.23.6" } }, "node_modules/motion-utils": { - "version": "11.18.1", - "resolved": "https://registry.npmjs.org/motion-utils/-/motion-utils-11.18.1.tgz", - "integrity": "sha512-49Kt+HKjtbJKLtgO/LKj9Ld+6vw9BjH5d9sc40R/kVyH8GLAXgT42M2NnuPcJNuA3s9ZfZBUcwIgpmZWGEE+hA==", + "version": "12.23.6", + "resolved": "https://registry.npmjs.org/motion-utils/-/motion-utils-12.23.6.tgz", + "integrity": "sha512-eAWoPgr4eFEOFfg2WjIsMoqJTW6Z8MTUCgn/GZ3VRpClWBdnbjryiA3ZSNLyxCTmCQx4RmYX6jX1iWHbenUPNQ==", "license": "MIT" }, "node_modules/ms": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index a9c56f98e..7a4e96074 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -22,7 +22,7 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "framer-motion": "^11.18.2", + "framer-motion": "^12.23.12", "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", From 26b4340de3ddc27775cddf716d3aa3a367808729 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 31 Aug 2025 17:47:31 -0700 Subject: [PATCH 022/124] chore(ui-deps): bump @types/node from 20.17.47 to 24.3.0 in /llama_stack/ui (#3290) Bumps [@types/node](https://github.com/DefinitelyTyped/DefinitelyTyped/tree/HEAD/types/node) from 20.17.47 to 24.3.0.
Commits

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 16 ++++++++-------- llama_stack/ui/package.json | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index e4bbca085..f67c471ce 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -39,7 +39,7 @@ "@testing-library/jest-dom": "^6.8.0", "@testing-library/react": "^16.3.0", "@types/jest": "^29.5.14", - "@types/node": "^20", + "@types/node": "^24", "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", @@ -3910,12 +3910,12 @@ "license": "MIT" }, "node_modules/@types/node": { - "version": "20.17.47", - "resolved": "https://registry.npmjs.org/@types/node/-/node-20.17.47.tgz", - "integrity": "sha512-3dLX0Upo1v7RvUimvxLeXqwrfyKxUINk0EAM83swP2mlSUcwV73sZy8XhNz8bcZ3VbsfQyC/y6jRdL5tgCNpDQ==", + "version": "24.3.0", + "resolved": "https://registry.npmjs.org/@types/node/-/node-24.3.0.tgz", + "integrity": "sha512-aPTXCrfwnDLj4VvXrm+UUCQjNEvJgNA8s5F1cvwQU+3KNltTOkBm1j30uNLyqqPNe7gE3KFzImYoZEfLhp4Yow==", "license": "MIT", "dependencies": { - "undici-types": "~6.19.2" + "undici-types": "~7.10.0" } }, "node_modules/@types/node-fetch": { @@ -13986,9 +13986,9 @@ } }, "node_modules/undici-types": { - "version": "6.19.8", - "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.19.8.tgz", - "integrity": "sha512-ve2KP6f/JnbPBFyobGHuerC9g1FYGn/F8n1LWTwNxCEzd6IfqTwUQcNXgEtmmQ6DlRrC1hrSrBnCZPokRrDHjw==", + "version": "7.10.0", + "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-7.10.0.tgz", + "integrity": "sha512-t5Fy/nfn+14LuOc2KNYg75vZqClpAiqscVvMygNnlsHBFpSXdJaYtXMcdNLpl/Qvc3P2cB3s6lOV51nqsFq4ag==", "license": "MIT" }, "node_modules/unified": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 7a4e96074..8cf2467f4 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -44,7 +44,7 @@ "@testing-library/jest-dom": "^6.8.0", "@testing-library/react": "^16.3.0", "@types/jest": "^29.5.14", - "@types/node": "^20", + "@types/node": "^24", "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", From 7cc059fe419d68bda7dee72f07661e6376bc0c79 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 10:18:15 +0200 Subject: [PATCH 023/124] chore(ui-deps): bump eslint-config-next from 15.3.2 to 15.5.2 in /llama_stack/ui (#3288) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [eslint-config-next](https://github.com/vercel/next.js/tree/HEAD/packages/eslint-config-next) from 15.3.2 to 15.5.2.
Release notes

Sourced from eslint-config-next's releases.

v15.5.2

[!NOTE]
This release is backporting bug fixes. It does not include all pending features/changes on canary.

Core Changes

  • fix: disable unknownatrules lint rule entirely (#83059)
  • revert: add ?dpl to fonts in /_next/static/media (#83062)

Credits

Huge thanks to @​bgub and @​ztanner for helping!

v15.5.1

[!NOTE]
This release is backporting bug fixes. It does not include all pending features/changes on canary.

Core Changes

  • fix: aliased navigations should apply scroll handling (#82900)
  • Turbopack: fix invalid NFT entry with file behind symlink (#82887)
  • fix: typesafe linking to route handlers and pages API routes (#82858)
  • fix: change "noUnknownAtRules" to "warn" for Biome (#82974)
  • fix: add path normalization to getRelativePath for Windows (#82918)
  • feat: add typesafety with config.typedRoutes to redirect() and permanentRedirect() (#82860)
  • fix: avoid importing types that will be unused (#82856)
  • fix: update the config.api.responseLimit type (#82852)
  • fix: update validation return types (#82854)

Credits

Huge thanks to @​bgub, @​mischnic, and @​ztanner for helping!

v15.5.1-canary.20

Misc Changes

  • Turbopack: hide blocking spans in trace server: #83167
  • Update Rspack production test manifest: #83207
  • [create-next-app] Generate route types after setup: #82956
  • Update Rspack development test manifest: #83208
  • docs: fix snippets in getting started: #83228

Credits

Huge thanks to @​sokra, @​vercel-release-bot, @​bgub, and @​icyJoseph for helping!

v15.5.1-canary.19

Core Changes

  • [sourcemaps] Always check for vendor chunks regardless of Node.js version: #83114
  • Turbopack: Remove undocumented legacy syntax for built-in conditions (e.g. foreign, browser): #83068
  • [metadata] update metadata routes cache headers: #83215

... (truncated)

Commits

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 16 ++++++++-------- llama_stack/ui/package.json | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index f67c471ce..4e9100d64 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -43,7 +43,7 @@ "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", - "eslint-config-next": "15.3.2", + "eslint-config-next": "15.5.2", "eslint-config-prettier": "^10.1.8", "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", @@ -1854,9 +1854,9 @@ "integrity": "sha512-OdiMrzCl2Xi0VTjiQQUK0Xh7bJHnOuET2s+3V+Y40WJBAXrJeGA3f+I8MZJ/YQ3mVGi5XGR1L66oFlgqXhQ4Vw==" }, "node_modules/@next/eslint-plugin-next": { - "version": "15.3.2", - "resolved": "https://registry.npmjs.org/@next/eslint-plugin-next/-/eslint-plugin-next-15.3.2.tgz", - "integrity": "sha512-ijVRTXBgnHT33aWnDtmlG+LJD+5vhc9AKTJPquGG5NKXjpKNjc62woIhFtrAcWdBobt8kqjCoaJ0q6sDQoX7aQ==", + "version": "15.5.2", + "resolved": "https://registry.npmjs.org/@next/eslint-plugin-next/-/eslint-plugin-next-15.5.2.tgz", + "integrity": "sha512-lkLrRVxcftuOsJNhWatf1P2hNVfh98k/omQHrCEPPriUypR6RcS13IvLdIrEvkm9AH2Nu2YpR5vLqBuy6twH3Q==", "dev": true, "license": "MIT", "dependencies": { @@ -6433,13 +6433,13 @@ } }, "node_modules/eslint-config-next": { - "version": "15.3.2", - "resolved": "https://registry.npmjs.org/eslint-config-next/-/eslint-config-next-15.3.2.tgz", - "integrity": "sha512-FerU4DYccO4FgeYFFglz0SnaKRe1ejXQrDb8kWUkTAg036YWi+jUsgg4sIGNCDhAsDITsZaL4MzBWKB6f4G1Dg==", + "version": "15.5.2", + "resolved": "https://registry.npmjs.org/eslint-config-next/-/eslint-config-next-15.5.2.tgz", + "integrity": "sha512-3hPZghsLupMxxZ2ggjIIrat/bPniM2yRpsVPVM40rp8ZMzKWOJp2CGWn7+EzoV2ddkUr5fxNfHpF+wU1hGt/3g==", "dev": true, "license": "MIT", "dependencies": { - "@next/eslint-plugin-next": "15.3.2", + "@next/eslint-plugin-next": "15.5.2", "@rushstack/eslint-patch": "^1.10.3", "@typescript-eslint/eslint-plugin": "^5.4.2 || ^6.0.0 || ^7.0.0 || ^8.0.0", "@typescript-eslint/parser": "^5.4.2 || ^6.0.0 || ^7.0.0 || ^8.0.0", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 8cf2467f4..e4d41d04d 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -48,7 +48,7 @@ "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", - "eslint-config-next": "15.3.2", + "eslint-config-next": "15.5.2", "eslint-config-prettier": "^10.1.8", "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", From 4499559ed164f76f0f53ab5bc26632a61c5760a0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 10:18:40 +0200 Subject: [PATCH 024/124] chore(ui-deps): bump prettier from 3.5.3 to 3.6.2 in /llama_stack/ui (#3289) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [prettier](https://github.com/prettier/prettier) from 3.5.3 to 3.6.2.
Release notes

Sourced from prettier's releases.

3.6.2

What's Changed

🔗 Changelog

3.6.1

  • Fix "Warning: File descriptor 39 closed but not opened in unmanaged mode" error when running --experimental-cli

🔗 Changelog

3.6.0

diff

🔗 Release note "Prettier 3.6: Experimental fast CLI and new OXC and Hermes plugins!"

Changelog

Sourced from prettier's changelog.

3.6.2

diff

Markdown: Add missing blank line around code block (#17675 by @​fisker)

<!-- Input -->
1. Some text, and code block below, with newline after code block

---
foo: bar



    

  1. Another
    2. 

  2. List
    3. 



<!-- Prettier 3.6.1 -->


    

  1. 

    Some text, and code block below, with newline after code block
    

    ---
foo: bar

    

      

    1. Another
      2. 

    2. List
      3. 

    

    2. 



<!-- Prettier 3.6.2 -->


    

  1. 

    Some text, and code block below, with newline after code block
    

    ---
foo: bar

    

      

    1. Another
      2. 

    2. List

3.6.1

diff

TypeScript: Allow const without initializer (#17650, #17654 by @​fisker)

// Input
</tr></table>

... (truncated)

Commits
  • 7a8b05f Release 3.6.2
  • 46526b4 Add missing blank line around code block (#17675)
  • a04ec11 chore(deps): update babel to v7.27.7 (#17684)
  • 32be5b6 chore(deps): update dependency flow-parser to v0.274.1 (#17676)
  • b55e777 Update docs about "TypeScript Configuration Files" (#17677)
  • b197c99 chore(deps): update dependency @​vitejs/plugin-react to v4.6.0 (#17674)
  • 1185f83 chore(deps): update dependency @​angular/compiler to v20.0.5 (#17680)
  • aa1316f chore(deps): update dependency browserslist to v4.25.1 (#17671)
  • c468d33 chore(deps): update dependency oxc-parser to v0.75.0 (#17672)
  • 3f46d91 chore(deps): update dependency vite to v7 (#17673)
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=prettier&package-manager=npm_and_yarn&previous-version=3.5.3&new-version=3.6.2)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 8 ++++---- llama_stack/ui/package.json | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 4e9100d64..da42958c3 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -48,7 +48,7 @@ "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", "jest-environment-jsdom": "^29.7.0", - "prettier": "3.5.3", + "prettier": "3.6.2", "tailwindcss": "^4", "ts-node": "^10.9.2", "tw-animate-css": "^1.2.9", @@ -12083,9 +12083,9 @@ } }, "node_modules/prettier": { - "version": "3.5.3", - "resolved": "https://registry.npmjs.org/prettier/-/prettier-3.5.3.tgz", - "integrity": "sha512-QQtaxnoDJeAkDvDKWCLiwIXkTgRhwYDEQCghU9Z6q03iyek/rxRh/2lC3HB7P8sWT2xC/y5JDctPLBIGzHKbhw==", + "version": "3.6.2", + "resolved": "https://registry.npmjs.org/prettier/-/prettier-3.6.2.tgz", + "integrity": "sha512-I7AIg5boAr5R0FFtJ6rCfD+LFsWHp81dolrFD8S79U9tb8Az2nGrJncnMSnys+bpQJfRUzqs9hnA81OAA3hCuQ==", "dev": true, "license": "MIT", "bin": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index e4d41d04d..9efde6dd4 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -53,7 +53,7 @@ "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", "jest-environment-jsdom": "^29.7.0", - "prettier": "3.5.3", + "prettier": "3.6.2", "tailwindcss": "^4", "ts-node": "^10.9.2", "tw-animate-css": "^1.2.9", From 9e5ef1af3c5d4df017710fa1d913081e9f18049f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 10:18:57 +0200 Subject: [PATCH 025/124] chore(ui-deps): bump @radix-ui/react-tooltip from 1.2.6 to 1.2.8 in /llama_stack/ui (#3287) Bumps [@radix-ui/react-tooltip](https://github.com/radix-ui/primitives) from 1.2.6 to 1.2.8.
Commits

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 231 +++++++++++++++++++++++++------ llama_stack/ui/package.json | 2 +- 2 files changed, 187 insertions(+), 46 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index da42958c3..7873cdfd5 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -14,7 +14,7 @@ "@radix-ui/react-select": "^2.2.5", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", - "@radix-ui/react-tooltip": "^1.2.6", + "@radix-ui/react-tooltip": "^1.2.8", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^12.23.12", @@ -2861,29 +2861,6 @@ } } }, - "node_modules/@radix-ui/react-select/node_modules/@radix-ui/react-visually-hidden": { - "version": "1.2.3", - "resolved": "https://registry.npmjs.org/@radix-ui/react-visually-hidden/-/react-visually-hidden-1.2.3.tgz", - "integrity": "sha512-pzJq12tEaaIhqjbzpCuv/OypJY/BPavOofm+dbab+MHLajy277+1lLm6JFcGgF5eskJ6mquGirhXY2GD/8u8Ug==", - "license": "MIT", - "dependencies": { - "@radix-ui/react-primitive": "2.1.3" - }, - "peerDependencies": { - "@types/react": "*", - "@types/react-dom": "*", - "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", - "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" - }, - "peerDependenciesMeta": { - "@types/react": { - "optional": true - }, - "@types/react-dom": { - "optional": true - } - } - }, "node_modules/@radix-ui/react-separator": { "version": "1.1.7", "resolved": "https://registry.npmjs.org/@radix-ui/react-separator/-/react-separator-1.1.7.tgz", @@ -2949,23 +2926,23 @@ } }, "node_modules/@radix-ui/react-tooltip": { - "version": "1.2.6", - "resolved": "https://registry.npmjs.org/@radix-ui/react-tooltip/-/react-tooltip-1.2.6.tgz", - "integrity": "sha512-zYb+9dc9tkoN2JjBDIIPLQtk3gGyz8FMKoqYTb8EMVQ5a5hBcdHPECrsZVI4NpPAUOixhkoqg7Hj5ry5USowfA==", + "version": "1.2.8", + "resolved": "https://registry.npmjs.org/@radix-ui/react-tooltip/-/react-tooltip-1.2.8.tgz", + "integrity": "sha512-tY7sVt1yL9ozIxvmbtN5qtmH2krXcBCfjEiCgKGLqunJHvgvZG2Pcl2oQ3kbcZARb1BGEHdkLzcYGO8ynVlieg==", "license": "MIT", "dependencies": { - "@radix-ui/primitive": "1.1.2", + "@radix-ui/primitive": "1.1.3", "@radix-ui/react-compose-refs": "1.1.2", "@radix-ui/react-context": "1.1.2", - "@radix-ui/react-dismissable-layer": "1.1.9", + "@radix-ui/react-dismissable-layer": "1.1.11", "@radix-ui/react-id": "1.1.1", - "@radix-ui/react-popper": "1.2.6", - "@radix-ui/react-portal": "1.1.8", - "@radix-ui/react-presence": "1.1.4", - "@radix-ui/react-primitive": "2.1.2", - "@radix-ui/react-slot": "1.2.2", + "@radix-ui/react-popper": "1.2.8", + "@radix-ui/react-portal": "1.1.9", + "@radix-ui/react-presence": "1.1.5", + "@radix-ui/react-primitive": "2.1.3", + "@radix-ui/react-slot": "1.2.3", "@radix-ui/react-use-controllable-state": "1.2.2", - "@radix-ui/react-visually-hidden": "1.2.2" + "@radix-ui/react-visually-hidden": "1.2.3" }, "peerDependencies": { "@types/react": "*", @@ -2982,21 +2959,162 @@ } } }, - "node_modules/@radix-ui/react-tooltip/node_modules/@radix-ui/react-slot": { - "version": "1.2.2", - "resolved": "https://registry.npmjs.org/@radix-ui/react-slot/-/react-slot-1.2.2.tgz", - "integrity": "sha512-y7TBO4xN4Y94FvcWIOIh18fM4R1A8S4q1jhoz4PNzOoHsFcN8pogcFmZrTYAm4F9VRUrWP/Mw7xSKybIeRI+CQ==", + "node_modules/@radix-ui/react-tooltip/node_modules/@radix-ui/primitive": { + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/@radix-ui/primitive/-/primitive-1.1.3.tgz", + "integrity": "sha512-JTF99U/6XIjCBo0wqkU5sK10glYe27MRRsfwoiq5zzOEZLHU3A3KCMa5X/azekYRCJ0HlwI0crAXS/5dEHTzDg==", + "license": "MIT" + }, + "node_modules/@radix-ui/react-tooltip/node_modules/@radix-ui/react-arrow": { + "version": "1.1.7", + "resolved": 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"license": "MIT", + "dependencies": { + "@floating-ui/react-dom": "^2.0.0", + "@radix-ui/react-arrow": "1.1.7", + "@radix-ui/react-compose-refs": "1.1.2", + "@radix-ui/react-context": "1.1.2", + "@radix-ui/react-primitive": "2.1.3", + "@radix-ui/react-use-callback-ref": "1.1.1", + "@radix-ui/react-use-layout-effect": "1.1.1", + "@radix-ui/react-use-rect": "1.1.1", + "@radix-ui/react-use-size": "1.1.1", + "@radix-ui/rect": "1.1.1" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@radix-ui/react-tooltip/node_modules/@radix-ui/react-portal": { + "version": "1.1.9", + "resolved": "https://registry.npmjs.org/@radix-ui/react-portal/-/react-portal-1.1.9.tgz", + "integrity": 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"1.1.1" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@radix-ui/react-tooltip/node_modules/@radix-ui/react-primitive": { + "version": "2.1.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-2.1.3.tgz", + "integrity": "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ==", + "license": "MIT", + "dependencies": { + "@radix-ui/react-slot": "1.2.3" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true } } }, @@ -3137,12 +3255,35 @@ } }, "node_modules/@radix-ui/react-visually-hidden": { - "version": "1.2.2", - "resolved": "https://registry.npmjs.org/@radix-ui/react-visually-hidden/-/react-visually-hidden-1.2.2.tgz", - "integrity": "sha512-ORCmRUbNiZIv6uV5mhFrhsIKw4UX/N3syZtyqvry61tbGm4JlgQuSn0hk5TwCARsCjkcnuRkSdCE3xfb+ADHew==", + "version": "1.2.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-visually-hidden/-/react-visually-hidden-1.2.3.tgz", + "integrity": "sha512-pzJq12tEaaIhqjbzpCuv/OypJY/BPavOofm+dbab+MHLajy277+1lLm6JFcGgF5eskJ6mquGirhXY2GD/8u8Ug==", "license": "MIT", "dependencies": { - "@radix-ui/react-primitive": "2.1.2" + "@radix-ui/react-primitive": "2.1.3" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@radix-ui/react-visually-hidden/node_modules/@radix-ui/react-primitive": { + "version": "2.1.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-2.1.3.tgz", + "integrity": "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ==", + "license": "MIT", + "dependencies": { + "@radix-ui/react-slot": "1.2.3" }, "peerDependencies": { "@types/react": "*", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 9efde6dd4..b37ff233f 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -19,7 +19,7 @@ "@radix-ui/react-select": "^2.2.5", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", - "@radix-ui/react-tooltip": "^1.2.6", + "@radix-ui/react-tooltip": "^1.2.8", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^12.23.12", From 9625ac6d0206148ef96694ae6c66237bbb2eca3b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 16:49:09 +0200 Subject: [PATCH 026/124] chore(python-deps): bump locust from 2.39.0 to 2.39.1 (#3284) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [locust](https://github.com/locustio/locust) from 2.39.0 to 2.39.1.
Release notes

Sourced from locust's releases.

2.39.1

What's Changed

New Contributors

Full Changelog: https://github.com/locustio/locust/compare/2.39.0...2.39.1

Changelog

Sourced from locust's changelog.

Detailed changelog

The most important changes can also be found in the documentation.

Commits
  • 934c5c3 changelog
  • 9350084 disable macos build for now
  • 705e2f6 Disable another unit test on macos because of annoying behavior on GH (really...
  • d888b9d Disable another unit test on macos because of annoying behavior on GH
  • 45bc4d8 Disable annoying test case on macos for now. Only has issues on GH. @​amadeupp...
  • 9d7710a unit tests: give extra time for testing on macOS
  • fcbc740 Avoid broken gevent version for now (#3196)
  • cd1f600 mypy
  • 0cf52dc Autogen changelog for 2.39.0
  • 094395e Merge pull request #3195 from JumboBear/pyproject
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=locust&package-manager=uv&previous-version=2.39.0&new-version=2.39.1)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- pyproject.toml | 2 +- uv.lock | 9 +++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1f87a3aaa..7881b79db 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -146,7 +146,7 @@ docs = [ ] codegen = ["rich", "pydantic", "jinja2>=3.1.6"] benchmark = [ - "locust>=2.37.14", + "locust>=2.39.1", ] [project.urls] diff --git a/uv.lock b/uv.lock index 73b52a3e9..0c658011a 100644 --- a/uv.lock +++ b/uv.lock @@ -1930,7 +1930,7 @@ requires-dist = [ provides-extras = ["ui"] [package.metadata.requires-dev] -benchmark = [{ name = "locust", specifier = ">=2.37.14" }] +benchmark = [{ name = "locust", specifier = ">=2.39.1" }] codegen = [ { name = "jinja2", specifier = ">=3.1.6" }, { name = "pydantic" }, @@ -2043,7 +2043,7 @@ wheels = [ [[package]] name = "locust" -version = "2.39.0" +version = "2.39.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "configargparse" }, @@ -2055,6 +2055,7 @@ dependencies = [ { name = "locust-cloud" }, { name = "msgpack" }, { name = "psutil" }, + { name = "python-engineio" }, { name = "python-socketio", extra = ["client"] }, { name = "pywin32", marker = "sys_platform == 'win32'" }, { name = "pyzmq" }, @@ -2062,9 +2063,9 @@ dependencies = [ { name = "setuptools" }, { name = "werkzeug" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/c4/6f/d6ca4483f4795747fbbd610d28e798ca4f5d4358e03f309343eb5bab128f/locust-2.39.0.tar.gz", hash = "sha256:71e82a68324f9d63d4b800035288488c08eab12811fa4c24ff07f031643b7b39", size = 1409879, upload-time = "2025-08-20T13:39:55.233Z" } +sdist = { url = "https://files.pythonhosted.org/packages/95/c8/10aa5445c404eed389b56877e6714c1787190cc09dd70059ce3765979ec5/locust-2.39.1.tar.gz", hash = "sha256:6bdd19e27edf9a1c84391d6cf6e9a737dfb832be7dfbf39053191ae31b9cc498", size = 1409902, upload-time = "2025-08-29T17:41:01.544Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/7c/94/7dc9a2b4ccb18a5b0c4be4bfadfa79b6c0fd860267a7114641402627e7db/locust-2.39.0-py3-none-any.whl", hash = "sha256:3817c4d7cca387b4b871da779c9e145c2a95fbb0b5602be5833976902b967a8f", size = 1428138, upload-time = "2025-08-20T13:39:52.549Z" }, + { url = "https://files.pythonhosted.org/packages/ec/b3/b2f4b2ca88b1e72eba7be2b2982533b887f8b709d222db78eb9602aa5121/locust-2.39.1-py3-none-any.whl", hash = "sha256:fd5148f2f1a4ed34aee968abc4393674e69d1b5e1b54db50a397f6eb09ce0b04", size = 1428155, upload-time = "2025-08-29T17:41:00.245Z" }, ] [[package]] From 4a59961a6cd764db739aefeba06601dfaee68d88 Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Mon, 1 Sep 2025 15:50:50 +0100 Subject: [PATCH 027/124] refactor: remove lama-api-client from pyproject.toml (#3299) # What does this PR do? This PR is eliminating `lama-api-client` dependency at `pyproject.toml` because it's not used in Llama Stack codebase ## Test Plan ` ./scripts/unit-tests.sh` --- pyproject.toml | 1 - uv.lock | 18 ------------------ 2 files changed, 19 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7881b79db..f615e632b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,6 @@ dependencies = [ "jinja2>=3.1.6", "jsonschema", "llama-stack-client>=0.2.20", - "llama-api-client>=0.1.2", "openai>=1.99.6", "prompt-toolkit", "python-dotenv", diff --git a/uv.lock b/uv.lock index 0c658011a..898194134 100644 --- a/uv.lock +++ b/uv.lock @@ -1748,22 +1748,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/5f/e4/f1546746049c99c6b8b247e2f34485b9eae36faa9322b84e2a17262e6712/litellm-1.74.9-py3-none-any.whl", hash = "sha256:ab8f8a6e4d8689d3c7c4f9c3bbc7e46212cc3ebc74ddd0f3c0c921bb459c9874", size = 8740449, upload-time = "2025-07-28T16:42:36.8Z" }, ] -[[package]] -name = "llama-api-client" -version = "0.2.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "anyio" }, - { name = "distro" }, - { name = "httpx" }, - { name = "pydantic" }, - { name = "sniffio" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/59/41/fa8521a0faff96bf5f810e2ab5b78c638f5ba44afd09aa86f94b6a1226ad/llama_api_client-0.2.0.tar.gz", hash = "sha256:b9bd5f5ad332b9133f0775a105f0940f057cbb311891f1d4487247d001c31f17", size = 117108, upload-time = "2025-08-12T17:07:07.734Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1d/11/198e65c1a50d9e839b4e3d346b4bd0f624e532446e468d1aba6c74ed7484/llama_api_client-0.2.0-py3-none-any.whl", hash = "sha256:50614ed991e1a72439e6a624a97e6000615ada1b9e2046ecc026fe62f107663c", size = 85002, upload-time = "2025-08-12T17:07:06.293Z" }, -] [[package]] name = "llama-stack" @@ -1780,7 +1764,6 @@ dependencies = [ { name = "huggingface-hub" }, { name = "jinja2" }, { name = "jsonschema" }, - { name = "llama-api-client" }, { name = "llama-stack-client" }, { name = "openai" }, { name = "opentelemetry-exporter-otlp-proto-http" }, @@ -1906,7 +1889,6 @@ requires-dist = [ { name = "huggingface-hub", specifier = ">=0.34.0,<1.0" }, { name = "jinja2", specifier = ">=3.1.6" }, { name = "jsonschema" }, - { name = "llama-api-client", specifier = ">=0.1.2" }, { name = "llama-stack-client", specifier = ">=0.2.20" }, { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.20" }, { name = "openai", specifier = ">=1.99.6" }, From 5c873d53dbaa8fd15d0167d1036126cf2488836a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 20:24:22 -0400 Subject: [PATCH 028/124] chore(python-deps): bump pymilvus from 2.6.0 to 2.6.1 (#3285) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [pymilvus](https://github.com/milvus-io/pymilvus) from 2.6.0 to 2.6.1.
Release notes

Sourced from pymilvus's releases.

PyMilvus v2.6.1 Release Notes

What's Changed

Full Changelog: https://github.com/milvus-io/pymilvus/compare/v2.6.0...v2.6.1

Commits
  • 0237c9f fix: [2.6]Passing unknown req.is_refresh to wait (#2964)
  • a083622 enhance: Multiple cherry picks from master branch (#2962)
  • 87e3c5a enhance: Add unixmsec in every RPC call (#2961)
  • 98077a2 fix: [2.6]Fix the existing version fmt (#2960)
  • 80e2e09 feat: Add partial update support for upsert operations (#2938) (#2940)
  • 0210ee9 [cherry-pick] support stageManager & stageFileManager (#2935)
  • 00fb8e6 [cherry-pick] bulkImport add objectUrls/token paramster & add example use (#2...
  • 442ef15 Avoid describe_collection when query by ids (#2930)
  • e704dd2 fix: Correct github actions on branch 2.6 (#2926)
  • See full diff in compare view

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- pyproject.toml | 4 ++-- uv.lock | 10 +++++----- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f615e632b..fb6d3a330 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -92,7 +92,7 @@ unit = [ "sqlalchemy[asyncio]>=2.0.41", "blobfile", "faiss-cpu", - "pymilvus>=2.5.12", + "pymilvus>=2.6.1", "milvus-lite>=2.5.0", "litellm", "together", @@ -120,7 +120,7 @@ test = [ "sqlalchemy", "sqlalchemy[asyncio]>=2.0.41", "requests", - "pymilvus>=2.5.12", + "pymilvus>=2.6.1", "milvus-lite>=2.5.0", "weaviate-client>=4.16.4", ] diff --git a/uv.lock b/uv.lock index 898194134..43cc59c7a 100644 --- a/uv.lock +++ b/uv.lock @@ -1963,7 +1963,7 @@ test = [ { name = "milvus-lite", specifier = ">=2.5.0" }, { name = "openai", specifier = ">=1.100.0" }, { name = "psycopg2-binary", specifier = ">=2.9.0" }, - { name = "pymilvus", specifier = ">=2.5.12" }, + { name = "pymilvus", specifier = ">=2.6.1" }, { name = "pypdf" }, { name = "requests" }, { name = "sqlalchemy" }, @@ -1988,7 +1988,7 @@ unit = [ { name = "ollama" }, { name = "openai" }, { name = "psycopg2-binary", specifier = ">=2.9.0" }, - { name = "pymilvus", specifier = ">=2.5.12" }, + { name = "pymilvus", specifier = ">=2.6.1" }, { name = "pypdf" }, { name = "qdrant-client" }, { name = "sqlalchemy" }, @@ -3491,7 +3491,7 @@ wheels = [ [[package]] name = "pymilvus" -version = "2.6.0" +version = "2.6.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "grpcio" }, @@ -3502,9 +3502,9 @@ dependencies = [ { name = "setuptools" }, { name = "ujson" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/86/21/5c25a975299415a5a8f26d4759ddf7852aefdf3595f002b5203c4aaf5c8e/pymilvus-2.6.0.tar.gz", hash = "sha256:2b2ca487e098abc34231755e33af2f5294e9f6a64d92d03551532defbac0a3fb", size = 1292994, upload-time = "2025-08-06T09:09:01.705Z" } +sdist = { url = "https://files.pythonhosted.org/packages/70/a9/b25af985972082d1bb0b26739fece8cea3f56370733b4b1de690c42a77cc/pymilvus-2.6.1.tar.gz", hash = "sha256:ef1d7f5039719398d131ca80c19e55bc2bccc7ab6609f2cca9a04217dcb0a7fb", size = 1322169, upload-time = "2025-08-29T10:03:50.523Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/f6/a2/dfc2a2225aeb90a7dff9443f2d26fe9d04f6f7bcefe537945b5d5220fddd/pymilvus-2.6.0-py3-none-any.whl", hash = "sha256:d743fdd928c9007184d24a52b4f5dfdd18d405a37b4dba66b5ea4bf196fac526", size = 248299, upload-time = "2025-08-06T09:08:58.272Z" }, + { url = "https://files.pythonhosted.org/packages/d4/1a/8b677e0f4ef683bbfb00d495960573fff0844ed509b3cf0abede79a48e90/pymilvus-2.6.1-py3-none-any.whl", hash = "sha256:e3d76d45ce04d3555a6849645a18a1e2992706e248d5b6dc58a00504d0b60165", size = 254252, upload-time = "2025-08-29T10:03:48.539Z" }, ] [[package]] From faf891b40c6236821ba9730e11d985582aad4b9a Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Tue, 2 Sep 2025 18:38:35 +0100 Subject: [PATCH 029/124] refactor: use generic WeightedInMemoryAggregator for hybrid search in SQLiteVecIndex (#3303) # What does this PR do? The purpose of this PR is to refactor `SQLiteVecIndex` to eliminate redundant code and simplify the code using generic `WeightedInMemoryAggregator` that can be used for any vector db provider. This pattern is already implemented for `PGVectorIndex` in #3064 CC: @franciscojavierarceo ## Test Plan 1. `./scripts/unit-tests.sh` 2. Integration tests in CI Workflow --- .../inline/vector_io/sqlite_vec/sqlite_vec.py | 67 ++----------------- 1 file changed, 5 insertions(+), 62 deletions(-) diff --git a/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py b/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py index 7cf163960..f34f8f6fb 100644 --- a/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py +++ b/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py @@ -30,11 +30,11 @@ from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin from llama_stack.providers.utils.memory.vector_store import ( RERANKER_TYPE_RRF, - RERANKER_TYPE_WEIGHTED, ChunkForDeletion, EmbeddingIndex, VectorDBWithIndex, ) +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator logger = get_logger(name=__name__, category="vector_io") @@ -66,59 +66,6 @@ def _create_sqlite_connection(db_path): return connection -def _normalize_scores(scores: dict[str, float]) -> dict[str, float]: - """Normalize scores to [0,1] range using min-max normalization.""" - if not scores: - return {} - min_score = min(scores.values()) - max_score = max(scores.values()) - score_range = max_score - min_score - if score_range > 0: - return {doc_id: (score - min_score) / score_range for doc_id, score in scores.items()} - return dict.fromkeys(scores, 1.0) - - -def _weighted_rerank( - vector_scores: dict[str, float], - keyword_scores: dict[str, float], - alpha: float = 0.5, -) -> dict[str, float]: - """ReRanker that uses weighted average of scores.""" - all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) - normalized_vector_scores = _normalize_scores(vector_scores) - normalized_keyword_scores = _normalize_scores(keyword_scores) - - return { - doc_id: (alpha * normalized_keyword_scores.get(doc_id, 0.0)) - + ((1 - alpha) * normalized_vector_scores.get(doc_id, 0.0)) - for doc_id in all_ids - } - - -def _rrf_rerank( - vector_scores: dict[str, float], - keyword_scores: dict[str, float], - impact_factor: float = 60.0, -) -> dict[str, float]: - """ReRanker that uses Reciprocal Rank Fusion.""" - # Convert scores to ranks - vector_ranks = { - doc_id: i + 1 for i, (doc_id, _) in enumerate(sorted(vector_scores.items(), key=lambda x: x[1], reverse=True)) - } - keyword_ranks = { - doc_id: i + 1 for i, (doc_id, _) in enumerate(sorted(keyword_scores.items(), key=lambda x: x[1], reverse=True)) - } - - all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) - rrf_scores = {} - for doc_id in all_ids: - vector_rank = vector_ranks.get(doc_id, float("inf")) - keyword_rank = keyword_ranks.get(doc_id, float("inf")) - # RRF formula: score = 1/(k + r) where k is impact_factor and r is the rank - rrf_scores[doc_id] = (1.0 / (impact_factor + vector_rank)) + (1.0 / (impact_factor + keyword_rank)) - return rrf_scores - - def _make_sql_identifier(name: str) -> str: return re.sub(r"[^a-zA-Z0-9_]", "_", name) @@ -398,14 +345,10 @@ class SQLiteVecIndex(EmbeddingIndex): for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False) } - # Combine scores using the specified reranker - if reranker_type == RERANKER_TYPE_WEIGHTED: - alpha = reranker_params.get("alpha", 0.5) - combined_scores = _weighted_rerank(vector_scores, keyword_scores, alpha) - else: - # Default to RRF for None, RRF, or any unknown types - impact_factor = reranker_params.get("impact_factor", 60.0) - combined_scores = _rrf_rerank(vector_scores, keyword_scores, impact_factor) + # Combine scores using the reranking utility + combined_scores = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type, reranker_params + ) # Sort by combined score and get top k results sorted_items = sorted(combined_scores.items(), key=lambda x: x[1], reverse=True) From c59d8c504742d0457b215ae4b6cfc60a67b67595 Mon Sep 17 00:00:00 2001 From: Varsha Date: Wed, 3 Sep 2025 00:59:16 -0700 Subject: [PATCH 030/124] fix: Fix mock vector DB schema in Qdrant tests (#3295) # What does this PR do? Fix: https://github.com/llamastack/llama-stack/issues/3293 ## Test Plan ``` ===================================================== test session starts ===================================================== platform darwin -- Python 3.12.11, pytest-7.4.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.12.11', 'Platform': 'macOS-14.7.7-arm64-arm-64bit', 'Packages': {'pytest': '7.4.4', 'pluggy': '1.5.0'}, 'Plugins': {'asyncio': '0.23.8', 'cov': '6.0.0', 'timeout': '2.2.0', 'socket': '0.7.0', 'xdist': '3.8.0', 'html': '3.1.1', 'langsmith': '0.3.39', 'anyio': '4.8.0', 'metadata': '3.0.0'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: asyncio-0.23.8, cov-6.0.0, timeout-2.2.0, socket-0.7.0, xdist-3.8.0, html-3.1.1, langsmith-0.3.39, anyio-4.8.0, metadata-3.0.0 asyncio: mode=Mode.AUTO collected 3 items tests/unit/providers/vector_io/test_qdrant.py::test_qdrant_adapter_returns_expected_chunks[2-2] PASSED [ 33%] tests/unit/providers/vector_io/test_qdrant.py::test_qdrant_adapter_returns_expected_chunks[100-60] PASSED [ 66%] tests/unit/providers/vector_io/test_qdrant.py::test_qdrant_register_and_unregister_vector_db PASSED [100%] ``` Co-authored-by: Francisco Arceo --- tests/unit/providers/vector_io/test_qdrant.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/unit/providers/vector_io/test_qdrant.py b/tests/unit/providers/vector_io/test_qdrant.py index d7900dbfd..aab5b6f45 100644 --- a/tests/unit/providers/vector_io/test_qdrant.py +++ b/tests/unit/providers/vector_io/test_qdrant.py @@ -54,7 +54,9 @@ def mock_vector_db(vector_db_id) -> MagicMock: mock_vector_db.identifier = vector_db_id mock_vector_db.embedding_dimension = 384 mock_vector_db.model_dump_json.return_value = ( - '{"identifier": "' + vector_db_id + '", "embedding_model": "embedding_model", "embedding_dimension": 384}' + '{"identifier": "' + + vector_db_id + + '", "provider_id": "qdrant", "embedding_model": "embedding_model", "embedding_dimension": 384}' ) return mock_vector_db From ccaf6aaa511a544948a6cf8a99e13ca200852c40 Mon Sep 17 00:00:00 2001 From: Cesare Pompeiano Date: Wed, 3 Sep 2025 11:33:35 +0200 Subject: [PATCH 031/124] chore(python-deps): replace ibm_watson_machine_learning with ibm_watsonx_ai (#3302) # What does this PR do? This PR updates the Watsonx provider dependencies from `ibm_watson_machine_learning` to `ibm_watsonx_ai`. The old package `ibm_watson_machine_learning` is in **deprecation mode** ([[PyPI link](https://pypi.org/project/ibm-watson-machine-learning/)](https://pypi.org/project/ibm-watson-machine-learning/)) and relies on older versions of dependencies such as `pandas`. Updating to `ibm_watsonx_ai` ensures compatibility with current dependency versions and ongoing support. ## Test Plan I verified the update by running an inference using a model provided by Watsonx. The model ran successfully, confirming that the new dependency works as expected. Co-authored-by: are-ces --- llama_stack/providers/registry/inference.py | 2 +- llama_stack/providers/remote/inference/watsonx/watsonx.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 6264de7c7..fb841afdf 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -292,7 +292,7 @@ Available Models: api=Api.inference, adapter=AdapterSpec( adapter_type="watsonx", - pip_packages=["ibm_watson_machine_learning"], + pip_packages=["ibm_watsonx_ai"], module="llama_stack.providers.remote.inference.watsonx", config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig", provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py index 78161d1cb..cb7fc175f 100644 --- a/llama_stack/providers/remote/inference/watsonx/watsonx.py +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -7,8 +7,8 @@ from collections.abc import AsyncGenerator, AsyncIterator from typing import Any -from ibm_watson_machine_learning.foundation_models import Model -from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams +from ibm_watsonx_ai.foundation_models import Model +from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams from openai import AsyncOpenAI from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem From d948e63340b37d1dd5f13fa2bb2e380db0f337ec Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 3 Sep 2025 07:11:59 -0700 Subject: [PATCH 032/124] chore: Improve error message for missing provider dependencies (#3315) Generated with CC: Replace cryptic KeyError with clear, actionable error message that shows: - Which API the failing provider belongs to - The provider ID and type that's failing - Which dependency is missing - Clear instructions on how to fix the issue ## Test plan Use a run config with Agents API and no safety provider Before: KeyError: After: Failed to resolve 'agents' provider 'meta-reference' of type 'inline::meta-reference': required dependency 'safety' is not available. Please add a 'safety' provider to your configuration or check if the provider is properly configured. --- llama_stack/core/resolver.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/llama_stack/core/resolver.py b/llama_stack/core/resolver.py index 7ac98dac8..a8ad03e1a 100644 --- a/llama_stack/core/resolver.py +++ b/llama_stack/core/resolver.py @@ -284,7 +284,15 @@ async def instantiate_providers( if provider.provider_id is None: continue - deps = {a: impls[a] for a in provider.spec.api_dependencies} + try: + deps = {a: impls[a] for a in provider.spec.api_dependencies} + except KeyError as e: + missing_api = e.args[0] + raise RuntimeError( + f"Failed to resolve '{provider.spec.api.value}' provider '{provider.provider_id}' of type '{provider.spec.provider_type}': " + f"required dependency '{missing_api.value}' is not available. " + f"Please add a '{missing_api.value}' provider to your configuration or check if the provider is properly configured." + ) from e for a in provider.spec.optional_api_dependencies: if a in impls: deps[a] = impls[a] From c3d3a0b83333a720886c4a846e41ed3ad2766e00 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 3 Sep 2025 11:33:03 -0700 Subject: [PATCH 033/124] feat(tests): auto-merge all model list responses and unify recordings (#3320) One needed to specify record-replay related environment variables for running integration tests. We could not use defaults because integration tests could be run against Ollama instances which could be running different models. For example, text vs vision tests needed separate instances of Ollama because a single instance typically cannot serve both of these models if you assume the standard CI worker configuration on Github. As a result, `client.list()` as returned by the Ollama client would be different between these runs and we'd end up overwriting responses. This PR "solves" it by adding a small amount of complexity -- we store model list responses specially, keyed by the hashes of the models they return. At replay time, we merge all of them and pretend that we have the union of all models available. ## Test Plan Re-recorded all the tests using `scripts/integration-tests.sh --inference-mode record`, including the vision tests. --- .gitignore | 2 + .../contributing/testing/record-replay.md | 9 +- .../remote/inference/ollama/ollama.py | 8 +- llama_stack/testing/inference_recorder.py | 154 +- scripts/integration-tests.sh | 14 +- tests/README.md | 12 - tests/integration/README.md | 19 +- .../recordings/responses/00ba04f74a96.json | 10 +- .../recordings/responses/04172112ffbb.json | 44 +- .../recordings/responses/0b27fd737699.json | 10 +- .../recordings/responses/0b3f2e4754ff.json | 32 +- .../recordings/responses/0e8f2b001dd9.json | 12 +- .../recordings/responses/10eea8c15ddc.json | 10 +- .../recordings/responses/140187e305dc.json | 12 +- .../recordings/responses/17253d7cc667.json | 10 +- .../recordings/responses/173ecb3aab28.json | 32 +- .../recordings/responses/174458ad71b2.json | 10 +- 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.../vision/responses/3877ecf1bc62.json | 22 - .../vision/responses/4096743baf8e.json | 56 - .../vision/responses/4a3a4447b16b.json | 68 - .../vision/responses/67198cbad48f.json | 56 - .../vision/responses/830a1fe14938.json | 56 - .../vision/responses/9c007f300365.json | 58 - .../vision/responses/c9667519ad7c.json | 58 - .../vision/responses/d0ac68cbde69.json | 19 - .../vision/responses/d4f56d7d1996.json | 56 - .../distribution/test_inference_recordings.py | 2 +- 122 files changed, 17460 insertions(+), 16129 deletions(-) rename tests/integration/recordings/{vision => }/responses/f1592dee71e5.json (99%) rename tests/integration/recordings/{vision => }/responses/ff7db0102b28.json (98%) create mode 100644 tests/integration/recordings/responses/models-4a3a4447b16b-3057338f.json delete mode 100644 tests/integration/recordings/vision/index.sqlite delete mode 100644 tests/integration/recordings/vision/responses/3877ecf1bc62.json delete mode 100644 tests/integration/recordings/vision/responses/4096743baf8e.json delete mode 100644 tests/integration/recordings/vision/responses/4a3a4447b16b.json delete mode 100644 tests/integration/recordings/vision/responses/67198cbad48f.json delete mode 100644 tests/integration/recordings/vision/responses/830a1fe14938.json delete mode 100644 tests/integration/recordings/vision/responses/9c007f300365.json delete mode 100644 tests/integration/recordings/vision/responses/c9667519ad7c.json delete mode 100644 tests/integration/recordings/vision/responses/d0ac68cbde69.json delete mode 100644 tests/integration/recordings/vision/responses/d4f56d7d1996.json diff --git a/.gitignore b/.gitignore index f3831f29c..11cc59847 100644 --- a/.gitignore +++ b/.gitignore @@ -26,5 +26,7 @@ venv/ pytest-report.xml .coverage .python-version +AGENTS.md +server.log CLAUDE.md .claude/ diff --git a/docs/source/contributing/testing/record-replay.md b/docs/source/contributing/testing/record-replay.md index 3049d333c..7b0f345b0 100644 --- a/docs/source/contributing/testing/record-replay.md +++ b/docs/source/contributing/testing/record-replay.md @@ -40,18 +40,15 @@ The system patches OpenAI and Ollama client methods to intercept calls before th ### Storage Architecture -Recordings use a two-tier storage system optimized for both speed and debuggability: +Recordings are stored as JSON files in the recording directory. They are looked up by their request hash. ``` recordings/ -├── index.sqlite # Fast lookup by request hash └── responses/ ├── abc123def456.json # Individual response files └── def789ghi012.json ``` -**SQLite index** enables O(log n) hash lookups and metadata queries without loading response bodies. - **JSON files** store complete request/response pairs in human-readable format for debugging. ## Recording Modes @@ -166,8 +163,8 @@ This preserves type safety - when replayed, you get the same Pydantic objects wi Control recording behavior globally: ```bash -export LLAMA_STACK_TEST_INFERENCE_MODE=replay -export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings +export LLAMA_STACK_TEST_INFERENCE_MODE=replay # this is the default +export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings # default is tests/integration/recordings pytest tests/integration/ ``` diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py index fcaf5ee92..d3d107e1d 100644 --- a/llama_stack/providers/remote/inference/ollama/ollama.py +++ b/llama_stack/providers/remote/inference/ollama/ollama.py @@ -118,10 +118,10 @@ class OllamaInferenceAdapter( async def initialize(self) -> None: logger.info(f"checking connectivity to Ollama at `{self.config.url}`...") - health_response = await self.health() - if health_response["status"] == HealthStatus.ERROR: + r = await self.health() + if r["status"] == HealthStatus.ERROR: logger.warning( - "Ollama Server is not running, make sure to start it using `ollama serve` in a separate terminal" + f"Ollama Server is not running (message: {r['message']}). Make sure to start it using `ollama serve` in a separate terminal" ) async def should_refresh_models(self) -> bool: @@ -156,7 +156,7 @@ class OllamaInferenceAdapter( ), Model( identifier="nomic-embed-text", - provider_resource_id="nomic-embed-text", + provider_resource_id="nomic-embed-text:latest", provider_id=provider_id, metadata={ "embedding_dimension": 768, diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 8fa5f5f2e..5b64e26d3 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -30,6 +30,9 @@ from openai.types.completion_choice import CompletionChoice CompletionChoice.model_fields["finish_reason"].annotation = Literal["stop", "length", "content_filter"] | None CompletionChoice.model_rebuild() +REPO_ROOT = Path(__file__).parent.parent.parent +DEFAULT_STORAGE_DIR = REPO_ROOT / "tests/integration/recordings" + class InferenceMode(StrEnum): LIVE = "live" @@ -51,7 +54,7 @@ def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict def get_inference_mode() -> InferenceMode: - return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "live").lower()) + return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "replay").lower()) def setup_inference_recording(): @@ -60,28 +63,18 @@ def setup_inference_recording(): to increase their reliability and reduce reliance on expensive, external services. Currently, this is only supported for OpenAI and Ollama clients. These should cover the vast majority of use cases. - Calls to the /models endpoint are not currently trapped. We probably need to add support for this. - Two environment variables are required: - - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. - - LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. + Two environment variables are supported: + - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. Default is 'replay'. + - LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. Default is 'tests/integration/recordings'. - The recordings are stored in a SQLite database and a JSON file for each request. The SQLite database is used to - quickly find the correct recording for a given request. The JSON files are used to store the request and response - bodies. + The recordings are stored as JSON files. """ mode = get_inference_mode() - - if mode not in InferenceMode: - raise ValueError(f"Invalid LLAMA_STACK_TEST_INFERENCE_MODE: {mode}. Must be 'live', 'record', or 'replay'") - if mode == InferenceMode.LIVE: return None - if "LLAMA_STACK_TEST_RECORDING_DIR" not in os.environ: - raise ValueError("LLAMA_STACK_TEST_RECORDING_DIR must be set for recording or replaying") - storage_dir = os.environ["LLAMA_STACK_TEST_RECORDING_DIR"] - + storage_dir = os.environ.get("LLAMA_STACK_TEST_RECORDING_DIR", DEFAULT_STORAGE_DIR) return inference_recording(mode=mode, storage_dir=storage_dir) @@ -134,8 +127,8 @@ class ResponseStorage: def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]): """Store a request/response pair.""" # Generate unique response filename - response_file = f"{request_hash[:12]}.json" - response_path = self.responses_dir / response_file + short_hash = request_hash[:12] + response_file = f"{short_hash}.json" # Serialize response body if needed serialized_response = dict(response) @@ -147,6 +140,14 @@ class ResponseStorage: # Handle single response serialized_response["body"] = _serialize_response(serialized_response["body"]) + # If this is an Ollama /api/tags recording, include models digest in filename to distinguish variants + endpoint = request.get("endpoint") + if endpoint in ("/api/tags", "/v1/models"): + digest = _model_identifiers_digest(endpoint, response) + response_file = f"models-{short_hash}-{digest}.json" + + response_path = self.responses_dir / response_file + # Save response to JSON file with open(response_path, "w") as f: json.dump({"request": request, "response": serialized_response}, f, indent=2) @@ -161,19 +162,85 @@ class ResponseStorage: if not response_path.exists(): return None - with open(response_path) as f: - data = json.load(f) + return _recording_from_file(response_path) - # Deserialize response body if needed - if "response" in data and "body" in data["response"]: - if isinstance(data["response"]["body"], list): - # Handle streaming responses - data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]] + def _model_list_responses(self, short_hash: str) -> list[dict[str, Any]]: + results: list[dict[str, Any]] = [] + for path in self.responses_dir.glob(f"models-{short_hash}-*.json"): + data = _recording_from_file(path) + results.append(data) + return results + + +def _recording_from_file(response_path) -> dict[str, Any]: + with open(response_path) as f: + data = json.load(f) + + # Deserialize response body if needed + if "response" in data and "body" in data["response"]: + if isinstance(data["response"]["body"], list): + # Handle streaming responses + data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]] + else: + # Handle single response + data["response"]["body"] = _deserialize_response(data["response"]["body"]) + + return cast(dict[str, Any], data) + + +def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str: + def _extract_model_identifiers(): + """Extract a stable set of identifiers for model-list endpoints. + + Supported endpoints: + - '/api/tags' (Ollama): response body has 'models': [ { name/model/digest/id/... }, ... ] + - '/v1/models' (OpenAI): response body has 'data': [ { id: ... }, ... ] + Returns a list of unique identifiers or None if structure doesn't match. + """ + body = response["body"] + if endpoint == "/api/tags": + items = body.get("models") + idents = [m.model for m in items] + else: + items = body.get("data") + idents = [m.id for m in items] + return sorted(set(idents)) + + identifiers = _extract_model_identifiers() + return hashlib.sha1(("|".join(identifiers)).encode("utf-8")).hexdigest()[:8] + + +def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]]) -> dict[str, Any] | None: + """Return a single, unioned recording for supported model-list endpoints.""" + seen: dict[str, dict[str, Any]] = {} + for rec in records: + body = rec["response"]["body"] + if endpoint == "/api/tags": + items = body.models + elif endpoint == "/v1/models": + items = body.data + else: + items = [] + + for m in items: + if endpoint == "/v1/models": + key = m.id else: - # Handle single response - data["response"]["body"] = _deserialize_response(data["response"]["body"]) + key = m.model + seen[key] = m - return cast(dict[str, Any], data) + ordered = [seen[k] for k in sorted(seen.keys())] + canonical = records[0] + canonical_req = canonical.get("request", {}) + if isinstance(canonical_req, dict): + canonical_req["endpoint"] = endpoint + if endpoint == "/v1/models": + body = {"data": ordered, "object": "list"} + else: + from ollama import ListResponse + + body = ListResponse(models=ordered) + return {"request": canonical_req, "response": {"body": body, "is_streaming": False}} async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs): @@ -195,8 +262,6 @@ async def _patched_inference_method(original_method, self, client_type, endpoint raise ValueError(f"Unknown client type: {client_type}") url = base_url.rstrip("/") + endpoint - - # Normalize request for matching method = "POST" headers = {} body = kwargs @@ -204,7 +269,12 @@ async def _patched_inference_method(original_method, self, client_type, endpoint request_hash = normalize_request(method, url, headers, body) if _current_mode == InferenceMode.REPLAY: - recording = _current_storage.find_recording(request_hash) + # Special handling for model-list endpoints: return union of all responses + if endpoint in ("/api/tags", "/v1/models"): + records = _current_storage._model_list_responses(request_hash[:12]) + recording = _combine_model_list_responses(endpoint, records) + else: + recording = _current_storage.find_recording(request_hash) if recording: response_body = recording["response"]["body"] @@ -274,12 +344,14 @@ def patch_inference_clients(): from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions from openai.resources.embeddings import AsyncEmbeddings + from openai.resources.models import AsyncModels # Store original methods for both OpenAI and Ollama clients _original_methods = { "chat_completions_create": AsyncChatCompletions.create, "completions_create": AsyncCompletions.create, "embeddings_create": AsyncEmbeddings.create, + "models_list": AsyncModels.list, "ollama_generate": OllamaAsyncClient.generate, "ollama_chat": OllamaAsyncClient.chat, "ollama_embed": OllamaAsyncClient.embed, @@ -304,10 +376,16 @@ def patch_inference_clients(): _original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs ) + async def patched_models_list(self, *args, **kwargs): + return await _patched_inference_method( + _original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs + ) + # Apply OpenAI patches AsyncChatCompletions.create = patched_chat_completions_create AsyncCompletions.create = patched_completions_create AsyncEmbeddings.create = patched_embeddings_create + AsyncModels.list = patched_models_list # Create patched methods for Ollama client async def patched_ollama_generate(self, *args, **kwargs): @@ -361,11 +439,13 @@ def unpatch_inference_clients(): from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions from openai.resources.embeddings import AsyncEmbeddings + from openai.resources.models import AsyncModels # Restore OpenAI client methods AsyncChatCompletions.create = _original_methods["chat_completions_create"] AsyncCompletions.create = _original_methods["completions_create"] AsyncEmbeddings.create = _original_methods["embeddings_create"] + AsyncModels.list = _original_methods["models_list"] # Restore Ollama client methods if they were patched OllamaAsyncClient.generate = _original_methods["ollama_generate"] @@ -379,16 +459,10 @@ def unpatch_inference_clients(): @contextmanager -def inference_recording(mode: str = "live", storage_dir: str | Path | None = None) -> Generator[None, None, None]: +def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Generator[None, None, None]: """Context manager for inference recording/replaying.""" global _current_mode, _current_storage - # Set defaults - if storage_dir is None: - storage_dir_path = Path.home() / ".llama" / "recordings" - else: - storage_dir_path = Path(storage_dir) - # Store previous state prev_mode = _current_mode prev_storage = _current_storage @@ -397,7 +471,9 @@ def inference_recording(mode: str = "live", storage_dir: str | Path | None = Non _current_mode = mode if mode in ["record", "replay"]: - _current_storage = ResponseStorage(storage_dir_path) + if storage_dir is None: + raise ValueError("storage_dir is required for record and replay modes") + _current_storage = ResponseStorage(Path(storage_dir)) patch_inference_clients() yield diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index e152444e1..104ba5cf3 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -140,13 +140,6 @@ THIS_DIR=$(dirname "$0") ROOT_DIR="$THIS_DIR/.." cd $ROOT_DIR -# Set recording directory -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - export LLAMA_STACK_TEST_RECORDING_DIR="tests/integration/recordings/vision" -else - export LLAMA_STACK_TEST_RECORDING_DIR="tests/integration/recordings" -fi - # check if "llama" and "pytest" are available. this script does not use `uv run` given # it can be used in a pre-release environment where we have not been able to tell # uv about pre-release dependencies properly (yet). @@ -298,5 +291,12 @@ echo "=== System Resources After Tests ===" free -h 2>/dev/null || echo "free command not available" df -h +# stop server +if [[ "$STACK_CONFIG" == *"server:"* ]]; then + echo "Stopping Llama Stack Server..." + kill $(lsof -i :8321 | awk 'NR>1 {print $2}') + echo "Llama Stack Server stopped" +fi + echo "" echo "=== Integration Tests Complete ===" diff --git a/tests/README.md b/tests/README.md index 3b129fbd9..81f025f86 100644 --- a/tests/README.md +++ b/tests/README.md @@ -38,26 +38,15 @@ For running integration tests, you must provide a few things: - a distribution name (e.g., `starter`) or a path to a `run.yaml` file - a comma-separated list of api=provider pairs, e.g. `inference=fireworks,safety=llama-guard,agents=meta-reference`. This is most useful for testing a single API surface. -- Whether you are using replay or live mode for inference. This is specified with the LLAMA_STACK_TEST_INFERENCE_MODE environment variable. The default mode currently is "live" -- that is certainly surprising, but we will fix this soon. - - Any API keys you need to use should be set in the environment, or can be passed in with the --env option. You can run the integration tests in replay mode with: ```bash # Run all tests with existing recordings -LLAMA_STACK_TEST_INFERENCE_MODE=replay \ - LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ uv run --group test \ pytest -sv tests/integration/ --stack-config=starter ``` -If you don't specify LLAMA_STACK_TEST_INFERENCE_MODE, by default it will be in "live" mode -- that is, it will make real API calls. - -```bash -# Test against live APIs -FIREWORKS_API_KEY=your_key pytest -sv tests/integration/inference --stack-config=starter -``` - ### Re-recording tests #### Local Re-recording (Manual Setup Required) @@ -66,7 +55,6 @@ If you want to re-record tests locally, you can do so with: ```bash LLAMA_STACK_TEST_INFERENCE_MODE=record \ - LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ uv run --group test \ pytest -sv tests/integration/ --stack-config=starter -k "" ``` diff --git a/tests/integration/README.md b/tests/integration/README.md index 46d66fd79..d177cbebf 100644 --- a/tests/integration/README.md +++ b/tests/integration/README.md @@ -98,29 +98,25 @@ pytest -s -v tests/integration/vector_io/ \ The testing system supports three modes controlled by environment variables: -### LIVE Mode (Default) -Tests make real API calls: +### REPLAY Mode (Default) +Uses cached responses instead of making API calls: ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/ +pytest tests/integration/ ``` - ### RECORD Mode Captures API interactions for later replay: ```bash LLAMA_STACK_TEST_INFERENCE_MODE=record \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ pytest tests/integration/inference/test_new_feature.py ``` -### REPLAY Mode -Uses cached responses instead of making API calls: +### LIVE Mode +Tests make real API calls (but not recorded): ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=replay \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ -pytest tests/integration/ +LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/ ``` -Note that right now you must specify the recording directory. This is because different tests use different recording directories and we don't (yet) have a fool-proof way to map a test to a recording directory. We are working on this. +By default, the recording directory is `tests/integration/recordings`. You can override this by setting the `LLAMA_STACK_TEST_RECORDING_DIR` environment variable. ## Managing Recordings @@ -146,7 +142,6 @@ See the [main testing guide](../README.md#remote-re-recording-recommended) for f ```bash # Re-record specific tests LLAMA_STACK_TEST_INFERENCE_MODE=record \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py ``` diff --git a/tests/integration/recordings/responses/00ba04f74a96.json b/tests/integration/recordings/responses/00ba04f74a96.json index d2e482d76..642c58414 100644 --- a/tests/integration/recordings/responses/00ba04f74a96.json +++ b/tests/integration/recordings/responses/00ba04f74a96.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.860911Z", + "created_at": "2025-09-03T17:37:35.23084Z", "done": true, "done_reason": "stop", - "total_duration": 249137667, - "load_duration": 152509542, + "total_duration": 195981375, + "load_duration": 110522917, "prompt_eval_count": 216, - "prompt_eval_duration": 71000000, + "prompt_eval_duration": 72393958, "eval_count": 2, - "eval_duration": 24000000, + "eval_duration": 11843000, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/04172112ffbb.json b/tests/integration/recordings/responses/04172112ffbb.json index bf94b0697..da5f58a50 100644 --- a/tests/integration/recordings/responses/04172112ffbb.json +++ b/tests/integration/recordings/responses/04172112ffbb.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.033900164Z", + "created_at": "2025-09-03T17:41:43.950283Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.213371151Z", + "created_at": "2025-09-03T17:41:43.991122Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.387513976Z", + "created_at": "2025-09-03T17:41:44.031378Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.564344287Z", + "created_at": "2025-09-03T17:41:44.073098Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.746579415Z", + "created_at": "2025-09-03T17:41:44.115961Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.923276047Z", + "created_at": "2025-09-03T17:41:44.156517Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.099961963Z", + "created_at": "2025-09-03T17:41:44.197079Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.275621884Z", + "created_at": "2025-09-03T17:41:44.237565Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.452204196Z", + "created_at": "2025-09-03T17:41:44.277755Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.626937514Z", + "created_at": "2025-09-03T17:41:44.318476Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.805566767Z", + "created_at": "2025-09-03T17:41:44.358628Z", "done": false, "done_reason": null, "total_duration": null, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.985987477Z", + "created_at": "2025-09-03T17:41:44.398984Z", "done": false, "done_reason": null, "total_duration": null, @@ -237,7 +237,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.166458601Z", + "created_at": "2025-09-03T17:41:44.439232Z", "done": false, "done_reason": null, "total_duration": null, @@ -255,7 +255,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.343346795Z", + "created_at": "2025-09-03T17:41:44.479478Z", "done": false, "done_reason": null, "total_duration": null, @@ -273,7 +273,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.525008091Z", + "created_at": "2025-09-03T17:41:44.520202Z", "done": false, "done_reason": null, "total_duration": null, @@ -291,7 +291,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.709087695Z", + "created_at": "2025-09-03T17:41:44.560517Z", "done": false, "done_reason": null, "total_duration": null, @@ -309,7 +309,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.887074305Z", + "created_at": "2025-09-03T17:41:44.601592Z", "done": false, "done_reason": null, "total_duration": null, @@ -327,15 +327,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:21.065244925Z", + "created_at": "2025-09-03T17:41:44.642064Z", "done": true, "done_reason": "stop", - "total_duration": 4373531496, - "load_duration": 44438132, + "total_duration": 887142667, + "load_duration": 119331417, "prompt_eval_count": 56, - "prompt_eval_duration": 1296273199, + "prompt_eval_duration": 74294709, "eval_count": 18, - "eval_duration": 3032321735, + "eval_duration": 692842791, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/0b27fd737699.json b/tests/integration/recordings/responses/0b27fd737699.json index e20c65c75..e25cde820 100644 --- a/tests/integration/recordings/responses/0b27fd737699.json +++ b/tests/integration/recordings/responses/0b27fd737699.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:13:57.556416Z", + "created_at": "2025-09-03T17:37:47.461886Z", "done": true, "done_reason": "stop", - "total_duration": 432363250, - "load_duration": 159296417, + "total_duration": 338927833, + "load_duration": 100895125, "prompt_eval_count": 223, - "prompt_eval_duration": 257000000, + "prompt_eval_duration": 221583042, "eval_count": 2, - "eval_duration": 14000000, + "eval_duration": 12341416, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/0b3f2e4754ff.json b/tests/integration/recordings/responses/0b3f2e4754ff.json index 28e923e9c..8496deeb0 100644 --- a/tests/integration/recordings/responses/0b3f2e4754ff.json +++ b/tests/integration/recordings/responses/0b3f2e4754ff.json @@ -24,7 +24,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -39,7 +39,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921333, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -50,7 +50,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -65,7 +65,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921333, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -76,7 +76,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -91,7 +91,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921333, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -102,7 +102,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -117,7 +117,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921333, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -128,7 +128,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -143,7 +143,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921334, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -154,7 +154,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -169,7 +169,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921334, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -180,7 +180,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -195,7 +195,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921334, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -206,7 +206,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -221,7 +221,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921334, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/0e8f2b001dd9.json b/tests/integration/recordings/responses/0e8f2b001dd9.json index 7c5973fae..6bcdfdfed 100644 --- a/tests/integration/recordings/responses/0e8f2b001dd9.json +++ b/tests/integration/recordings/responses/0e8f2b001dd9.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-368", + "id": "chatcmpl-161", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "Saturn is known for its extensive ring system.", + "content": "The answer is Saturn.", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754081853, + "created": 1756921364, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 11, + "completion_tokens": 6, "prompt_tokens": 39, - "total_tokens": 50, + "total_tokens": 45, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/10eea8c15ddc.json b/tests/integration/recordings/responses/10eea8c15ddc.json index 71496da9a..bc608ef09 100644 --- a/tests/integration/recordings/responses/10eea8c15ddc.json +++ b/tests/integration/recordings/responses/10eea8c15ddc.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:51.682357Z", + "created_at": "2025-09-03T17:37:33.473237Z", "done": true, "done_reason": "stop", - "total_duration": 238161000, - "load_duration": 72494750, + "total_duration": 279025042, + "load_duration": 162673250, "prompt_eval_count": 212, - "prompt_eval_duration": 87000000, + "prompt_eval_duration": 73595834, "eval_count": 5, - "eval_duration": 74000000, + "eval_duration": 41950291, "response": "unsafe\nS8", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/140187e305dc.json b/tests/integration/recordings/responses/140187e305dc.json index 44d00c96f..69b9712eb 100644 --- a/tests/integration/recordings/responses/140187e305dc.json +++ b/tests/integration/recordings/responses/140187e305dc.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-876", + "id": "chatcmpl-974", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'm afraid I don't have a built-in ability to directly interface with or \"test\" OpenAI models, including the original GPT-1 model. However, I can explain how you might approach this task:\n\nThe OpenAI GPT-1 is a large transformer-based language model that was trained on a massive dataset of text and achieved state-of-the-art results in various natural language processing tasks.\n\nTo test or evaluate the performance of a model like GPT-1, you would typically follow these steps:\n\n1. **Get access to the OpenAI API**: The OpenAI API provides a way for developers to interact with the GPT-1 model programmatically. You can sign up for an API key on the OpenAI website.\n2. **Choose a testing platform or environment**: You'll need a compute platform that supports the necessary algorithms and data structures to run inference on the GPT-1 model. Some popular options include AWS, Google Cloud, or Azure Compute Virtual Machines.\n3. **Prepare your test input data**: This will involve creating text inputs in the format expected by the OpenAI API (i.e., a JSON object containing the text to be processed).\n4. **Use the OpenAI Python library or SDK**: The OpenAI Python library provides an easy-to-use interface for interacting with the GPT-1 model through the API.\n\nHere's some example code that demonstrates how you might use the OpenAI Flask API to test a single input:\n\n```python\nfrom flask import Flask, request, jsonify\nimport json\n\napp = Flask(__name__)\n\n@ app . route ( '/ /gpt-en ', ' Text ', methods = ['POST'])\ndef gpt_en () -> Json :\n data = request . get_json ()\n if not data or \"message\" in ( data ):\n return None , 400 , { ' error' : \"Input must be a text string.\" }\n response = []\n while True:\n message = \"\"\n for token in data [\"input\"]:\n response_text = f\"{data['prompt']} {token}\"\n data[\"input\"] = [response_text]\n new_response = gpt_en()(data)\n if all([not item or not isinstance(item, dict) for item in new_response]):\n break\n\n message = json . dumps ({}\"text\": response_text})\n response.append(message)\n\n return jsonify ({\"output\": response}), 200 , {}\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n```\n\n5. **Evaluate the output**: Once you have processed your test input data using the GPT-1 model, you can evaluate the accuracy of the generated responses.\n\nKeep in mind that this is just a basic example to illustrate how you might approach testing the OpenAI GPT-1 model.", + "content": "I'm happy to help you test the OpenAI API, however I can not access the API.\n\nInstead why don't we follow these steps:\n\n* Check documentation\n* Contact support\n* Reach out to their community forum. \n\nLet me know if I can be of any additional assistance", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510050, + "created": 1756921202, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 567, + "completion_tokens": 61, "prompt_tokens": 31, - "total_tokens": 598, + "total_tokens": 92, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/17253d7cc667.json b/tests/integration/recordings/responses/17253d7cc667.json index 1013a8b08..290c0395b 100644 --- a/tests/integration/recordings/responses/17253d7cc667.json +++ b/tests/integration/recordings/responses/17253d7cc667.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.919624Z", + "created_at": "2025-09-03T17:37:34.308033Z", "done": true, "done_reason": "stop", - "total_duration": 201956834, - "load_duration": 105132584, + "total_duration": 200296000, + "load_duration": 115974708, "prompt_eval_count": 212, - "prompt_eval_duration": 75000000, + "prompt_eval_duration": 72173459, "eval_count": 2, - "eval_duration": 20000000, + "eval_duration": 11536750, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/173ecb3aab28.json b/tests/integration/recordings/responses/173ecb3aab28.json index bc550edd5..0c29b278b 100644 --- a/tests/integration/recordings/responses/173ecb3aab28.json +++ b/tests/integration/recordings/responses/173ecb3aab28.json @@ -40,7 +40,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -55,7 +55,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -66,7 +66,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -81,7 +81,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -92,7 +92,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -107,7 +107,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -118,7 +118,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -133,7 +133,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -144,7 +144,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -159,7 +159,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -170,7 +170,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -185,7 +185,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -196,7 +196,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -211,7 +211,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -222,7 +222,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -237,7 +237,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/174458ad71b2.json b/tests/integration/recordings/responses/174458ad71b2.json index 2dcb85262..ba99d54e6 100644 --- a/tests/integration/recordings/responses/174458ad71b2.json +++ b/tests/integration/recordings/responses/174458ad71b2.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.580806Z", + "created_at": "2025-09-03T17:37:34.994704Z", "done": true, "done_reason": "stop", - "total_duration": 205732750, - "load_duration": 98967000, + "total_duration": 339570875, + "load_duration": 262794125, "prompt_eval_count": 213, - "prompt_eval_duration": 86000000, + "prompt_eval_duration": 64061000, "eval_count": 2, - "eval_duration": 18000000, + "eval_duration": 11839042, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/178016edef0e.json b/tests/integration/recordings/responses/178016edef0e.json index be545c221..83746aa33 100644 --- a/tests/integration/recordings/responses/178016edef0e.json +++ b/tests/integration/recordings/responses/178016edef0e.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.354566Z", + "created_at": "2025-09-03T17:37:33.769233Z", "done": true, "done_reason": "stop", - "total_duration": 605192500, - "load_duration": 457087166, + "total_duration": 253836584, + "load_duration": 138624959, "prompt_eval_count": 210, - "prompt_eval_duration": 63000000, + "prompt_eval_duration": 69496125, "eval_count": 5, - "eval_duration": 84000000, + "eval_duration": 45062833, "response": "unsafe\nS12", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/197228e26971.json b/tests/integration/recordings/responses/197228e26971.json index 6c1730df2..4fa9e2126 100644 --- a/tests/integration/recordings/responses/197228e26971.json +++ b/tests/integration/recordings/responses/197228e26971.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.686478Z", + "created_at": "2025-09-03T17:37:34.074233Z", "done": true, "done_reason": "stop", - "total_duration": 304136208, - "load_duration": 155977000, + "total_duration": 270746375, + "load_duration": 156423042, "prompt_eval_count": 213, - "prompt_eval_duration": 71000000, + "prompt_eval_duration": 70338083, "eval_count": 5, - "eval_duration": 76000000, + "eval_duration": 43379167, "response": "unsafe\nS2", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/198ef7208389.json b/tests/integration/recordings/responses/198ef7208389.json index b196d3be2..f0f9d6a7d 100644 --- a/tests/integration/recordings/responses/198ef7208389.json +++ b/tests/integration/recordings/responses/198ef7208389.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:51.186501Z", + "created_at": "2025-09-03T17:37:32.84197Z", "done": true, "done_reason": "stop", - "total_duration": 3146184459, - "load_duration": 2533467917, + "total_duration": 21572898667, + "load_duration": 21155275042, "prompt_eval_count": 212, - "prompt_eval_duration": 526000000, + "prompt_eval_duration": 371898125, "eval_count": 5, - "eval_duration": 83000000, + "eval_duration": 43290458, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1adfaa0e062e.json b/tests/integration/recordings/responses/1adfaa0e062e.json index 5a3d44394..253c230d9 100644 --- a/tests/integration/recordings/responses/1adfaa0e062e.json +++ b/tests/integration/recordings/responses/1adfaa0e062e.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.332041Z", + "created_at": "2025-09-03T17:37:34.607413Z", "done": true, "done_reason": "stop", - "total_duration": 365895333, - "load_duration": 257825208, + "total_duration": 267812042, + "load_duration": 181570000, "prompt_eval_count": 213, - "prompt_eval_duration": 78000000, + "prompt_eval_duration": 73947375, "eval_count": 2, - "eval_duration": 28000000, + "eval_duration": 11708000, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1b8394f90636.json b/tests/integration/recordings/responses/1b8394f90636.json index f5885805b..6857c6840 100644 --- a/tests/integration/recordings/responses/1b8394f90636.json +++ b/tests/integration/recordings/responses/1b8394f90636.json @@ -22,15 +22,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:05.685988Z", + "created_at": "2025-09-03T17:36:13.821929Z", "done": true, "done_reason": "stop", - "total_duration": 14128980625, - "load_duration": 7220159208, + "total_duration": 1907912167, + "load_duration": 90979292, "prompt_eval_count": 18, - "prompt_eval_duration": 4658000000, + "prompt_eval_duration": 77350291, "eval_count": 43, - "eval_duration": 2224000000, + "eval_duration": 1738568334, "response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1b92be674e2a.json b/tests/integration/recordings/responses/1b92be674e2a.json index 2ed061949..e5f05bf54 100644 --- a/tests/integration/recordings/responses/1b92be674e2a.json +++ b/tests/integration/recordings/responses/1b92be674e2a.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:50:06.140190726Z", + "created_at": "2025-09-03T17:39:38.236797Z", "done": true, "done_reason": "stop", - "total_duration": 5213341378, - "load_duration": 43943569, + "total_duration": 1296281500, + "load_duration": 283393917, "prompt_eval_count": 23, - "prompt_eval_duration": 1049424427, + "prompt_eval_duration": 75453042, "eval_count": 24, - "eval_duration": 4119422888, + "eval_duration": 936860125, "response": "Mark Zuckerberg is the founder, chairman and CEO of Meta, which he originally founded as Facebook in 2004.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1e11c2b20ff8.json b/tests/integration/recordings/responses/1e11c2b20ff8.json index 98e855fdf..6131b1d5e 100644 --- a/tests/integration/recordings/responses/1e11c2b20ff8.json +++ b/tests/integration/recordings/responses/1e11c2b20ff8.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - 0.042499725, - -0.061890375, - -0.07846951, - 0.006408736, - 0.031287834, - 0.008066364, - 0.058032244, - 0.025457833, - 0.016401615, - 0.04601607, - -0.028947692, - 0.04452766, - 0.056886304, - -0.0153307365, - -0.070184045, - -0.057157565, - -0.0768682, - 0.0067744707, - 0.0043326365, - -0.1236485, - 0.0031424984, - -0.032562014, - -0.029376298, - 0.024144078, - -0.028531333, - 0.102257624, - 0.0021518522, - -0.0069792354, - 0.02530627, - -0.055496883, - 0.031227645, - -0.0070384145, - 0.08432449, - -0.028390806, - -0.083012834, - 0.009549195, - -0.020060178, - -0.00240923, - -0.007700305, - 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Earth.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/23506e73bb9e.json b/tests/integration/recordings/responses/23506e73bb9e.json index d6e34c3f9..20ec9f1d1 100644 --- a/tests/integration/recordings/responses/23506e73bb9e.json +++ b/tests/integration/recordings/responses/23506e73bb9e.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.055977955, - 0.075997174, - -0.09249559, - 0.014318654, - 0.05876127, - -0.032458965, - 0.020946832, - 0.028819378, - -0.06590933, - 0.013517223, - 0.13000485, - 0.0045786807, - -0.0069082035, - -0.055431433, - -0.04756826, - -0.02912152, - -0.12239366, - -0.05359766, - -0.014712379, - 0.059826344, - 0.034466766, - 0.02072927, - -0.048724595, - 0.013531463, - 0.05862551, - -0.0030636105, - -0.031532496, - 0.08256397, - -0.031230088, - -0.12059464, - 0.03833127, - 0.06573049, - 0.064165965, - 0.03838281, - 0.12570563, - 0.031128457, - 0.10817016, - -0.001977333, - -0.024726717, - 0.028785817, - 0.012688804, 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-58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:01.99605Z", + "created_at": "2025-09-03T17:37:50.519952Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:02.049974Z", + "created_at": "2025-09-03T17:37:50.561433Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:02.102027Z", + "created_at": "2025-09-03T17:37:50.603624Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:02.158416Z", + "created_at": 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"llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:11.991247Z", + "created_at": "2025-09-03T17:37:56.609308Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.043953Z", + "created_at": "2025-09-03T17:37:56.651314Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.096001Z", + "created_at": "2025-09-03T17:37:56.693185Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.150454Z", + "created_at": "2025-09-03T17:37:56.734643Z", "done": false, "done_reason": null, "total_duration": null, @@ 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null } ], - "created": 1754081854, + "created": 1756921366, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/382c2f22274c.json b/tests/integration/recordings/responses/382c2f22274c.json index 6d05649a5..eb4a24f47 100644 --- a/tests/integration/recordings/responses/382c2f22274c.json +++ b/tests/integration/recordings/responses/382c2f22274c.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -22,14 +22,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-339", + "id": "chatcmpl-442", "choices": [ { "finish_reason": "length", "index": 0, "logprobs": null, "message": { - "content": "I can guide you through the process, but please note that this is not an official OpenAI API call. OpenAI's API terms and conditions prohibit using their models for malicious purposes.\n\nTo test a model like \"text-temperature\" with a temperature of 0 (i.e., no noise or randomness), we'll need to use a third-party library that connects to the OpenAI API. One such library is `transformers`.\n\nFirst, you need to install the `transformers` and `", + "content": "I can guide you on how to use the `test-temperature` parameter with OpenAI's API, but please note that using a temperature of 0 may not produce meaningful results. Temperature is a hyperparameter that controls the level of randomness in the model's output.\n\nOpenAI's API uses a variant of the GPT-3 model, which is trained on a large corpus of text data. The `test-temperature` parameter allows you to adjust the level of randomness in the model's output", "refusal": null, "role": "assistant", "annotations": null, @@ -39,7 +39,7 @@ } } ], - "created": 1754510065, + "created": 1756921254, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/3c0bf9ba81b2.json b/tests/integration/recordings/responses/3c0bf9ba81b2.json index 1b5f16c22..3d2b85e8d 100644 --- a/tests/integration/recordings/responses/3c0bf9ba81b2.json +++ b/tests/integration/recordings/responses/3c0bf9ba81b2.json @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-651", + "id": "chatcmpl-334", "choices": [ { "finish_reason": "length", "index": 0, "logprobs": null, "message": { - "content": "I'm ready to help", + "content": "It looks like we've", "refusal": null, "role": "assistant", "annotations": null, @@ -37,7 +37,7 @@ } } ], - "created": 1755294941, + "created": 1756921086, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/3c3f13cb7794.json b/tests/integration/recordings/responses/3c3f13cb7794.json index a1f240a9c..117fbcceb 100644 --- a/tests/integration/recordings/responses/3c3f13cb7794.json +++ b/tests/integration/recordings/responses/3c3f13cb7794.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.338232Z", + "created_at": "2025-09-03T17:36:18.136699Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.39419Z", + "created_at": "2025-09-03T17:36:18.177622Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.445346Z", + "created_at": "2025-09-03T17:36:18.218104Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.496701Z", + "created_at": "2025-09-03T17:36:18.258837Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.546804Z", + "created_at": "2025-09-03T17:36:18.299715Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.601009Z", + "created_at": "2025-09-03T17:36:18.341602Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.652788Z", + "created_at": "2025-09-03T17:36:18.385504Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.703325Z", + "created_at": "2025-09-03T17:36:18.429427Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.754033Z", + "created_at": "2025-09-03T17:36:18.473547Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.804654Z", + "created_at": "2025-09-03T17:36:18.516327Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,15 +201,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.854841Z", + "created_at": "2025-09-03T17:36:18.559332Z", "done": true, "done_reason": "stop", - "total_duration": 652371000, - "load_duration": 42086042, + "total_duration": 628034000, + "load_duration": 116384417, "prompt_eval_count": 26, - "prompt_eval_duration": 78000000, + "prompt_eval_duration": 87798792, "eval_count": 11, - "eval_duration": 531000000, + "eval_duration": 423189583, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/3ca695048bee.json b/tests/integration/recordings/responses/3ca695048bee.json index bed6762e7..b307b2f98 100644 --- a/tests/integration/recordings/responses/3ca695048bee.json +++ b/tests/integration/recordings/responses/3ca695048bee.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -39,7 +39,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-490", + "id": "chatcmpl-3", "choices": [ { "delta": { @@ -50,7 +50,7 @@ "tool_calls": [ { "index": 0, - "id": "call_rolv1ozt", + "id": "call_3kigugt3", "function": { "arguments": "{\"city\":\"Tokyo\"}", "name": "get_weather" @@ -64,7 +64,7 @@ "logprobs": null } ], - "created": 1754081852, + "created": 1756921361, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -75,7 +75,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-490", + "id": "chatcmpl-3", "choices": [ { "delta": { @@ -85,12 +85,12 @@ "role": "assistant", "tool_calls": null }, - "finish_reason": "stop", + "finish_reason": "tool_calls", "index": 0, "logprobs": null } ], - "created": 1754081852, + "created": 1756921361, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/3dff18060ebc.json b/tests/integration/recordings/responses/3dff18060ebc.json index e04bb8be7..c3da2998e 100644 --- a/tests/integration/recordings/responses/3dff18060ebc.json +++ b/tests/integration/recordings/responses/3dff18060ebc.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.060643002, - 0.063731536, - -0.059394535, - -0.010293381, - -0.119798504, - 0.033409704, - 0.056838214, - -0.006487789, - 0.029893834, - -0.05035498, - 0.015207984, - -0.0634482, - 0.015118864, - -0.08356639, - 0.009297568, - 0.04425259, - -0.02442732, - -0.050995167, - -0.028106945, 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b/tests/integration/recordings/responses/44a1d9de0602.json @@ -20,7 +20,7 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-987", + "id": "chatcmpl-507", "choices": [ { "finish_reason": "length", @@ -37,7 +37,7 @@ } } ], - "created": 1755294921, + "created": 1756921150, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/44fb9cf5875f.json b/tests/integration/recordings/responses/44fb9cf5875f.json index c7b0333f2..17c538862 100644 --- a/tests/integration/recordings/responses/44fb9cf5875f.json +++ b/tests/integration/recordings/responses/44fb9cf5875f.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:42.166585642Z", + "created_at": "2025-09-03T17:41:49.581065Z", "done": true, "done_reason": "stop", - "total_duration": 9490295253, - "load_duration": 42349084, + "total_duration": 2391571708, + "load_duration": 182022958, "prompt_eval_count": 20, - "prompt_eval_duration": 545470166, + "prompt_eval_duration": 74456583, "eval_count": 51, - "eval_duration": 8901928284, + "eval_duration": 2134471458, "response": "It seems like you're trying to test the system, but I'm not sure what specific functionality or feature you'd like to test. Could you please provide more context or clarify what you're looking for? I'll do my best to assist you!", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/48d2fb183a2a.json b/tests/integration/recordings/responses/48d2fb183a2a.json index c8fbcb07d..1b5ee286c 100644 --- a/tests/integration/recordings/responses/48d2fb183a2a.json +++ b/tests/integration/recordings/responses/48d2fb183a2a.json @@ -67,15 +67,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:40.583477Z", + "created_at": "2025-09-03T17:36:40.283084Z", "done": true, "done_reason": "stop", - "total_duration": 3928481500, - "load_duration": 151903250, + "total_duration": 2900042958, + "load_duration": 83372125, "prompt_eval_count": 259, - "prompt_eval_duration": 468000000, + "prompt_eval_duration": 352890750, "eval_count": 60, - "eval_duration": 3306000000, + "eval_duration": 2462885208, "response": "{\n \"first_name\": \"Michael\",\n \"last_name\": \"Jordan\",\n \"year_of_birth\": 1963,\n \"nba_stats\": {\n \"year_for_draft\": 1984,\n \"num_seasons_in_nba\": 15\n }\n}", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/50340cd4d253.json b/tests/integration/recordings/responses/50340cd4d253.json index f35923c06..3101fa9d8 100644 --- a/tests/integration/recordings/responses/50340cd4d253.json +++ b/tests/integration/recordings/responses/50340cd4d253.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:19.298378Z", + "created_at": "2025-09-03T17:38:01.239743Z", "done": true, "done_reason": "stop", - "total_duration": 266786083, - "load_duration": 53820458, + "total_duration": 207264667, + "load_duration": 73437959, "prompt_eval_count": 216, - "prompt_eval_duration": 192000000, + "prompt_eval_duration": 121657333, "eval_count": 2, - "eval_duration": 17000000, + "eval_duration": 11348417, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/5370751803dc.json b/tests/integration/recordings/responses/5370751803dc.json index 1edae9956..af1d8efab 100644 --- a/tests/integration/recordings/responses/5370751803dc.json +++ b/tests/integration/recordings/responses/5370751803dc.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.07649938, - 0.021244217, - -0.036287725, - -0.0011695292, - -0.048568938, - -0.13184524, - -0.08424354, - 0.059378363, - -0.06171173, - -0.009400254, - -0.08092405, - 0.05547966, - 0.05243954, - 0.026002606, - 0.06304219, - -0.062263194, - -0.06520713, - -0.022376515, - 0.017407224, - -0.11619268, - -0.03641897, - 0.04050772, - -0.032505907, - -0.017739171, - 0.057254575, - 0.012360873, - -0.018550506, - -0.029990712, - 0.00235547, - 0.0067841834, - -0.088615544, - 0.07800687, - 0.037015557, - 0.029492933, - -0.019656634, - 0.054334868, - -0.0006793985, - -0.08961444, - -0.05305694, - -0.012659472, - -0.0860912, - 0.07697376, - 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"ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.736161Z", + "created_at": "2025-09-03T17:42:32.710947Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.809857Z", + "created_at": "2025-09-03T17:42:32.752286Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.883599Z", + "created_at": "2025-09-03T17:42:32.793309Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.942471Z", + "created_at": "2025-09-03T17:42:32.834578Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.999844Z", + "created_at": "2025-09-03T17:42:32.876536Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.050862Z", + "created_at": "2025-09-03T17:42:32.918807Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.104589Z", + "created_at": "2025-09-03T17:42:32.960101Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.158301Z", + "created_at": "2025-09-03T17:42:33.00196Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.210985Z", + "created_at": "2025-09-03T17:42:33.043876Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.263525Z", + "created_at": "2025-09-03T17:42:33.08756Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,15 +238,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.314455Z", + "created_at": "2025-09-03T17:42:33.12966Z", "done": true, "done_reason": "stop", - "total_duration": 914060542, - "load_duration": 63705209, + "total_duration": 648814958, + "load_duration": 75300875, "prompt_eval_count": 408, - "prompt_eval_duration": 95000000, + "prompt_eval_duration": 66740291, "eval_count": 13, - "eval_duration": 753000000, + "eval_duration": 505313125, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/554de3cd986f.json b/tests/integration/recordings/responses/554de3cd986f.json index 990de1928..7a359c50e 100644 --- a/tests/integration/recordings/responses/554de3cd986f.json +++ b/tests/integration/recordings/responses/554de3cd986f.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.40585Z", + "created_at": "2025-09-03T17:37:51.805591Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.455647Z", + "created_at": "2025-09-03T17:37:51.850067Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.509581Z", + "created_at": "2025-09-03T17:37:51.892443Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.56592Z", + "created_at": "2025-09-03T17:37:51.934364Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.616979Z", + "created_at": "2025-09-03T17:37:51.978382Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.671413Z", + "created_at": "2025-09-03T17:37:52.019332Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.725494Z", + "created_at": "2025-09-03T17:37:52.060708Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.779905Z", + "created_at": "2025-09-03T17:37:52.102717Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.829791Z", + "created_at": "2025-09-03T17:37:52.143996Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.880729Z", + "created_at": "2025-09-03T17:37:52.185479Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.93338Z", + "created_at": "2025-09-03T17:37:52.227562Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.981714Z", + "created_at": "2025-09-03T17:37:52.270178Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:05.036068Z", + "created_at": "2025-09-03T17:37:52.31151Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:05.088069Z", + "created_at": "2025-09-03T17:37:52.35278Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:05.144485Z", + "created_at": "2025-09-03T17:37:52.393954Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:05.203042Z", + "created_at": "2025-09-03T17:37:52.435238Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": 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19, - "eval_duration": 967000000, + "eval_duration": 755260750, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/561746e1c8de.json b/tests/integration/recordings/responses/561746e1c8de.json index 120f40661..1bb8a3345 100644 --- a/tests/integration/recordings/responses/561746e1c8de.json +++ b/tests/integration/recordings/responses/561746e1c8de.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-15T20:24:49.18651486Z", + "created_at": "2025-09-03T17:36:20.465701Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-15T20:24:49.370611348Z", + "created_at": "2025-09-03T17:36:20.507671Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": 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a/tests/integration/recordings/responses/563b994bb7d1.json +++ b/tests/integration/recordings/responses/563b994bb7d1.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:13.25248Z", + "created_at": "2025-09-03T17:36:19.594923Z", "done": true, "done_reason": "stop", - "total_duration": 1344654917, - "load_duration": 200585375, + "total_duration": 988472417, + "load_duration": 117976625, "prompt_eval_count": 326, - "prompt_eval_duration": 564000000, + "prompt_eval_duration": 451625542, "eval_count": 11, - "eval_duration": 578000000, + "eval_duration": 418313417, "response": "[get_weather(location=\"San Francisco, CA\")]", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/5f5d16afadb4.json b/tests/integration/recordings/responses/5f5d16afadb4.json index 8b4061494..f93d688c4 100644 --- a/tests/integration/recordings/responses/5f5d16afadb4.json +++ b/tests/integration/recordings/responses/5f5d16afadb4.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:13.354888Z", + "created_at": "2025-09-03T17:36:19.808372Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:13.427569Z", + "created_at": "2025-09-03T17:36:19.84991Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:13.486244Z", + "created_at": "2025-09-03T17:36:19.892111Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": 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0.019454133, + 0.111560374, + -0.01269702, + -0.06466137, + -0.09346588, + -0.050038446, + -0.042178612, + 0.0599713, + 0.034831088, + -0.014957726, + 0.014484159, + -0.022619838, + 0.06916277, + -0.088544875, + 0.021478733, + 0.01378541, + -0.0075770007, + 0.027888266, + 0.015526889, + 0.0052174823, + 0.010616002, + -0.022908956, + -0.02535865, + -0.04139556, + -0.08375561, + 0.092626974, + 0.051755503, + 0.09296614, + 0.011223383, + -0.016759252 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/6906a6e71988.json b/tests/integration/recordings/responses/6906a6e71988.json index 9d4125823..6574cab53 100644 --- a/tests/integration/recordings/responses/6906a6e71988.json +++ b/tests/integration/recordings/responses/6906a6e71988.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:18.886381Z", + "created_at": "2025-09-03T17:38:00.98692Z", "done": true, "done_reason": "stop", - "total_duration": 488566500, - "load_duration": 113477291, + "total_duration": 332473583, + "load_duration": 90611333, "prompt_eval_count": 317, - "prompt_eval_duration": 361000000, + "prompt_eval_duration": 229691000, "eval_count": 2, - "eval_duration": 12000000, + "eval_duration": 11571291, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6cc063bbd7d3.json b/tests/integration/recordings/responses/6cc063bbd7d3.json index 2e7841626..ab6e12602 100644 --- a/tests/integration/recordings/responses/6cc063bbd7d3.json +++ b/tests/integration/recordings/responses/6cc063bbd7d3.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:55.9885Z", + "created_at": "2025-09-03T17:42:17.402486Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.054143Z", + "created_at": "2025-09-03T17:42:17.444334Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.117658Z", + "created_at": "2025-09-03T17:42:17.484625Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.179422Z", + "created_at": "2025-09-03T17:42:17.525063Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.240328Z", + "created_at": "2025-09-03T17:42:17.565015Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.295992Z", + "created_at": "2025-09-03T17:42:17.60499Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.355683Z", + "created_at": "2025-09-03T17:42:17.64509Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.412176Z", + "created_at": "2025-09-03T17:42:17.685566Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.466952Z", + "created_at": "2025-09-03T17:42:17.725855Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.517222Z", + "created_at": "2025-09-03T17:42:17.766056Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.570491Z", + "created_at": "2025-09-03T17:42:17.806415Z", "done": false, "done_reason": null, "total_duration": null, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.623189Z", + "created_at": "2025-09-03T17:42:17.847273Z", "done": false, "done_reason": null, "total_duration": null, @@ -237,7 +237,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.679221Z", + "created_at": "2025-09-03T17:42:17.888576Z", "done": false, "done_reason": null, "total_duration": null, @@ -255,7 +255,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.731373Z", + "created_at": "2025-09-03T17:42:17.928952Z", "done": false, "done_reason": null, "total_duration": null, @@ -273,7 +273,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.781364Z", + "created_at": "2025-09-03T17:42:17.969744Z", "done": false, "done_reason": null, "total_duration": null, @@ -291,7 +291,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.831951Z", + "created_at": "2025-09-03T17:42:18.010869Z", "done": false, "done_reason": null, "total_duration": null, @@ -309,7 +309,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.888381Z", + "created_at": "2025-09-03T17:42:18.051109Z", "done": false, "done_reason": null, "total_duration": null, @@ -327,7 +327,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.943539Z", + "created_at": "2025-09-03T17:42:18.093266Z", "done": false, "done_reason": null, "total_duration": null, @@ -345,7 +345,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.997422Z", + "created_at": "2025-09-03T17:42:18.135749Z", "done": false, "done_reason": null, "total_duration": null, @@ -363,15 +363,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:57.056259Z", + "created_at": "2025-09-03T17:42:18.176649Z", "done": true, "done_reason": "stop", - "total_duration": 1289815458, - "load_duration": 119745583, + "total_duration": 907420000, + "load_duration": 66756750, "prompt_eval_count": 26, - "prompt_eval_duration": 98000000, + "prompt_eval_duration": 62900875, "eval_count": 20, - "eval_duration": 1071000000, + "eval_duration": 777306958, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6d35c91287e2.json b/tests/integration/recordings/responses/6d35c91287e2.json index 699493f45..a7af894e8 100644 --- a/tests/integration/recordings/responses/6d35c91287e2.json +++ b/tests/integration/recordings/responses/6d35c91287e2.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.362667Z", + "created_at": "2025-09-03T17:38:03.549266Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.427435Z", + "created_at": "2025-09-03T17:38:03.592203Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.484198Z", + "created_at": "2025-09-03T17:38:03.63417Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.537031Z", + "created_at": "2025-09-03T17:38:03.677268Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.591198Z", + "created_at": "2025-09-03T17:38:03.719768Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.643336Z", + "created_at": "2025-09-03T17:38:03.762204Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.698589Z", + "created_at": "2025-09-03T17:38:03.80404Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.752904Z", + "created_at": "2025-09-03T17:38:03.845678Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.804Z", + "created_at": "2025-09-03T17:38:03.887086Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.855633Z", + "created_at": "2025-09-03T17:38:03.928422Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.906918Z", + "created_at": "2025-09-03T17:38:03.969641Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.958729Z", + "created_at": "2025-09-03T17:38:04.011212Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,15 +238,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:23.011279Z", + "created_at": "2025-09-03T17:38:04.052626Z", "done": true, "done_reason": "stop", - "total_duration": 793500292, - "load_duration": 55339750, + "total_duration": 731936583, + "load_duration": 147334791, "prompt_eval_count": 417, - "prompt_eval_duration": 83000000, + "prompt_eval_duration": 79443792, "eval_count": 13, - "eval_duration": 653000000, + "eval_duration": 504352750, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6f96090aa955.json b/tests/integration/recordings/responses/6f96090aa955.json index d5131d389..d0ac20442 100644 --- a/tests/integration/recordings/responses/6f96090aa955.json +++ b/tests/integration/recordings/responses/6f96090aa955.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -21,7 +21,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -36,7 +36,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -47,7 +47,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -62,7 +62,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -73,11 +73,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " Welcome", + "content": " It", "function_call": null, "refusal": null, "role": "assistant", @@ -88,7 +88,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -99,7 +99,59 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", + "choices": [ + { + "delta": { + "content": "'s", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921359, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-698", + "choices": [ + { + "delta": { + "content": " nice", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921359, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -114,7 +166,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -125,11 +177,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " our", + "content": " meet", "function_call": null, "refusal": null, "role": "assistant", @@ -140,7 +192,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -151,11 +203,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " conversation", + "content": " you", "function_call": null, "refusal": null, "role": "assistant", @@ -166,7 +218,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -177,7 +229,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -192,7 +244,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -203,7 +255,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -218,7 +270,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -229,7 +281,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -244,7 +296,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -255,7 +307,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -270,7 +322,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -281,7 +333,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -296,7 +348,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -307,7 +359,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -322,7 +374,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -333,7 +385,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -348,7 +400,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -359,7 +411,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -374,7 +426,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -385,7 +437,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -400,7 +452,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -411,33 +463,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", - "choices": [ - { - "delta": { - "content": ",", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081850, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -452,7 +478,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -463,7 +489,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -478,7 +504,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -489,7 +515,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -504,7 +530,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -515,7 +541,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -530,7 +556,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -541,7 +567,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -556,7 +582,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -567,7 +593,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -582,7 +608,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -593,7 +619,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -608,7 +634,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -619,7 +645,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -634,7 +660,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/6fbea1abca7c.json b/tests/integration/recordings/responses/6fbea1abca7c.json index 576fc7de1..c16fe1268 100644 --- a/tests/integration/recordings/responses/6fbea1abca7c.json +++ b/tests/integration/recordings/responses/6fbea1abca7c.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.337763Z", + "created_at": "2025-09-03T17:38:01.89965Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.394358Z", + "created_at": "2025-09-03T17:38:01.941253Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.451349Z", + "created_at": "2025-09-03T17:38:01.982621Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.504443Z", + "created_at": "2025-09-03T17:38:02.024144Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.555779Z", + "created_at": "2025-09-03T17:38:02.065495Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.607807Z", + "created_at": "2025-09-03T17:38:02.107529Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.660627Z", + "created_at": "2025-09-03T17:38:02.149217Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.711562Z", + "created_at": "2025-09-03T17:38:02.190357Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.761822Z", + "created_at": "2025-09-03T17:38:02.231501Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.81712Z", + "created_at": "2025-09-03T17:38:02.272546Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.868755Z", + "created_at": "2025-09-03T17:38:02.313561Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.921049Z", + "created_at": "2025-09-03T17:38:02.354563Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.973584Z", + "created_at": "2025-09-03T17:38:02.395585Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.030707Z", + "created_at": "2025-09-03T17:38:02.436854Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.082015Z", + "created_at": "2025-09-03T17:38:02.47814Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.132945Z", + "created_at": "2025-09-03T17:38:02.519661Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.187452Z", + "created_at": "2025-09-03T17:38:02.561119Z", "done": false, "done_reason": null, "total_duration": null, @@ -328,7 +328,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.239827Z", + "created_at": "2025-09-03T17:38:02.602821Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,15 +346,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.294154Z", + "created_at": "2025-09-03T17:38:02.644633Z", "done": true, "done_reason": "stop", - "total_duration": 1929211666, - "load_duration": 61298666, + "total_duration": 1375629459, + "load_duration": 94090250, "prompt_eval_count": 386, - "prompt_eval_duration": 908000000, + "prompt_eval_duration": 535119167, "eval_count": 19, - "eval_duration": 959000000, + "eval_duration": 745684041, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6fe1d4fedf12.json b/tests/integration/recordings/responses/6fe1d4fedf12.json index 733c7bd55..8fd079a85 100644 --- a/tests/integration/recordings/responses/6fe1d4fedf12.json +++ b/tests/integration/recordings/responses/6fe1d4fedf12.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -24,7 +24,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", + "id": "chatcmpl-358", "choices": [ { "delta": { @@ -39,7 +39,7 @@ "logprobs": null } ], - "created": 1755228961, + "created": 1756921324, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -50,11 +50,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", + "id": "chatcmpl-358", "choices": [ { "delta": { - "content": " don", + "content": "'m", "function_call": null, "refusal": null, "role": "assistant", @@ -65,7 +65,7 @@ "logprobs": null } ], - "created": 1755228961, + "created": 1756921324, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -76,11 +76,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", + "id": "chatcmpl-358", "choices": [ { "delta": { - "content": "'t", + "content": " not", "function_call": null, "refusal": null, "role": "assistant", @@ -91,7 +91,7 @@ "logprobs": null } ], - "created": 1755228961, + "created": 1756921324, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -102,11 +102,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", + "id": "chatcmpl-358", "choices": [ { "delta": { - "content": " have", + "content": " able", "function_call": null, "refusal": null, "role": "assistant", @@ -117,7 +117,7 @@ "logprobs": null } ], - "created": 1755228961, + "created": 1756921324, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -128,85 +128,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", - "choices": [ - { - "delta": { - "content": " real", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1755228961, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-381", - "choices": [ - { - "delta": { - "content": "-time", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1755228961, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": 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30, - "prompt_eval_duration": 99000000, + "prompt_eval_duration": 72578583, "eval_count": 70, - "eval_duration": 3712000000, + "eval_duration": 2819555375, "response": "The answer is Saturn! Saturn's ring system is one of the most iconic and well-known in our solar system. The rings are made up of ice particles, rock debris, and dust that orbit around the planet due to its gravitational pull.\n\nWould you like to know more about Saturn's rings or is there something else I can help you with?", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/72c1126ff2f9.json b/tests/integration/recordings/responses/72c1126ff2f9.json index b474c7e21..f50c68953 100644 --- a/tests/integration/recordings/responses/72c1126ff2f9.json +++ b/tests/integration/recordings/responses/72c1126ff2f9.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.024362812, - 0.016713308, - 0.03763492, - -0.009156733, - -0.030551745, - -0.017125947, - 0.07426094, - 0.045657348, - -0.0093097305, - 0.009920903, - -0.005690781, - 0.0076895193, - 0.039548296, - 0.015248784, - -0.083151944, - 0.019454934, - -0.02207085, - -0.033246633, - -0.1810784, - -0.1302997, - -0.0022484967, - 0.013480844, - -0.024304103, - -0.03698983, - 0.001961629, - 0.08568096, - 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-0.008556093, + 0.06409403, + -0.033037152, + -0.03049978, + 0.0945846, + -0.008954658, + -0.029921891, + -0.132985, + 0.059934624, + -0.011668423, + 0.0071737366, + 0.035627652, + 0.0041028745, + 0.056198087, + 0.07656151, + -0.010067092, + 0.05678312, + 0.023536043, + -0.063770495, + 0.08934554, + 0.043756966, + 0.04337246, + 0.046287052, + -0.07039028 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/7354ec181984.json b/tests/integration/recordings/responses/7354ec181984.json index 0404c6a6a..b73a7cd50 100644 --- a/tests/integration/recordings/responses/7354ec181984.json +++ b/tests/integration/recordings/responses/7354ec181984.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:51:16.201313167Z", + "created_at": "2025-09-03T17:39:54.374714Z", "done": true, "done_reason": "stop", - "total_duration": 27475921912, - "load_duration": 40564716, + "total_duration": 6321793333, + "load_duration": 182255958, "prompt_eval_count": 25, - "prompt_eval_duration": 964907432, + "prompt_eval_duration": 67964459, "eval_count": 150, - "eval_duration": 26469935419, + "eval_duration": 6070867875, "response": "The smallest country in the world is the Vatican City, which has a total area of approximately 0.44 km\u00b2 (0.17 sq mi). It is an independent city-state located within Rome, Italy, and is home to the Pope and the central government of the Catholic Church.\n\nTo put that into perspective, the Vatican City is smaller than a golf course! Despite its tiny size, it has its own government, currency, postal system, and even its own police force. It's also home to numerous iconic landmarks like St. Peter's Basilica and the Sistine Chapel.\n\nInterestingly, the Vatican City is not only the smallest country in the world but also the most densely populated, with a population of just over 800 people!", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/75d0dd9d0fa3.json b/tests/integration/recordings/responses/75d0dd9d0fa3.json index 52c5d574d..561fa1e67 100644 --- a/tests/integration/recordings/responses/75d0dd9d0fa3.json +++ b/tests/integration/recordings/responses/75d0dd9d0fa3.json @@ -45,15 +45,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:10.58267Z", + "created_at": "2025-09-03T17:36:17.508028Z", "done": true, "done_reason": "stop", - "total_duration": 1981967792, - "load_duration": 63184458, + "total_duration": 1529591917, + "load_duration": 84990667, "prompt_eval_count": 119, - "prompt_eval_duration": 259000000, + "prompt_eval_duration": 189045583, "eval_count": 29, - "eval_duration": 1582000000, + "eval_duration": 1254813583, "response": "{ \"name\": \"Michael Jordan\", \"year_born\": \"1963\", \"year_retired\": \"2003\"}\n ", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/7b25b702ea18.json b/tests/integration/recordings/responses/7b25b702ea18.json index bf8fb73d9..29a978e07 100644 --- a/tests/integration/recordings/responses/7b25b702ea18.json +++ b/tests/integration/recordings/responses/7b25b702ea18.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - 0.06829144, - 0.061772227, - -0.0064161597, - 0.082678765, - -0.07824987, - 0.026521353, - 0.13125585, - 0.041369338, - -0.019540362, - -0.02709599, - 0.0887907, - -0.10275329, - 0.050712623, - -0.07134879, - -0.009282846, - -0.039247703, - 0.028860288, - -0.01049117, - -0.024684245, - -0.035460133, - -0.04094595, - -0.009883736, - -0.026154075, - 0.057957783, - 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+ -0.01730519, + -0.03837497, + -0.00043787624, + -0.008650636, + -0.026787039, + -0.06598753, + -0.14336495, + 0.041543495, + -0.048590284, + 0.012749011, + -0.08499328, + -0.010950221, + -0.038154602, + 0.030090204, + -0.03886871, + -0.03670644, + 0.046492297, + 0.03623469, + 0.052362714, + -0.09623828, + -0.04149126, + 0.050219554, + -2.084757e-05, + 0.0019338154, + 0.019553935 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/7b4815aba6c5.json b/tests/integration/recordings/responses/7b4815aba6c5.json index 2843b8a9c..f1e8e7165 100644 --- a/tests/integration/recordings/responses/7b4815aba6c5.json +++ b/tests/integration/recordings/responses/7b4815aba6c5.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.222059Z", + "created_at": "2025-09-03T17:37:48.840898Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ 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"done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.48928Z", + "created_at": "2025-09-03T17:37:49.049125Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.547102Z", + "created_at": "2025-09-03T17:37:49.090893Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.60579Z", + "created_at": "2025-09-03T17:37:49.132101Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"2025-08-01T23:13:56.463658Z", + "created_at": "2025-09-03T17:37:46.708948Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.51846Z", + "created_at": "2025-09-03T17:37:46.749031Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.569676Z", + "created_at": "2025-09-03T17:37:46.790192Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.621666Z", + "created_at": "2025-09-03T17:37:46.831093Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.675114Z", + "created_at": "2025-09-03T17:37:46.873135Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.727649Z", + "created_at": "2025-09-03T17:37:46.91375Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.780249Z", + "created_at": "2025-09-03T17:37:46.95439Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.834148Z", + "created_at": "2025-09-03T17:37:46.995224Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.885509Z", + "created_at": "2025-09-03T17:37:47.035887Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,15 +184,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.936635Z", + "created_at": "2025-09-03T17:37:47.076806Z", "done": true, "done_reason": "stop", - "total_duration": 1572591291, - "load_duration": 77121041, + "total_duration": 2069654958, + "load_duration": 177579833, "prompt_eval_count": 31, - "prompt_eval_duration": 1019000000, + "prompt_eval_duration": 1521851250, "eval_count": 10, - "eval_duration": 474000000, + "eval_duration": 369478042, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/8295382a8e7c.json b/tests/integration/recordings/responses/8295382a8e7c.json index 6e1dc793d..6a38dde20 100644 --- a/tests/integration/recordings/responses/8295382a8e7c.json +++ b/tests/integration/recordings/responses/8295382a8e7c.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-99", + "id": "chatcmpl-713", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'd be happy to help you test the OpenAI 2 architecture!\n\nOpenAI 2 is a neural network model developed by OpenAI, and it's not exactly possible for me to directly \"test\" it. However, I can guide you through a simplified example of how to verify if OpenAI 2 has been implemented correctly in a specific codebase.\n\nHere's an outline of the steps:\n\n1. **Understand the basics**: Before we dive into testing, make sure you understand the architectural and functional details of OpenAI 2.\n2. **Get access to the model**: You'll need to obtain a trained OpenAI 2 model or implement it from scratch using a language like PyTorch or TensorFlow.\n3. **Implement a validation framework**: Create a simple validation framework that uses common tasks, such as classification on the GLUE benchmark, to evaluate the performance of your OpenAI 2 model.\n\nHere's a simplified code example in PyTorch:\n```python\nimport torch\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\n# Load pre-trained OpenAI 2 Model(s)\nmodel_name = \"github/openai/OpenAIAccelerated-Text-To-Speech\"\nmodel_class = AutoModelForSequenceClassification\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Initialize the model and tokenizer\nmodel = model_class(pretrained=True, num_labels=8) # adjust label number according to your task\ntokenizer = tokenizer\n\ndef evaluate_model():\n batch_size = 100\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n # Create a validation dataset from the GLUE benchmark\n glue_datasets = [ datasets[\"glue\"][\"sst2\"], datasets[\"glue\"][\"mnli\"] ]\n val_dataset = []\n for i, gds in enumerate(glue_datasets):\n data = gds[:10000] # take only first few examples to speed up evaluation\n input_ids = tokenizer encodings(data[\"sentence1\"], \n attention_mask=data[\"attention_mask\"],\n max_length=512,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\")\n\n for example in data:\n for sentence in [example['sentence1'], example['sentence2']]:\n input_ids = input_ids.to(device)\n outputs = model(input_ids, labels=None) # adjust label to empty\n \n\n # Compute evaluation metrics\n predictions = []\n labels = []\n accuracy = torch.zeros(8).to(device)\n\n for sentence in data.values():\n sentenceids = [input_ids[\"input_ids\"].flatten()]\n _, pred_labels = model(sentenceids)\n if len(predictions) == 0:\n labels.extend([1, 2])\n else:\n assert len(labels)==len(sentences), 'error'\n labels.append(preds[-1]) \n\n # Append the prediction to the list\n predictions.append(pred)\n\n return accuracy\n\naccuracy = evaluate_model()\nprint(\"Accuracy:\", accuracy)\n```\n\nAfter running this code, you should get an estimate of the model's performance on the GLUE benchmark. Keep in mind that this is a simplified example and real-world openai 2 testing may require more involved validation processes.\n\nI hope this helps! Let me know if you have any further questions or if there are any specific areas where you'd like more information.", + "content": "I'd be happy to help you test the Transformer-XL (denoted as \"Test Trace OpenAI 2\") model, but first I need to clarify a few things:\n\n1. **Transformer-XL** is not an official name from OpenAI. It's actually a variant of the Transformer model proposed in the paper \"Long-Short Term Memory Are General: A Study on The Curvature of Time\" (2017) by Jinyu Chen, et al.\n2. **Trace OpenAI 2** sounds like a specific version or configuration of the Transformer-XL model, possibly developed by OpenAI.\n\nGiven these clarifications, I'll provide you with a general idea of how to test the Transformer-XL (or \"Test Trace OpenAI 2\") model using popular frameworks and libraries. Please note that this is not an exhaustive or definitive guide.\n\nTo test the Transformer-XL model, you can follow these steps:\n\n1. **Install the necessary dependencies**: You'll need a deep learning framework like TensorFlow or PyTorch, as well as a library for natural language processing (NLP) like Hugging Face's transformers.\n2. **Load the pre-trained weights**: You can use a pre-trained model checkpoint from Hugging Face's Transformers library or load your own weights trained on a specific task or dataset.\n3. **Prepare your data**: Load your text data into tokens, such as words or characters, and preprocess it according to the specific requirements of the Transformer-XL architecture (e.g., tokenization, padding, etc.).\n4. **Configure the model**: Adjust the hyperparameters to suit your specific test case, including the model's configuration, batch size, learning rate, etc.\n5. **Run the inference**: Use the loaded pre-trained weights to perform inference on your test data.\n\nHere's some sample Python code using PyTorch and Hugging Face's Transformers library to get you started:\n```python\nimport torch\nfrom transformers import LongformerForSequenceClassification, LongformerTokenizer\n\n# Load pre-trained weights\nmodel = LongformerForSequenceClassification.from_pretrained('test-trace-openai-2')\n\n# Prepare data\ntokenizer = model.tokenizer\ntext = \"This is a test sentence\"\ninputs = tokenizer(text, return_tensors='pt')\noutput = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])\n\n# Print the results\nprint(output.logits)\n```\nPlease note that this code snippet is just an example and may not work as-is. You'll need to adapt it to your specific requirements and test data.\n\nKeep in mind that testing a model's performance on a specific task or dataset requires careful consideration of factors like:\n\n* **Test data quality**: Your test data should accurately represent the underlying distribution of your target dataset.\n* **Model evaluation metrics**: Choose relevant evaluation metrics that measure the model's performance on your specific task, such as accuracy, precision, recall, F1-score, etc.\n\nFeel free to ask if you have any further questions or need more guidance!", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510064, + "created": 1756921250, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 694, + "completion_tokens": 614, "prompt_tokens": 31, - "total_tokens": 725, + "total_tokens": 645, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/836f51dfb3c5.json b/tests/integration/recordings/responses/836f51dfb3c5.json index 833545737..85f3aff00 100644 --- a/tests/integration/recordings/responses/836f51dfb3c5.json +++ b/tests/integration/recordings/responses/836f51dfb3c5.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:03.770002Z", + "created_at": "2025-09-03T17:37:51.562847Z", "done": true, "done_reason": "stop", - "total_duration": 395965875, - "load_duration": 178888708, + "total_duration": 272296250, + "load_duration": 131747125, "prompt_eval_count": 214, - "prompt_eval_duration": 170000000, + "prompt_eval_duration": 124006709, "eval_count": 2, - "eval_duration": 44000000, + "eval_duration": 15572291, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/840fbb380b73.json b/tests/integration/recordings/responses/840fbb380b73.json index a3fb7ccd8..4367d8788 100644 --- a/tests/integration/recordings/responses/840fbb380b73.json +++ b/tests/integration/recordings/responses/840fbb380b73.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:13:57.935921Z", + "created_at": "2025-09-03T17:37:47.871962Z", "done": true, "done_reason": "stop", - "total_duration": 313787333, - "load_duration": 89797542, + "total_duration": 301629042, + "load_duration": 102832917, "prompt_eval_count": 233, - "prompt_eval_duration": 167000000, + "prompt_eval_duration": 154806625, "eval_count": 5, - "eval_duration": 55000000, + "eval_duration": 43361542, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/84cab42e1f5c.json b/tests/integration/recordings/responses/84cab42e1f5c.json index 423dd16da..611e67218 100644 --- a/tests/integration/recordings/responses/84cab42e1f5c.json +++ b/tests/integration/recordings/responses/84cab42e1f5c.json @@ -17,7 +17,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -26,7 +26,7 @@ "text": "Blue" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -36,7 +36,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -45,7 +45,7 @@ "text": ".\n\n" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -55,7 +55,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -64,7 +64,7 @@ "text": "My" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -74,16 +74,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " response" + "text": " answer" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -93,7 +93,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -102,7 +102,7 @@ "text": " is" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -112,634 +112,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " based" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " on" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " a" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " common" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " English" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " rhyme" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " or" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " poem" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " that" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " completes" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " the" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " sentence" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " with" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " the" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " word" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " \"" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "blue" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "\"." - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " The" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " complete" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " phrase" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " is" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": ":" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " \"" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "R" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "oses" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " are" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " red" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "," - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " v" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "io" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "lets" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " are" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -748,7 +121,7 @@ "text": " blue" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -758,16 +131,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": "\".\n\n" + "text": " because" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -777,16 +150,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": "The" + "text": " it" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -796,16 +169,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " use" + "text": "'s" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -815,7 +188,121 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " a" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " common" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " and" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " well" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "-known" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " completion" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -824,7 +311,7 @@ "text": " of" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -834,7 +321,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -843,7 +330,7 @@ "text": " the" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -853,16 +340,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " word" + "text": " classic" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -872,7 +359,64 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " tongue" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "-tw" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "ister" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -881,7 +425,7 @@ "text": " \"" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -891,16 +435,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": "blue" + "text": "R" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -910,7 +454,159 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "oses" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " are" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " red" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "," + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " v" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "io" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "lets" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " are" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -919,7 +615,7 @@ "text": "\"" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -929,7 +625,292 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " \u2013" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " often" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " followed" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " by" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " the" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " phrase" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " \"" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "blue" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": ".\"" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " This" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " rhyme" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " has" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " been" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " widely" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " used" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -938,7 +919,7 @@ "text": " in" } ], - "created": 1754348151, + "created": 1756921027, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -948,16 +929,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " this" + "text": " literature" } ], - "created": 1754348151, + "created": 1756921027, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -967,7 +948,26 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "," + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": "length", @@ -976,7 +976,7 @@ "text": "" } ], - "created": 1754348151, + "created": 1756921027, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", diff --git a/tests/integration/recordings/responses/85594a69d74a.json b/tests/integration/recordings/responses/85594a69d74a.json index 286b8da11..c4a01bc33 100644 --- a/tests/integration/recordings/responses/85594a69d74a.json +++ b/tests/integration/recordings/responses/85594a69d74a.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:54.634929Z", + "created_at": "2025-09-03T17:37:36.046489Z", "done": true, "done_reason": "stop", - "total_duration": 233222375, - "load_duration": 136303125, + "total_duration": 198969250, + "load_duration": 110421000, "prompt_eval_count": 213, - "prompt_eval_duration": 78000000, + "prompt_eval_duration": 76196541, "eval_count": 2, - "eval_duration": 17000000, + "eval_duration": 11832042, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/97d3812bfccb.json b/tests/integration/recordings/responses/97d3812bfccb.json index 8a9b076fd..11e0fb402 100644 --- a/tests/integration/recordings/responses/97d3812bfccb.json +++ b/tests/integration/recordings/responses/97d3812bfccb.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:06.082832Z", + "created_at": "2025-09-03T17:37:52.965106Z", "done": true, "done_reason": "stop", - "total_duration": 421905083, - "load_duration": 88557750, + "total_duration": 376594792, + "load_duration": 158273792, "prompt_eval_count": 217, - "prompt_eval_duration": 278000000, + "prompt_eval_duration": 177001375, "eval_count": 5, - "eval_duration": 54000000, + "eval_duration": 40927500, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/97e259c0d3e5.json b/tests/integration/recordings/responses/97e259c0d3e5.json index cd083c9a8..2e47bca80 100644 --- a/tests/integration/recordings/responses/97e259c0d3e5.json +++ b/tests/integration/recordings/responses/97e259c0d3e5.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.138696Z", + "created_at": "2025-09-03T17:37:53.505006Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.195013Z", + "created_at": "2025-09-03T17:37:53.547032Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.246591Z", + "created_at": "2025-09-03T17:37:53.588985Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.29736Z", + "created_at": "2025-09-03T17:37:53.631139Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.347941Z", + "created_at": "2025-09-03T17:37:53.67269Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.399151Z", + "created_at": "2025-09-03T17:37:53.714798Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.452488Z", + "created_at": "2025-09-03T17:37:53.756492Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.50538Z", + "created_at": "2025-09-03T17:37:53.798115Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.558656Z", + "created_at": "2025-09-03T17:37:53.840012Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.610408Z", + "created_at": "2025-09-03T17:37:53.882555Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.66358Z", + "created_at": "2025-09-03T17:37:53.924566Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.717638Z", + "created_at": "2025-09-03T17:37:53.966279Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.769423Z", + "created_at": "2025-09-03T17:37:54.008483Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.819395Z", + "created_at": "2025-09-03T17:37:54.050042Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.871391Z", + "created_at": "2025-09-03T17:37:54.092416Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.924892Z", + "created_at": "2025-09-03T17:37:54.134857Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.976557Z", + "created_at": "2025-09-03T17:37:54.176408Z", "done": false, "done_reason": null, "total_duration": null, @@ -328,7 +328,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:08.029579Z", + "created_at": "2025-09-03T17:37:54.217553Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,15 +346,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:08.082749Z", + "created_at": "2025-09-03T17:37:54.259141Z", "done": true, "done_reason": "stop", - "total_duration": 1425800209, - "load_duration": 138858459, + "total_duration": 1008303875, + "load_duration": 119709875, "prompt_eval_count": 384, - "prompt_eval_duration": 340000000, + "prompt_eval_duration": 132645959, "eval_count": 19, - "eval_duration": 945000000, + "eval_duration": 755215708, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/9c140a29ae09.json b/tests/integration/recordings/responses/9c140a29ae09.json index 41b070cc5..a436484d7 100644 --- a/tests/integration/recordings/responses/9c140a29ae09.json +++ b/tests/integration/recordings/responses/9c140a29ae09.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:09.83858Z", + "created_at": "2025-09-03T17:37:55.13567Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:09.891488Z", + "created_at": "2025-09-03T17:37:55.17774Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:09.945656Z", + "created_at": "2025-09-03T17:37:55.220061Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:09.996898Z", + "created_at": "2025-09-03T17:37:55.261406Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.053632Z", + "created_at": "2025-09-03T17:37:55.302615Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.105753Z", + "created_at": "2025-09-03T17:37:55.343879Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": 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-0.034636553, + 0.025459053, + -0.046185397, + -0.067215376, + 0.06057241, + -0.041255984, + -0.019857686, + -0.013778329, + -0.06125949, + 0.014752149, + -0.07630465, + -0.056748062, + 0.0505062, + -0.036068004, + 0.12241577, + 0.06429002, + -0.038303368 ], "index": 3, "object": "embedding" diff --git a/tests/integration/recordings/responses/9e7a83d3d596.json b/tests/integration/recordings/responses/9e7a83d3d596.json index 4965aa3c7..a9054d729 100644 --- a/tests/integration/recordings/responses/9e7a83d3d596.json +++ b/tests/integration/recordings/responses/9e7a83d3d596.json @@ -15,23 +15,23 @@ "body": { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-43", + "id": "cmpl-775", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, - "text": "Blue.\n\nMy response is based on the traditional English rhyme that pairs the colors of roses (red) with violets in a poetic and somewhat whimsical way. This specific version of the rhyme goes like this:\n\n\"Roses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.\"\n\nIn modern times, variations of this rhyme can deviate from the original \"blue\" for violets, but in my complete sentence as requested, sticking with a widely recognized completion adds an air of timelessness and familiarity to the phrase." + "text": "Blue.\n\nMy response is based on the traditional rhyme \"Roses are Red, Violets are Blue,\" which is a well-known poem or phrase often used as a greeting or way to express affection. The exact wording may vary slightly depending on the source, but the general meaning remains the same: violets are typically depicted as blue-colored flowers in this rhyme." } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 113, + "completion_tokens": 75, "prompt_tokens": 50, - "total_tokens": 163, + "total_tokens": 125, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/9fadf5a3d68f.json b/tests/integration/recordings/responses/9fadf5a3d68f.json index bc3c3ca22..aba45bcd3 100644 --- a/tests/integration/recordings/responses/9fadf5a3d68f.json +++ b/tests/integration/recordings/responses/9fadf5a3d68f.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:22.168612Z", + "created_at": "2025-09-03T17:38:03.270261Z", "done": true, "done_reason": "stop", - "total_duration": 198446125, - "load_duration": 31859666, + "total_duration": 244051875, + "load_duration": 111239500, "prompt_eval_count": 224, - "prompt_eval_duration": 151000000, + "prompt_eval_duration": 120962791, "eval_count": 2, - "eval_duration": 13000000, + "eval_duration": 11306292, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/a0c4df33879f.json b/tests/integration/recordings/responses/a0c4df33879f.json index f134e0bed..7898e5b02 100644 --- a/tests/integration/recordings/responses/a0c4df33879f.json +++ b/tests/integration/recordings/responses/a0c4df33879f.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -21,7 +21,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -36,7 +36,7 @@ "logprobs": null } ], - "created": 1754081845, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -47,7 +47,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -62,7 +62,7 @@ "logprobs": null } ], - "created": 1754081845, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -73,319 +73,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " word", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081845, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " for", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081845, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " the", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081845, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " Sun", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081845, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " is", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081845, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " \"", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081845, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", - "choices": 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"fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -400,7 +88,7 @@ "logprobs": null } ], - "created": 1754081846, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -411,7 +99,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -426,7 +114,7 @@ "logprobs": null } ], - "created": 1754081846, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -437,7 +125,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -452,7 +140,7 @@ "logprobs": null } ], - "created": 1754081846, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -463,7 +151,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -478,7 +166,7 @@ "logprobs": null } ], - "created": 1754081846, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -489,11 +177,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { - "content": ",", + "content": " is", "function_call": null, "refusal": null, "role": "assistant", @@ -504,7 +192,7 @@ "logprobs": null } ], - "created": 1754081846, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -515,11 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+ "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-792", + "choices": [ + { + "delta": { + "content": " the", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921358, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-792", + "choices": [ + { + "delta": { + "content": " solar", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921358, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-792", + "choices": [ + { + "delta": { + "content": " system", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921358, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-792", + "choices": [ + { + "delta": { + "content": ".", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921358, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -1726,7 +1882,7 @@ "logprobs": null } ], - "created": 1754081848, + "created": 1756921358, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/a4c8d19bb1eb.json b/tests/integration/recordings/responses/a4c8d19bb1eb.json index a3aba2bff..89f52f82e 100644 --- a/tests/integration/recordings/responses/a4c8d19bb1eb.json +++ b/tests/integration/recordings/responses/a4c8d19bb1eb.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-560", + "id": "chatcmpl-715", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "Hello! It's nice to meet you. How can I assist you today?", + "content": "Hello! It's nice to meet you. Is there something I can help you with or would you like to chat?", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754081856, + "created": 1756921367, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 17, + "completion_tokens": 25, "prompt_tokens": 29, - "total_tokens": 46, + "total_tokens": 54, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/a5187d9d5057.json b/tests/integration/recordings/responses/a5187d9d5057.json index 0dedba066..edacd5fa6 100644 --- a/tests/integration/recordings/responses/a5187d9d5057.json +++ b/tests/integration/recordings/responses/a5187d9d5057.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-64", + "id": "chatcmpl-376", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "You want to test the capabilities of the OpenAI Text-to-Text model (also known as T0).\n\nPlease note that I'll be using a pre-trained model, so my responses might not be entirely customized to your specific prompt or context. That being said, I'll do my best to mimic the behavior of the original model.\n\nWhat would you like to test or ask? Please provide a prompt or question, and I'll respond accordingly.\n\n(Note: if you'd like to run a longer experiment or try out specific models like text completion or code generation, feel free to let me know and we can figure out a way to collaborate.)", + "content": "I'll simulate a test for the LA-1030/5B linear accelerator, specifically for the H8 (High Voltage) model found in early models of the test rail. Note that this is not meant to be taken as actual test results but rather a demonstration.\n\n### Introduction:\nThe LA-1030/5B was used primarily for high-energy physics and nuclear research during the 1970s and 1980s. This linear accelerator was capable of producing proton beams with energies up to several GeV. The H8 model, also known as the 'High Voltage' component, is a series of power supplies that drive the high voltage DC (HV) accelerators.\n\n### Test Setup:\n\n- **Test Goal:** Measure the output of the LA-1030/5B H8 model linear accelerator and assess its ability to generate stable, high-voltage direct current (HVDC) to power it properly.\n \n - The setup consists of a single test rail containing one of these H8 modules. A precise DC voltage is supplied to the linear accelerator via an external DC source.\n\n### Operating Parameters:\n\n- **Input Voltage:** To ensure the linear accelerator operates within its safe operating parameters, input voltages will be varied from 20KV to 140KV.\n- **Current Delivery:** Monitoring current at these different output levels requires a precise multimeter or oscilloscope. \n- **Voltage Level and Current Stability:** The voltage should stabilize as close as possible to the desired output level.\n\n### Potential Issues\n\n1.) Over-Pressure in H8 Modules\n - During high voltage levels, there's a risk of over-pressurization in the component casing due to the vacuum properties within the modules.\n - Check for any external signs of stress or leakage.\n2.) Current Limitation and Arcing\n - High current requirements demand close monitoring of the accelerator components and associated connections.\n - An excessive arc can be detrimental to electronics connected to the system.\n3.) Interlocks and Safe Guards\n\n- **Ensure alignment:** Prevent accidental triggering.\n\n4.) Insulation integrity \n - Potential risks from faulty or non-insulated components\n\n### Results Analysis:\n\nBased on this hypothetical test, some potential results could include:\n1. Output voltage stability for the chosen input voltages\n2. Ability to exceed the accelerator's highest voltage ratings.\n3. Consistency between different current levels at various output voltage tests.\n\nThis exercise is a general simulation and might not reflect real-world conditions or performance specifications of an actual LA-1030/5B linear accelerator. The focus here was on demonstrating how one could analyze data from such a system, given typical components involved in linear accelerators at that time period.", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510052, + "created": 1756921225, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 129, + "completion_tokens": 547, "prompt_tokens": 31, - "total_tokens": 160, + "total_tokens": 578, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/a59d0d7c1485.json b/tests/integration/recordings/responses/a59d0d7c1485.json index 80e2462d5..c951596ce 100644 --- a/tests/integration/recordings/responses/a59d0d7c1485.json +++ b/tests/integration/recordings/responses/a59d0d7c1485.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:23.46316Z", + "created_at": "2025-09-03T17:38:04.367295Z", "done": true, "done_reason": "stop", - "total_duration": 270313833, - "load_duration": 71668791, + "total_duration": 276503250, + "load_duration": 125852000, "prompt_eval_count": 238, - "prompt_eval_duration": 169000000, + "prompt_eval_duration": 138575125, "eval_count": 2, - "eval_duration": 25000000, + "eval_duration": 11277208, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/a6810c23eda8.json b/tests/integration/recordings/responses/a6810c23eda8.json index 6d9747d28..d5b5c5a6d 100644 --- a/tests/integration/recordings/responses/a6810c23eda8.json +++ b/tests/integration/recordings/responses/a6810c23eda8.json @@ -23,7 +23,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:05.992185Z", + "created_at": "2025-09-03T17:36:13.985194Z", "done": false, "done_reason": null, "total_duration": null, @@ -41,7 +41,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.047726Z", + "created_at": "2025-09-03T17:36:14.027686Z", "done": false, "done_reason": null, "total_duration": null, @@ -59,7 +59,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.123375Z", + "created_at": "2025-09-03T17:36:14.068694Z", "done": false, "done_reason": null, "total_duration": null, @@ -77,7 +77,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.182233Z", + "created_at": "2025-09-03T17:36:14.10959Z", "done": false, "done_reason": null, "total_duration": null, @@ -95,7 +95,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.244655Z", + "created_at": "2025-09-03T17:36:14.150266Z", "done": false, "done_reason": null, "total_duration": null, @@ -113,7 +113,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.304777Z", + "created_at": "2025-09-03T17:36:14.190959Z", "done": false, "done_reason": null, "total_duration": null, @@ -131,7 +131,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.361584Z", + "created_at": "2025-09-03T17:36:14.231689Z", "done": false, "done_reason": null, "total_duration": null, @@ -149,7 +149,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.419647Z", + "created_at": "2025-09-03T17:36:14.272328Z", "done": false, "done_reason": null, "total_duration": null, @@ -167,7 +167,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.477037Z", + "created_at": "2025-09-03T17:36:14.312774Z", "done": false, "done_reason": null, "total_duration": null, @@ -185,7 +185,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.534717Z", + "created_at": "2025-09-03T17:36:14.353348Z", "done": false, "done_reason": null, "total_duration": null, @@ -203,7 +203,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.600289Z", + "created_at": "2025-09-03T17:36:14.393886Z", "done": false, "done_reason": null, "total_duration": null, @@ -221,7 +221,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.658769Z", + "created_at": "2025-09-03T17:36:14.434753Z", "done": false, "done_reason": null, "total_duration": null, @@ -239,7 +239,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.71323Z", + "created_at": "2025-09-03T17:36:14.474992Z", "done": false, "done_reason": null, "total_duration": null, @@ -257,7 +257,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.764206Z", + "created_at": "2025-09-03T17:36:14.515133Z", "done": false, "done_reason": null, "total_duration": null, @@ -275,7 +275,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.815428Z", + "created_at": "2025-09-03T17:36:14.555579Z", "done": false, "done_reason": null, "total_duration": null, @@ -293,7 +293,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.86906Z", + "created_at": "2025-09-03T17:36:14.596355Z", "done": false, "done_reason": null, "total_duration": null, @@ -311,7 +311,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.92191Z", + "created_at": "2025-09-03T17:36:14.637241Z", "done": false, "done_reason": null, "total_duration": null, @@ -329,7 +329,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.97464Z", + "created_at": "2025-09-03T17:36:14.679196Z", "done": false, "done_reason": null, "total_duration": null, @@ -347,7 +347,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.026686Z", + "created_at": "2025-09-03T17:36:14.719878Z", "done": false, "done_reason": null, "total_duration": null, @@ -365,7 +365,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.078382Z", + "created_at": "2025-09-03T17:36:14.759719Z", "done": false, "done_reason": null, "total_duration": null, @@ -383,7 +383,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.131717Z", + "created_at": "2025-09-03T17:36:14.79997Z", "done": false, "done_reason": null, "total_duration": null, @@ -401,7 +401,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.188206Z", + "created_at": "2025-09-03T17:36:14.84053Z", "done": false, "done_reason": null, "total_duration": null, @@ -419,7 +419,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.243218Z", + "created_at": "2025-09-03T17:36:14.881964Z", "done": false, "done_reason": null, "total_duration": null, @@ -437,7 +437,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.298542Z", + "created_at": "2025-09-03T17:36:14.921986Z", "done": false, "done_reason": null, "total_duration": null, @@ -455,7 +455,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.355167Z", + "created_at": "2025-09-03T17:36:14.962551Z", "done": false, "done_reason": null, "total_duration": null, @@ -473,7 +473,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"created_at": "2025-08-04T22:55:07.626345Z", + "created_at": "2025-09-03T17:36:15.165202Z", "done": false, "done_reason": null, "total_duration": null, @@ -563,7 +563,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.680673Z", + "created_at": "2025-09-03T17:36:15.205416Z", "done": false, "done_reason": null, "total_duration": null, @@ -581,7 +581,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.736803Z", + "created_at": "2025-09-03T17:36:15.245854Z", "done": false, "done_reason": null, "total_duration": null, @@ -599,7 +599,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.789556Z", + "created_at": "2025-09-03T17:36:15.286352Z", "done": false, "done_reason": null, "total_duration": null, @@ -617,7 +617,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.841142Z", + "created_at": "2025-09-03T17:36:15.326952Z", "done": false, "done_reason": null, "total_duration": null, @@ -635,7 +635,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.896607Z", + "created_at": "2025-09-03T17:36:15.367575Z", "done": false, "done_reason": null, "total_duration": null, @@ -653,7 +653,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.953628Z", + "created_at": "2025-09-03T17:36:15.408069Z", "done": false, "done_reason": null, "total_duration": null, @@ -671,7 +671,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.007575Z", + "created_at": "2025-09-03T17:36:15.448413Z", "done": false, "done_reason": null, "total_duration": null, @@ -689,7 +689,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.061895Z", + "created_at": "2025-09-03T17:36:15.489223Z", "done": false, "done_reason": null, "total_duration": null, @@ -707,7 +707,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.121698Z", + "created_at": "2025-09-03T17:36:15.530477Z", "done": false, "done_reason": null, "total_duration": null, @@ -725,7 +725,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.175866Z", + "created_at": "2025-09-03T17:36:15.571317Z", "done": false, "done_reason": null, "total_duration": null, @@ -743,7 +743,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.231661Z", + "created_at": "2025-09-03T17:36:15.612263Z", "done": false, "done_reason": null, "total_duration": null, @@ -761,7 +761,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.285188Z", + "created_at": "2025-09-03T17:36:15.652533Z", "done": false, "done_reason": null, "total_duration": null, @@ -779,15 +779,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.334914Z", + "created_at": "2025-09-03T17:36:15.692748Z", "done": true, "done_reason": "stop", - "total_duration": 2543128958, - "load_duration": 133497375, + "total_duration": 1808812333, + "load_duration": 57887042, "prompt_eval_count": 18, - "prompt_eval_duration": 62000000, + "prompt_eval_duration": 42042750, "eval_count": 43, - "eval_duration": 2346000000, + "eval_duration": 1708293042, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/ae1c22f18ecc.json b/tests/integration/recordings/responses/ae1c22f18ecc.json index 595b6668d..c9a47657b 100644 --- a/tests/integration/recordings/responses/ae1c22f18ecc.json +++ b/tests/integration/recordings/responses/ae1c22f18ecc.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:32.661124541Z", + "created_at": "2025-09-03T17:41:47.144448Z", "done": true, "done_reason": "stop", - "total_duration": 11391290133, - "load_duration": 42154800, + "total_duration": 2462760250, + "load_duration": 83668541, "prompt_eval_count": 20, - "prompt_eval_duration": 1208581216, + "prompt_eval_duration": 74227125, "eval_count": 58, - "eval_duration": 10140044676, + "eval_duration": 2304346166, "response": "I'm happy to help you with your test, but I don't see what kind of test we are testing. Could you please provide more context or clarify what kind of test you would like me to perform? Is it a programming test, a language proficiency test, or something else?", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/ae6835cfe70e.json b/tests/integration/recordings/responses/ae6835cfe70e.json index 1bc383707..9766c6023 100644 --- a/tests/integration/recordings/responses/ae6835cfe70e.json +++ b/tests/integration/recordings/responses/ae6835cfe70e.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:57.955211Z", + "created_at": "2025-09-03T17:42:18.871277Z", "done": true, "done_reason": "stop", - "total_duration": 842946458, - "load_duration": 91343000, + "total_duration": 644170416, + "load_duration": 69749500, "prompt_eval_count": 386, - "prompt_eval_duration": 685000000, + "prompt_eval_duration": 531218583, "eval_count": 2, - "eval_duration": 64000000, + "eval_duration": 42446084, "response": "[]", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/b14ff438ca99.json b/tests/integration/recordings/responses/b14ff438ca99.json index c445e7d42..180ec3286 100644 --- a/tests/integration/recordings/responses/b14ff438ca99.json +++ b/tests/integration/recordings/responses/b14ff438ca99.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:51:39.104140157Z", + "created_at": "2025-09-03T17:39:59.708499Z", "done": true, "done_reason": "stop", - "total_duration": 22895811031, - "load_duration": 41692686, + "total_duration": 5293681583, + "load_duration": 196095541, "prompt_eval_count": 23, - "prompt_eval_duration": 793961939, + "prompt_eval_duration": 72668042, "eval_count": 124, - "eval_duration": 22059637137, + "eval_duration": 5024327166, "response": "The official currency of Japan is the Japanese yen (\u00a5). It is abbreviated as \"JPY\" and its symbol is \u00a5. The yen is divided into 100 sen, although the sen has been officially discontinued since 1967.\n\nYou can exchange your money for yen at banks, currency exchange offices, or use ATMs to withdraw cash from an ATM. Credit cards are also widely accepted in Japan, especially among major retailers and restaurants.\n\nIt's worth noting that some businesses may not accept foreign currencies other than US dollars, so it's a good idea to have some local currency on hand when traveling to Japan.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/b5e3ed420986.json b/tests/integration/recordings/responses/b5e3ed420986.json index 871708ea0..f5a6e2400 100644 --- a/tests/integration/recordings/responses/b5e3ed420986.json +++ b/tests/integration/recordings/responses/b5e3ed420986.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.054539014, - -0.016468922, - -0.010608761, - 0.02301095, - 0.011758054, - -0.11193683, - -0.0096305525, - 0.019113416, - 0.048967674, - -0.040160257, - -0.022335947, - 0.016229406, - 0.009204825, - 0.05479278, - 0.049229205, - -0.09585555, - -0.031133035, - -0.010217964, - -0.029166166, - -0.08954575, - -0.0006925836, - 0.034955315, - 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"2025-07-29T23:46:35.850399Z", + "created_at": "2025-09-03T17:34:22.916043Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.89419Z", + "created_at": "2025-09-03T17:34:22.957379Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.938049Z", + "created_at": "2025-09-03T17:34:23.00029Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.980392Z", + "created_at": "2025-09-03T17:34:23.043332Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.023004Z", + "created_at": "2025-09-03T17:34:23.085324Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.065467Z", + "created_at": "2025-09-03T17:34:23.128181Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.108189Z", + "created_at": "2025-09-03T17:34:23.172026Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,15 +147,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.150902Z", + "created_at": "2025-09-03T17:34:23.216706Z", "done": true, "done_reason": "stop", - "total_duration": 468910417, - "load_duration": 93969000, + "total_duration": 516060000, + "load_duration": 127260334, "prompt_eval_count": 479, - "prompt_eval_duration": 72596750, + "prompt_eval_duration": 87107292, "eval_count": 8, - "eval_duration": 301590375, + "eval_duration": 299381042, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/c2199d6064db.json b/tests/integration/recordings/responses/c2199d6064db.json index 73194cc00..ff7298e86 100644 --- a/tests/integration/recordings/responses/c2199d6064db.json +++ b/tests/integration/recordings/responses/c2199d6064db.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.021827588, - 0.08818103, - -0.10864717, - 0.0027738505, - 0.049183175, - -0.030155653, - -0.015535575, - 0.027562236, - -0.025055608, - 0.016142149, - 0.12481904, - 0.0027390872, - -0.033304155, - -0.007155499, - -0.07006565, - -0.028012667, - -0.0974939, - -0.09156265, - 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-0.009361379, + -0.01570009, + -0.01836736, + 0.12301148, + 0.1185397, + 0.12366319, + 0.022782512, + -0.020027133, + -0.07401259, + -0.0047104736, + -0.024872223, + 0.006070436, + -0.06660639, + -0.08130306, + -0.0873992, + -0.0634906, + -0.039198957, + -0.11274462, + -0.030654918, + 0.026607778, + -0.063220546, + 0.042023618, + -0.039010853, + -0.009214424, + 0.005044682, + 0.0015641748, + -0.058640927, + 0.043107104, + 0.06682025, + 0.062172387, + 0.021147223, + -0.041068073 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/c9cba6f3ee38.json b/tests/integration/recordings/responses/c9cba6f3ee38.json index 488ac6563..02363c70e 100644 --- a/tests/integration/recordings/responses/c9cba6f3ee38.json +++ b/tests/integration/recordings/responses/c9cba6f3ee38.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:21.909783Z", + "created_at": "2025-09-03T17:38:03.002753Z", "done": true, "done_reason": "stop", - "total_duration": 311036333, - "load_duration": 37569542, + "total_duration": 334941166, + "load_duration": 149512166, "prompt_eval_count": 219, - "prompt_eval_duration": 259000000, + "prompt_eval_duration": 173843500, "eval_count": 2, - "eval_duration": 12000000, + "eval_duration": 11119166, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/cb3df2a1dc22.json b/tests/integration/recordings/responses/cb3df2a1dc22.json index d65945ac1..41db65a5e 100644 --- a/tests/integration/recordings/responses/cb3df2a1dc22.json +++ b/tests/integration/recordings/responses/cb3df2a1dc22.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-877", + "id": "chatcmpl-271", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'm not capable of directly testing the functionality of external systems like Telemetry. However, I can provide you with some general information about creating telemetry data and offer suggestions on how to set up a basic telemetry system.\r\n\r\nTelemetry is the automatic measurement, reporting, and transmission of data from sensors or other devices. In the context of OpenAI, telemetry refers to the collection and analysis of data related to the company's products and services.\r\n\r\nTo create telemetry creation using the OpenAI APIs you would need to follow these steps:\r\n\r\n1. Register for an OpenAI account and get an access token.\r\n2. Choose the OpenAI API that you want to use (e.g., GPT-3).\r\n3. Create a new file or project in your preferred programming language or framework.\r\n4. Import the necessary libraries and modules to interact with the OpenAI API.\r\n5. Use the OpenAI API to create and send telemetry data.\r\n\r\nHere is an example of how you might create a basic telemetry system using Python and the OpenAI GPT-3 API:\r\n\r\n```python\r\nimport os\r\nimport json\r\n\r\n# Set your OpenAI access token\r\naccess_token = \"YOUR_OPENAI_ACCESS_TOKEN\"\r\n\r\n# Define the telemetry data\r\ntelemetry_data = {\r\n \"name\": \"example-telemetry\",\r\n \"description\": \"Example telemetry data.\r\n\r\n # Define the telemetry metrics\r\n \"metrics\": [\r\n {\"key\": \"users\", \"value\": 100},\r\n {\"key\": \" engagement\", \"value\": 20}\r\n ]\r\n}\r\n\r\n# Convert the telemetry data to JSON\r\ntelemetry_json = json.dumps(telemetry_data)\r\n\r\n# Set the OpenAI API endpoint and headers\r\napi_endpoint = \"https://api.openai.com/v1/telemetry\"\r\nheaders = {\r\n \"Authorization\": f\"Bearer {access_token}\",\r\n \"Content-Type\": \"application/json\"\r\n}\r\n\r\n# Send the telemetry data to the OpenAI API\r\nimport requests\r\n\r\nresponse = requests.post(api_endpoint, headers=headers, data=telemetry_json)\r\n\r\n# Check if the request was successful\r\nif response.status_code == 200:\r\n print(\"Telemetry data sent successfully\")\r\nelse:\r\n print(\"Error sending telemetry data\")\r\n```\n\nPlease note that this is a basic example and you should adjust it according to your needs. Also, the specific implementation details may vary depending on the OpenAI API you're using and the programming language or framework you're working with.\r\n\r\nI hope this helps! Let me know if you have any further questions.", + "content": "# OpenAI Telemetry Creation Testing\n\nThis guide provides a test environment for creating and analyzing telemetries using the OpenAI API.\n\n## Prerequisites\n\n- Python 3.8 or higher\n- `requests` library\n- An OpenAI API key (create an account on the [OpenAI website](https://openai.com/))\n\n## Step 1: Install Required Libraries\n\nFirst, install the required libraries by running the following command in your terminal:\n\n```bash\npip install requests\n```\n\n## Step 2: Create a Telemetry Object\n\nCreate a new Python file (e.g., `telemetry.py`) and import the required library. Define a telemetry object with the relevant data:\n\n```python\n# Import the requests library\nimport requests\n\nclass Telemetry:\n def __init__(self, api_key):\n \"\"\"\n Initialize the telemetry object.\n\n Args:\n api_key (str): The OpenAI API key.\n \"\"\"\n\n self.api_key = api_key\n\n def create_telemetry(self, id, data):\n \"\"\"\n Create a new telemetry entry using the OpenAI API.\n\n Args:\n id (str): The ID of the model or dataset.\n data (dict): The telemetry data to be created.\n\n Returns:\n dict: The response from the OpenAI API.\n\n Raises:\n ValueError: If the request fails.\n \"\"\"\n\n url = f\"https://api.openai.com/v1/models/{id}/telemetry\"\n\n headers = {\n \"Authorization\": self.api_key,\n \"Content-Type\": \"application/json\",\n }\n\n telemetry_data = {\"events\": data}\n\n response = requests.post(url, json=telemetry_data, headers=headers)\n\n if not response.ok:\n raise ValueError(\"Failed to create telemetry\")\n\n return response.json()\n```\n\n## Step 3: Usage Example\n\nHere's an example usage of the `Telemetry` class:\n\n```python\n# Create a new Telemetry object with your OpenAI API key\ntelemetry = Telemetry(\n \"YOUR_OPENAI_API_KEY_HERE\"\n)\n\n# Define the telemetry data\ndata = {\"event\": \"example_event\"}\n\n# Create a new telemetry entry\nid = \"my_model_id\" # Replace with your model or dataset ID\n\ntry:\n result = telemetry.create_telemetry(id, data)\n print(result)\nexcept ValueError as e:\n print(e)\n```\n\nThis code creates a new `Telemetry` object, defines some sample telemetry data, and uses the `create_telemetry` method to create a new telemetry entry. The response from the OpenAI API is printed out.\n\nNote: Replace `\"YOUR_OPENAI_API_KEY_HERE\"` with your actual OpenAI API key.\n\n## Conclusion\n\nThis guide provides a basic example of how to create telemetries using the OpenAI API. You can modify the code and implement additional features as needed for your project.\n\nStay updated on our latest tutorials and guides:\n\n* [Check out our Discord channel](link): https://discord.gg/openai-exists\n\nHappy coding!", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510083, + "created": 1756921299, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 505, + "completion_tokens": 633, "prompt_tokens": 30, - "total_tokens": 535, + "total_tokens": 663, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/cd094caaf1c0.json b/tests/integration/recordings/responses/cd094caaf1c0.json index c0b3873d3..70a3d334d 100644 --- a/tests/integration/recordings/responses/cd094caaf1c0.json +++ b/tests/integration/recordings/responses/cd094caaf1c0.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.822116Z", + "created_at": "2025-09-03T17:36:21.138019Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.874482Z", + "created_at": "2025-09-03T17:36:21.179853Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.926533Z", + "created_at": "2025-09-03T17:36:21.220635Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.980659Z", + "created_at": "2025-09-03T17:36:21.261418Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.036126Z", + "created_at": "2025-09-03T17:36:21.301991Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.087015Z", + "created_at": "2025-09-03T17:36:21.3425Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.137306Z", + "created_at": "2025-09-03T17:36:21.38302Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.189129Z", + "created_at": "2025-09-03T17:36:21.423862Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.240264Z", + "created_at": "2025-09-03T17:36:21.464611Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.291201Z", + "created_at": "2025-09-03T17:36:21.505714Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.341476Z", + "created_at": "2025-09-03T17:36:21.547075Z", "done": false, "done_reason": null, "total_duration": null, 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@@ -1011,7 +1011,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.746336Z", + "created_at": "2025-09-03T17:36:23.37817Z", "done": false, "done_reason": null, "total_duration": null, @@ -1029,7 +1029,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.801383Z", + "created_at": "2025-09-03T17:36:23.418119Z", "done": false, "done_reason": null, "total_duration": null, @@ -1047,7 +1047,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.854621Z", + "created_at": "2025-09-03T17:36:23.458074Z", "done": false, "done_reason": null, "total_duration": null, @@ -1065,7 +1065,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.911212Z", + 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"2025-08-04T22:55:18.500596Z", + "created_at": "2025-09-03T17:36:23.944762Z", "done": false, "done_reason": null, "total_duration": null, @@ -1281,7 +1281,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.552919Z", + "created_at": "2025-09-03T17:36:23.985199Z", "done": false, "done_reason": null, "total_duration": null, @@ -1299,7 +1299,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.605569Z", + "created_at": "2025-09-03T17:36:24.025821Z", "done": false, "done_reason": null, "total_duration": null, @@ -1317,7 +1317,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.657753Z", + "created_at": "2025-09-03T17:36:24.066639Z", "done": false, "done_reason": null, "total_duration": null, @@ -1335,7 +1335,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.712933Z", + "created_at": "2025-09-03T17:36:24.109215Z", "done": false, "done_reason": null, "total_duration": null, @@ -1353,7 +1353,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.765708Z", + "created_at": "2025-09-03T17:36:24.15123Z", "done": false, "done_reason": null, "total_duration": null, @@ -1371,7 +1371,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.81852Z", + "created_at": "2025-09-03T17:36:24.192856Z", "done": false, "done_reason": null, "total_duration": null, @@ -1389,7 +1389,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.870752Z", + "created_at": "2025-09-03T17:36:24.23433Z", 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"llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.076061Z", + "created_at": "2025-09-03T17:36:24.396181Z", "done": false, "done_reason": null, "total_duration": null, @@ -1479,7 +1479,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.126893Z", + "created_at": "2025-09-03T17:36:24.43716Z", "done": false, "done_reason": null, "total_duration": null, @@ -1497,7 +1497,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.179123Z", + "created_at": "2025-09-03T17:36:24.478009Z", "done": false, "done_reason": null, "total_duration": null, @@ -1515,7 +1515,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.230189Z", + "created_at": "2025-09-03T17:36:24.519697Z", "done": false, "done_reason": null, "total_duration": 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"2025-08-04T22:55:19.980096Z", + "created_at": "2025-09-03T17:36:25.127555Z", "done": false, "done_reason": null, "total_duration": null, @@ -1803,7 +1803,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.03407Z", + "created_at": "2025-09-03T17:36:25.168437Z", "done": false, "done_reason": null, "total_duration": null, @@ -1821,7 +1821,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.090735Z", + "created_at": "2025-09-03T17:36:25.20913Z", "done": false, "done_reason": null, "total_duration": null, @@ -1839,7 +1839,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.153924Z", + "created_at": "2025-09-03T17:36:25.249991Z", "done": false, "done_reason": null, "total_duration": null, @@ -1857,7 +1857,7 @@ "__type__": 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"__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.118611Z", + "created_at": "2025-09-03T17:36:25.743383Z", "done": false, "done_reason": null, "total_duration": null, @@ -2073,7 +2073,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.182092Z", + "created_at": "2025-09-03T17:36:25.785523Z", "done": false, "done_reason": null, "total_duration": null, @@ -2091,7 +2091,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.241399Z", + "created_at": "2025-09-03T17:36:25.828276Z", "done": false, "done_reason": null, "total_duration": null, @@ -2109,7 +2109,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.296699Z", + "created_at": 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"2025-08-04T22:55:22.024705Z", + "created_at": "2025-09-03T17:36:26.335776Z", "done": false, "done_reason": null, "total_duration": null, @@ -2325,7 +2325,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.079411Z", + "created_at": "2025-09-03T17:36:26.377421Z", "done": false, "done_reason": null, "total_duration": null, @@ -2343,7 +2343,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.132835Z", + "created_at": "2025-09-03T17:36:26.419324Z", "done": false, "done_reason": null, "total_duration": null, @@ -2361,7 +2361,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.189848Z", + "created_at": "2025-09-03T17:36:26.460598Z", "done": false, "done_reason": null, "total_duration": null, @@ -2379,7 +2379,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.252016Z", + "created_at": "2025-09-03T17:36:26.502926Z", "done": false, "done_reason": null, "total_duration": null, @@ -2397,7 +2397,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.316246Z", + "created_at": "2025-09-03T17:36:26.545467Z", "done": false, "done_reason": null, "total_duration": null, @@ -2415,7 +2415,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.384612Z", + "created_at": "2025-09-03T17:36:26.587384Z", "done": false, "done_reason": null, "total_duration": null, @@ -2433,7 +2433,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.444066Z", + "created_at": 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"2025-08-04T22:55:23.116141Z", + "created_at": "2025-09-03T17:36:27.082117Z", "done": false, "done_reason": null, "total_duration": null, @@ -2649,7 +2649,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.171904Z", + "created_at": "2025-09-03T17:36:27.124286Z", "done": false, "done_reason": null, "total_duration": null, @@ -2667,7 +2667,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.226341Z", + "created_at": "2025-09-03T17:36:27.165354Z", "done": false, "done_reason": null, "total_duration": null, @@ -2685,7 +2685,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.279164Z", + "created_at": "2025-09-03T17:36:27.206517Z", "done": false, "done_reason": null, "total_duration": null, @@ -2703,7 +2703,7 @@ "__type__": 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"2025-08-04T22:55:24.689009Z", + "created_at": "2025-09-03T17:36:28.271506Z", "done": false, "done_reason": null, "total_duration": null, @@ -3171,7 +3171,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.742834Z", + "created_at": "2025-09-03T17:36:28.313533Z", "done": false, "done_reason": null, "total_duration": null, @@ -3189,7 +3189,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.79443Z", + "created_at": "2025-09-03T17:36:28.356508Z", "done": false, "done_reason": null, "total_duration": null, @@ -3207,7 +3207,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.845937Z", + "created_at": "2025-09-03T17:36:28.397379Z", "done": false, "done_reason": null, "total_duration": null, @@ -3225,7 +3225,7 @@ "__type__": 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- "created_at": "2025-08-04T22:55:25.274019Z", + "created_at": "2025-09-03T17:36:28.724325Z", "done": false, "done_reason": null, "total_duration": null, @@ -3369,7 +3369,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.324668Z", + "created_at": "2025-09-03T17:36:28.764731Z", "done": false, "done_reason": null, "total_duration": null, @@ -3387,7 +3387,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.377987Z", + "created_at": "2025-09-03T17:36:28.805214Z", "done": false, "done_reason": null, "total_duration": null, @@ -3405,7 +3405,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.429358Z", + "created_at": "2025-09-03T17:36:28.845962Z", "done": false, "done_reason": null, "total_duration": null, @@ -3423,7 +3423,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.481004Z", + "created_at": "2025-09-03T17:36:28.886874Z", "done": false, "done_reason": null, "total_duration": null, @@ -3441,7 +3441,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.534764Z", + "created_at": "2025-09-03T17:36:28.927442Z", "done": false, "done_reason": null, "total_duration": null, @@ -3459,7 +3459,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.587324Z", + "created_at": "2025-09-03T17:36:28.967837Z", "done": false, "done_reason": null, "total_duration": null, @@ -3477,7 +3477,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.639379Z", + "created_at": 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"2025-08-04T22:55:26.241555Z", + "created_at": "2025-09-03T17:36:29.459743Z", "done": false, "done_reason": null, "total_duration": null, @@ -3693,7 +3693,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.292588Z", + "created_at": "2025-09-03T17:36:29.500928Z", "done": false, "done_reason": null, "total_duration": null, @@ -3711,7 +3711,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.345649Z", + "created_at": "2025-09-03T17:36:29.541823Z", "done": false, "done_reason": null, "total_duration": null, @@ -3729,7 +3729,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.39865Z", + "created_at": "2025-09-03T17:36:29.583225Z", "done": false, "done_reason": null, "total_duration": null, @@ -3747,7 +3747,7 @@ "__type__": 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- "created_at": "2025-08-04T22:55:26.813607Z", + "created_at": "2025-09-03T17:36:29.909608Z", "done": false, "done_reason": null, "total_duration": null, @@ -3891,7 +3891,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.866943Z", + "created_at": "2025-09-03T17:36:29.950119Z", "done": false, "done_reason": null, "total_duration": null, @@ -3909,7 +3909,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.918563Z", + "created_at": "2025-09-03T17:36:29.990856Z", "done": false, "done_reason": null, "total_duration": null, @@ -3927,7 +3927,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.969428Z", + "created_at": "2025-09-03T17:36:30.031737Z", "done": false, "done_reason": null, "total_duration": null, @@ -3945,7 +3945,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.023314Z", + "created_at": "2025-09-03T17:36:30.072804Z", "done": false, "done_reason": null, "total_duration": null, @@ -3963,7 +3963,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.075325Z", + "created_at": "2025-09-03T17:36:30.115879Z", "done": false, "done_reason": null, "total_duration": null, @@ -3981,7 +3981,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.128289Z", + "created_at": "2025-09-03T17:36:30.157268Z", "done": false, "done_reason": null, "total_duration": null, @@ -3999,7 +3999,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.190218Z", + "created_at": 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- "created_at": "2025-08-04T22:55:28.427518Z", + "created_at": "2025-09-03T17:36:31.097989Z", "done": false, "done_reason": null, "total_duration": null, @@ -4413,7 +4413,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.479545Z", + "created_at": "2025-09-03T17:36:31.13892Z", "done": false, "done_reason": null, "total_duration": null, @@ -4431,7 +4431,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.531416Z", + "created_at": "2025-09-03T17:36:31.179559Z", "done": false, "done_reason": null, "total_duration": null, @@ -4449,7 +4449,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.58181Z", + "created_at": "2025-09-03T17:36:31.220282Z", "done": false, "done_reason": null, "total_duration": null, @@ -4467,7 +4467,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.632489Z", + "created_at": "2025-09-03T17:36:31.260847Z", "done": false, "done_reason": null, "total_duration": null, @@ -4485,7 +4485,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.684096Z", + "created_at": "2025-09-03T17:36:31.301689Z", "done": false, "done_reason": null, "total_duration": null, @@ -4503,7 +4503,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.737131Z", + "created_at": "2025-09-03T17:36:31.342413Z", "done": false, "done_reason": null, "total_duration": null, @@ -4521,7 +4521,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.789945Z", + "created_at": 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"2025-08-04T22:55:29.374321Z", + "created_at": "2025-09-03T17:36:31.836637Z", "done": false, "done_reason": null, "total_duration": null, @@ -4737,7 +4737,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.427106Z", + "created_at": "2025-09-03T17:36:31.878115Z", "done": false, "done_reason": null, "total_duration": null, @@ -4755,7 +4755,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.479022Z", + "created_at": "2025-09-03T17:36:31.919569Z", "done": false, "done_reason": null, "total_duration": null, @@ -4773,7 +4773,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.536933Z", + "created_at": "2025-09-03T17:36:31.960615Z", "done": false, "done_reason": null, "total_duration": null, @@ -4791,7 +4791,7 @@ "__type__": 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"2025-08-04T22:55:30.314187Z", + "created_at": "2025-09-03T17:36:32.572701Z", "done": false, "done_reason": null, "total_duration": null, @@ -5061,7 +5061,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.368683Z", + "created_at": "2025-09-03T17:36:32.614093Z", "done": false, "done_reason": null, "total_duration": null, @@ -5079,7 +5079,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.423991Z", + "created_at": "2025-09-03T17:36:32.655113Z", "done": false, "done_reason": null, "total_duration": null, @@ -5097,7 +5097,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.475926Z", + "created_at": "2025-09-03T17:36:32.696438Z", "done": false, "done_reason": null, "total_duration": null, @@ -5115,7 +5115,7 @@ "__type__": 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null, @@ -5313,7 +5313,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.1225Z", + "created_at": "2025-09-03T17:36:33.192643Z", "done": false, "done_reason": null, "total_duration": null, @@ -5331,7 +5331,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.17487Z", + "created_at": "2025-09-03T17:36:33.233604Z", "done": false, "done_reason": null, "total_duration": null, @@ -5349,7 +5349,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.226231Z", + "created_at": "2025-09-03T17:36:33.274665Z", "done": false, "done_reason": null, "total_duration": null, @@ -5367,7 +5367,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.28044Z", + 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.496638Z", + "created_at": "2025-09-03T17:36:33.478995Z", "done": false, "done_reason": null, "total_duration": null, @@ -5457,7 +5457,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.549024Z", + "created_at": "2025-09-03T17:36:33.520178Z", "done": false, "done_reason": null, "total_duration": null, @@ -5475,7 +5475,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.604983Z", + "created_at": "2025-09-03T17:36:33.561169Z", "done": false, "done_reason": null, "total_duration": null, @@ -5493,7 +5493,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.657366Z", + "created_at": "2025-09-03T17:36:33.602614Z", "done": false, "done_reason": 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"2025-08-04T22:55:31.864427Z", + "created_at": "2025-09-03T17:36:33.788023Z", "done": false, "done_reason": null, "total_duration": null, @@ -5583,7 +5583,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.915242Z", + "created_at": "2025-09-03T17:36:33.830123Z", "done": false, "done_reason": null, "total_duration": null, @@ -5601,7 +5601,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.967322Z", + "created_at": "2025-09-03T17:36:33.873234Z", "done": false, "done_reason": null, "total_duration": null, @@ -5619,7 +5619,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.018589Z", + "created_at": "2025-09-03T17:36:33.91574Z", "done": false, "done_reason": null, "total_duration": null, @@ -5637,7 +5637,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.070624Z", + "created_at": "2025-09-03T17:36:33.958165Z", "done": false, "done_reason": null, "total_duration": null, @@ -5655,7 +5655,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.121703Z", + "created_at": "2025-09-03T17:36:34.000544Z", "done": false, "done_reason": null, "total_duration": null, @@ -5673,7 +5673,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.174718Z", + "created_at": "2025-09-03T17:36:34.043824Z", "done": false, "done_reason": null, "total_duration": null, @@ -5691,7 +5691,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.23641Z", + "created_at": "2025-09-03T17:36:34.086339Z", "done": false, "done_reason": null, "total_duration": null, @@ -5709,7 +5709,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.294487Z", + "created_at": "2025-09-03T17:36:34.128863Z", "done": false, "done_reason": null, "total_duration": null, @@ -5727,7 +5727,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.354809Z", + "created_at": "2025-09-03T17:36:34.171675Z", "done": false, "done_reason": null, "total_duration": null, @@ -5745,7 +5745,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.409827Z", + "created_at": "2025-09-03T17:36:34.214025Z", "done": false, "done_reason": null, "total_duration": null, @@ -5763,7 +5763,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.467898Z", + "created_at": "2025-09-03T17:36:34.256135Z", "done": false, "done_reason": null, "total_duration": null, @@ -5781,7 +5781,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.525406Z", + "created_at": "2025-09-03T17:36:34.298571Z", "done": false, "done_reason": null, "total_duration": null, @@ -5799,7 +5799,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.580356Z", + "created_at": "2025-09-03T17:36:34.340742Z", "done": false, "done_reason": null, "total_duration": null, @@ -5817,7 +5817,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.637738Z", + "created_at": "2025-09-03T17:36:34.38192Z", "done": false, "done_reason": null, "total_duration": null, @@ -5835,7 +5835,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.691339Z", + "created_at": "2025-09-03T17:36:34.423807Z", "done": false, "done_reason": null, "total_duration": null, @@ -5853,7 +5853,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.75193Z", + "created_at": "2025-09-03T17:36:34.465059Z", "done": false, "done_reason": null, "total_duration": null, @@ -5871,7 +5871,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.809022Z", + "created_at": "2025-09-03T17:36:34.506527Z", "done": false, "done_reason": null, "total_duration": null, @@ -5889,7 +5889,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.868509Z", + "created_at": "2025-09-03T17:36:34.547797Z", "done": false, "done_reason": null, "total_duration": null, @@ -5907,7 +5907,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.927239Z", + "created_at": "2025-09-03T17:36:34.589189Z", "done": false, "done_reason": null, "total_duration": null, @@ -5925,7 +5925,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.985536Z", + "created_at": "2025-09-03T17:36:34.632479Z", "done": false, "done_reason": null, "total_duration": null, @@ -5943,7 +5943,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.040875Z", + "created_at": "2025-09-03T17:36:34.673914Z", "done": false, "done_reason": null, "total_duration": null, @@ -5961,7 +5961,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.099492Z", + "created_at": "2025-09-03T17:36:34.714561Z", "done": false, "done_reason": null, "total_duration": null, @@ -5979,7 +5979,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.151102Z", + "created_at": "2025-09-03T17:36:34.755794Z", "done": false, "done_reason": null, "total_duration": null, @@ -5997,7 +5997,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.2036Z", + "created_at": "2025-09-03T17:36:34.797365Z", "done": false, "done_reason": null, "total_duration": null, @@ -6015,7 +6015,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.255217Z", + "created_at": "2025-09-03T17:36:34.839305Z", "done": false, "done_reason": 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"2025-08-04T22:55:33.473848Z", + "created_at": "2025-09-03T17:36:35.005594Z", "done": false, "done_reason": null, "total_duration": null, @@ -6105,7 +6105,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.530364Z", + "created_at": "2025-09-03T17:36:35.047897Z", "done": false, "done_reason": null, "total_duration": null, @@ -6123,7 +6123,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.588387Z", + "created_at": "2025-09-03T17:36:35.088945Z", "done": false, "done_reason": null, "total_duration": null, @@ -6141,7 +6141,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.644848Z", + "created_at": "2025-09-03T17:36:35.130496Z", "done": false, "done_reason": null, "total_duration": null, @@ -6159,7 +6159,7 @@ "__type__": 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"done": false, "done_reason": null, "total_duration": null, @@ -6231,7 +6231,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.921183Z", + "created_at": "2025-09-03T17:36:35.335904Z", "done": false, "done_reason": null, "total_duration": null, @@ -6249,7 +6249,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.972308Z", + "created_at": "2025-09-03T17:36:35.376911Z", "done": false, "done_reason": null, "total_duration": null, @@ -6267,7 +6267,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.024699Z", + "created_at": "2025-09-03T17:36:35.417931Z", "done": false, "done_reason": null, "total_duration": null, @@ -6285,7 +6285,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": 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"2025-08-04T22:55:35.049568Z", + "created_at": "2025-09-03T17:36:36.206212Z", "done": false, "done_reason": null, "total_duration": null, @@ -6627,7 +6627,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.101649Z", + "created_at": "2025-09-03T17:36:36.24761Z", "done": false, "done_reason": null, "total_duration": null, @@ -6645,7 +6645,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.151407Z", + "created_at": "2025-09-03T17:36:36.288872Z", "done": false, "done_reason": null, "total_duration": null, @@ -6663,7 +6663,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.20241Z", + "created_at": "2025-09-03T17:36:36.330688Z", "done": false, "done_reason": null, "total_duration": null, @@ -6681,7 +6681,7 @@ "__type__": 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"total_duration": null, @@ -6879,7 +6879,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.834386Z", + "created_at": "2025-09-03T17:36:36.833512Z", "done": false, "done_reason": null, "total_duration": null, @@ -6897,7 +6897,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.887658Z", + "created_at": "2025-09-03T17:36:36.875114Z", "done": false, "done_reason": null, "total_duration": null, @@ -6915,7 +6915,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.940042Z", + "created_at": "2025-09-03T17:36:36.916425Z", "done": false, "done_reason": null, "total_duration": null, @@ -6933,7 +6933,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.996154Z", + "created_at": "2025-09-03T17:36:36.959229Z", "done": false, "done_reason": null, "total_duration": null, @@ -6951,7 +6951,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.054767Z", + "created_at": "2025-09-03T17:36:37.000732Z", "done": false, "done_reason": null, "total_duration": null, @@ -6969,7 +6969,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.110188Z", + "created_at": "2025-09-03T17:36:37.042352Z", "done": false, "done_reason": null, "total_duration": null, @@ -6987,7 +6987,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.172356Z", + "created_at": "2025-09-03T17:36:37.083572Z", "done": false, "done_reason": null, "total_duration": null, @@ -7005,7 +7005,7 @@ "__type__": 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"2025-09-03T17:36:37.249261Z", "done": false, "done_reason": null, "total_duration": null, @@ -7077,7 +7077,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.455985Z", + "created_at": "2025-09-03T17:36:37.291638Z", "done": false, "done_reason": null, "total_duration": null, @@ -7095,15 +7095,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.508093Z", + "created_at": "2025-09-03T17:36:37.333479Z", "done": true, "done_reason": "stop", - "total_duration": 21827314917, - "load_duration": 60502000, + "total_duration": 16422193500, + "load_duration": 146702667, "prompt_eval_count": 36, - "prompt_eval_duration": 75000000, + "prompt_eval_duration": 78361500, "eval_count": 394, - "eval_duration": 21690000000, + "eval_duration": 16196482750, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/d0ac68cbde69.json b/tests/integration/recordings/responses/d0ac68cbde69.json index 5c19e7c5a..750c5c69b 100644 --- a/tests/integration/recordings/responses/d0ac68cbde69.json +++ b/tests/integration/recordings/responses/d0ac68cbde69.json @@ -13,21 +13,21 @@ "__data__": { "models": [ { - "model": "llama3.2:3b-instruct-fp16", - "name": "llama3.2:3b-instruct-fp16", - "digest": "195a8c01d91ec3cb1e0aad4624a51f2602c51fa7d96110f8ab5a20c84081804d", - "expires_at": "2025-08-18T13:47:44.262256-07:00", - "size": 7919570944, - "size_vram": 7919570944, + "model": "llama3.2-vision:11b", + "name": "llama3.2-vision:11b", + "digest": "6f2f9757ae97e8a3f8ea33d6adb2b11d93d9a35bef277cd2c0b1b5af8e8d0b1e", + "expires_at": "2025-09-03T11:51:35.966409-07:00", + "size": 12401209008, + "size_vram": 12401209008, "details": { "parent_model": "", "format": "gguf", - "family": "llama", + "family": "mllama", "families": [ - "llama" + "mllama" ], - "parameter_size": "3.2B", - "quantization_level": "F16" + "parameter_size": "10.7B", + "quantization_level": "Q4_K_M" } } ] diff --git a/tests/integration/recordings/responses/d4c86ac355fb.json b/tests/integration/recordings/responses/d4c86ac355fb.json index 399c99e96..5dd3c7cc2 100644 --- a/tests/integration/recordings/responses/d4c86ac355fb.json +++ b/tests/integration/recordings/responses/d4c86ac355fb.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:54.357928Z", + "created_at": "2025-09-03T17:37:35.824092Z", "done": true, "done_reason": "stop", - "total_duration": 227148458, - "load_duration": 113314916, + "total_duration": 270017875, + "load_duration": 183186083, "prompt_eval_count": 220, - "prompt_eval_duration": 83000000, + "prompt_eval_duration": 74457250, "eval_count": 2, - "eval_duration": 27000000, + "eval_duration": 11684125, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/d86d4fc1eaca.json b/tests/integration/recordings/responses/d86d4fc1eaca.json index 165e65093..b22354c20 100644 --- a/tests/integration/recordings/responses/d86d4fc1eaca.json +++ b/tests/integration/recordings/responses/d86d4fc1eaca.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.043112263, - 0.008686894, - 0.06879597, - 0.018093547, - 0.04600579, - 0.0026370327, - -0.0032194739, - -0.04128641, - -0.090751864, - -0.03311354, - -0.026625047, - 0.007723082, - 0.02020638, - -0.032501053, - -0.03582959, - 0.031117352, - -0.03921459, - -0.011261255, - -0.10972644, - -0.12942035, - 0.0180839, - 0.011446483, - -0.07227963, - -0.013646516, - 0.035441313, - 0.024786202, - 0.033887945, - 0.072541736, - -0.012643559, - -0.058576923, - 0.05788946, - -0.08161914, - 0.064951725, - 0.0013679718, - -0.067565694, - 0.03500105, - -0.04499739, - -0.004745917, - 0.04001028, - -0.010447466, - 0.01971203, - -0.09853681, - -0.012831109, - 0.018893523, - 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a/tests/integration/recordings/responses/dd226d71f844.json b/tests/integration/recordings/responses/dd226d71f844.json index 2b8b52a63..ba2810bc9 100644 --- a/tests/integration/recordings/responses/dd226d71f844.json +++ b/tests/integration/recordings/responses/dd226d71f844.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.381208Z", + "created_at": "2025-09-03T17:38:05.682744Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.441511Z", + "created_at": "2025-09-03T17:38:05.72605Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.499052Z", + "created_at": "2025-09-03T17:38:05.770654Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.577259Z", + "created_at": "2025-09-03T17:38:05.819087Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.635016Z", + "created_at": "2025-09-03T17:38:05.862915Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.68944Z", + "created_at": "2025-09-03T17:38:05.913209Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.742314Z", + "created_at": "2025-09-03T17:38:05.951646Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.795086Z", + "created_at": "2025-09-03T17:38:05.996738Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.847905Z", + "created_at": "2025-09-03T17:38:06.046726Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.898666Z", + "created_at": "2025-09-03T17:38:06.08508Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.952292Z", + "created_at": "2025-09-03T17:38:06.128566Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:26.001903Z", + "created_at": "2025-09-03T17:38:06.173309Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,15 +238,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:26.053764Z", + "created_at": "2025-09-03T17:38:06.218818Z", "done": true, "done_reason": "stop", - "total_duration": 880684833, - "load_duration": 101945250, + "total_duration": 755252250, + "load_duration": 141479625, "prompt_eval_count": 402, - "prompt_eval_duration": 100000000, + "prompt_eval_duration": 76304166, "eval_count": 13, - "eval_duration": 677000000, + "eval_duration": 536202125, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/dd9e7d5913e9.json b/tests/integration/recordings/responses/dd9e7d5913e9.json index 8f4b0ef30..e3d8b41f5 100644 --- a/tests/integration/recordings/responses/dd9e7d5913e9.json +++ b/tests/integration/recordings/responses/dd9e7d5913e9.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:41.559883Z", + "created_at": "2025-09-03T17:36:40.972565Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,15 +39,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:41.619829Z", + "created_at": "2025-09-03T17:36:41.014682Z", "done": true, "done_reason": "stop", - "total_duration": 915493834, - "load_duration": 167838417, + "total_duration": 693115125, + "load_duration": 114019375, "prompt_eval_count": 386, - "prompt_eval_duration": 683000000, + "prompt_eval_duration": 535931209, "eval_count": 2, - "eval_duration": 63000000, + "eval_duration": 42505166, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/decfd950646c.json b/tests/integration/recordings/responses/decfd950646c.json index f62340c27..c46fa8686 100644 --- a/tests/integration/recordings/responses/decfd950646c.json +++ b/tests/integration/recordings/responses/decfd950646c.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -44,32 +44,22 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-620", + "id": "chatcmpl-202", "choices": [ { "delta": { - "content": "", + "content": "{\"name\":\"get_weather\",\"parameters{\"key\"]=\"Tokyo\"}}", "function_call": null, "refusal": null, "role": "assistant", - "tool_calls": [ - { - "index": 0, - "id": "call_490d5ur7", - "function": { - "arguments": "{\"city\":\"Tokyo\"}", - "name": "get_weather" - }, - "type": "function" - } - ] + "tool_calls": null }, "finish_reason": null, "index": 0, "logprobs": null } ], - "created": 1755228972, + "created": 1756921363, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -80,7 +70,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-620", + "id": "chatcmpl-202", "choices": [ { "delta": { @@ -90,12 +80,12 @@ "role": "assistant", "tool_calls": null }, - "finish_reason": "tool_calls", + "finish_reason": "stop", "index": 0, "logprobs": null } ], - "created": 1755228972, + "created": 1756921363, "model": "llama3.2:3b-instruct-fp16", 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0.016800206, + 0.005279432, + 0.030968234, + -0.05446385, + 0.0048696757, + 0.070877954, + 0.06684445, + 0.017715273, + -0.029237686 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/e2c9b07709fe.json b/tests/integration/recordings/responses/e2c9b07709fe.json index 47fa23233..0bab360ba 100644 --- a/tests/integration/recordings/responses/e2c9b07709fe.json +++ b/tests/integration/recordings/responses/e2c9b07709fe.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -22,14 +22,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-494", + "id": "chatcmpl-662", "choices": [ { "finish_reason": "length", "index": 0, "logprobs": null, "message": { - "content": "To test the OpenAI API with a temperature of 1, you can use the following Python code:\n\n```python\nimport requests\n\ndef generate_text(model_name, prompt, temperature=1):\n # Set the API endpoint and parameters\n url = \"https://api.openai.com/v1/models/\" + model_name + \"/generate\"\n params = {\n \"prompt\": prompt,\n \"temperature\": temperature\n }\n\n # Send a GET request to the API\n response =", + "content": "To test the prompt understanding of OpenAI's text generation capabilities, I'll simulate a conversation. \n\nYou mentioned testing the model with a temperature setting of 1. The temperature parameter in OpenAI's text models controls the diversity and coherence of generated text.\n\nA temperature of 1 is considered \"colder\" than usual, meaning the model will generate more coherent but potentially less diverse text compared to higher temperatures (e.g., 0.5 or 0.7).\n\nPlease provide a prompt for", "refusal": null, "role": "assistant", "annotations": null, @@ -39,7 +39,7 @@ } } ], - "created": 1754510067, + "created": 1756921259, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/e96152610712.json b/tests/integration/recordings/responses/e96152610712.json index b55e02825..aa758da0d 100644 --- a/tests/integration/recordings/responses/e96152610712.json +++ b/tests/integration/recordings/responses/e96152610712.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:51.421145Z", + "created_at": "2025-09-03T17:37:33.16899Z", "done": true, "done_reason": "stop", - "total_duration": 201670125, - "load_duration": 70275459, + "total_duration": 300698625, + "load_duration": 179823875, "prompt_eval_count": 207, - "prompt_eval_duration": 71000000, + "prompt_eval_duration": 65083666, "eval_count": 5, - "eval_duration": 58000000, + "eval_duration": 55216084, "response": "unsafe\nS2", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/e9c8a0e4f0e0.json b/tests/integration/recordings/responses/e9c8a0e4f0e0.json index 85adb5734..87a208405 100644 --- a/tests/integration/recordings/responses/e9c8a0e4f0e0.json +++ b/tests/integration/recordings/responses/e9c8a0e4f0e0.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-57", + "id": "chatcmpl-957", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "Humans live on Earth. It is the third planet from the Sun and is the only known planet in the universe that currently supports human life.", + "content": "Humans live on Earth. It's a terrestrial planet in the Solar System, located in the outer reaches of the Sun's gravitational pull.", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754081845, + "created": 1756921355, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 29, + "completion_tokens": 28, "prompt_tokens": 32, - "total_tokens": 61, + "total_tokens": 60, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/ed9e9b34008d.json b/tests/integration/recordings/responses/ed9e9b34008d.json index ae46f481a..d0591dbc1 100644 --- a/tests/integration/recordings/responses/ed9e9b34008d.json +++ b/tests/integration/recordings/responses/ed9e9b34008d.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:50:48.719062652Z", + "created_at": "2025-09-03T17:39:48.030217Z", "done": true, "done_reason": "stop", - "total_duration": 42572007410, - "load_duration": 42791399, + "total_duration": 9760536750, + "load_duration": 242188583, "prompt_eval_count": 26, - "prompt_eval_duration": 1301967184, + "prompt_eval_duration": 83819333, "eval_count": 232, - "eval_duration": 41226696354, + "eval_duration": 9434009042, "response": "The largest planet in our solar system is Jupiter. It is a gas giant, meaning it is primarily composed of hydrogen and helium gases. Jupiter has a diameter of approximately 142,984 kilometers (88,846 miles), which is more than 11 times the diameter of Earth.\n\nJupiter is not only the largest planet in terms of size, but also the most massive planet in our solar system, with a mass that is more than 318 times that of Earth. It has a thick atmosphere and a strong magnetic field, and is known for its distinctive banded appearance, which is caused by strong winds in the upper atmosphere.\n\nJupiter's massive size and gravitational pull have a significant impact on the surrounding space, including the orbits of nearby planets and asteroids. Its moons are also notable, with four large ones: Io, Europa, Ganymede, and Callisto, which are known as the Galilean moons due to their discovery by Galileo Galilei in 1610.\n\nJupiter is a fascinating planet that continues to be studied by astronomers and space agencies around the world, offering insights into the formation and evolution of our solar system.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/eee47930e3ae.json b/tests/integration/recordings/responses/eee47930e3ae.json index 20ec83476..283416a09 100644 --- a/tests/integration/recordings/responses/eee47930e3ae.json +++ b/tests/integration/recordings/responses/eee47930e3ae.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:23.842191Z", + "created_at": "2025-09-03T17:38:04.631107Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:23.903756Z", + "created_at": "2025-09-03T17:38:04.673105Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:23.962295Z", + "created_at": "2025-09-03T17:38:04.714459Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.019479Z", + "created_at": "2025-09-03T17:38:04.755882Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.076158Z", + "created_at": "2025-09-03T17:38:04.797494Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.142903Z", + "created_at": "2025-09-03T17:38:04.839382Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.202616Z", + "created_at": "2025-09-03T17:38:04.881062Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.25501Z", + "created_at": "2025-09-03T17:38:04.921976Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.308017Z", + "created_at": "2025-09-03T17:38:04.962922Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.360014Z", + "created_at": "2025-09-03T17:38:05.00411Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.413785Z", + "created_at": "2025-09-03T17:38:05.04532Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.466618Z", + "created_at": "2025-09-03T17:38:05.086979Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.519141Z", + "created_at": "2025-09-03T17:38:05.128195Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.572343Z", + "created_at": "2025-09-03T17:38:05.169221Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.626495Z", + "created_at": "2025-09-03T17:38:05.210938Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.683554Z", + "created_at": "2025-09-03T17:38:05.252232Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.736715Z", + "created_at": "2025-09-03T17:38:05.293529Z", "done": false, "done_reason": null, "total_duration": null, @@ -328,7 +328,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.789545Z", + "created_at": "2025-09-03T17:38:05.334965Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,15 +346,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.842095Z", + "created_at": "2025-09-03T17:38:05.376741Z", "done": true, "done_reason": "stop", - "total_duration": 1141228125, - "load_duration": 38375333, + "total_duration": 936717042, + "load_duration": 109245542, "prompt_eval_count": 371, - "prompt_eval_duration": 99000000, + "prompt_eval_duration": 80430583, "eval_count": 19, - "eval_duration": 1002000000, + "eval_duration": 746422917, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/ef59cbff54d0.json b/tests/integration/recordings/responses/ef59cbff54d0.json index e16cf605c..559930873 100644 --- a/tests/integration/recordings/responses/ef59cbff54d0.json +++ b/tests/integration/recordings/responses/ef59cbff54d0.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:54.110896Z", + "created_at": "2025-09-03T17:37:35.524155Z", "done": true, "done_reason": "stop", - "total_duration": 219323916, - "load_duration": 109411750, + "total_duration": 251173708, + "load_duration": 165988125, "prompt_eval_count": 213, - "prompt_eval_duration": 86000000, + "prompt_eval_duration": 73363375, "eval_count": 2, - "eval_duration": 22000000, + "eval_duration": 11249792, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/ef757a75ed08.json b/tests/integration/recordings/responses/ef757a75ed08.json index b2d68f4d6..05860c4bb 100644 --- a/tests/integration/recordings/responses/ef757a75ed08.json +++ b/tests/integration/recordings/responses/ef757a75ed08.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.212563Z", + "created_at": "2025-09-03T17:34:22.272912Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.254896Z", + "created_at": "2025-09-03T17:34:22.31501Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.297152Z", + "created_at": "2025-09-03T17:34:22.356888Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.339477Z", + "created_at": "2025-09-03T17:34:22.398576Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.382245Z", + "created_at": "2025-09-03T17:34:22.440412Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.423387Z", + "created_at": "2025-09-03T17:34:22.482165Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.465286Z", + "created_at": "2025-09-03T17:34:22.523773Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.507249Z", + "created_at": "2025-09-03T17:34:22.565072Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,15 +165,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.549072Z", + "created_at": "2025-09-03T17:34:22.607117Z", "done": true, "done_reason": "stop", - "total_duration": 5519843458, - "load_duration": 4110366375, + "total_duration": 1386049708, + "load_duration": 96970583, "prompt_eval_count": 456, - "prompt_eval_duration": 1070783708, + "prompt_eval_duration": 952471625, "eval_count": 9, - "eval_duration": 337120750, + "eval_duration": 335924459, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/vision/responses/f1592dee71e5.json b/tests/integration/recordings/responses/f1592dee71e5.json similarity index 99% rename from tests/integration/recordings/vision/responses/f1592dee71e5.json rename to tests/integration/recordings/responses/f1592dee71e5.json index a30aa460b..d95497ee2 100644 --- a/tests/integration/recordings/vision/responses/f1592dee71e5.json +++ b/tests/integration/recordings/responses/f1592dee71e5.json @@ -30,18 +30,18 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:06:12.068973125Z", + "created_at": "2025-09-03T17:54:32.086616Z", "done": true, "done_reason": "stop", - "total_duration": 44793549354, - "load_duration": 51960915, + "total_duration": 3537246333, + "load_duration": 130547125, "prompt_eval_count": 18, - "prompt_eval_duration": 579363429, - "eval_count": 110, - "eval_duration": 44156162976, + "prompt_eval_duration": 140216250, + "eval_count": 56, + "eval_duration": 3262609875, "message": { "role": "assistant", - "content": "The image features a close-up of a golden retriever puppy, with its mouth open and tongue out, as if it is smiling or panting. The puppy's fur is a light golden color, and its ears are floppy and hanging down on either side of its head. The background of the image is blurred, but it appears to be a natural setting, possibly a field or a park, with a greenish-yellow color. The overall atmosphere of the image is one of happiness and playfulness, as the puppy seems to be enjoying itself.", + "content": "The image is of a golden retriever puppy. The puppy is looking directly at the camera with its mouth open and tongue out. The puppy is white with golden ears and a black nose. 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"http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -38,7 +38,7 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-549", + "id": "chatcmpl-10", "choices": [ { "finish_reason": "tool_calls", @@ -53,7 +53,7 @@ "function_call": null, "tool_calls": [ { - "id": "call_ybj7t2qt", + "id": "call_7cm57k1b", "function": { "arguments": "{\"city\":\"Tokyo\"}", "name": "get_weather" @@ -65,7 +65,7 @@ } } ], - "created": 1754081857, + "created": 1756921368, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/fcdef245da95.json b/tests/integration/recordings/responses/fcdef245da95.json index 04606b914..d2801b9c6 100644 --- a/tests/integration/recordings/responses/fcdef245da95.json +++ b/tests/integration/recordings/responses/fcdef245da95.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:13:55.309172Z", + "created_at": "2025-09-03T17:37:44.986629Z", "done": true, "done_reason": "stop", - "total_duration": 2252068541, - "load_duration": 240932958, + "total_duration": 285693167, + "load_duration": 110888542, "prompt_eval_count": 212, - "prompt_eval_duration": 1979000000, + "prompt_eval_duration": 163158250, "eval_count": 2, - "eval_duration": 25000000, + "eval_duration": 11080125, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/vision/responses/ff7db0102b28.json b/tests/integration/recordings/responses/ff7db0102b28.json similarity index 98% rename from tests/integration/recordings/vision/responses/ff7db0102b28.json rename to tests/integration/recordings/responses/ff7db0102b28.json index 160e0a607..f1866d1f4 100644 --- a/tests/integration/recordings/vision/responses/ff7db0102b28.json +++ b/tests/integration/recordings/responses/ff7db0102b28.json @@ -31,7 +31,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:49.339347876Z", + "created_at": "2025-09-03T17:54:22.358461Z", "done": false, "done_reason": null, "total_duration": null, @@ -53,7 +53,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:49.747466769Z", + "created_at": "2025-09-03T17:54:22.416981Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.156146804Z", + "created_at": "2025-09-03T17:54:22.477481Z", "done": false, "done_reason": null, "total_duration": null, @@ -97,7 +97,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.566195243Z", + "created_at": "2025-09-03T17:54:22.53807Z", "done": false, "done_reason": null, "total_duration": null, @@ -119,7 +119,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.975121211Z", + "created_at": "2025-09-03T17:54:22.59701Z", "done": false, "done_reason": null, "total_duration": null, @@ -141,7 +141,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:51.388779549Z", + "created_at": "2025-09-03T17:54:22.655848Z", "done": false, "done_reason": null, "total_duration": null, @@ -163,7 +163,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:51.79897453Z", + "created_at": "2025-09-03T17:54:22.715363Z", "done": false, "done_reason": null, "total_duration": null, @@ -185,7 +185,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:52.209608504Z", + "created_at": "2025-09-03T17:54:22.773865Z", "done": false, "done_reason": null, "total_duration": null, @@ -207,7 +207,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:52.619045995Z", + "created_at": "2025-09-03T17:54:22.832338Z", "done": false, "done_reason": null, "total_duration": null, @@ -229,7 +229,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:53.026501007Z", + "created_at": "2025-09-03T17:54:22.890824Z", "done": false, "done_reason": null, "total_duration": null, @@ -251,7 +251,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:53.436015071Z", + "created_at": "2025-09-03T17:54:22.949237Z", "done": false, "done_reason": null, "total_duration": null, @@ -273,7 +273,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:53.843369446Z", + "created_at": "2025-09-03T17:54:23.008374Z", "done": false, "done_reason": null, "total_duration": null, @@ -295,7 +295,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:54.255794451Z", + "created_at": "2025-09-03T17:54:23.066921Z", "done": false, "done_reason": null, "total_duration": null, @@ -317,7 +317,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:54.663263793Z", + "created_at": "2025-09-03T17:54:23.125544Z", "done": false, "done_reason": null, "total_duration": null, @@ -339,7 +339,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:55.073162133Z", + "created_at": "2025-09-03T17:54:23.184923Z", "done": false, "done_reason": null, "total_duration": null, @@ -361,7 +361,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:55.48667439Z", + "created_at": "2025-09-03T17:54:23.244278Z", "done": false, "done_reason": null, "total_duration": null, @@ -383,7 +383,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:55.897947147Z", + "created_at": "2025-09-03T17:54:23.303383Z", "done": false, "done_reason": null, "total_duration": null, @@ -405,7 +405,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:56.31639321Z", + "created_at": "2025-09-03T17:54:23.36246Z", "done": false, "done_reason": null, "total_duration": null, @@ -427,7 +427,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:56.729288843Z", + "created_at": "2025-09-03T17:54:23.421703Z", "done": false, "done_reason": null, "total_duration": null, @@ -449,7 +449,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:57.142647132Z", + "created_at": "2025-09-03T17:54:23.481027Z", "done": false, "done_reason": null, "total_duration": null, @@ -471,7 +471,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:57.55091814Z", + "created_at": "2025-09-03T17:54:23.540282Z", "done": false, "done_reason": null, "total_duration": null, @@ -493,7 +493,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:57.959494633Z", + "created_at": "2025-09-03T17:54:23.59938Z", "done": false, "done_reason": null, "total_duration": null, @@ -515,7 +515,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:58.367117419Z", + "created_at": "2025-09-03T17:54:23.658742Z", "done": false, "done_reason": null, "total_duration": null, @@ -537,7 +537,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:58.77560425Z", + "created_at": "2025-09-03T17:54:23.718569Z", "done": false, "done_reason": null, "total_duration": null, @@ -559,7 +559,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:59.183890868Z", + "created_at": "2025-09-03T17:54:23.777758Z", "done": false, "done_reason": null, "total_duration": null, @@ -581,51 +581,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:59.596163097Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - 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"__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-517", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "I'm happy to help you test OpenAI's telemetry creation feature. However, I need to inform you that OpenAI's models are not designed for direct testing and may not support the kind of feedback you're looking for.\n\nThat being said, we can try a simulated testing process using this chat interface. Here's how we can go about it:\n\n1. **Test the chat model:** Before we dive into telemetry creation, let's test the conversation system itself.\n2. **Try out general queries and statements**: See if I can respond to various questions and prompt topics with accuracy. This will help you gauge the effectiveness of my language processing abilities within this interface.\n3. **Create a simulated telemetry request:** Based on your feedback about our chat, describe what kind of information would be needed as a telemetry point for monitoring conversations like ours.\n\nGo ahead and give me some test data or prompt topics so we can proceed with creating a simulated \"telemetry\" creation process.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003724, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 195, - "prompt_tokens": 30, - "total_tokens": 225, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/830a1fe14938.json b/tests/integration/recordings/vision/responses/830a1fe14938.json deleted file mode 100644 index 2202416c9..000000000 --- a/tests/integration/recordings/vision/responses/830a1fe14938.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai 1" - } - ], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-434", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "I don't have information on testing \"OpenAI\" as a product has not been released.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003706, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 20, - "prompt_tokens": 31, - "total_tokens": 51, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/9c007f300365.json b/tests/integration/recordings/vision/responses/9c007f300365.json deleted file mode 100644 index f776e16a0..000000000 --- a/tests/integration/recordings/vision/responses/9c007f300365.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai with temperature 0" - } - ], - "max_tokens": 100, - "stream": false, - "temperature": 0.7 - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-413", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "I can't provide information or guidance on illegal or harmful activities, including testing the OpenAI model at a temperature of 0. Is there anything else I can help you with?", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003714, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 37, - "prompt_tokens": 33, - "total_tokens": 70, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/c9667519ad7c.json b/tests/integration/recordings/vision/responses/c9667519ad7c.json deleted file mode 100644 index ce0322da9..000000000 --- a/tests/integration/recordings/vision/responses/c9667519ad7c.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai with temperature 1" - } - ], - "max_tokens": 100, - "stream": false, - "temperature": 0.7 - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-82", - "choices": [ - { - "finish_reason": "length", - "index": 0, - "logprobs": null, - "message": { - "content": "To test the trace functionality of OpenAI's API with a temperature of 1, you can use the following Python code:\n```\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\n# Load pre-trained model and tokenizer\nmodel_name = \"CompVis/transformers-base-tiny\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Set temperature to 1\ntemperature = 1.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003715, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 100, - "prompt_tokens": 33, - "total_tokens": 133, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/d0ac68cbde69.json b/tests/integration/recordings/vision/responses/d0ac68cbde69.json deleted file mode 100644 index b37962fb6..000000000 --- a/tests/integration/recordings/vision/responses/d0ac68cbde69.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/ps", - "headers": {}, - "body": {}, - "endpoint": "/api/ps", - "model": "" - }, - "response": { - "body": { - "__type__": "ollama._types.ProcessResponse", - "__data__": { - "models": [] - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/d4f56d7d1996.json b/tests/integration/recordings/vision/responses/d4f56d7d1996.json deleted file mode 100644 index 47468b71e..000000000 --- a/tests/integration/recordings/vision/responses/d4f56d7d1996.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai 2" - } - ], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-661", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "You want to test the text-to-image capabilities of the OpenAI 2 model. To do this, we can use a simple interface in Python to prompt the model and see if it generates an image.\n\nHere's an example code snippet that shows how you can test the model:\n```\nimport numpy as np\nfrom PIL import Image\nfrom io import BytesIO\n\n# Load the OpenAI 2 model weights\nmodel_weights = \"path/to/openai2/model_weights.json\"\n\n# Load the model\nmodel = torch.hub.load(\"openai\", \"image-model\", pretrain_model_path=model_weights)\n\n# Set up a prompt for the model\nprompt = \"A picture of a futuristic cityscape at sunset\"\n\n# Use the model to generate an image\nwith torch.no_grad():\n image = model(prompt, return_tensor=True).numpy()\n\n# Save the generated image to a file\nimg = Image.fromarray(np.uint8(image))\nimg.save(\"generated_image.png\")\n\nprint(\"Generated image saved to 'generated_image.png'\")\n```\nPlease note that:\n\n1. You need to have PyTorch installed (`pip install torch torchvision`) and downloaded the OpenAI 2 model weights from their repository.\n2. The `image-model` library is used for text-to-image synthesis, which can be installed with `pip install image-model`.\n3. You may need to adjust the prompt and the output settings according to your specific use case.\n\nAlso note that, the openai2 model requires pre-trained on CelebA and FFHQ datasets and its text-to-image capabilities might not work as well as trained specifically for this type of task.\n\nYou can find more information about how to use the `image-model` library at their official documentation: https://github.com/karpathy/vis-dlg\n\nAlso, you can try other text-to-image models like DALL-E or Stable Diffusion using Python libraries like Hugging Face Transformers and PyTorch.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003713, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 395, - "prompt_tokens": 31, - "total_tokens": 426, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/unit/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py index dd80b0caf..c69cf319b 100644 --- a/tests/unit/distribution/test_inference_recordings.py +++ b/tests/unit/distribution/test_inference_recordings.py @@ -266,7 +266,7 @@ class TestInferenceRecording: return real_openai_chat_response with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): - with inference_recording(mode=InferenceMode.LIVE): + with inference_recording(mode=InferenceMode.LIVE, storage_dir="foo"): client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") response = await client.chat.completions.create( From 02f6e0f53122e9dff575e4b85cfcad83de88eef3 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 3 Sep 2025 15:57:17 -0700 Subject: [PATCH 034/124] fix(tests): set inference mode to be replay by default (#3326) `construct_stack()` relies on the environment variable to know when to setup the patching infrastructure. https://github.com/llamastack/llama-stack/blob/c3d3a0b83333a720886c4a846e41ed3ad2766e00/llama_stack/core/stack.py#L314 --- tests/integration/conftest.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 234d762ce..fd9a54d04 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -30,6 +30,8 @@ def pytest_runtest_makereport(item, call): def pytest_sessionstart(session): # stop macOS from complaining about duplicate OpenMP libraries os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" + if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ: + os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = "replay" def pytest_runtest_teardown(item): From 5d52e0d2c5c4550eceeabed5115cf552295819db Mon Sep 17 00:00:00 2001 From: ehhuang Date: Thu, 4 Sep 2025 04:23:18 -0700 Subject: [PATCH 035/124] chore: handle missing finish_reason (#3328) # What does this PR do? Sometimes the stream don't have chunks with finish_reason, e.g. canceled stream, which throws a pydantic error as OpenAIChoice.finish_reason: str ## Test Plan observe no more such error when benchmarking --- llama_stack/core/routers/inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 4b66601bb..8dcad85e3 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -755,7 +755,7 @@ class InferenceRouter(Inference): choices_data[idx] = { "content_parts": [], "tool_calls_builder": {}, - "finish_reason": None, + "finish_reason": "stop", "logprobs_content_parts": [], } current_choice_data = choices_data[idx] From 64d2306dd5481be3fdfaa11c415efe3f2788358e Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 4 Sep 2025 16:56:32 +0100 Subject: [PATCH 036/124] fix: distro-codegen pre-commit hook file pattern (#3337) Update the file pattern from 'llama_stack/templates' to 'llama_stack/distributions' to properly trigger the Distribution Template Codegen hook when distribution files change. Signed-off-by: Derek Higgins --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 514fe6d2e..b7880a9fc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -86,7 +86,7 @@ repos: language: python pass_filenames: false require_serial: true - files: ^llama_stack/templates/.*$|^llama_stack/providers/.*/inference/.*/models\.py$ + files: ^llama_stack/distributions/.*$|^llama_stack/providers/.*/inference/.*/models\.py$ - id: provider-codegen name: Provider Codegen additional_dependencies: From 85f33762d7cb7495b9aa5946284e4b6c85f5ad96 Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Thu, 4 Sep 2025 17:15:13 +0100 Subject: [PATCH 037/124] refactor(server): remove hardcoded 409 and 404 status codes in server.py using httpx constants (#3333) # What does this PR do? This PR is eliminating hardcoded status codes: `409` CONFLICT and `404` NOT_FOUND in `server.py` using `httpx` built-in constants. This implementation will follow the existing structure to improve readability, extensibility and developer experience. This is already was implemented in #3131 ## Test Plan `./scripts/unit-tests.sh` --- llama_stack/core/server/server.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama_stack/core/server/server.py b/llama_stack/core/server/server.py index d6dfc3435..b247a610d 100644 --- a/llama_stack/core/server/server.py +++ b/llama_stack/core/server/server.py @@ -132,9 +132,9 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro }, ) elif isinstance(exc, ConflictError): - return HTTPException(status_code=409, detail=str(exc)) + return HTTPException(status_code=httpx.codes.CONFLICT, detail=str(exc)) elif isinstance(exc, ResourceNotFoundError): - return HTTPException(status_code=404, detail=str(exc)) + return HTTPException(status_code=httpx.codes.NOT_FOUND, detail=str(exc)) elif isinstance(exc, ValueError): return HTTPException(status_code=httpx.codes.BAD_REQUEST, detail=f"Invalid value: {str(exc)}") elif isinstance(exc, BadRequestError): From 5bbca56cfc24cd1a1d4d5aff6a9c1c4ad12a741a Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 4 Sep 2025 18:58:41 +0100 Subject: [PATCH 038/124] fix: Make SentenceTransformer embedding operations non-blocking (#3335) - Wrap model loading with asyncio.to_thread() to prevent blocking during model download/initialization - Wrap encoding operations with asyncio.to_thread() to run in background thread - Convert _load_sentence_transformer_model() to async method This ensures the async event loop remains responsive during embedding operations. Closes: #3332 Signed-off-by: Derek Higgins Co-authored-by: Francisco Arceo --- .../utils/inference/embedding_mixin.py | 23 ++++++++++++------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/llama_stack/providers/utils/inference/embedding_mixin.py b/llama_stack/providers/utils/inference/embedding_mixin.py index 65ba2854b..9bd0aa8ce 100644 --- a/llama_stack/providers/utils/inference/embedding_mixin.py +++ b/llama_stack/providers/utils/inference/embedding_mixin.py @@ -4,6 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import asyncio import base64 import struct from typing import TYPE_CHECKING @@ -43,9 +44,11 @@ class SentenceTransformerEmbeddingMixin: task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) - embedding_model = self._load_sentence_transformer_model(model.provider_resource_id) - embeddings = embedding_model.encode( - [interleaved_content_as_str(content) for content in contents], show_progress_bar=False + embedding_model = await self._load_sentence_transformer_model(model.provider_resource_id) + embeddings = await asyncio.to_thread( + embedding_model.encode, + [interleaved_content_as_str(content) for content in contents], + show_progress_bar=False, ) return EmbeddingsResponse(embeddings=embeddings) @@ -64,8 +67,8 @@ class SentenceTransformerEmbeddingMixin: # Get the model and generate embeddings model_obj = await self.model_store.get_model(model) - embedding_model = self._load_sentence_transformer_model(model_obj.provider_resource_id) - embeddings = embedding_model.encode(input_list, show_progress_bar=False) + embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id) + embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False) # Convert embeddings to the requested format data = [] @@ -93,7 +96,7 @@ class SentenceTransformerEmbeddingMixin: usage=usage, ) - def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer": + async def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer": global EMBEDDING_MODELS loaded_model = EMBEDDING_MODELS.get(model) @@ -101,8 +104,12 @@ class SentenceTransformerEmbeddingMixin: return loaded_model log.info(f"Loading sentence transformer for {model}...") - from sentence_transformers import SentenceTransformer - loaded_model = SentenceTransformer(model) + def _load_model(): + from sentence_transformers import SentenceTransformer + + return SentenceTransformer(model) + + loaded_model = await asyncio.to_thread(_load_model) EMBEDDING_MODELS[model] = loaded_model return loaded_model From bcc7f2c7d0a24a4671e98134e6c28f74e6acff26 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Thu, 4 Sep 2025 11:37:46 -0700 Subject: [PATCH 039/124] chore: async inference store write (#3318) # What does this PR do? ## Test Plan ``` cd /docs/source/distributions/k8s-benchmark # start mock server python openai-mock-server.py --port 8000 # start stack server uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml # run benchmark script uv run python3 benchmark.py --duration 30 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct ``` Before: ============================================================ BENCHMARK RESULTS ============================================================ Total time: 30.00s Concurrent users: 50 Total requests: 1267 Successful requests: 1267 Failed requests: 0 Success rate: 100.0% Requests per second: 42.23 After: ============================================================ BENCHMARK RESULTS ============================================================ Total time: 30.00s Concurrent users: 50 Total requests: 1449 Successful requests: 1449 Failed requests: 0 Success rate: 100.0% Requests per second: 48.30 --- .../distributions/k8s-benchmark/stack_run_config.yaml | 8 ++++++++ llama_stack/core/routers/inference.py | 4 ++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml b/docs/source/distributions/k8s-benchmark/stack_run_config.yaml index ceb1ba2d9..5a810639e 100644 --- a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml +++ b/docs/source/distributions/k8s-benchmark/stack_run_config.yaml @@ -3,6 +3,7 @@ image_name: kubernetes-benchmark-demo apis: - agents - inference +- safety - telemetry - tool_runtime - vector_io @@ -30,6 +31,11 @@ providers: db: ${env.POSTGRES_DB:=llamastack} user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] agents: - provider_id: meta-reference provider_type: inline::meta-reference @@ -95,6 +101,8 @@ models: - model_id: ${env.INFERENCE_MODEL} provider_id: vllm-inference model_type: llm +shields: +- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} vector_dbs: [] datasets: [] scoring_fns: [] diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 8dcad85e3..045093fe0 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -527,7 +527,7 @@ class InferenceRouter(Inference): # Store the response with the ID that will be returned to the client if self.store: - await self.store.store_chat_completion(response, messages) + asyncio.create_task(self.store.store_chat_completion(response, messages)) if self.telemetry: metrics = self._construct_metrics( @@ -855,4 +855,4 @@ class InferenceRouter(Inference): object="chat.completion", ) logger.debug(f"InferenceRouter.completion_response: {final_response}") - await self.store.store_chat_completion(final_response, messages) + asyncio.create_task(self.store.store_chat_completion(final_response, messages)) From 561d2fc6b8226f167bb9782e6619d86a092af8e8 Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Thu, 4 Sep 2025 11:47:46 -0700 Subject: [PATCH 040/124] fix: Move to older version for docker container failure [fireworks-ai] (#3338) # What does this PR do? Noticed the test https://github.com/llamastack/llama-stack-ops/actions/workflows/test-maybe-cut.yaml are still failing randomly. Earlier fixed this with 0.18.0 of fireworks here https://github.com/llamastack/llama-stack/pull/3267, the local testing may have inadvertently picked a lower version with `<=` which I assumed picks latest version. Now tested with `==` to find the version where it broke and pinning to version(`<=`) where it was passing. ## Test Plan Tested locally with the following commands to start a container Build container `llama stack build --distro starter --image-type container` start container `docker run -d -p 8321:8321 --name llama-stack-test distribution-starter:0.2.20` check health `http://localhost:8321/v1/health` Above steps fails without the fix Tested with `==` to ensure the same version is picked in local testing instead of anything lower. Following here for the fix from `fireworks-ai` https://discord.com/channels/1137072072808472616/1410674695597981778/1410674695597981778 https://github.com/llamastack/llama-stack/issues/3273 --- llama_stack/providers/registry/inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index fb841afdf..50956f58c 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -116,7 +116,7 @@ def available_providers() -> list[ProviderSpec]: adapter=AdapterSpec( adapter_type="fireworks", pip_packages=[ - "fireworks-ai<=0.18.0", + "fireworks-ai<=0.17.16", ], module="llama_stack.providers.remote.inference.fireworks", config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig", From 55a8c5f439b710cfbb1256e3d6c41a6d82601970 Mon Sep 17 00:00:00 2001 From: Sumanth Kamenani Date: Thu, 4 Sep 2025 16:25:02 -0400 Subject: [PATCH 041/124] fix: show descriptive MCP server connection errors instead of generic 500s (#3256) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit What does this PR do? Fixes error handling when MCP server connections fail. Instead of returning generic 500 errors, now provides descriptive error messages with proper HTTP status codes. Closes #3107 Test Plan Before fix: curl -X GET "http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server" Returns: {"detail": "Internal server error: An unexpected error occurred."} (500) After fix: curl -X GET "http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server" Returns: {"error": {"detail": "Failed to connect to MCP server at http://localhost:9999/sse: Connection refused"}} (502) Tests: - Added unit test for ConnectionError → 502 translation - Manually tested with unreachable MCP servers (connection refused) --- llama_stack/core/server/server.py | 2 ++ llama_stack/providers/utils/tools/mcp.py | 32 ++++++++++++++++++++++++ tests/unit/server/test_server.py | 9 +++++++ 3 files changed, 43 insertions(+) diff --git a/llama_stack/core/server/server.py b/llama_stack/core/server/server.py index b247a610d..288bf46e1 100644 --- a/llama_stack/core/server/server.py +++ b/llama_stack/core/server/server.py @@ -141,6 +141,8 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro return HTTPException(status_code=httpx.codes.BAD_REQUEST, detail=str(exc)) elif isinstance(exc, PermissionError | AccessDeniedError): return HTTPException(status_code=httpx.codes.FORBIDDEN, detail=f"Permission denied: {str(exc)}") + elif isinstance(exc, ConnectionError | httpx.ConnectError): + return HTTPException(status_code=httpx.codes.BAD_GATEWAY, detail=str(exc)) elif isinstance(exc, asyncio.TimeoutError | TimeoutError): return HTTPException(status_code=httpx.codes.GATEWAY_TIMEOUT, detail=f"Operation timed out: {str(exc)}") elif isinstance(exc, NotImplementedError): diff --git a/llama_stack/providers/utils/tools/mcp.py b/llama_stack/providers/utils/tools/mcp.py index 02f7aaf8a..fc8e2f377 100644 --- a/llama_stack/providers/utils/tools/mcp.py +++ b/llama_stack/providers/utils/tools/mcp.py @@ -67,6 +67,38 @@ async def client_wrapper(endpoint: str, headers: dict[str, str]) -> AsyncGenerat raise AuthenticationRequiredError(exc) from exc if i == len(connection_strategies) - 1: raise + except* httpx.ConnectError as eg: + # Connection refused, server down, network unreachable + if i == len(connection_strategies) - 1: + error_msg = f"Failed to connect to MCP server at {endpoint}: Connection refused" + logger.error(f"MCP connection error: {error_msg}") + raise ConnectionError(error_msg) from eg + else: + logger.warning( + f"failed to connect to MCP server at {endpoint} via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) + except* httpx.TimeoutException as eg: + # Request timeout, server too slow + if i == len(connection_strategies) - 1: + error_msg = f"MCP server at {endpoint} timed out" + logger.error(f"MCP timeout error: {error_msg}") + raise TimeoutError(error_msg) from eg + else: + logger.warning( + f"MCP server at {endpoint} timed out via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) + except* httpx.RequestError as eg: + # DNS resolution failures, network errors, invalid URLs + if i == len(connection_strategies) - 1: + # Get the first exception's message for the error string + exc_msg = str(eg.exceptions[0]) if eg.exceptions else "Unknown error" + error_msg = f"Network error connecting to MCP server at {endpoint}: {exc_msg}" + logger.error(f"MCP network error: {error_msg}") + raise ConnectionError(error_msg) from eg + else: + logger.warning( + f"network error connecting to MCP server at {endpoint} via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) except* McpError: if i < len(connection_strategies) - 1: logger.warning( diff --git a/tests/unit/server/test_server.py b/tests/unit/server/test_server.py index 803111fc7..f21bbdd67 100644 --- a/tests/unit/server/test_server.py +++ b/tests/unit/server/test_server.py @@ -113,6 +113,15 @@ class TestTranslateException: assert result.status_code == 504 assert result.detail == "Operation timed out: " + def test_translate_connection_error(self): + """Test that ConnectionError is translated to 502 HTTP status.""" + exc = ConnectionError("Failed to connect to MCP server at http://localhost:9999/sse: Connection refused") + result = translate_exception(exc) + + assert isinstance(result, HTTPException) + assert result.status_code == 502 + assert result.detail == "Failed to connect to MCP server at http://localhost:9999/sse: Connection refused" + def test_translate_not_implemented_error(self): """Test that NotImplementedError is translated to 501 HTTP status.""" exc = NotImplementedError("Not implemented") From 3a7ac4227d4a2c0a74e3a7e5eda459ae19761c03 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Thu, 4 Sep 2025 15:13:31 -0700 Subject: [PATCH 042/124] chore: unbreak inference store test (#3340) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? The inference store writes were moved to asyncio.create_task and not await anymore ## Test Plan ❯ OLLAMA_URL=http://localhost:11434 LLAMA_STACK_CONFIG=server:starter uv run --with pytest-repeat pytest tests/integration/inference --text-model="ollama/llama3.2:3b-instruct-fp16" -vvs -k "test_inference_store_tool_calls and 3b-instruct-fp16-True" --count=10 Uninstalled 2 packages in 102ms Installed 2 packages in 138ms INFO 2025-09-04 14:10:17,775 tests.integration.conftest:66 tests: Setting DISABLE_CODE_SANDBOX=1 for macOS ========================================================================================================== test session starts =========================================================================================================== platform darwin -- Python 3.12.3, pytest-8.4.1, pluggy-1.6.0 -- /Users/erichuang/.cache/uv/builds-v0/.tmpSGMlgt/bin/python cachedir: .pytest_cache metadata: {'Python': '3.12.3', 'Platform': 'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '8.4.1', 'pluggy': '1.6.0'}, 'Plugins': {'repeat': '0.9.4', 'anyio': '4.9.0', 'html': '4.1.1', 'socket': '0.7.0', 'asyncio': '1.1.0', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'cov': '6.2.1', 'nbval': '0.11.0'}} rootdir: /Users/erichuang/projects/llama-stack-git configfile: pyproject.toml plugins: repeat-0.9.4, anyio-4.9.0, html-4.1.1, socket-0.7.0, asyncio-1.1.0, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, cov-6.2.1, nbval-0.11.0 asyncio: mode=Mode.AUTO, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 970 items / 950 deselected / 20 selected tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-1-10] instantiating llama_stack_client Starting llama stack server with config 'starter' on port 8321... 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(15.7s elapsed) Server is ready at http://localhost:8321 llama_stack_client instantiated in 20.583s PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-2-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-3-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-4-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-5-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-6-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-7-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-8-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-9-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-10-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-1-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-2-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-3-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-4-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-5-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-6-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-7-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-8-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-9-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-10-10] PASSEDTerminating llama stack server process... Terminating process 53307 and its group... Server process and children terminated gracefully --- .../inference/test_openai_completion.py | 25 +++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 72137662d..62185e470 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -5,6 +5,8 @@ # the root directory of this source tree. +import time + import pytest from ..test_cases.test_case import TestCase @@ -323,8 +325,15 @@ def test_inference_store(compat_client, client_with_models, text_model_id, strea response_id = response.id content = response.choices[0].message.content - responses = client.chat.completions.list(limit=1000) - assert response_id in [r.id for r in responses.data] + tries = 0 + while tries < 10: + responses = client.chat.completions.list(limit=1000) + if response_id in [r.id for r in responses.data]: + break + else: + tries += 1 + time.sleep(0.1) + assert tries < 10, f"Response {response_id} not found after 1 second" retrieved_response = client.chat.completions.retrieve(response_id) assert retrieved_response.id == response_id @@ -388,6 +397,18 @@ def test_inference_store_tool_calls(compat_client, client_with_models, text_mode response_id = response.id content = response.choices[0].message.content + # wait for the response to be stored + tries = 0 + while tries < 10: + responses = client.chat.completions.list(limit=1000) + if response_id in [r.id for r in responses.data]: + break + else: + tries += 1 + time.sleep(0.1) + + assert tries < 10, f"Response {response_id} not found after 1 second" + responses = client.chat.completions.list(limit=1000) assert response_id in [r.id for r in responses.data] From 0b00c68d59d28ea4a502ccb54708bd384c4dea73 Mon Sep 17 00:00:00 2001 From: Sumanth Kamenani Date: Fri, 5 Sep 2025 04:45:11 -0400 Subject: [PATCH 043/124] fix: use lambda pattern for bedrock config env vars (#3307) # What does this PR do? Improved bedrock provider config to read from environment variables like AWS_ACCESS_KEY_ID. Updated all fields to use default_factory with lambda patterns like the nvidia provider does. Now the environment variables work as documented. Closes #3305 ## Test Plan Ran the new bedrock config tests: ```bash python -m pytest tests/unit/providers/inference/bedrock/test_config.py -v Verified existing provider tests still work: python -m pytest tests/unit/providers/test_configs.py -v --- .../providers/inference/remote_bedrock.md | 4 +- .../source/providers/safety/remote_bedrock.md | 4 +- llama_stack/providers/utils/bedrock/config.py | 22 ++++--- .../inference/bedrock/test_config.py | 63 +++++++++++++++++++ 4 files changed, 79 insertions(+), 14 deletions(-) create mode 100644 tests/unit/providers/inference/bedrock/test_config.py diff --git a/docs/source/providers/inference/remote_bedrock.md b/docs/source/providers/inference/remote_bedrock.md index 1454c54c2..216dd4adb 100644 --- a/docs/source/providers/inference/remote_bedrock.md +++ b/docs/source/providers/inference/remote_bedrock.md @@ -15,8 +15,8 @@ AWS Bedrock inference provider for accessing various AI models through AWS's man | `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE | | `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS | | `retry_mode` | `str \| None` | No | | A string representing the type of retries Boto3 will perform.Default use environment variable: AWS_RETRY_MODE | -| `connect_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | -| `read_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | +| `connect_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | +| `read_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | | `session_ttl` | `int \| None` | No | 3600 | The time in seconds till a session expires. The default is 3600 seconds (1 hour). | ## Sample Configuration diff --git a/docs/source/providers/safety/remote_bedrock.md b/docs/source/providers/safety/remote_bedrock.md index 3c1d6bcb0..99d77dd72 100644 --- a/docs/source/providers/safety/remote_bedrock.md +++ b/docs/source/providers/safety/remote_bedrock.md @@ -15,8 +15,8 @@ AWS Bedrock safety provider for content moderation using AWS's safety services. | `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE | | `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS | | `retry_mode` | `str \| None` | No | | A string representing the type of retries Boto3 will perform.Default use environment variable: AWS_RETRY_MODE | -| `connect_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | -| `read_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | +| `connect_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | +| `read_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | | `session_ttl` | `int \| None` | No | 3600 | The time in seconds till a session expires. The default is 3600 seconds (1 hour). | ## Sample Configuration diff --git a/llama_stack/providers/utils/bedrock/config.py b/llama_stack/providers/utils/bedrock/config.py index b25617d76..2745c88cb 100644 --- a/llama_stack/providers/utils/bedrock/config.py +++ b/llama_stack/providers/utils/bedrock/config.py @@ -4,53 +4,55 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import os + from pydantic import BaseModel, Field class BedrockBaseConfig(BaseModel): aws_access_key_id: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_ACCESS_KEY_ID"), description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID", ) aws_secret_access_key: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_SECRET_ACCESS_KEY"), description="The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY", ) aws_session_token: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_SESSION_TOKEN"), description="The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN", ) region_name: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_DEFAULT_REGION"), description="The default AWS Region to use, for example, us-west-1 or us-west-2." "Default use environment variable: AWS_DEFAULT_REGION", ) profile_name: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_PROFILE"), description="The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE", ) total_max_attempts: int | None = Field( - default=None, + default_factory=lambda: int(val) if (val := os.getenv("AWS_MAX_ATTEMPTS")) else None, description="An integer representing the maximum number of attempts that will be made for a single request, " "including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS", ) retry_mode: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_RETRY_MODE"), description="A string representing the type of retries Boto3 will perform." "Default use environment variable: AWS_RETRY_MODE", ) connect_timeout: float | None = Field( - default=60, + default_factory=lambda: float(os.getenv("AWS_CONNECT_TIMEOUT", "60")), description="The time in seconds till a timeout exception is thrown when attempting to make a connection. " "The default is 60 seconds.", ) read_timeout: float | None = Field( - default=60, + default_factory=lambda: float(os.getenv("AWS_READ_TIMEOUT", "60")), description="The time in seconds till a timeout exception is thrown when attempting to read from a connection." "The default is 60 seconds.", ) session_ttl: int | None = Field( - default=3600, + default_factory=lambda: int(os.getenv("AWS_SESSION_TTL", "3600")), description="The time in seconds till a session expires. The default is 3600 seconds (1 hour).", ) diff --git a/tests/unit/providers/inference/bedrock/test_config.py b/tests/unit/providers/inference/bedrock/test_config.py new file mode 100644 index 000000000..1b8639f2e --- /dev/null +++ b/tests/unit/providers/inference/bedrock/test_config.py @@ -0,0 +1,63 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +from unittest.mock import patch + +from llama_stack.providers.utils.bedrock.config import BedrockBaseConfig + + +class TestBedrockBaseConfig: + def test_defaults_work_without_env_vars(self): + with patch.dict(os.environ, {}, clear=True): + config = BedrockBaseConfig() + + # Basic creds should be None + assert config.aws_access_key_id is None + assert config.aws_secret_access_key is None + assert config.region_name is None + + # Timeouts get defaults + assert config.connect_timeout == 60.0 + assert config.read_timeout == 60.0 + assert config.session_ttl == 3600 + + def test_env_vars_get_picked_up(self): + env_vars = { + "AWS_ACCESS_KEY_ID": "AKIATEST123", + "AWS_SECRET_ACCESS_KEY": "secret123", + "AWS_DEFAULT_REGION": "us-west-2", + "AWS_MAX_ATTEMPTS": "5", + "AWS_RETRY_MODE": "adaptive", + "AWS_CONNECT_TIMEOUT": "30", + } + + with patch.dict(os.environ, env_vars, clear=True): + config = BedrockBaseConfig() + + assert config.aws_access_key_id == "AKIATEST123" + assert config.aws_secret_access_key == "secret123" + assert config.region_name == "us-west-2" + assert config.total_max_attempts == 5 + assert config.retry_mode == "adaptive" + assert config.connect_timeout == 30.0 + + def test_partial_env_setup(self): + # Just setting one timeout var + with patch.dict(os.environ, {"AWS_CONNECT_TIMEOUT": "120"}, clear=True): + config = BedrockBaseConfig() + + assert config.connect_timeout == 120.0 + assert config.read_timeout == 60.0 # still default + assert config.aws_access_key_id is None + + def test_bad_max_attempts_breaks(self): + with patch.dict(os.environ, {"AWS_MAX_ATTEMPTS": "not_a_number"}, clear=True): + try: + BedrockBaseConfig() + raise AssertionError("Should have failed on bad int conversion") + except ValueError: + pass # expected From 64b297716247317a0e0714b61fc7ee77bbfb9e52 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Fri, 5 Sep 2025 13:09:36 +0100 Subject: [PATCH 044/124] fix: Fix locations of distrubution runtime directories (#3336) The defaults were mixed up Signed-off-by: Derek Higgins --- llama_stack/distributions/ci-tests/ci_tests.py | 4 +--- llama_stack/distributions/ci-tests/run.yaml | 18 +++++++++--------- llama_stack/distributions/starter-gpu/run.yaml | 18 +++++++++--------- .../distributions/starter-gpu/starter_gpu.py | 4 +--- llama_stack/distributions/starter/starter.py | 3 +-- 5 files changed, 21 insertions(+), 26 deletions(-) diff --git a/llama_stack/distributions/ci-tests/ci_tests.py b/llama_stack/distributions/ci-tests/ci_tests.py index 8fb61faca..ab102f5f3 100644 --- a/llama_stack/distributions/ci-tests/ci_tests.py +++ b/llama_stack/distributions/ci-tests/ci_tests.py @@ -11,9 +11,7 @@ from ..starter.starter import get_distribution_template as get_starter_distribut def get_distribution_template() -> DistributionTemplate: - template = get_starter_distribution_template() - name = "ci-tests" - template.name = name + template = get_starter_distribution_template(name="ci-tests") template.description = "CI tests for Llama Stack" return template diff --git a/llama_stack/distributions/ci-tests/run.yaml b/llama_stack/distributions/ci-tests/run.yaml index 7523df581..26a677c7a 100644 --- a/llama_stack/distributions/ci-tests/run.yaml +++ b/llama_stack/distributions/ci-tests/run.yaml @@ -89,28 +89,28 @@ providers: config: kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/faiss_store.db - provider_id: sqlite-vec provider_type: inline::sqlite-vec config: - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec_registry.db - provider_id: ${env.MILVUS_URL:+milvus} provider_type: inline::milvus config: - db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db + db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/ci-tests}/milvus.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/milvus_registry.db - provider_id: ${env.CHROMADB_URL:+chromadb} provider_type: remote::chromadb config: url: ${env.CHROMADB_URL:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests/}/chroma_remote_registry.db - provider_id: ${env.PGVECTOR_DB:+pgvector} provider_type: remote::pgvector config: @@ -121,15 +121,15 @@ providers: password: ${env.PGVECTOR_PASSWORD:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/pgvector_registry.db files: - provider_id: meta-reference-files provider_type: inline::localfs config: - storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ci-tests/files} metadata_store: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/llama_stack/distributions/starter-gpu/run.yaml b/llama_stack/distributions/starter-gpu/run.yaml index 8aed61519..5d9dfcb27 100644 --- a/llama_stack/distributions/starter-gpu/run.yaml +++ b/llama_stack/distributions/starter-gpu/run.yaml @@ -89,28 +89,28 @@ providers: config: kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/faiss_store.db - provider_id: sqlite-vec provider_type: inline::sqlite-vec config: - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec_registry.db - provider_id: ${env.MILVUS_URL:+milvus} provider_type: inline::milvus config: - db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db + db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter-gpu}/milvus.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/milvus_registry.db - provider_id: ${env.CHROMADB_URL:+chromadb} provider_type: remote::chromadb config: url: ${env.CHROMADB_URL:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu/}/chroma_remote_registry.db - provider_id: ${env.PGVECTOR_DB:+pgvector} provider_type: remote::pgvector config: @@ -121,15 +121,15 @@ providers: password: ${env.PGVECTOR_PASSWORD:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/pgvector_registry.db files: - provider_id: meta-reference-files provider_type: inline::localfs config: - storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter-gpu/files} metadata_store: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/llama_stack/distributions/starter-gpu/starter_gpu.py b/llama_stack/distributions/starter-gpu/starter_gpu.py index 245334749..e7efcb283 100644 --- a/llama_stack/distributions/starter-gpu/starter_gpu.py +++ b/llama_stack/distributions/starter-gpu/starter_gpu.py @@ -11,9 +11,7 @@ from ..starter.starter import get_distribution_template as get_starter_distribut def get_distribution_template() -> DistributionTemplate: - template = get_starter_distribution_template() - name = "starter-gpu" - template.name = name + template = get_starter_distribution_template(name="starter-gpu") template.description = "Quick start template for running Llama Stack with several popular providers. This distribution is intended for GPU-enabled environments." template.providers["post_training"] = [ diff --git a/llama_stack/distributions/starter/starter.py b/llama_stack/distributions/starter/starter.py index a4bbc6371..2fca52700 100644 --- a/llama_stack/distributions/starter/starter.py +++ b/llama_stack/distributions/starter/starter.py @@ -99,9 +99,8 @@ def get_remote_inference_providers() -> list[Provider]: return inference_providers -def get_distribution_template() -> DistributionTemplate: +def get_distribution_template(name: str = "starter") -> DistributionTemplate: remote_inference_providers = get_remote_inference_providers() - name = "starter" providers = { "inference": [BuildProvider(provider_type=p.provider_type, module=p.module) for p in remote_inference_providers] From e2fe39aee108c1796ddc1be1e44acd25c800082e Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Fri, 5 Sep 2025 07:40:34 -0600 Subject: [PATCH 045/124] feat!: Migrate Vector DB IDs to Vector Store IDs (breaking change) (#3253) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? This change migrates the VectorDB id generation to Vector Stores. This is a breaking change for **_some users_** that may have application code using the `vector_db_id` parameter in the request of the VectorDB protocol instead of the `VectorDB.identifier` in the response. By default we will now create a Vector Store every time we register a VectorDB. The caveat with this approach is that this maps the `vector_db_id` → `vector_store.name`. This is a reasonable tradeoff to transition users towards OpenAI Vector Stores. As an added benefit, registering VectorDBs will result in them appearing in the VectorStores admin UI. ### Why? This PR makes the `POST` API call to `/v1/vector-dbs` swap the `vector_db_id` parameter in the **request body** into the VectorStore's name field and sets the `vector_db_id` to the generated vector store id (e.g., `vs_038247dd-4bbb-4dbb-a6be-d5ecfd46cfdb`). That means that users would have to do something like follows in their application code: ```python res = client.vector_dbs.register( vector_db_id='my-vector-db-id', embedding_model='ollama/all-minilm:l6-v2', embedding_dimension=384, ) vector_db_id = res.identifier ``` And then the rest of their code would behave, including `VectorIO`'s insert protocol using `vector_db_id` in the request. An alternative implementation would be to just delete the `vector_db_id` parameter in `VectorDB` but the end result would still require users having to write `vector_db_id = res.identifier` since `VectorStores.create()` generates the ID for you. So this approach felt the easiest way to migrate users towards VectorStores (subsequent PRs will be added to trigger `files.create()` and `vector_stores.files.create()`). ## Test Plan Unit tests and integration tests have been added. Signed-off-by: Francisco Javier Arceo --- llama_stack/core/routing_tables/vector_dbs.py | 26 +++- tests/integration/vector_io/test_vector_io.py | 75 +++++++---- .../routers/test_routing_tables.py | 30 ++++- .../routing_tables/test_vector_dbs.py | 127 ++++++++++++++++-- 4 files changed, 209 insertions(+), 49 deletions(-) diff --git a/llama_stack/core/routing_tables/vector_dbs.py b/llama_stack/core/routing_tables/vector_dbs.py index 00f71b4fe..497894064 100644 --- a/llama_stack/core/routing_tables/vector_dbs.py +++ b/llama_stack/core/routing_tables/vector_dbs.py @@ -52,7 +52,6 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): provider_vector_db_id: str | None = None, vector_db_name: str | None = None, ) -> VectorDB: - provider_vector_db_id = provider_vector_db_id or vector_db_id if provider_id is None: if len(self.impls_by_provider_id) > 0: provider_id = list(self.impls_by_provider_id.keys())[0] @@ -69,14 +68,33 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding) if "embedding_dimension" not in model.metadata: raise ValueError(f"Model {embedding_model} does not have an embedding dimension") + + provider = self.impls_by_provider_id[provider_id] + logger.warning( + "VectorDB is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly." + ) + vector_store = await provider.openai_create_vector_store( + name=vector_db_name or vector_db_id, + embedding_model=embedding_model, + embedding_dimension=model.metadata["embedding_dimension"], + provider_id=provider_id, + provider_vector_db_id=provider_vector_db_id, + ) + + vector_store_id = vector_store.id + actual_provider_vector_db_id = provider_vector_db_id or vector_store_id + logger.warning( + f"Ignoring vector_db_id {vector_db_id} and using vector_store_id {vector_store_id} instead. Setting VectorDB {vector_db_id} to VectorDB.vector_db_name" + ) + vector_db_data = { - "identifier": vector_db_id, + "identifier": vector_store_id, "type": ResourceType.vector_db.value, "provider_id": provider_id, - "provider_resource_id": provider_vector_db_id, + "provider_resource_id": actual_provider_vector_db_id, "embedding_model": embedding_model, "embedding_dimension": model.metadata["embedding_dimension"], - "vector_db_name": vector_db_name, + "vector_db_name": vector_store.name, } vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data) await self.register_object(vector_db) diff --git a/tests/integration/vector_io/test_vector_io.py b/tests/integration/vector_io/test_vector_io.py index 07faa0db1..979eff6bb 100644 --- a/tests/integration/vector_io/test_vector_io.py +++ b/tests/integration/vector_io/test_vector_io.py @@ -47,34 +47,45 @@ def client_with_empty_registry(client_with_models): def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension): - # Register a memory bank first - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + # Retrieve the memory bank and validate its properties - response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=vector_db_id) + response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=actual_vector_db_id) assert response is not None - assert response.identifier == vector_db_id + assert response.identifier == actual_vector_db_id assert response.embedding_model == embedding_model_id - assert response.provider_resource_id == vector_db_id + assert response.identifier.startswith("vs_") def test_vector_db_register(client_with_empty_registry, embedding_model_id, embedding_dimension): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) - vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_dbs_after_register == [vector_db_id] + actual_vector_db_id = response.identifier + assert actual_vector_db_id.startswith("vs_") + assert actual_vector_db_id != vector_db_name - client_with_empty_registry.vector_dbs.unregister(vector_db_id=vector_db_id) + vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + assert vector_dbs_after_register == [actual_vector_db_id] + + vector_stores = client_with_empty_registry.vector_stores.list() + assert len(vector_stores.data) == 1 + vector_store = vector_stores.data[0] + assert vector_store.id == actual_vector_db_id + assert vector_store.name == vector_db_name + + client_with_empty_registry.vector_dbs.unregister(vector_db_id=actual_vector_db_id) vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] assert len(vector_dbs) == 0 @@ -91,20 +102,22 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe ], ) def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding_dimension, sample_chunks, test_case): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=sample_chunks, ) response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="What is the capital of France?", ) assert response is not None @@ -113,7 +126,7 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding query, expected_doc_id = test_case response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query=query, ) assert response is not None @@ -128,13 +141,15 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e "remote::qdrant": {"score_threshold": -1.0}, "inline::qdrant": {"score_threshold": -1.0}, } - vector_db_id = "test_precomputed_embeddings_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_precomputed_embeddings_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + chunks_with_embeddings = [ Chunk( content="This is a test chunk with precomputed embedding.", @@ -144,13 +159,13 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e ] client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=chunks_with_embeddings, ) provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0] response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="precomputed embedding test", params=vector_io_provider_params_dict.get(provider, None), ) @@ -173,13 +188,15 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb( "remote::qdrant": {"score_threshold": 0.0}, "inline::qdrant": {"score_threshold": 0.0}, } - vector_db_id = "test_precomputed_embeddings_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_precomputed_embeddings_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + chunks_with_embeddings = [ Chunk( content="duplicate", @@ -189,13 +206,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb( ] client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=chunks_with_embeddings, ) provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0] response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="duplicate", params=vector_io_provider_params_dict.get(provider, None), ) diff --git a/tests/unit/distribution/routers/test_routing_tables.py b/tests/unit/distribution/routers/test_routing_tables.py index 2652f5c8d..1ceee81c6 100644 --- a/tests/unit/distribution/routers/test_routing_tables.py +++ b/tests/unit/distribution/routers/test_routing_tables.py @@ -146,6 +146,20 @@ class VectorDBImpl(Impl): async def unregister_vector_db(self, vector_db_id: str): return vector_db_id + async def openai_create_vector_store(self, **kwargs): + import time + import uuid + + from llama_stack.apis.vector_io.vector_io import VectorStoreFileCounts, VectorStoreObject + + vector_store_id = kwargs.get("provider_vector_db_id") or f"vs_{uuid.uuid4()}" + return VectorStoreObject( + id=vector_store_id, + name=kwargs.get("name", vector_store_id), + created_at=int(time.time()), + file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0), + ) + async def test_models_routing_table(cached_disk_dist_registry): table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) @@ -247,17 +261,21 @@ async def test_vectordbs_routing_table(cached_disk_dist_registry): ) # Register multiple vector databases and verify listing - await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test_provider/test-model") - await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test_provider/test-model") + vdb1 = await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test_provider/test-model") + vdb2 = await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test_provider/test-model") vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 2 vector_db_ids = {v.identifier for v in vector_dbs.data} - assert "test-vectordb" in vector_db_ids - assert "test-vectordb-2" in vector_db_ids + assert vdb1.identifier in vector_db_ids + assert vdb2.identifier in vector_db_ids - await table.unregister_vector_db(vector_db_id="test-vectordb") - await table.unregister_vector_db(vector_db_id="test-vectordb-2") + # Verify they have UUID-based identifiers + assert vdb1.identifier.startswith("vs_") + assert vdb2.identifier.startswith("vs_") + + await table.unregister_vector_db(vector_db_id=vdb1.identifier) + await table.unregister_vector_db(vector_db_id=vdb2.identifier) vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 0 diff --git a/tests/unit/distribution/routing_tables/test_vector_dbs.py b/tests/unit/distribution/routing_tables/test_vector_dbs.py index 789eda433..3444f64c2 100644 --- a/tests/unit/distribution/routing_tables/test_vector_dbs.py +++ b/tests/unit/distribution/routing_tables/test_vector_dbs.py @@ -7,6 +7,7 @@ # Unit tests for the routing tables vector_dbs import time +import uuid from unittest.mock import AsyncMock import pytest @@ -34,6 +35,7 @@ from tests.unit.distribution.routers.test_routing_tables import Impl, InferenceI class VectorDBImpl(Impl): def __init__(self): super().__init__(Api.vector_io) + self.vector_stores = {} async def register_vector_db(self, vector_db: VectorDB): return vector_db @@ -114,8 +116,35 @@ class VectorDBImpl(Impl): async def openai_delete_vector_store_file(self, vector_store_id, file_id): return VectorStoreFileDeleteResponse(id=file_id, deleted=True) + async def openai_create_vector_store( + self, + name=None, + embedding_model=None, + embedding_dimension=None, + provider_id=None, + provider_vector_db_id=None, + **kwargs, + ): + vector_store_id = provider_vector_db_id or f"vs_{uuid.uuid4()}" + vector_store = VectorStoreObject( + id=vector_store_id, + name=name or vector_store_id, + created_at=int(time.time()), + file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0), + ) + self.vector_stores[vector_store_id] = vector_store + return vector_store + + async def openai_list_vector_stores(self, **kwargs): + from llama_stack.apis.vector_io.vector_io import VectorStoreListResponse + + return VectorStoreListResponse( + data=list(self.vector_stores.values()), has_more=False, first_id=None, last_id=None + ) + async def test_vectordbs_routing_table(cached_disk_dist_registry): + n = 10 table = VectorDBsRoutingTable({"test_provider": VectorDBImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -129,22 +158,98 @@ async def test_vectordbs_routing_table(cached_disk_dist_registry): ) # Register multiple vector databases and verify listing - await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test-model") - await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test-model") + vdb_dict = {} + for i in range(n): + vdb_dict[i] = await table.register_vector_db(vector_db_id=f"test-vectordb-{i}", embedding_model="test-model") + vector_dbs = await table.list_vector_dbs() - assert len(vector_dbs.data) == 2 + assert len(vector_dbs.data) == len(vdb_dict) vector_db_ids = {v.identifier for v in vector_dbs.data} - assert "test-vectordb" in vector_db_ids - assert "test-vectordb-2" in vector_db_ids - - await table.unregister_vector_db(vector_db_id="test-vectordb") - await table.unregister_vector_db(vector_db_id="test-vectordb-2") + for k in vdb_dict: + assert vdb_dict[k].identifier in vector_db_ids + for k in vdb_dict: + await table.unregister_vector_db(vector_db_id=vdb_dict[k].identifier) vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 0 +async def test_vector_db_and_vector_store_id_mapping(cached_disk_dist_registry): + n = 10 + impl = VectorDBImpl() + table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, {}) + await table.initialize() + + m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) + await m_table.initialize() + await m_table.register_model( + model_id="test-model", + provider_id="test_provider", + metadata={"embedding_dimension": 128}, + model_type=ModelType.embedding, + ) + + vdb_dict = {} + for i in range(n): + vdb_dict[i] = await table.register_vector_db(vector_db_id=f"test-vectordb-{i}", embedding_model="test-model") + + vector_dbs = await table.list_vector_dbs() + vector_db_ids = {v.identifier for v in vector_dbs.data} + + vector_stores = await impl.openai_list_vector_stores() + vector_store_ids = {v.id for v in vector_stores.data} + + assert vector_db_ids == vector_store_ids, ( + f"Vector DB IDs {vector_db_ids} don't match vector store IDs {vector_store_ids}" + ) + + for vector_store in vector_stores.data: + vector_db = await table.get_vector_db(vector_store.id) + assert vector_store.name == vector_db.vector_db_name, ( + f"Vector store name {vector_store.name} doesn't match vector store ID {vector_store.id}" + ) + + for vector_db_id in vector_db_ids: + await table.unregister_vector_db(vector_db_id) + + assert len((await table.list_vector_dbs()).data) == 0 + + +async def test_vector_db_id_becomes_vector_store_name(cached_disk_dist_registry): + impl = VectorDBImpl() + table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, {}) + await table.initialize() + + m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) + await m_table.initialize() + await m_table.register_model( + model_id="test-model", + provider_id="test_provider", + metadata={"embedding_dimension": 128}, + model_type=ModelType.embedding, + ) + + user_provided_id = "my-custom-vector-db" + await table.register_vector_db(vector_db_id=user_provided_id, embedding_model="test-model") + + vector_stores = await impl.openai_list_vector_stores() + assert len(vector_stores.data) == 1 + + vector_store = vector_stores.data[0] + + assert vector_store.name == user_provided_id + + assert vector_store.id.startswith("vs_") + assert vector_store.id != user_provided_id + + vector_dbs = await table.list_vector_dbs() + assert len(vector_dbs.data) == 1 + assert vector_dbs.data[0].identifier == vector_store.id + + await table.unregister_vector_db(vector_store.id) + + async def test_openai_vector_stores_routing_table_roles(cached_disk_dist_registry): impl = VectorDBImpl() impl.openai_retrieve_vector_store = AsyncMock(return_value="OK") @@ -164,7 +269,8 @@ async def test_openai_vector_stores_routing_table_roles(cached_disk_dist_registr authorized_user = User(principal="alice", attributes={"roles": [authorized_team]}) with request_provider_data_context({}, authorized_user): - _ = await table.register_vector_db(vector_db_id="vs1", embedding_model="test-model") + registered_vdb = await table.register_vector_db(vector_db_id="vs1", embedding_model="test-model") + authorized_table = registered_vdb.identifier # Use the actual generated ID # Authorized reader with request_provider_data_context({}, authorized_user): @@ -227,7 +333,8 @@ async def test_openai_vector_stores_routing_table_actions(cached_disk_dist_regis ) with request_provider_data_context({}, admin_user): - await table.register_vector_db(vector_db_id=vector_db_id, embedding_model="test-model") + registered_vdb = await table.register_vector_db(vector_db_id=vector_db_id, embedding_model="test-model") + vector_db_id = registered_vdb.identifier # Use the actual generated ID read_methods = [ (table.openai_retrieve_vector_store, (vector_db_id,), {}), From df1526991f6d5cfca05d3c4f1077b67f4832d93e Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 5 Sep 2025 14:59:57 -0400 Subject: [PATCH 046/124] feat(batches, completions): add /v1/completions support to /v1/batches (#3309) # What does this PR do? add support for /v1/completions to the /v1/batches api ## Test Plan ci --- .../inline/batches/reference/batches.py | 69 +++++++++++++------ tests/integration/batches/test_batches.py | 55 +++++++++++++++ .../recordings/responses/41e27b9b5d09.json | 42 +++++++++++ .../unit/providers/batches/test_reference.py | 65 +++++++++++++++-- 4 files changed, 205 insertions(+), 26 deletions(-) create mode 100644 tests/integration/recordings/responses/41e27b9b5d09.json diff --git a/llama_stack/providers/inline/batches/reference/batches.py b/llama_stack/providers/inline/batches/reference/batches.py index 26f0ad15a..e049518a4 100644 --- a/llama_stack/providers/inline/batches/reference/batches.py +++ b/llama_stack/providers/inline/batches/reference/batches.py @@ -178,9 +178,9 @@ class ReferenceBatchesImpl(Batches): # TODO: set expiration time for garbage collection - if endpoint not in ["/v1/chat/completions"]: + if endpoint not in ["/v1/chat/completions", "/v1/completions"]: raise ValueError( - f"Invalid endpoint: {endpoint}. Supported values: /v1/chat/completions. Code: invalid_value. Param: endpoint", + f"Invalid endpoint: {endpoint}. Supported values: /v1/chat/completions, /v1/completions. Code: invalid_value. Param: endpoint", ) if completion_window != "24h": @@ -424,13 +424,21 @@ class ReferenceBatchesImpl(Batches): ) valid = False - for param, expected_type, type_string in [ - ("model", str, "a string"), - # messages is specific to /v1/chat/completions - # we could skip validating messages here and let inference fail. however, - # that would be a very expensive way to find out messages is wrong. - ("messages", list, "an array"), # TODO: allow messages to be a string? - ]: + if batch.endpoint == "/v1/chat/completions": + required_params = [ + ("model", str, "a string"), + # messages is specific to /v1/chat/completions + # we could skip validating messages here and let inference fail. however, + # that would be a very expensive way to find out messages is wrong. + ("messages", list, "an array"), # TODO: allow messages to be a string? + ] + else: # /v1/completions + required_params = [ + ("model", str, "a string"), + ("prompt", str, "a string"), # TODO: allow prompt to be a list of strings?? + ] + + for param, expected_type, type_string in required_params: if param not in body: errors.append( BatchError( @@ -591,20 +599,37 @@ class ReferenceBatchesImpl(Batches): try: # TODO(SECURITY): review body for security issues - request.body["messages"] = [convert_to_openai_message_param(msg) for msg in request.body["messages"]] - chat_response = await self.inference_api.openai_chat_completion(**request.body) + if request.url == "/v1/chat/completions": + request.body["messages"] = [convert_to_openai_message_param(msg) for msg in request.body["messages"]] + chat_response = await self.inference_api.openai_chat_completion(**request.body) - # this is for mypy, we don't allow streaming so we'll get the right type - assert hasattr(chat_response, "model_dump_json"), "Chat response must have model_dump_json method" - return { - "id": request_id, - "custom_id": request.custom_id, - "response": { - "status_code": 200, - "request_id": request_id, # TODO: should this be different? - "body": chat_response.model_dump_json(), - }, - } + # this is for mypy, we don't allow streaming so we'll get the right type + assert hasattr(chat_response, "model_dump_json"), "Chat response must have model_dump_json method" + return { + "id": request_id, + "custom_id": request.custom_id, + "response": { + "status_code": 200, + "request_id": request_id, # TODO: should this be different? + "body": chat_response.model_dump_json(), + }, + } + else: # /v1/completions + completion_response = await self.inference_api.openai_completion(**request.body) + + # this is for mypy, we don't allow streaming so we'll get the right type + assert hasattr(completion_response, "model_dump_json"), ( + "Completion response must have model_dump_json method" + ) + return { + "id": request_id, + "custom_id": request.custom_id, + "response": { + "status_code": 200, + "request_id": request_id, + "body": completion_response.model_dump_json(), + }, + } except Exception as e: logger.info(f"Error processing request {request.custom_id} in batch {batch_id}: {e}") return { diff --git a/tests/integration/batches/test_batches.py b/tests/integration/batches/test_batches.py index 59811b7a4..d55a68bd3 100644 --- a/tests/integration/batches/test_batches.py +++ b/tests/integration/batches/test_batches.py @@ -268,3 +268,58 @@ class TestBatchesIntegration: deleted_error_file = openai_client.files.delete(final_batch.error_file_id) assert deleted_error_file.deleted, f"Error file {final_batch.error_file_id} was not deleted successfully" + + def test_batch_e2e_completions(self, openai_client, batch_helper, text_model_id): + """Run an end-to-end batch with a single successful text completion request.""" + request_body = {"model": text_model_id, "prompt": "Say completions", "max_tokens": 20} + + batch_requests = [ + { + "custom_id": "success-1", + "method": "POST", + "url": "/v1/completions", + "body": request_body, + } + ] + + with batch_helper.create_file(batch_requests) as uploaded_file: + batch = openai_client.batches.create( + input_file_id=uploaded_file.id, + endpoint="/v1/completions", + completion_window="24h", + metadata={"test": "e2e_completions_success"}, + ) + + final_batch = batch_helper.wait_for( + batch.id, + max_wait_time=3 * 60, + expected_statuses={"completed"}, + timeout_action="skip", + ) + + assert final_batch.status == "completed" + assert final_batch.request_counts is not None + assert final_batch.request_counts.total == 1 + assert final_batch.request_counts.completed == 1 + assert final_batch.output_file_id is not None + + output_content = openai_client.files.content(final_batch.output_file_id) + if isinstance(output_content, str): + output_text = output_content + else: + output_text = output_content.content.decode("utf-8") + + output_lines = output_text.strip().split("\n") + assert len(output_lines) == 1 + + result = json.loads(output_lines[0]) + assert result["custom_id"] == "success-1" + assert "response" in result + assert result["response"]["status_code"] == 200 + + deleted_output_file = openai_client.files.delete(final_batch.output_file_id) + assert deleted_output_file.deleted + + if final_batch.error_file_id is not None: + deleted_error_file = openai_client.files.delete(final_batch.error_file_id) + assert deleted_error_file.deleted diff --git a/tests/integration/recordings/responses/41e27b9b5d09.json b/tests/integration/recordings/responses/41e27b9b5d09.json new file mode 100644 index 000000000..45d140843 --- /dev/null +++ b/tests/integration/recordings/responses/41e27b9b5d09.json @@ -0,0 +1,42 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + "prompt": "Say completions", + "max_tokens": 20 + }, + "endpoint": "/v1/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-271", + "choices": [ + { + "finish_reason": "length", + "index": 0, + "logprobs": null, + "text": "You want me to respond with a completion, but you didn't specify what I should complete. Could" + } + ], + "created": 1756846620, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 20, + "prompt_tokens": 28, + "total_tokens": 48, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/unit/providers/batches/test_reference.py b/tests/unit/providers/batches/test_reference.py index 0ca866f7b..dfef5e040 100644 --- a/tests/unit/providers/batches/test_reference.py +++ b/tests/unit/providers/batches/test_reference.py @@ -46,7 +46,8 @@ The tests are categorized and outlined below, keep this updated: * test_validate_input_url_mismatch (negative) * test_validate_input_multiple_errors_per_request (negative) * test_validate_input_invalid_request_format (negative) - * test_validate_input_missing_parameters (parametrized negative - custom_id, method, url, body, model, messages missing validation) + * test_validate_input_missing_parameters_chat_completions (parametrized negative - custom_id, method, url, body, model, messages missing validation for chat/completions) + * test_validate_input_missing_parameters_completions (parametrized negative - custom_id, method, url, body, model, prompt missing validation for completions) * test_validate_input_invalid_parameter_types (parametrized negative - custom_id, url, method, body, model, messages type validation) The tests use temporary SQLite databases for isolation and mock external @@ -213,7 +214,6 @@ class TestReferenceBatchesImpl: "endpoint", [ "/v1/embeddings", - "/v1/completions", "/v1/invalid/endpoint", "", ], @@ -499,8 +499,10 @@ class TestReferenceBatchesImpl: ("messages", "body.messages", "invalid_request", "Messages parameter is required"), ], ) - async def test_validate_input_missing_parameters(self, provider, param_name, param_path, error_code, error_message): - """Test _validate_input when file contains request with missing required parameters.""" + async def test_validate_input_missing_parameters_chat_completions( + self, provider, param_name, param_path, error_code, error_message + ): + """Test _validate_input when file contains request with missing required parameters for chat completions.""" provider.files_api.openai_retrieve_file = AsyncMock() mock_response = MagicMock() @@ -541,6 +543,61 @@ class TestReferenceBatchesImpl: assert errors[0].message == error_message assert errors[0].param == param_path + @pytest.mark.parametrize( + "param_name,param_path,error_code,error_message", + [ + ("custom_id", "custom_id", "missing_required_parameter", "Missing required parameter: custom_id"), + ("method", "method", "missing_required_parameter", "Missing required parameter: method"), + ("url", "url", "missing_required_parameter", "Missing required parameter: url"), + ("body", "body", "missing_required_parameter", "Missing required parameter: body"), + ("model", "body.model", "invalid_request", "Model parameter is required"), + ("prompt", "body.prompt", "invalid_request", "Prompt parameter is required"), + ], + ) + async def test_validate_input_missing_parameters_completions( + self, provider, param_name, param_path, error_code, error_message + ): + """Test _validate_input when file contains request with missing required parameters for text completions.""" + provider.files_api.openai_retrieve_file = AsyncMock() + mock_response = MagicMock() + + base_request = { + "custom_id": "req-1", + "method": "POST", + "url": "/v1/completions", + "body": {"model": "test-model", "prompt": "Hello"}, + } + + # Remove the specific parameter being tested + if "." in param_path: + top_level, nested_param = param_path.split(".", 1) + del base_request[top_level][nested_param] + else: + del base_request[param_name] + + mock_response.body = json.dumps(base_request).encode() + provider.files_api.openai_retrieve_file_content = AsyncMock(return_value=mock_response) + + batch = BatchObject( + id="batch_test", + object="batch", + endpoint="/v1/completions", + input_file_id=f"missing_{param_name}_file", + completion_window="24h", + status="validating", + created_at=1234567890, + ) + + errors, requests = await provider._validate_input(batch) + + assert len(errors) == 1 + assert len(requests) == 0 + + assert errors[0].code == error_code + assert errors[0].line == 1 + assert errors[0].message == error_message + assert errors[0].param == param_path + async def test_validate_input_url_mismatch(self, provider): """Test _validate_input when file contains request with URL that doesn't match batch endpoint.""" provider.files_api.openai_retrieve_file = AsyncMock() From 0c2757a05b504bafef1bc589376712b9ac9a1c52 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 5 Sep 2025 15:00:09 -0400 Subject: [PATCH 047/124] chore(sambanova test): skip with_n tests for sambanova, it is not implemented server-side (#3342) # What does this PR do? skip a test that cannot pass for sambanova see https://docs-legacy.sambanova.ai/sambastudio/latest/open-ai-api.html\#_example_requests_using_openai_client ## Test Plan ci --- .../inference/test_openai_completion.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 62185e470..bb447b3c1 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -58,6 +58,15 @@ def skip_if_model_doesnt_support_suffix(client_with_models, model_id): pytest.skip(f"Provider {provider.provider_type} doesn't support suffix.") +def skip_if_doesnt_support_n(client_with_models, model_id): + provider = provider_from_model(client_with_models, model_id) + if provider.provider_type in ( + "remote::sambanova", + "remote::ollama", + ): + pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") + + def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, model_id): provider = provider_from_model(client_with_models, model_id) if provider.provider_type in ( @@ -262,10 +271,7 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex ) def test_openai_chat_completion_streaming_with_n(compat_client, client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id) - - provider = provider_from_model(client_with_models, text_model_id) - if provider.provider_type == "remote::ollama": - pytest.skip(f"Model {text_model_id} hosted by {provider.provider_type} doesn't support n > 1.") + skip_if_doesnt_support_n(client_with_models, text_model_id) tc = TestCase(test_case) question = tc["question"] From 47b640370e275dd178c0bb9fcf822467e032120d Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Fri, 5 Sep 2025 13:58:49 -0700 Subject: [PATCH 048/124] feat(tests): introduce a test "suite" concept to encompass dirs, options (#3339) Our integration tests need to be 'grouped' because each group often needs a specific set of models it works with. We separated vision tests due to this, and we have a separate set of tests which test "Responses" API. This PR makes this system a bit more official so it is very easy to target these groups and apply all testing infrastructure towards all the groups (for example, record-replay) uniformly. There are three suites declared: - base - vision - responses Note that our CI currently runs the "base" and "vision" suites. You can use the `--suite` option when running pytest (or any of the testing scripts or workflows.) For example: ``` OLLAMA_URL=http://localhost:11434 \ pytest -s -v tests/integration/ --stack-config starter --suite vision ``` --- .../actions/run-and-record-tests/action.yml | 30 ++-- .github/actions/setup-ollama/action.yml | 8 +- .../actions/setup-test-environment/action.yml | 8 +- .github/workflows/README.md | 2 +- .github/workflows/integration-tests.yml | 20 +-- .../workflows/record-integration-tests.yml | 32 ++--- scripts/github/schedule-record-workflow.sh | 32 +++-- scripts/integration-tests.sh | 133 +++++++----------- tests/README.md | 2 +- tests/integration/README.md | 21 +++ tests/integration/conftest.py | 75 ++++++++-- .../{non_ci => }/responses/__init__.py | 0 .../responses/fixtures/__init__.py | 0 .../responses/fixtures/fixtures.py | 0 .../fixtures/images/vision_test_1.jpg | Bin .../fixtures/images/vision_test_2.jpg | Bin .../fixtures/images/vision_test_3.jpg | Bin .../fixtures/pdfs/llama_stack_and_models.pdf | Bin .../responses/fixtures/test_cases.py | 0 .../{non_ci => }/responses/helpers.py | 0 .../responses/streaming_assertions.py | 0 .../responses/test_basic_responses.py | 0 .../responses/test_file_search.py | 0 .../responses/test_tool_responses.py | 0 tests/integration/suites.py | 53 +++++++ 25 files changed, 255 insertions(+), 161 deletions(-) rename tests/integration/{non_ci => }/responses/__init__.py (100%) rename tests/integration/{non_ci => }/responses/fixtures/__init__.py (100%) rename tests/integration/{non_ci => }/responses/fixtures/fixtures.py (100%) rename tests/integration/{non_ci => }/responses/fixtures/images/vision_test_1.jpg (100%) rename tests/integration/{non_ci => }/responses/fixtures/images/vision_test_2.jpg (100%) rename tests/integration/{non_ci => }/responses/fixtures/images/vision_test_3.jpg (100%) rename tests/integration/{non_ci => }/responses/fixtures/pdfs/llama_stack_and_models.pdf (100%) rename tests/integration/{non_ci => }/responses/fixtures/test_cases.py (100%) rename tests/integration/{non_ci => }/responses/helpers.py (100%) rename tests/integration/{non_ci => }/responses/streaming_assertions.py (100%) rename tests/integration/{non_ci => }/responses/test_basic_responses.py (100%) rename tests/integration/{non_ci => }/responses/test_file_search.py (100%) rename tests/integration/{non_ci => }/responses/test_tool_responses.py (100%) create mode 100644 tests/integration/suites.py diff --git a/.github/actions/run-and-record-tests/action.yml b/.github/actions/run-and-record-tests/action.yml index 60550cfdc..7f028b104 100644 --- a/.github/actions/run-and-record-tests/action.yml +++ b/.github/actions/run-and-record-tests/action.yml @@ -2,13 +2,6 @@ name: 'Run and Record Tests' description: 'Run integration tests and handle recording/artifact upload' inputs: - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - required: true - test-pattern: - description: 'Regex pattern to pass to pytest -k' - required: false - default: '' stack-config: description: 'Stack configuration to use' required: true @@ -18,10 +11,18 @@ inputs: inference-mode: description: 'Inference mode (record or replay)' required: true - run-vision-tests: - description: 'Whether to run vision tests' + test-suite: + description: 'Test suite to use: base, responses, vision, etc.' required: false - default: 'false' + default: '' + test-subdirs: + description: 'Comma-separated list of test subdirectories to run; overrides test-suite' + required: false + default: '' + test-pattern: + description: 'Regex pattern to pass to pytest -k' + required: false + default: '' runs: using: 'composite' @@ -42,7 +43,7 @@ runs: --test-subdirs '${{ inputs.test-subdirs }}' \ --test-pattern '${{ inputs.test-pattern }}' \ --inference-mode '${{ inputs.inference-mode }}' \ - ${{ inputs.run-vision-tests == 'true' && '--run-vision-tests' || '' }} \ + --test-suite '${{ inputs.test-suite }}' \ | tee pytest-${{ inputs.inference-mode }}.log @@ -57,12 +58,7 @@ runs: echo "New recordings detected, committing and pushing" git add tests/integration/recordings/ - if [ "${{ inputs.run-vision-tests }}" == "true" ]; then - git commit -m "Recordings update from CI (vision)" - else - git commit -m "Recordings update from CI" - fi - + git commit -m "Recordings update from CI (test-suite: ${{ inputs.test-suite }})" git fetch origin ${{ github.ref_name }} git rebase origin/${{ github.ref_name }} echo "Rebased successfully" diff --git a/.github/actions/setup-ollama/action.yml b/.github/actions/setup-ollama/action.yml index e57876cb0..dc2f87e8c 100644 --- a/.github/actions/setup-ollama/action.yml +++ b/.github/actions/setup-ollama/action.yml @@ -1,17 +1,17 @@ name: Setup Ollama description: Start Ollama inputs: - run-vision-tests: - description: 'Run vision tests: "true" or "false"' + test-suite: + description: 'Test suite to use: base, responses, vision, etc.' required: false - default: 'false' + default: '' runs: using: "composite" steps: - name: Start Ollama shell: bash run: | - if [ "${{ inputs.run-vision-tests }}" == "true" ]; then + if [ "${{ inputs.test-suite }}" == "vision" ]; then image="ollama-with-vision-model" else image="ollama-with-models" diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index d830e3d13..3be76f009 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -12,10 +12,10 @@ inputs: description: 'Provider to setup (ollama or vllm)' required: true default: 'ollama' - run-vision-tests: - description: 'Whether to setup provider for vision tests' + test-suite: + description: 'Test suite to use: base, responses, vision, etc.' required: false - default: 'false' + default: '' inference-mode: description: 'Inference mode (record or replay)' required: true @@ -33,7 +33,7 @@ runs: if: ${{ inputs.provider == 'ollama' && inputs.inference-mode == 'record' }} uses: ./.github/actions/setup-ollama with: - run-vision-tests: ${{ inputs.run-vision-tests }} + test-suite: ${{ inputs.test-suite }} - name: Setup vllm if: ${{ inputs.provider == 'vllm' && inputs.inference-mode == 'record' }} diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 8344d12a4..2e0df58b8 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -8,7 +8,7 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl | Installer CI | [install-script-ci.yml](install-script-ci.yml) | Test the installation script | | Integration Auth Tests | [integration-auth-tests.yml](integration-auth-tests.yml) | Run the integration test suite with Kubernetes authentication | | SqlStore Integration Tests | [integration-sql-store-tests.yml](integration-sql-store-tests.yml) | Run the integration test suite with SqlStore | -| Integration Tests (Replay) | [integration-tests.yml](integration-tests.yml) | Run the integration test suite from tests/integration in replay mode | +| Integration Tests (Replay) | [integration-tests.yml](integration-tests.yml) | Run the integration test suites from tests/integration in replay mode | | Vector IO Integration Tests | [integration-vector-io-tests.yml](integration-vector-io-tests.yml) | Run the integration test suite with various VectorIO providers | | Pre-commit | [pre-commit.yml](pre-commit.yml) | Run pre-commit checks | | Test Llama Stack Build | [providers-build.yml](providers-build.yml) | Test llama stack build | diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 57e582b20..bb53eea2f 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -1,6 +1,6 @@ name: Integration Tests (Replay) -run-name: Run the integration test suite from tests/integration in replay mode +run-name: Run the integration test suites from tests/integration in replay mode on: push: @@ -32,14 +32,6 @@ on: description: 'Test against a specific provider' type: string default: 'ollama' - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - type: string - default: '' - test-pattern: - description: 'Regex pattern to pass to pytest -k' - type: string - default: '' concurrency: # Skip concurrency for pushes to main - each commit should be tested independently @@ -50,7 +42,7 @@ jobs: run-replay-mode-tests: runs-on: ubuntu-latest - name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, vision={4})', matrix.client-type, matrix.provider, matrix.python-version, matrix.client-version, matrix.run-vision-tests) }} + name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.provider, matrix.python-version, matrix.client-version, matrix.test-suite) }} strategy: fail-fast: false @@ -61,7 +53,7 @@ jobs: # Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12 python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }} client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }} - run-vision-tests: [true, false] + test-suite: [base, vision] steps: - name: Checkout repository @@ -73,15 +65,13 @@ jobs: python-version: ${{ matrix.python-version }} client-version: ${{ matrix.client-version }} provider: ${{ matrix.provider }} - run-vision-tests: ${{ matrix.run-vision-tests }} + test-suite: ${{ matrix.test-suite }} inference-mode: 'replay' - name: Run tests uses: ./.github/actions/run-and-record-tests with: - test-subdirs: ${{ inputs.test-subdirs }} - test-pattern: ${{ inputs.test-pattern }} stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || 'server:ci-tests' }} provider: ${{ matrix.provider }} inference-mode: 'replay' - run-vision-tests: ${{ matrix.run-vision-tests }} + test-suite: ${{ matrix.test-suite }} diff --git a/.github/workflows/record-integration-tests.yml b/.github/workflows/record-integration-tests.yml index d4f5586e2..01797a54b 100644 --- a/.github/workflows/record-integration-tests.yml +++ b/.github/workflows/record-integration-tests.yml @@ -10,18 +10,18 @@ run-name: Run the integration test suite from tests/integration on: workflow_dispatch: inputs: - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - type: string - default: '' test-provider: description: 'Test against a specific provider' type: string default: 'ollama' - run-vision-tests: - description: 'Whether to run vision tests' - type: boolean - default: false + test-suite: + description: 'Test suite to use: base, responses, vision, etc.' + type: string + default: '' + test-subdirs: + description: 'Comma-separated list of test subdirectories to run; overrides test-suite' + type: string + default: '' test-pattern: description: 'Regex pattern to pass to pytest -k' type: string @@ -38,11 +38,11 @@ jobs: - name: Echo workflow inputs run: | echo "::group::Workflow Inputs" - echo "test-subdirs: ${{ inputs.test-subdirs }}" - echo "test-provider: ${{ inputs.test-provider }}" - echo "run-vision-tests: ${{ inputs.run-vision-tests }}" - echo "test-pattern: ${{ inputs.test-pattern }}" echo "branch: ${{ github.ref_name }}" + echo "test-provider: ${{ inputs.test-provider }}" + echo "test-suite: ${{ inputs.test-suite }}" + echo "test-subdirs: ${{ inputs.test-subdirs }}" + echo "test-pattern: ${{ inputs.test-pattern }}" echo "::endgroup::" - name: Checkout repository @@ -56,15 +56,15 @@ jobs: python-version: "3.12" # Use single Python version for recording client-version: "latest" provider: ${{ inputs.test-provider || 'ollama' }} - run-vision-tests: ${{ inputs.run-vision-tests }} + test-suite: ${{ inputs.test-suite }} inference-mode: 'record' - name: Run and record tests uses: ./.github/actions/run-and-record-tests with: - test-pattern: ${{ inputs.test-pattern }} - test-subdirs: ${{ inputs.test-subdirs }} stack-config: 'server:ci-tests' # recording must be done with server since more tests are run provider: ${{ inputs.test-provider || 'ollama' }} inference-mode: 'record' - run-vision-tests: ${{ inputs.run-vision-tests }} + test-suite: ${{ inputs.test-suite }} + test-subdirs: ${{ inputs.test-subdirs }} + test-pattern: ${{ inputs.test-pattern }} diff --git a/scripts/github/schedule-record-workflow.sh b/scripts/github/schedule-record-workflow.sh index e381b60b6..09e055611 100755 --- a/scripts/github/schedule-record-workflow.sh +++ b/scripts/github/schedule-record-workflow.sh @@ -15,7 +15,7 @@ set -euo pipefail BRANCH="" TEST_SUBDIRS="" TEST_PROVIDER="ollama" -RUN_VISION_TESTS=false +TEST_SUITE="base" TEST_PATTERN="" # Help function @@ -27,9 +27,9 @@ Trigger the integration test recording workflow remotely. This way you do not ne OPTIONS: -b, --branch BRANCH Branch to run the workflow on (defaults to current branch) - -s, --test-subdirs DIRS Comma-separated list of test subdirectories to run (REQUIRED) -p, --test-provider PROVIDER Test provider to use: vllm or ollama (default: ollama) - -v, --run-vision-tests Include vision tests in the recording + -t, --test-suite SUITE Test suite to use: base, responses, vision, etc. (default: base) + -s, --test-subdirs DIRS Comma-separated list of test subdirectories to run (overrides suite) -k, --test-pattern PATTERN Regex pattern to pass to pytest -k -h, --help Show this help message @@ -38,7 +38,7 @@ EXAMPLES: $0 --test-subdirs "agents" # Record tests for specific branch with vision tests - $0 -b my-feature-branch --test-subdirs "inference" --run-vision-tests + $0 -b my-feature-branch --test-suite vision # Record multiple test subdirectories with specific provider $0 --test-subdirs "agents,inference" --test-provider vllm @@ -71,9 +71,9 @@ while [[ $# -gt 0 ]]; do TEST_PROVIDER="$2" shift 2 ;; - -v|--run-vision-tests) - RUN_VISION_TESTS=true - shift + -t|--test-suite) + TEST_SUITE="$2" + shift 2 ;; -k|--test-pattern) TEST_PATTERN="$2" @@ -92,11 +92,11 @@ while [[ $# -gt 0 ]]; do done # Validate required parameters -if [[ -z "$TEST_SUBDIRS" ]]; then - echo "Error: --test-subdirs is required" - echo "Please specify which test subdirectories to run, e.g.:" +if [[ -z "$TEST_SUBDIRS" && -z "$TEST_SUITE" ]]; then + echo "Error: --test-subdirs or --test-suite is required" + echo "Please specify which test subdirectories to run or test suite to use, e.g.:" echo " $0 --test-subdirs \"agents,inference\"" - echo " $0 --test-subdirs \"inference\" --run-vision-tests" + echo " $0 --test-suite vision" echo "" exit 1 fi @@ -239,17 +239,19 @@ echo "Triggering integration test recording workflow..." echo "Branch: $BRANCH" echo "Test provider: $TEST_PROVIDER" echo "Test subdirs: $TEST_SUBDIRS" -echo "Run vision tests: $RUN_VISION_TESTS" +echo "Test suite: $TEST_SUITE" echo "Test pattern: ${TEST_PATTERN:-"(none)"}" echo "" # Prepare inputs for gh workflow run -INPUTS="-f test-subdirs='$TEST_SUBDIRS'" +if [[ -n "$TEST_SUBDIRS" ]]; then + INPUTS="-f test-subdirs='$TEST_SUBDIRS'" +fi if [[ -n "$TEST_PROVIDER" ]]; then INPUTS="$INPUTS -f test-provider='$TEST_PROVIDER'" fi -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - INPUTS="$INPUTS -f run-vision-tests=true" +if [[ -n "$TEST_SUITE" ]]; then + INPUTS="$INPUTS -f test-suite='$TEST_SUITE'" fi if [[ -n "$TEST_PATTERN" ]]; then INPUTS="$INPUTS -f test-pattern='$TEST_PATTERN'" diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index 104ba5cf3..ab7e37579 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -16,7 +16,7 @@ STACK_CONFIG="" PROVIDER="" TEST_SUBDIRS="" TEST_PATTERN="" -RUN_VISION_TESTS="false" +TEST_SUITE="base" INFERENCE_MODE="replay" EXTRA_PARAMS="" @@ -28,12 +28,16 @@ Usage: $0 [OPTIONS] Options: --stack-config STRING Stack configuration to use (required) --provider STRING Provider to use (ollama, vllm, etc.) (required) - --test-subdirs STRING Comma-separated list of test subdirectories to run (default: 'inference') - --run-vision-tests Run vision tests instead of regular tests + --test-suite STRING Comma-separated list of test suites to run (default: 'base') --inference-mode STRING Inference mode: record or replay (default: replay) + --test-subdirs STRING Comma-separated list of test subdirectories to run (overrides suite) --test-pattern STRING Regex pattern to pass to pytest -k --help Show this help message +Suites are defined in tests/integration/suites.py. They are used to narrow the collection of tests and provide default model options. + +You can also specify subdirectories (of tests/integration) to select tests from, which will override the suite. + Examples: # Basic inference tests with ollama $0 --stack-config server:ci-tests --provider ollama @@ -42,7 +46,7 @@ Examples: $0 --stack-config server:ci-tests --provider vllm --test-subdirs 'inference,agents' # Vision tests with ollama - $0 --stack-config server:ci-tests --provider ollama --run-vision-tests + $0 --stack-config server:ci-tests --provider ollama --test-suite vision # Record mode for updating test recordings $0 --stack-config server:ci-tests --provider ollama --inference-mode record @@ -64,9 +68,9 @@ while [[ $# -gt 0 ]]; do TEST_SUBDIRS="$2" shift 2 ;; - --run-vision-tests) - RUN_VISION_TESTS="true" - shift + --test-suite) + TEST_SUITE="$2" + shift 2 ;; --inference-mode) INFERENCE_MODE="$2" @@ -92,22 +96,25 @@ done # Validate required parameters if [[ -z "$STACK_CONFIG" ]]; then echo "Error: --stack-config is required" - usage exit 1 fi if [[ -z "$PROVIDER" ]]; then echo "Error: --provider is required" - usage + exit 1 +fi + +if [[ -z "$TEST_SUITE" && -z "$TEST_SUBDIRS" ]]; then + echo "Error: --test-suite or --test-subdirs is required" exit 1 fi echo "=== Llama Stack Integration Test Runner ===" echo "Stack Config: $STACK_CONFIG" echo "Provider: $PROVIDER" -echo "Test Subdirs: $TEST_SUBDIRS" -echo "Vision Tests: $RUN_VISION_TESTS" echo "Inference Mode: $INFERENCE_MODE" +echo "Test Suite: $TEST_SUITE" +echo "Test Subdirs: $TEST_SUBDIRS" echo "Test Pattern: $TEST_PATTERN" echo "" @@ -194,84 +201,46 @@ if [[ -n "$TEST_PATTERN" ]]; then PYTEST_PATTERN="${PYTEST_PATTERN} and $TEST_PATTERN" fi -# Run vision tests if specified -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - echo "Running vision tests..." - set +e - pytest -s -v tests/integration/inference/test_vision_inference.py \ - --stack-config="$STACK_CONFIG" \ - -k "$PYTEST_PATTERN" \ - --vision-model=ollama/llama3.2-vision:11b \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 \ - --color=yes $EXTRA_PARAMS \ - --capture=tee-sys - exit_code=$? - set -e - - if [ $exit_code -eq 0 ]; then - echo "✅ Vision tests completed successfully" - elif [ $exit_code -eq 5 ]; then - echo "⚠️ No vision tests collected (pattern matched no tests)" - else - echo "❌ Vision tests failed" - exit 1 - fi - exit 0 -fi - -# Run regular tests -if [[ -z "$TEST_SUBDIRS" ]]; then - TEST_SUBDIRS=$(find tests/integration -maxdepth 1 -mindepth 1 -type d | - sed 's|tests/integration/||' | - grep -Ev "^(__pycache__|fixtures|test_cases|recordings|non_ci|post_training)$" | - sort) -fi echo "Test subdirs to run: $TEST_SUBDIRS" -# Collect all test files for the specified test types -TEST_FILES="" -for test_subdir in $(echo "$TEST_SUBDIRS" | tr ',' '\n'); do - # Skip certain test types for vllm provider - if [[ "$PROVIDER" == "vllm" ]]; then - if [[ "$test_subdir" == "safety" ]] || [[ "$test_subdir" == "post_training" ]] || [[ "$test_subdir" == "tool_runtime" ]]; then - echo "Skipping $test_subdir for vllm provider" - continue +if [[ -n "$TEST_SUBDIRS" ]]; then + # Collect all test files for the specified test types + TEST_FILES="" + for test_subdir in $(echo "$TEST_SUBDIRS" | tr ',' '\n'); do + if [[ -d "tests/integration/$test_subdir" ]]; then + # Find all Python test files in this directory + test_files=$(find tests/integration/$test_subdir -name "test_*.py" -o -name "*_test.py") + if [[ -n "$test_files" ]]; then + TEST_FILES="$TEST_FILES $test_files" + echo "Added test files from $test_subdir: $(echo $test_files | wc -w) files" + fi + else + echo "Warning: Directory tests/integration/$test_subdir does not exist" fi + done + + if [[ -z "$TEST_FILES" ]]; then + echo "No test files found for the specified test types" + exit 1 fi - if [[ "$STACK_CONFIG" != *"server:"* ]] && [[ "$test_subdir" == "batches" ]]; then - echo "Skipping $test_subdir for library client until types are supported" - continue - fi + echo "" + echo "=== Running all collected tests in a single pytest command ===" + echo "Total test files: $(echo $TEST_FILES | wc -w)" - if [[ -d "tests/integration/$test_subdir" ]]; then - # Find all Python test files in this directory - test_files=$(find tests/integration/$test_subdir -name "test_*.py" -o -name "*_test.py") - if [[ -n "$test_files" ]]; then - TEST_FILES="$TEST_FILES $test_files" - echo "Added test files from $test_subdir: $(echo $test_files | wc -w) files" - fi - else - echo "Warning: Directory tests/integration/$test_subdir does not exist" - fi -done - -if [[ -z "$TEST_FILES" ]]; then - echo "No test files found for the specified test types" - exit 1 + PYTEST_TARGET="$TEST_FILES" + EXTRA_PARAMS="$EXTRA_PARAMS --text-model=$TEXT_MODEL --embedding-model=sentence-transformers/all-MiniLM-L6-v2" +else + PYTEST_TARGET="tests/integration/" + EXTRA_PARAMS="$EXTRA_PARAMS --suite=$TEST_SUITE" fi -echo "" -echo "=== Running all collected tests in a single pytest command ===" -echo "Total test files: $(echo $TEST_FILES | wc -w)" - set +e -pytest -s -v $TEST_FILES \ +pytest -s -v $PYTEST_TARGET \ --stack-config="$STACK_CONFIG" \ -k "$PYTEST_PATTERN" \ - --text-model="$TEXT_MODEL" \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 \ - --color=yes $EXTRA_PARAMS \ + $EXTRA_PARAMS \ + --color=yes \ --capture=tee-sys exit_code=$? set -e @@ -294,7 +263,13 @@ df -h # stop server if [[ "$STACK_CONFIG" == *"server:"* ]]; then echo "Stopping Llama Stack Server..." - kill $(lsof -i :8321 | awk 'NR>1 {print $2}') + pids=$(lsof -i :8321 | awk 'NR>1 {print $2}') + if [[ -n "$pids" ]]; then + echo "Killing Llama Stack Server processes: $pids" + kill -9 $pids + else + echo "No Llama Stack Server processes found ?!" + fi echo "Llama Stack Server stopped" fi diff --git a/tests/README.md b/tests/README.md index 81f025f86..c00829d3e 100644 --- a/tests/README.md +++ b/tests/README.md @@ -77,7 +77,7 @@ You must be careful when re-recording. CI workflows assume a specific setup for ./scripts/github/schedule-record-workflow.sh --test-subdirs "agents,inference" # Record with vision tests enabled -./scripts/github/schedule-record-workflow.sh --test-subdirs "inference" --run-vision-tests +./scripts/github/schedule-record-workflow.sh --test-suite vision # Record with specific provider ./scripts/github/schedule-record-workflow.sh --test-subdirs "agents" --test-provider vllm diff --git a/tests/integration/README.md b/tests/integration/README.md index d177cbebf..b05beeb98 100644 --- a/tests/integration/README.md +++ b/tests/integration/README.md @@ -42,6 +42,27 @@ Model parameters can be influenced by the following options: Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped if no model is specified. +### Suites (fast selection + sane defaults) + +- `--suite`: comma-separated list of named suites that both narrow which tests are collected and prefill common model options (unless you pass them explicitly). +- Available suites: + - `responses`: collects tests under `tests/integration/responses`; this is a separate suite because it needs a strong tool-calling model. + - `vision`: collects only `tests/integration/inference/test_vision_inference.py`; defaults `--vision-model=ollama/llama3.2-vision:11b`, `--embedding-model=sentence-transformers/all-MiniLM-L6-v2`. +- Explicit flags always win. For example, `--suite=responses --text-model=` overrides the suite’s text model. + +Examples: + +```bash +# Fast responses run with defaults +pytest -s -v tests/integration --stack-config=server:starter --suite=responses + +# Fast single-file vision run with defaults +pytest -s -v tests/integration --stack-config=server:starter --suite=vision + +# Combine suites and override a default +pytest -s -v tests/integration --stack-config=server:starter --suite=responses,vision --embedding-model=text-embedding-3-small +``` + ## Examples ### Testing against a Server diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index fd9a54d04..96260fdb7 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -6,15 +6,17 @@ import inspect import itertools import os -import platform import textwrap import time +from pathlib import Path import pytest from dotenv import load_dotenv from llama_stack.log import get_logger +from .suites import SUITE_DEFINITIONS + logger = get_logger(__name__, category="tests") @@ -61,9 +63,22 @@ def pytest_configure(config): key, value = env_var.split("=", 1) os.environ[key] = value - if platform.system() == "Darwin": # Darwin is the system name for macOS - os.environ["DISABLE_CODE_SANDBOX"] = "1" - logger.info("Setting DISABLE_CODE_SANDBOX=1 for macOS") + suites_raw = config.getoption("--suite") + suites: list[str] = [] + if suites_raw: + suites = [p.strip() for p in str(suites_raw).split(",") if p.strip()] + unknown = [p for p in suites if p not in SUITE_DEFINITIONS] + if unknown: + raise pytest.UsageError( + f"Unknown suite(s): {', '.join(unknown)}. Available: {', '.join(sorted(SUITE_DEFINITIONS.keys()))}" + ) + for suite in suites: + suite_def = SUITE_DEFINITIONS.get(suite, {}) + defaults: dict = suite_def.get("defaults", {}) + for dest, value in defaults.items(): + current = getattr(config.option, dest, None) + if not current: + setattr(config.option, dest, value) def pytest_addoption(parser): @@ -105,16 +120,21 @@ def pytest_addoption(parser): default=384, help="Output dimensionality of the embedding model to use for testing. Default: 384", ) - parser.addoption( - "--record-responses", - action="store_true", - help="Record new API responses instead of using cached ones.", - ) parser.addoption( "--report", help="Path where the test report should be written, e.g. --report=/path/to/report.md", ) + available_suites = ", ".join(sorted(SUITE_DEFINITIONS.keys())) + suite_help = ( + "Comma-separated integration test suites to narrow collection and prefill defaults. " + "Available: " + f"{available_suites}. " + "Explicit CLI flags (e.g., --text-model) override suite defaults. " + "Examples: --suite=responses or --suite=responses,vision." + ) + parser.addoption("--suite", help=suite_help) + MODEL_SHORT_IDS = { "meta-llama/Llama-3.2-3B-Instruct": "3B", @@ -197,3 +217,40 @@ def pytest_generate_tests(metafunc): pytest_plugins = ["tests.integration.fixtures.common"] + + +def pytest_ignore_collect(path: str, config: pytest.Config) -> bool: + """Skip collecting paths outside the selected suite roots for speed.""" + suites_raw = config.getoption("--suite") + if not suites_raw: + return False + + names = [p.strip() for p in str(suites_raw).split(",") if p.strip()] + roots: list[str] = [] + for name in names: + suite_def = SUITE_DEFINITIONS.get(name) + if suite_def: + roots.extend(suite_def.get("roots", [])) + if not roots: + return False + + p = Path(str(path)).resolve() + + # Only constrain within tests/integration to avoid ignoring unrelated tests + integration_root = (Path(str(config.rootpath)) / "tests" / "integration").resolve() + if not p.is_relative_to(integration_root): + return False + + for r in roots: + rp = (Path(str(config.rootpath)) / r).resolve() + if rp.is_file(): + # Allow the exact file and any ancestor directories so pytest can walk into it. + if p == rp: + return False + if p.is_dir() and rp.is_relative_to(p): + return False + else: + # Allow anything inside an allowed directory + if p.is_relative_to(rp): + return False + return True diff --git a/tests/integration/non_ci/responses/__init__.py b/tests/integration/responses/__init__.py similarity index 100% rename from tests/integration/non_ci/responses/__init__.py rename to tests/integration/responses/__init__.py diff --git a/tests/integration/non_ci/responses/fixtures/__init__.py b/tests/integration/responses/fixtures/__init__.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/__init__.py rename to tests/integration/responses/fixtures/__init__.py diff --git a/tests/integration/non_ci/responses/fixtures/fixtures.py b/tests/integration/responses/fixtures/fixtures.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/fixtures.py rename to tests/integration/responses/fixtures/fixtures.py diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_1.jpg b/tests/integration/responses/fixtures/images/vision_test_1.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_1.jpg rename to tests/integration/responses/fixtures/images/vision_test_1.jpg diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_2.jpg b/tests/integration/responses/fixtures/images/vision_test_2.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_2.jpg rename to tests/integration/responses/fixtures/images/vision_test_2.jpg diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_3.jpg b/tests/integration/responses/fixtures/images/vision_test_3.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_3.jpg rename to tests/integration/responses/fixtures/images/vision_test_3.jpg diff --git a/tests/integration/non_ci/responses/fixtures/pdfs/llama_stack_and_models.pdf b/tests/integration/responses/fixtures/pdfs/llama_stack_and_models.pdf similarity index 100% rename from tests/integration/non_ci/responses/fixtures/pdfs/llama_stack_and_models.pdf rename to tests/integration/responses/fixtures/pdfs/llama_stack_and_models.pdf diff --git a/tests/integration/non_ci/responses/fixtures/test_cases.py b/tests/integration/responses/fixtures/test_cases.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/test_cases.py rename to tests/integration/responses/fixtures/test_cases.py diff --git a/tests/integration/non_ci/responses/helpers.py b/tests/integration/responses/helpers.py similarity index 100% rename from tests/integration/non_ci/responses/helpers.py rename to tests/integration/responses/helpers.py diff --git a/tests/integration/non_ci/responses/streaming_assertions.py b/tests/integration/responses/streaming_assertions.py similarity index 100% rename from tests/integration/non_ci/responses/streaming_assertions.py rename to tests/integration/responses/streaming_assertions.py diff --git a/tests/integration/non_ci/responses/test_basic_responses.py b/tests/integration/responses/test_basic_responses.py similarity index 100% rename from tests/integration/non_ci/responses/test_basic_responses.py rename to tests/integration/responses/test_basic_responses.py diff --git a/tests/integration/non_ci/responses/test_file_search.py b/tests/integration/responses/test_file_search.py similarity index 100% rename from tests/integration/non_ci/responses/test_file_search.py rename to tests/integration/responses/test_file_search.py diff --git a/tests/integration/non_ci/responses/test_tool_responses.py b/tests/integration/responses/test_tool_responses.py similarity index 100% rename from tests/integration/non_ci/responses/test_tool_responses.py rename to tests/integration/responses/test_tool_responses.py diff --git a/tests/integration/suites.py b/tests/integration/suites.py new file mode 100644 index 000000000..602855055 --- /dev/null +++ b/tests/integration/suites.py @@ -0,0 +1,53 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +# Central definition of integration test suites. You can use these suites by passing --suite=name to pytest. +# For example: +# +# ```bash +# pytest tests/integration/ --suite=vision +# ``` +# +# Each suite can: +# - restrict collection to specific roots (dirs or files) +# - provide default CLI option values (e.g. text_model, embedding_model, etc.) + +from pathlib import Path + +this_dir = Path(__file__).parent +default_roots = [ + str(p) + for p in this_dir.glob("*") + if p.is_dir() + and p.name not in ("__pycache__", "fixtures", "test_cases", "recordings", "responses", "post_training") +] + +SUITE_DEFINITIONS: dict[str, dict] = { + "base": { + "description": "Base suite that includes most tests but runs them with a text Ollama model", + "roots": default_roots, + "defaults": { + "text_model": "ollama/llama3.2:3b-instruct-fp16", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + }, + "responses": { + "description": "Suite that includes only the OpenAI Responses tests; needs a strong tool-calling model", + "roots": ["tests/integration/responses"], + "defaults": { + "text_model": "openai/gpt-4o", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + }, + "vision": { + "description": "Suite that includes only the vision tests", + "roots": ["tests/integration/inference/test_vision_inference.py"], + "defaults": { + "vision_model": "ollama/llama3.2-vision:11b", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + }, +} From 7cd1c2c238969cb41ddb3d87b7dcfe5331350be1 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Sat, 6 Sep 2025 07:26:34 -0600 Subject: [PATCH 049/124] feat: Updating Rag Tool to use Files API and Vector Stores API (#3344) --- docs/source/getting_started/demo_script.py | 5 +- .../inline/tool_runtime/rag/__init__.py | 2 +- .../inline/tool_runtime/rag/memory.py | 74 ++++++++++++++----- .../providers/registry/tool_runtime.py | 2 +- .../integration/tool_runtime/test_rag_tool.py | 41 ++++++---- tests/unit/rag/test_rag_query.py | 8 +- 6 files changed, 93 insertions(+), 39 deletions(-) diff --git a/docs/source/getting_started/demo_script.py b/docs/source/getting_started/demo_script.py index 777fc78c2..2ea67739f 100644 --- a/docs/source/getting_started/demo_script.py +++ b/docs/source/getting_started/demo_script.py @@ -18,12 +18,13 @@ embedding_model_id = ( ).identifier embedding_dimension = em.metadata["embedding_dimension"] -_ = client.vector_dbs.register( +vector_db = client.vector_dbs.register( vector_db_id=vector_db_id, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, provider_id="faiss", ) +vector_db_id = vector_db.identifier source = "https://www.paulgraham.com/greatwork.html" print("rag_tool> Ingesting document:", source) document = RAGDocument( @@ -35,7 +36,7 @@ document = RAGDocument( client.tool_runtime.rag_tool.insert( documents=[document], vector_db_id=vector_db_id, - chunk_size_in_tokens=50, + chunk_size_in_tokens=100, ) agent = Agent( client, diff --git a/llama_stack/providers/inline/tool_runtime/rag/__init__.py b/llama_stack/providers/inline/tool_runtime/rag/__init__.py index f9a6e5c55..f9a7e7b89 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/__init__.py +++ b/llama_stack/providers/inline/tool_runtime/rag/__init__.py @@ -14,6 +14,6 @@ from .config import RagToolRuntimeConfig async def get_provider_impl(config: RagToolRuntimeConfig, deps: dict[Api, Any]): from .memory import MemoryToolRuntimeImpl - impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference]) + impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference], deps[Api.files]) await impl.initialize() return impl diff --git a/llama_stack/providers/inline/tool_runtime/rag/memory.py b/llama_stack/providers/inline/tool_runtime/rag/memory.py index a1543457b..cb526e8ee 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/memory.py +++ b/llama_stack/providers/inline/tool_runtime/rag/memory.py @@ -5,10 +5,15 @@ # the root directory of this source tree. import asyncio +import base64 +import io +import mimetypes import secrets import string from typing import Any +import httpx +from fastapi import UploadFile from pydantic import TypeAdapter from llama_stack.apis.common.content_types import ( @@ -17,6 +22,7 @@ from llama_stack.apis.common.content_types import ( InterleavedContentItem, TextContentItem, ) +from llama_stack.apis.files import Files, OpenAIFilePurpose from llama_stack.apis.inference import Inference from llama_stack.apis.tools import ( ListToolDefsResponse, @@ -30,13 +36,18 @@ from llama_stack.apis.tools import ( ToolParameter, ToolRuntime, ) -from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO +from llama_stack.apis.vector_io import ( + QueryChunksResponse, + VectorIO, + VectorStoreChunkingStrategyStatic, + VectorStoreChunkingStrategyStaticConfig, +) from llama_stack.log import get_logger from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str from llama_stack.providers.utils.memory.vector_store import ( content_from_doc, - make_overlapped_chunks, + parse_data_url, ) from .config import RagToolRuntimeConfig @@ -55,10 +66,12 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti config: RagToolRuntimeConfig, vector_io_api: VectorIO, inference_api: Inference, + files_api: Files, ): self.config = config self.vector_io_api = vector_io_api self.inference_api = inference_api + self.files_api = files_api async def initialize(self): pass @@ -78,27 +91,50 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti vector_db_id: str, chunk_size_in_tokens: int = 512, ) -> None: - chunks = [] + if not documents: + return + for doc in documents: - content = await content_from_doc(doc) - # TODO: we should add enrichment here as URLs won't be added to the metadata by default - chunks.extend( - make_overlapped_chunks( - doc.document_id, - content, - chunk_size_in_tokens, - chunk_size_in_tokens // 4, - doc.metadata, + if isinstance(doc.content, URL): + if doc.content.uri.startswith("data:"): + parts = parse_data_url(doc.content.uri) + file_data = base64.b64decode(parts["data"]) if parts["is_base64"] else parts["data"].encode() + mime_type = parts["mimetype"] + else: + async with httpx.AsyncClient() as client: + response = await client.get(doc.content.uri) + file_data = response.content + mime_type = doc.mime_type or response.headers.get("content-type", "application/octet-stream") + else: + content_str = await content_from_doc(doc) + file_data = content_str.encode("utf-8") + mime_type = doc.mime_type or "text/plain" + + file_extension = mimetypes.guess_extension(mime_type) or ".txt" + filename = doc.metadata.get("filename", f"{doc.document_id}{file_extension}") + + file_obj = io.BytesIO(file_data) + file_obj.name = filename + + upload_file = UploadFile(file=file_obj, filename=filename) + + created_file = await self.files_api.openai_upload_file( + file=upload_file, purpose=OpenAIFilePurpose.ASSISTANTS + ) + + chunking_strategy = VectorStoreChunkingStrategyStatic( + static=VectorStoreChunkingStrategyStaticConfig( + max_chunk_size_tokens=chunk_size_in_tokens, + chunk_overlap_tokens=chunk_size_in_tokens // 4, ) ) - if not chunks: - return - - await self.vector_io_api.insert_chunks( - chunks=chunks, - vector_db_id=vector_db_id, - ) + await self.vector_io_api.openai_attach_file_to_vector_store( + vector_store_id=vector_db_id, + file_id=created_file.id, + attributes=doc.metadata, + chunking_strategy=chunking_strategy, + ) async def query( self, diff --git a/llama_stack/providers/registry/tool_runtime.py b/llama_stack/providers/registry/tool_runtime.py index 661851443..5a58fa7af 100644 --- a/llama_stack/providers/registry/tool_runtime.py +++ b/llama_stack/providers/registry/tool_runtime.py @@ -32,7 +32,7 @@ def available_providers() -> list[ProviderSpec]: ], module="llama_stack.providers.inline.tool_runtime.rag", config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig", - api_dependencies=[Api.vector_io, Api.inference], + api_dependencies=[Api.vector_io, Api.inference, Api.files], description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.", ), remote_provider_spec( diff --git a/tests/integration/tool_runtime/test_rag_tool.py b/tests/integration/tool_runtime/test_rag_tool.py index 2affe2a2d..b208500d8 100644 --- a/tests/integration/tool_runtime/test_rag_tool.py +++ b/tests/integration/tool_runtime/test_rag_tool.py @@ -17,10 +17,14 @@ def client_with_empty_registry(client_with_models): client_with_models.vector_dbs.unregister(vector_db_id=vector_db_id) clear_registry() + + try: + client_with_models.toolgroups.register(toolgroup_id="builtin::rag", provider_id="rag-runtime") + except Exception: + pass + yield client_with_models - # you must clean after the last test if you were running tests against - # a stateful server instance clear_registry() @@ -66,12 +70,13 @@ def assert_valid_text_response(response): def test_vector_db_insert_inline_and_query( client_with_empty_registry, sample_documents, embedding_model_id, embedding_dimension ): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + vector_db = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + vector_db_id = vector_db.identifier client_with_empty_registry.tool_runtime.rag_tool.insert( documents=sample_documents, @@ -134,7 +139,11 @@ def test_vector_db_insert_from_url_and_query( # list to check memory bank is successfully registered available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_db_id in available_vector_dbs + # VectorDB is being migrated to VectorStore, so the ID will be different + # Just check that at least one vector DB was registered + assert len(available_vector_dbs) > 0 + # Use the actual registered vector_db_id for subsequent operations + actual_vector_db_id = available_vector_dbs[0] urls = [ "memory_optimizations.rst", @@ -153,13 +162,13 @@ def test_vector_db_insert_from_url_and_query( client_with_empty_registry.tool_runtime.rag_tool.insert( documents=documents, - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunk_size_in_tokens=512, ) # Query for the name of method response1 = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="What's the name of the fine-tunning method used?", ) assert_valid_chunk_response(response1) @@ -167,7 +176,7 @@ def test_vector_db_insert_from_url_and_query( # Query for the name of model response2 = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="Which Llama model is mentioned?", ) assert_valid_chunk_response(response2) @@ -187,7 +196,11 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i ) available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_db_id in available_vector_dbs + # VectorDB is being migrated to VectorStore, so the ID will be different + # Just check that at least one vector DB was registered + assert len(available_vector_dbs) > 0 + # Use the actual registered vector_db_id for subsequent operations + actual_vector_db_id = available_vector_dbs[0] urls = [ "memory_optimizations.rst", @@ -206,19 +219,19 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i client_with_empty_registry.tool_runtime.rag_tool.insert( documents=documents, - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunk_size_in_tokens=512, ) response_with_metadata = client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", ) assert_valid_text_response(response_with_metadata) assert any("metadata:" in chunk.text.lower() for chunk in response_with_metadata.content) response_without_metadata = client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", query_config={ "include_metadata_in_content": True, @@ -230,7 +243,7 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i with pytest.raises((ValueError, BadRequestError)): client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", query_config={ "chunk_template": "This should raise a ValueError because it is missing the proper template variables", diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index 05ccecb99..d18d90716 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -19,12 +19,16 @@ from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRunti class TestRagQuery: async def test_query_raises_on_empty_vector_db_ids(self): - rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) + rag_tool = MemoryToolRuntimeImpl( + config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock() + ) with pytest.raises(ValueError): await rag_tool.query(content=MagicMock(), vector_db_ids=[]) async def test_query_chunk_metadata_handling(self): - rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) + rag_tool = MemoryToolRuntimeImpl( + config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock() + ) content = "test query content" vector_db_ids = ["db1"] From d6c3b363904eedd9c5323594603dc4f2d5b581eb Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 15:22:20 -0400 Subject: [PATCH 050/124] chore: update the gemini inference impl to use openai-python for openai-compat functions (#3351) # What does this PR do? update the Gemini inference provider to use openai-python for the openai-compat endpoints partially addresses #3349, does not address /inference/completion or /inference/chat-completion ## Test Plan ci --- llama_stack/providers/registry/inference.py | 2 +- llama_stack/providers/remote/inference/gemini/gemini.py | 8 +++++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 50956f58c..1bb3c3147 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -207,7 +207,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="gemini", - pip_packages=["litellm"], + pip_packages=["litellm", "openai"], module="llama_stack.providers.remote.inference.gemini", config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig", provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/gemini/gemini.py b/llama_stack/providers/remote/inference/gemini/gemini.py index b6048eff7..569227fdd 100644 --- a/llama_stack/providers/remote/inference/gemini/gemini.py +++ b/llama_stack/providers/remote/inference/gemini/gemini.py @@ -5,12 +5,13 @@ # the root directory of this source tree. from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import GeminiConfig from .models import MODEL_ENTRIES -class GeminiInferenceAdapter(LiteLLMOpenAIMixin): +class GeminiInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): def __init__(self, config: GeminiConfig) -> None: LiteLLMOpenAIMixin.__init__( self, @@ -21,6 +22,11 @@ class GeminiInferenceAdapter(LiteLLMOpenAIMixin): ) self.config = config + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self): + return "https://generativelanguage.googleapis.com/v1beta/openai/" + async def initialize(self) -> None: await super().initialize() From 4c28544c04ab96c5c4d188dda3a53dc2eab0415d Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 15:22:44 -0400 Subject: [PATCH 051/124] chore(gemini, tests): add skips for n and completions, gemini api does not support them (#3350) # What does this PR do? the gemini api endpoints do not support the n param or completions ## Test Plan ci --- tests/integration/inference/test_openai_completion.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index bb447b3c1..099032578 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -37,6 +37,7 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) "remote::sambanova", "remote::tgi", "remote::vertexai", + "remote::gemini", # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404 ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.") @@ -63,6 +64,9 @@ def skip_if_doesnt_support_n(client_with_models, model_id): if provider.provider_type in ( "remote::sambanova", "remote::ollama", + # Error code: 400 - [{'error': {'code': 400, 'message': 'Only one candidate can be specified in the + # current model', 'status': 'INVALID_ARGUMENT'}}] + "remote::gemini", ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") From bf02cd846fdc39db80291746e06ca547e5afdbdb Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 15:25:13 -0400 Subject: [PATCH 052/124] chore: update the sambanova inference impl to use openai-python for openai-compat functions (#3345) # What does this PR do? update SambaNova inference provider to use OpenAIMixin for openai-compat endpoints ## Test Plan ``` $ SAMBANOVA_API_KEY=... uv run llama stack build --image-type venv --providers inference=remote::sambanova --run ... $ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model sambanova/Meta-Llama-3.3-70B-Instruct tests/integration/inference -k 'not store' ... FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=sambanova/Meta-Llama-3.3-70B-Instruct-inference:chat_completion:tool_calling_tools_absent-True] - AttributeError: 'NoneType' object has no attribute 'delta' FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=sambanova/Meta-Llama-3.3-70B-Instruct-inference:chat_completion:tool_calling_tools_absent-False] - llama_stack_client.InternalServerError: Error code: 500 - {'detail': 'Internal server error: An une... =========== 2 failed, 16 passed, 68 skipped, 8 deselected, 3 xfailed, 13 warnings in 15.85s ============ ``` the two failures also exist before this change. they are part of the deprecated inference.chat_completion tests that flow through litellm. they can be resolved later. --- llama_stack/providers/registry/inference.py | 2 +- .../remote/inference/sambanova/sambanova.py | 26 ++++++++++++++++++- 2 files changed, 26 insertions(+), 2 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 1bb3c3147..7a95fd089 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -270,7 +270,7 @@ Available Models: api=Api.inference, adapter=AdapterSpec( adapter_type="sambanova", - pip_packages=["litellm"], + pip_packages=["litellm", "openai"], module="llama_stack.providers.remote.inference.sambanova", config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig", provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/sambanova/sambanova.py b/llama_stack/providers/remote/inference/sambanova/sambanova.py index 96469acac..ee3b0f648 100644 --- a/llama_stack/providers/remote/inference/sambanova/sambanova.py +++ b/llama_stack/providers/remote/inference/sambanova/sambanova.py @@ -4,13 +4,26 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. + from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import SambaNovaImplConfig from .models import MODEL_ENTRIES -class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): +class SambaNovaInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): + """ + SambaNova Inference Adapter for Llama Stack. + + Note: The inheritance order is important here. OpenAIMixin must come before + LiteLLMOpenAIMixin to ensure that OpenAIMixin.check_model_availability() + is used instead of LiteLLMOpenAIMixin.check_model_availability(). + + - OpenAIMixin.check_model_availability() queries the /v1/models to check if a model exists + - LiteLLMOpenAIMixin.check_model_availability() checks the static registry within LiteLLM + """ + def __init__(self, config: SambaNovaImplConfig): self.config = config self.environment_available_models = [] @@ -24,3 +37,14 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): download_images=True, # SambaNova requires base64 image encoding json_schema_strict=False, # SambaNova doesn't support strict=True yet ) + + # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + """ + Get the base URL for OpenAI mixin. + + :return: The SambaNova base URL + """ + return self.config.url From 9252d9fc018487ef7bd6ac400ed329cd5db1e8c4 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 15:35:30 -0400 Subject: [PATCH 053/124] chore(groq test): skip with_n tests for groq, it is not supported server-side (#3346) # What does this PR do? skip the with_n test for groq, because it isn't supported by the provider's service see https://console.groq.com/docs/openai#currently-unsupported-openai-features Co-authored-by: raghotham --- tests/integration/inference/test_openai_completion.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 099032578..2043f6aeb 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -64,6 +64,9 @@ def skip_if_doesnt_support_n(client_with_models, model_id): if provider.provider_type in ( "remote::sambanova", "remote::ollama", + # https://console.groq.com/docs/openai#currently-unsupported-openai-features + # -> Error code: 400 - {'error': {'message': "'n' : number must be at most 1", 'type': 'invalid_request_error'}} + "remote::groq", # Error code: 400 - [{'error': {'code': 400, 'message': 'Only one candidate can be specified in the # current model', 'status': 'INVALID_ARGUMENT'}}] "remote::gemini", From ecd9d8dc1a70ea70ea06253869392afac3abdb40 Mon Sep 17 00:00:00 2001 From: Charlie Doern Date: Sat, 6 Sep 2025 15:40:33 -0400 Subject: [PATCH 054/124] test: introduce api conformance test (#3257) # What does this PR do? this test runs on each PR and uses a new conformance workflow to compare the base (main) branch openapi spec to the one on this PR if one of our "stable" APIs change, the test will fail. this workflow uses `oasdiff` to identify breaking changes for paths we want to ensure comptability for. specifically this is using `oasdiff breaking` with `--match-path` which only checks breaking changes for the specified paths. As a follow up to this, we can add an optional way to make it so that it is OK to make these change if properly documented or in a changelog or something. or by using a label on the PR to override the failing test. related to #3237 ## Test Plan conformance test should pass given there are no changes Signed-off-by: Charlie Doern --- .github/workflows/README.md | 1 + .github/workflows/conformance.yml | 57 +++++++++++++++++++++++++++++++ 2 files changed, 58 insertions(+) create mode 100644 .github/workflows/conformance.yml diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 2e0df58b8..059bb873f 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -5,6 +5,7 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl | Name | File | Purpose | | ---- | ---- | ------- | | Update Changelog | [changelog.yml](changelog.yml) | Creates PR for updating the CHANGELOG.md | +| API Conformance Tests | [conformance.yml](conformance.yml) | Run the API Conformance test suite on the changes. | | Installer CI | [install-script-ci.yml](install-script-ci.yml) | Test the installation script | | Integration Auth Tests | [integration-auth-tests.yml](integration-auth-tests.yml) | Run the integration test suite with Kubernetes authentication | | SqlStore Integration Tests | [integration-sql-store-tests.yml](integration-sql-store-tests.yml) | Run the integration test suite with SqlStore | diff --git a/.github/workflows/conformance.yml b/.github/workflows/conformance.yml new file mode 100644 index 000000000..2433b0203 --- /dev/null +++ b/.github/workflows/conformance.yml @@ -0,0 +1,57 @@ +# API Conformance Tests +# This workflow ensures that API changes maintain backward compatibility and don't break existing integrations +# It runs schema validation and OpenAPI diff checks to catch breaking changes early + +name: API Conformance Tests + +run-name: Run the API Conformance test suite on the changes. + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + types: [opened, synchronize, reopened] + paths: + - 'llama_stack/**' + - '!llama_stack/ui/**' + - 'tests/**' + - 'uv.lock' + - 'pyproject.toml' + - '.github/workflows/conformance.yml' # This workflow itself + +concurrency: + group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }} + # Cancel in-progress runs when new commits are pushed to avoid wasting CI resources + cancel-in-progress: true + +jobs: + # Job to check if API schema changes maintain backward compatibility + check-schema-compatibility: + runs-on: ubuntu-latest + steps: + # Using specific version 4.1.7 because 5.0.0 fails when trying to run this locally using `act` + # This ensures consistent behavior between local testing and CI + - name: Checkout PR Code + uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 + + # Checkout the base branch to compare against (usually main) + # This allows us to diff the current changes against the previous state + - name: Checkout Base Branch + uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 + with: + ref: ${{ github.event.pull_request.base.ref }} + path: 'base' + + # Install oasdiff: https://github.com/oasdiff/oasdiff, a tool for detecting breaking changes in OpenAPI specs. + - name: Install oasdiff + run: | + curl -fsSL https://raw.githubusercontent.com/oasdiff/oasdiff/main/install.sh | sh + + # Run oasdiff to detect breaking changes in the API specification + # This step will fail if incompatible changes are detected, preventing breaking changes from being merged + - name: Run OpenAPI Breaking Change Diff + run: | + oasdiff breaking --fail-on ERR base/docs/_static/llama-stack-spec.yaml docs/_static/llama-stack-spec.yaml --match-path '^/v1/openai/v1' \ + --match-path '^/v1/vector-io' \ + --match-path '^/v1/vector-dbs' From d23607483fc8ca63ea15db5a95f672aec249d2e1 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 18:36:27 -0400 Subject: [PATCH 055/124] chore: update the groq inference impl to use openai-python for openai-compat functions (#3348) # What does this PR do? update Groq inference provider to use OpenAIMixin for openai-compat endpoints changes on api.groq.com - - json_schema is now supported for specific models, see https://console.groq.com/docs/structured-outputs#supported-models - response_format with streaming is now supported for models that support response_format - groq no longer returns a 400 error if tools are provided and tool_choice is not "required" ## Test Plan ``` $ GROQ_API_KEY=... uv run llama stack build --image-type venv --providers inference=remote::groq --run ... $ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model groq/llama-3.3-70b-versatile tests/integration/inference/test_openai_completion.py -k 'not store' ... SKIPPED [3] tests/integration/inference/test_openai_completion.py:44: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support OpenAI completions. SKIPPED [3] tests/integration/inference/test_openai_completion.py:94: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support vllm extra_body parameters. SKIPPED [4] tests/integration/inference/test_openai_completion.py:73: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support n param. SKIPPED [1] tests/integration/inference/test_openai_completion.py:100: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support chat completion calls with base64 encoded files. ======================= 8 passed, 11 skipped, 8 deselected, 2 warnings in 5.13s ======================== ``` --------- Co-authored-by: raghotham --- llama_stack/providers/registry/inference.py | 2 +- .../providers/remote/inference/groq/groq.py | 139 +----------------- .../test_inference_client_caching.py | 3 +- 3 files changed, 10 insertions(+), 134 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 7a95fd089..4176f85a6 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -248,7 +248,7 @@ Available Models: api=Api.inference, adapter=AdapterSpec( adapter_type="groq", - pip_packages=["litellm"], + pip_packages=["litellm", "openai"], module="llama_stack.providers.remote.inference.groq", config_class="llama_stack.providers.remote.inference.groq.GroqConfig", provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/groq/groq.py b/llama_stack/providers/remote/inference/groq/groq.py index fd7212de4..888953af0 100644 --- a/llama_stack/providers/remote/inference/groq/groq.py +++ b/llama_stack/providers/remote/inference/groq/groq.py @@ -4,30 +4,15 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from collections.abc import AsyncIterator -from typing import Any -from openai import AsyncOpenAI - -from llama_stack.apis.inference import ( - OpenAIChatCompletion, - OpenAIChatCompletionChunk, - OpenAIChoiceDelta, - OpenAIChunkChoice, - OpenAIMessageParam, - OpenAIResponseFormatParam, - OpenAISystemMessageParam, -) from llama_stack.providers.remote.inference.groq.config import GroqConfig from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin -from llama_stack.providers.utils.inference.openai_compat import ( - prepare_openai_completion_params, -) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .models import MODEL_ENTRIES -class GroqInferenceAdapter(LiteLLMOpenAIMixin): +class GroqInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): _config: GroqConfig def __init__(self, config: GroqConfig): @@ -40,122 +25,14 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin): ) self.config = config + # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + return f"{self.config.url}/openai/v1" + async def initialize(self): await super().initialize() async def shutdown(self): await super().shutdown() - - def _get_openai_client(self) -> AsyncOpenAI: - return AsyncOpenAI( - base_url=f"{self.config.url}/openai/v1", - api_key=self.get_api_key(), - ) - - async def openai_chat_completion( - self, - model: str, - messages: list[OpenAIMessageParam], - frequency_penalty: float | None = None, - function_call: str | dict[str, Any] | None = None, - functions: list[dict[str, Any]] | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_completion_tokens: int | None = None, - max_tokens: int | None = None, - n: int | None = None, - parallel_tool_calls: bool | None = None, - presence_penalty: float | None = None, - response_format: OpenAIResponseFormatParam | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - tool_choice: str | dict[str, Any] | None = None, - tools: list[dict[str, Any]] | None = None, - top_logprobs: int | None = None, - top_p: float | None = None, - user: str | None = None, - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - model_obj = await self.model_store.get_model(model) - - # Groq does not support json_schema response format, so we need to convert it to json_object - if response_format and response_format.type == "json_schema": - response_format.type = "json_object" - schema = response_format.json_schema.get("schema", {}) - response_format.json_schema = None - json_instructions = f"\nYour response should be a JSON object that matches the following schema: {schema}" - if messages and messages[0].role == "system": - messages[0].content = messages[0].content + json_instructions - else: - messages.insert(0, OpenAISystemMessageParam(content=json_instructions)) - - # Groq returns a 400 error if tools are provided but none are called - # So, set tool_choice to "required" to attempt to force a call - if tools and (not tool_choice or tool_choice == "auto"): - tool_choice = "required" - - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - - # Groq does not support streaming requests that set response_format - fake_stream = False - if stream and response_format: - params["stream"] = False - fake_stream = True - - response = await self._get_openai_client().chat.completions.create(**params) - - if fake_stream: - chunk_choices = [] - for choice in response.choices: - delta = OpenAIChoiceDelta( - content=choice.message.content, - role=choice.message.role, - tool_calls=choice.message.tool_calls, - ) - chunk_choice = OpenAIChunkChoice( - delta=delta, - finish_reason=choice.finish_reason, - index=choice.index, - logprobs=None, - ) - chunk_choices.append(chunk_choice) - chunk = OpenAIChatCompletionChunk( - id=response.id, - choices=chunk_choices, - object="chat.completion.chunk", - created=response.created, - model=response.model, - ) - - async def _fake_stream_generator(): - yield chunk - - return _fake_stream_generator() - else: - return response diff --git a/tests/unit/providers/inference/test_inference_client_caching.py b/tests/unit/providers/inference/test_inference_client_caching.py index b371cf907..f4b3201e9 100644 --- a/tests/unit/providers/inference/test_inference_client_caching.py +++ b/tests/unit/providers/inference/test_inference_client_caching.py @@ -33,8 +33,7 @@ def test_groq_provider_openai_client_caching(): with request_provider_data_context( {"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})} ): - openai_client = inference_adapter._get_openai_client() - assert openai_client.api_key == api_key + assert inference_adapter.client.api_key == api_key def test_openai_provider_openai_client_caching(): From 78cab5331a78d27b084a04c0d8e302a51d5d2c4d Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 19:21:55 -0400 Subject: [PATCH 056/124] chore(groq test): skip completions tests for groq, api is not supported server-side (#3347) # What does this PR do? skip /v1/completions tests on groq, endpoint is not supported Co-authored-by: raghotham --- tests/integration/inference/test_openai_completion.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 2043f6aeb..aee75b21d 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -37,6 +37,9 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) "remote::sambanova", "remote::tgi", "remote::vertexai", + # {"error":{"message":"Unknown request URL: GET /openai/v1/completions. Please check the URL for typos, + # or see the docs at https://console.groq.com/docs/","type":"invalid_request_error","code":"unknown_url"}} + "remote::groq", "remote::gemini", # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404 ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.") From 6a35bd7bb6186d2ee60bbc3de0e2a349b12796f0 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sun, 7 Sep 2025 17:00:42 -0400 Subject: [PATCH 057/124] chore: update the anthropic inference impl to use openai-python for openai-compat functions (#3366) # What does this PR do? update the Anthropic inference provider to use openai-python for the openai-compat endpoints ## Test Plan ci Co-authored-by: raghotham --- .../providers/remote/inference/anthropic/anthropic.py | 8 +++++++- tests/integration/inference/test_openai_completion.py | 3 +++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/llama_stack/providers/remote/inference/anthropic/anthropic.py b/llama_stack/providers/remote/inference/anthropic/anthropic.py index 31626082b..0f247218d 100644 --- a/llama_stack/providers/remote/inference/anthropic/anthropic.py +++ b/llama_stack/providers/remote/inference/anthropic/anthropic.py @@ -5,12 +5,13 @@ # the root directory of this source tree. from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import AnthropicConfig from .models import MODEL_ENTRIES -class AnthropicInferenceAdapter(LiteLLMOpenAIMixin): +class AnthropicInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): def __init__(self, config: AnthropicConfig) -> None: LiteLLMOpenAIMixin.__init__( self, @@ -26,3 +27,8 @@ class AnthropicInferenceAdapter(LiteLLMOpenAIMixin): async def shutdown(self) -> None: await super().shutdown() + + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self): + return "https://api.anthropic.com/v1" diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index aee75b21d..df1184f1c 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -41,6 +41,7 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) # or see the docs at https://console.groq.com/docs/","type":"invalid_request_error","code":"unknown_url"}} "remote::groq", "remote::gemini", # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404 + "remote::anthropic", # at least claude-3-{5,7}-{haiku,sonnet}-* / claude-{sonnet,opus}-4-* are not supported ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.") @@ -73,6 +74,8 @@ def skip_if_doesnt_support_n(client_with_models, model_id): # Error code: 400 - [{'error': {'code': 400, 'message': 'Only one candidate can be specified in the # current model', 'status': 'INVALID_ARGUMENT'}}] "remote::gemini", + # https://docs.anthropic.com/en/api/openai-sdk#simple-fields + "remote::anthropic", ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") From fe134d90e5488725da179d5283f126787d61a89b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 09:58:45 +0200 Subject: [PATCH 058/124] chore(ui-deps): bump react-dom and @types/react-dom in /llama_stack/ui (#3360) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [react-dom](https://github.com/facebook/react/tree/HEAD/packages/react-dom) and [@types/react-dom](https://github.com/DefinitelyTyped/DefinitelyTyped/tree/HEAD/types/react-dom). These dependencies needed to be updated together. Updates `react-dom` from 19.1.0 to 19.1.1
Release notes

Sourced from react-dom's releases.

19.1.1 (July 28, 2025)

React

  • Fixed Owner Stacks to work with ES2015 function.name semantics (#33680 by @​hoxyq)
Changelog

Sourced from react-dom's changelog.

19.1.1 (July 28, 2025)

React

  • Fixed Owner Stacks to work with ES2015 function.name semantics (#33680 by @​hoxyq)
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Updates `@types/react-dom` from 19.1.5 to 19.1.9
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 22 +++++++++++----------- llama_stack/ui/package.json | 2 +- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 7873cdfd5..05ccdb708 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -24,7 +24,7 @@ "next-auth": "^4.24.11", "next-themes": "^0.4.6", "react": "^19.0.0", - "react-dom": "^19.0.0", + "react-dom": "^19.1.1", "react-markdown": "^10.1.0", "remark-gfm": "^4.0.1", "remeda": "^2.30.0", @@ -4079,9 +4079,9 @@ } }, "node_modules/@types/react-dom": { - "version": "19.1.5", - "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-19.1.5.tgz", - "integrity": "sha512-CMCjrWucUBZvohgZxkjd6S9h0nZxXjzus6yDfUb+xLxYM7VvjKNH1tQrE9GWLql1XoOP4/Ds3bwFqShHUYraGg==", + "version": "19.1.9", + "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-19.1.9.tgz", + "integrity": "sha512-qXRuZaOsAdXKFyOhRBg6Lqqc0yay13vN7KrIg4L7N4aaHN68ma9OK3NE1BoDFgFOTfM7zg+3/8+2n8rLUH3OKQ==", "devOptional": true, "license": "MIT", "peerDependencies": { @@ -12448,24 +12448,24 @@ } }, "node_modules/react": { - "version": "19.1.0", - "resolved": "https://registry.npmjs.org/react/-/react-19.1.0.tgz", - "integrity": "sha512-FS+XFBNvn3GTAWq26joslQgWNoFu08F4kl0J4CgdNKADkdSGXQyTCnKteIAJy96Br6YbpEU1LSzV5dYtjMkMDg==", + "version": "19.1.1", + "resolved": "https://registry.npmjs.org/react/-/react-19.1.1.tgz", + "integrity": "sha512-w8nqGImo45dmMIfljjMwOGtbmC/mk4CMYhWIicdSflH91J9TyCyczcPFXJzrZ/ZXcgGRFeP6BU0BEJTw6tZdfQ==", "license": "MIT", "engines": { "node": ">=0.10.0" } }, "node_modules/react-dom": { - "version": "19.1.0", - "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.1.0.tgz", - "integrity": "sha512-Xs1hdnE+DyKgeHJeJznQmYMIBG3TKIHJJT95Q58nHLSrElKlGQqDTR2HQ9fx5CN/Gk6Vh/kupBTDLU11/nDk/g==", + "version": "19.1.1", + "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.1.1.tgz", + "integrity": "sha512-Dlq/5LAZgF0Gaz6yiqZCf6VCcZs1ghAJyrsu84Q/GT0gV+mCxbfmKNoGRKBYMJ8IEdGPqu49YWXD02GCknEDkw==", "license": "MIT", "dependencies": { "scheduler": "^0.26.0" }, "peerDependencies": { - "react": "^19.1.0" + "react": "^19.1.1" } }, "node_modules/react-is": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index b37ff233f..558a9fe2f 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -29,7 +29,7 @@ "next-auth": "^4.24.11", "next-themes": "^0.4.6", "react": "^19.0.0", - "react-dom": "^19.0.0", + "react-dom": "^19.1.1", "react-markdown": "^10.1.0", "remark-gfm": "^4.0.1", "remeda": "^2.30.0", From 91c7c4570e3abc3f9571795a8fd6ae520a0d9853 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 09:59:02 +0200 Subject: [PATCH 059/124] chore(ui-deps): bump sonner from 2.0.6 to 2.0.7 in /llama_stack/ui (#3364) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [sonner](https://github.com/emilkowalski/sonner) from 2.0.6 to 2.0.7.
Release notes

Sourced from sonner's releases.

v2.0.7

Sonner now supports multiple <Toaster /> components, see more here.

What's Changed

New Contributors

Full Changelog: https://github.com/emilkowalski/sonner/compare/v2.0.6...v2.0.7

Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=sonner&package-manager=npm_and_yarn&previous-version=2.0.6&new-version=2.0.7)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 8 ++++---- llama_stack/ui/package.json | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 05ccdb708..f0ca72e92 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -29,7 +29,7 @@ "remark-gfm": "^4.0.1", "remeda": "^2.30.0", "shiki": "^1.29.2", - "sonner": "^2.0.6", + "sonner": "^2.0.7", "tailwind-merge": "^3.3.1" }, "devDependencies": { @@ -13285,9 +13285,9 @@ } }, "node_modules/sonner": { - "version": "2.0.6", - "resolved": "https://registry.npmjs.org/sonner/-/sonner-2.0.6.tgz", - "integrity": "sha512-yHFhk8T/DK3YxjFQXIrcHT1rGEeTLliVzWbO0xN8GberVun2RiBnxAjXAYpZrqwEVHBG9asI/Li8TAAhN9m59Q==", + "version": "2.0.7", + "resolved": "https://registry.npmjs.org/sonner/-/sonner-2.0.7.tgz", + "integrity": "sha512-W6ZN4p58k8aDKA4XPcx2hpIQXBRAgyiWVkYhT7CvK6D3iAu7xjvVyhQHg2/iaKJZ1XVJ4r7XuwGL+WGEK37i9w==", "license": "MIT", "peerDependencies": { "react": "^18.0.0 || ^19.0.0 || ^19.0.0-rc", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 558a9fe2f..6f35fc83f 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -34,7 +34,7 @@ "remark-gfm": "^4.0.1", "remeda": "^2.30.0", "shiki": "^1.29.2", - "sonner": "^2.0.6", + "sonner": "^2.0.7", "tailwind-merge": "^3.3.1" }, "devDependencies": { From e508aef320f209d8ed9bb37c64a1fc5be35c05db Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 09:59:24 +0200 Subject: [PATCH 060/124] chore(ui-deps): bump lucide-react from 0.510.0 to 0.542.0 in /llama_stack/ui (#3363) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [lucide-react](https://github.com/lucide-icons/lucide/tree/HEAD/packages/lucide-react) from 0.510.0 to 0.542.0.
Release notes

Sourced from lucide-react's releases.

Version 0.542.0

What's Changed

New Contributors

Full Changelog: https://github.com/lucide-icons/lucide/compare/0.541.0...0.542.0

Version 0.541.0

What's Changed

Full Changelog: https://github.com/lucide-icons/lucide/compare/0.540.0...0.541.0

Version 0.540.0

What's Changed

Full Changelog: https://github.com/lucide-icons/lucide/compare/0.539.0...0.540.0

Version 0.539.0

What's Changed

Full Changelog: https://github.com/lucide-icons/lucide/compare/0.538.0...0.539.0

Version 0.538.0

What's Changed

... (truncated)

Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=lucide-react&package-manager=npm_and_yarn&previous-version=0.510.0&new-version=0.542.0)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 8 ++++---- llama_stack/ui/package.json | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index f0ca72e92..198b98fdf 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -19,7 +19,7 @@ "clsx": "^2.1.1", "framer-motion": "^12.23.12", "llama-stack-client": "^0.2.20", - "lucide-react": "^0.510.0", + "lucide-react": "^0.542.0", "next": "15.3.3", "next-auth": "^4.24.11", "next-themes": "^0.4.6", @@ -10240,9 +10240,9 @@ "license": "ISC" }, "node_modules/lucide-react": { - "version": "0.510.0", - "resolved": "https://registry.npmjs.org/lucide-react/-/lucide-react-0.510.0.tgz", - "integrity": "sha512-p8SQRAMVh7NhsAIETokSqDrc5CHnDLbV29mMnzaXx+Vc/hnqQzwI2r0FMWCcoTXnbw2KEjy48xwpGdEL+ck06Q==", + "version": "0.542.0", + "resolved": "https://registry.npmjs.org/lucide-react/-/lucide-react-0.542.0.tgz", + "integrity": "sha512-w3hD8/SQB7+lzU2r4VdFyzzOzKnUjTZIF/MQJGSSvni7Llewni4vuViRppfRAa2guOsY5k4jZyxw/i9DQHv+dw==", "license": "ISC", "peerDependencies": { "react": "^16.5.1 || ^17.0.0 || ^18.0.0 || ^19.0.0" diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 6f35fc83f..f892f4f9b 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -24,7 +24,7 @@ "clsx": "^2.1.1", "framer-motion": "^12.23.12", "llama-stack-client": "^0.2.20", - "lucide-react": "^0.510.0", + "lucide-react": "^0.542.0", "next": "15.3.3", "next-auth": "^4.24.11", "next-themes": "^0.4.6", From e1b81ce1fc4820480a95471bbe0f504a952110b6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 09:59:44 +0200 Subject: [PATCH 061/124] chore(ui-deps): bump @radix-ui/react-dropdown-menu from 2.1.14 to 2.1.16 in /llama_stack/ui (#3361) Bumps [@radix-ui/react-dropdown-menu](https://github.com/radix-ui/primitives) from 2.1.14 to 2.1.16.
Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=@radix-ui/react-dropdown-menu&package-manager=npm_and_yarn&previous-version=2.1.14&new-version=2.1.16)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 443 ++++++++++++++++++++----------- llama_stack/ui/package.json | 2 +- 2 files changed, 288 insertions(+), 157 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 198b98fdf..cbe8e5557 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -10,7 +10,7 @@ "dependencies": { "@radix-ui/react-collapsible": "^1.1.12", "@radix-ui/react-dialog": "^1.1.13", - "@radix-ui/react-dropdown-menu": "^2.1.14", + "@radix-ui/react-dropdown-menu": "^2.1.16", "@radix-ui/react-select": "^2.2.5", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", @@ -2066,12 +2066,35 @@ "license": "MIT" }, "node_modules/@radix-ui/react-arrow": { - "version": "1.1.6", - "resolved": "https://registry.npmjs.org/@radix-ui/react-arrow/-/react-arrow-1.1.6.tgz", - "integrity": "sha512-2JMfHJf/eVnwq+2dewT3C0acmCWD3XiVA1Da+jTDqo342UlU13WvXtqHhG+yJw5JeQmu4ue2eMy6gcEArLBlcw==", + "version": "1.1.7", + "resolved": "https://registry.npmjs.org/@radix-ui/react-arrow/-/react-arrow-1.1.7.tgz", + "integrity": "sha512-F+M1tLhO+mlQaOWspE8Wstg+z6PwxwRd8oQ8IXceWz92kfAmalTRf0EjrouQeo7QssEPfCn05B4Ihs1K9WQ/7w==", "license": "MIT", "dependencies": { - "@radix-ui/react-primitive": "2.1.2" + "@radix-ui/react-primitive": "2.1.3" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@radix-ui/react-arrow/node_modules/@radix-ui/react-primitive": { + "version": "2.1.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-2.1.3.tgz", + "integrity": "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ==", + "license": "MIT", + "dependencies": { + "@radix-ui/react-slot": "1.2.3" }, "peerDependencies": { "@types/react": "*", @@ -2172,15 +2195,15 @@ } }, "node_modules/@radix-ui/react-collection": { - "version": "1.1.6", - "resolved": "https://registry.npmjs.org/@radix-ui/react-collection/-/react-collection-1.1.6.tgz", - 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"resolved": "https://registry.npmjs.org/@radix-ui/react-arrow/-/react-arrow-1.1.7.tgz", - "integrity": "sha512-F+M1tLhO+mlQaOWspE8Wstg+z6PwxwRd8oQ8IXceWz92kfAmalTRf0EjrouQeo7QssEPfCn05B4Ihs1K9WQ/7w==", - "license": "MIT", - "dependencies": { - "@radix-ui/react-primitive": "2.1.3" - }, - "peerDependencies": { - "@types/react": "*", - "@types/react-dom": "*", - "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", - "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" - }, - "peerDependenciesMeta": { - "@types/react": { - "optional": true - }, - "@types/react-dom": { - "optional": true - } - } - }, "node_modules/@radix-ui/react-tooltip/node_modules/@radix-ui/react-dismissable-layer": { "version": "1.1.11", "resolved": "https://registry.npmjs.org/@radix-ui/react-dismissable-layer/-/react-dismissable-layer-1.1.11.tgz", @@ -3015,38 +3178,6 @@ } } }, - "node_modules/@radix-ui/react-tooltip/node_modules/@radix-ui/react-popper": { - "version": "1.2.8", - "resolved": "https://registry.npmjs.org/@radix-ui/react-popper/-/react-popper-1.2.8.tgz", - "integrity": "sha512-0NJQ4LFFUuWkE7Oxf0htBKS6zLkkjBH+hM1uk7Ng705ReR8m/uelduy1DBo0PyBXPKVnBA6YBlU94MBGXrSBCw==", - "license": "MIT", - "dependencies": { - "@floating-ui/react-dom": "^2.0.0", - "@radix-ui/react-arrow": "1.1.7", - "@radix-ui/react-compose-refs": "1.1.2", - "@radix-ui/react-context": "1.1.2", - "@radix-ui/react-primitive": "2.1.3", - "@radix-ui/react-use-callback-ref": "1.1.1", - "@radix-ui/react-use-layout-effect": "1.1.1", - "@radix-ui/react-use-rect": "1.1.1", - "@radix-ui/react-use-size": "1.1.1", - "@radix-ui/rect": "1.1.1" - }, - "peerDependencies": { - "@types/react": "*", - "@types/react-dom": "*", - "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", - "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" - }, - "peerDependenciesMeta": { - "@types/react": { - "optional": true - }, - "@types/react-dom": { - "optional": true - } - } - }, "node_modules/@radix-ui/react-tooltip/node_modules/@radix-ui/react-portal": { "version": "1.1.9", "resolved": "https://registry.npmjs.org/@radix-ui/react-portal/-/react-portal-1.1.9.tgz", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index f892f4f9b..e817e9ae3 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -15,7 +15,7 @@ "dependencies": { "@radix-ui/react-collapsible": "^1.1.12", "@radix-ui/react-dialog": "^1.1.13", - "@radix-ui/react-dropdown-menu": "^2.1.14", + "@radix-ui/react-dropdown-menu": "^2.1.16", "@radix-ui/react-select": "^2.2.5", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", From 51012a82a38e6b8aa2b31907487cacbfd1d8b645 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 10:00:41 +0200 Subject: [PATCH 062/124] chore(github-deps): bump astral-sh/setup-uv from 6.6.0 to 6.6.1 (#3355) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 6.6.0 to 6.6.1.
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v6.6.1 🌈 Fix exclusions in cache-dependency-glob

Changes

Exclusions with a leading ! in the cache-dependency-glob did not work and got fixed with this release. Thank you @​KnisterPeter for raising this!

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/python-build-test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-build-test.yml b/.github/workflows/python-build-test.yml index bf9a3e057..00f0950c7 100644 --- a/.github/workflows/python-build-test.yml +++ b/.github/workflows/python-build-test.yml @@ -24,7 +24,7 @@ jobs: uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 - name: Install uv - uses: astral-sh/setup-uv@4959332f0f014c5280e7eac8b70c90cb574c9f9b # v6.6.0 + uses: astral-sh/setup-uv@557e51de59eb14aaaba2ed9621916900a91d50c6 # v6.6.1 with: python-version: ${{ matrix.python-version }} activate-environment: true From 2f91344c1faf8fdb10b31d74cba07d46de694382 Mon Sep 17 00:00:00 2001 From: Yuan Tang Date: Mon, 8 Sep 2025 04:01:41 -0400 Subject: [PATCH 063/124] docs: Update changelog (#3343) This updates the changelog doc to include the latest updates. Signed-off-by: Yuan Tang --- CHANGELOG.md | 98 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 98 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2f47c3ae3..c51a1b2aa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,103 @@ # Changelog +# v0.2.20 +Published on: 2025-08-29T22:25:32Z + +Here are some key changes that are coming as part of this release. + +### Build and Environment + +- Environment improvements: fixed env var replacement to preserve types. +- Docker stability: fixed container startup failures for Fireworks AI provider. +- Removed absolute paths in build for better portability. + +### Features + +- UI Enhancements: Implemented file upload and VectorDB creation/configuration directly in UI. +- Vector Store Improvements: Added keyword, vector, and hybrid search inside vector store. +- Added S3 authorization support for file providers. +- SQL Store: Added inequality support to where clause. + +### Documentation + +- Fixed post-training docs. +- Added Contributor Guidelines for creating Internal vs. External providers. + +### Fixes + +- Removed unsupported bfcl scoring function. +- Multiple reliability and configuration fixes for providers and environment handling. + +### Engineering / Chores + +- Cleaner internal development setup with consistent paths. +- Incremental improvements to provider integration and vector store behavior. + + +### New Contributors +- @omertuc made their first contribution in #3270 +- @r3v5 made their first contribution in vector store hybrid search + +--- + +# v0.2.19 +Published on: 2025-08-26T22:06:55Z + +## Highlights +* feat: Add CORS configuration support for server by @skamenan7 in https://github.com/llamastack/llama-stack/pull/3201 +* feat(api): introduce /rerank by @ehhuang in https://github.com/llamastack/llama-stack/pull/2940 +* feat: Add S3 Files Provider by @mattf in https://github.com/llamastack/llama-stack/pull/3202 + + +--- + +# v0.2.18 +Published on: 2025-08-20T01:09:27Z + +## Highlights +* Add moderations create API +* Hybrid search in Milvus +* Numerous Responses API improvements +* Documentation updates + + +--- + +# v0.2.17 +Published on: 2025-08-05T01:51:14Z + +## Highlights + +* feat(tests): introduce inference record/replay to increase test reliability by @ashwinb in https://github.com/meta-llama/llama-stack/pull/2941 +* fix(library_client): improve initialization error handling and prevent AttributeError by @mattf in https://github.com/meta-llama/llama-stack/pull/2944 +* fix: use OLLAMA_URL to activate Ollama provider in starter by @ashwinb in https://github.com/meta-llama/llama-stack/pull/2963 +* feat(UI): adding MVP playground UI by @franciscojavierarceo in https://github.com/meta-llama/llama-stack/pull/2828 +* Standardization of errors (@nathan-weinberg) +* feat: Enable DPO training with HuggingFace inline provider by @Nehanth in https://github.com/meta-llama/llama-stack/pull/2825 +* chore: rename templates to distributions by @ashwinb in https://github.com/meta-llama/llama-stack/pull/3035 + + +--- + +# v0.2.16 +Published on: 2025-07-28T23:35:23Z + +## Highlights + +* Automatic model registration for self-hosted providers (ollama and vllm currently). No need for `INFERENCE_MODEL` environment variables which need to be updated, etc. +* Much simplified starter distribution. Most `ENABLE_` env variables are now gone. When you set `VLLM_URL`, the `vllm` provider is auto-enabled. Similar for `MILVUS_URL`, `PGVECTOR_DB`, etc. Check the [run.yaml](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/templates/starter/run.yaml) for more details. +* All tests migrated to pytest now (thanks @Elbehery) +* DPO implementation in the post-training provider (thanks @Nehanth) +* (Huge!) Support for external APIs and providers thereof (thanks @leseb, @cdoern and others). This is a really big deal -- you can now add more APIs completely out of tree and experiment with them before (optionally) wanting to contribute back. +* `inline::vllm` provider is gone thank you very much +* several improvements to OpenAI inference implementations and LiteLLM backend (thanks @mattf) +* Chroma now supports Vector Store API (thanks @franciscojavierarceo). +* Authorization improvements: Vector Store/File APIs now supports access control (thanks @franciscojavierarceo); Telemetry read APIs are gated according to logged-in user's roles. + + + +--- + # v0.2.15 Published on: 2025-07-16T03:30:01Z From 58c61d85c86029bf0935b6f21d1750117ef861ac Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 10:04:41 +0200 Subject: [PATCH 064/124] chore(github-deps): bump actions/stale from 9.1.0 to 10.0.0 (#3352) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [actions/stale](https://github.com/actions/stale) from 9.1.0 to 10.0.0.
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v10.0.0

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Full Changelog: https://github.com/actions/stale/compare/v9...v10.0.0

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/stale_bot.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/stale_bot.yml b/.github/workflows/stale_bot.yml index 087df72d7..502a78f8e 100644 --- a/.github/workflows/stale_bot.yml +++ b/.github/workflows/stale_bot.yml @@ -24,7 +24,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Stale Action - uses: actions/stale@5bef64f19d7facfb25b37b414482c7164d639639 # v9.1.0 + uses: actions/stale@3a9db7e6a41a89f618792c92c0e97cc736e1b13f # v10.0.0 with: stale-issue-label: 'stale' stale-issue-message: > From dfa13d68f193b7f701c725d705251458c065e64a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 10:05:00 +0200 Subject: [PATCH 065/124] chore(github-deps): bump actions/setup-node from 4.4.0 to 5.0.0 (#3353) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [actions/setup-node](https://github.com/actions/setup-node) from 4.4.0 to 5.0.0.
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v5.0.0

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This update, introduces automatic caching when a valid packageManager field is present in your package.json. This aims to improve workflow performance and make dependency management more seamless. To disable this automatic caching, set package-manager-cache: false

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- uses: actions/checkout@v5
- uses: actions/setup-node@v5
  with:
    package-manager-cache: false

Make sure your runner is on version v2.327.1 or later to ensure compatibility with this release. See Release Notes

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/pre-commit.yml | 2 +- .github/workflows/ui-unit-tests.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 5f13620f7..27db5fbe7 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -37,7 +37,7 @@ jobs: .pre-commit-config.yaml - name: Set up Node.js - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: '20' cache: 'npm' diff --git a/.github/workflows/ui-unit-tests.yml b/.github/workflows/ui-unit-tests.yml index 2afb92bee..c16f512d1 100644 --- a/.github/workflows/ui-unit-tests.yml +++ b/.github/workflows/ui-unit-tests.yml @@ -29,7 +29,7 @@ jobs: uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 - name: Setup Node.js - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: ${{ matrix.node-version }} cache: 'npm' From d458817af51a59b1f00c0860a3d8a9d9418153a0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 10:05:34 +0200 Subject: [PATCH 066/124] chore(github-deps): bump actions/setup-python from 5.6.0 to 6.0.0 (#3354) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5.6.0 to 6.0.0.
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v6.0.0

What's Changed

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Make sure your runner is on version v2.327.1 or later to ensure compatibility with this release. See Release Notes

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Full Changelog: https://github.com/actions/setup-python/compare/v5...v6.0.0

Commits
  • e797f83 Upgrade to node 24 (#1164)
  • 3d1e2d2 Revert "Enhance cache-dependency-path handling to support files outside the w...
  • 65b0712 Clarify pythonLocation behavior for PyPy and GraalPy in environment variables...
  • 5b668cf Bump actions/checkout from 4 to 5 (#1181)
  • f62a0e2 Change missing cache directory error to warning (#1182)
  • 9322b3c Upgrade setuptools to 78.1.1 to fix path traversal vulnerability in PackageIn...
  • fbeb884 Bump form-data to fix critical vulnerabilities #182 & #183 (#1163)
  • 03bb615 Bump idna from 2.9 to 3.7 in /tests/data (#843)
  • 36da51d Add version parsing from Pipfile (#1067)
  • 3c6f142 update documentation (#1156)
  • Additional commits viewable in compare view

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/pre-commit.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 27db5fbe7..792162262 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -28,7 +28,7 @@ jobs: fetch-depth: ${{ github.actor == 'dependabot[bot]' && 0 || 1 }} - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: '3.12' cache: pip From 44e1a405950f773354ae06c7062ce146f3ea8219 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 10:07:03 +0200 Subject: [PATCH 067/124] chore(github-deps): bump actions/checkout from 4.1.7 to 5.0.0 (#3357) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [//]: # (dependabot-start) ⚠️ **Dependabot is rebasing this PR** ⚠️ Rebasing might not happen immediately, so don't worry if this takes some time. Note: if you make any changes to this PR yourself, they will take precedence over the rebase. --- [//]: # (dependabot-end) Bumps [actions/checkout](https://github.com/actions/checkout) from 4.1.7 to 5.0.0.
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/conformance.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/conformance.yml b/.github/workflows/conformance.yml index 2433b0203..c0a7795a3 100644 --- a/.github/workflows/conformance.yml +++ b/.github/workflows/conformance.yml @@ -33,12 +33,12 @@ jobs: # Using specific version 4.1.7 because 5.0.0 fails when trying to run this locally using `act` # This ensures consistent behavior between local testing and CI - name: Checkout PR Code - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 + uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 # Checkout the base branch to compare against (usually main) # This allows us to diff the current changes against the previous state - name: Checkout Base Branch - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 + uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: ref: ${{ github.event.pull_request.base.ref }} path: 'base' From 072dca0609c55205cd8a8ada5a727c631ce0cc3b Mon Sep 17 00:00:00 2001 From: Akram Ben Aissi Date: Mon, 8 Sep 2025 11:25:10 +0200 Subject: [PATCH 068/124] feat: Add Kubernetes auth provider to use SelfSubjectReview and kubernetes api server (#2559) # What does this PR do? Add Kubernetes authentication provider support - Add KubernetesAuthProvider class for token validation using Kubernetes SelfSubjectReview API - Add KubernetesAuthProviderConfig with configurable API server URL, TLS settings, and claims mapping - Implement authentication via POST requests to /apis/authentication.k8s.io/v1/selfsubjectreviews endpoint - Add support for parsing Kubernetes SelfSubjectReview response format to extract user information - Add KUBERNETES provider type to AuthProviderType enum - Update create_auth_provider factory function to handle 'kubernetes' provider type - Add comprehensive unit tests for KubernetesAuthProvider functionality - Add documentation with configuration examples and usage instructions The provider validates tokens by sending SelfSubjectReview requests to the Kubernetes API server and extracts user information from the userInfo structure in the response. ## Test Plan What This Verifies: Authentication header validation Token validation with Kubernetes SelfSubjectReview and kubernetes server API endpoint Error handling for invalid tokens and HTTP errors Request payload structure and headers ``` python -m pytest tests/unit/server/test_auth.py -k "kubernetes" -v ``` Signed-off-by: Akram Ben Aissi --- docs/source/distributions/configuration.md | 41 +++++++ llama_stack/apis/common/errors.py | 7 ++ llama_stack/core/datatypes.py | 41 ++++++- llama_stack/core/server/auth_providers.py | 93 +++++++++++++- tests/unit/server/test_auth.py | 133 +++++++++++++++++++++ 5 files changed, 311 insertions(+), 4 deletions(-) diff --git a/docs/source/distributions/configuration.md b/docs/source/distributions/configuration.md index c9677b3b6..452c3d95f 100644 --- a/docs/source/distributions/configuration.md +++ b/docs/source/distributions/configuration.md @@ -354,6 +354,47 @@ You can easily validate a request by running: curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://127.0.0.1:8321/v1/providers ``` +#### Kubernetes Authentication Provider + +The server can be configured to use Kubernetes SelfSubjectReview API to validate tokens directly against the Kubernetes API server: + +```yaml +server: + auth: + provider_config: + type: "kubernetes" + api_server_url: "https://kubernetes.default.svc" + claims_mapping: + username: "roles" + groups: "roles" + uid: "uid_attr" + verify_tls: true + tls_cafile: "/path/to/ca.crt" +``` + +Configuration options: +- `api_server_url`: The Kubernetes API server URL (e.g., https://kubernetes.default.svc:6443) +- `verify_tls`: Whether to verify TLS certificates (default: true) +- `tls_cafile`: Path to CA certificate file for TLS verification +- `claims_mapping`: Mapping of Kubernetes user claims to access attributes + +The provider validates tokens by sending a SelfSubjectReview request to the Kubernetes API server at `/apis/authentication.k8s.io/v1/selfsubjectreviews`. The provider extracts user information from the response: +- Username from the `userInfo.username` field +- Groups from the `userInfo.groups` field +- UID from the `userInfo.uid` field + +To obtain a token for testing: +```bash +kubectl create namespace llama-stack +kubectl create serviceaccount llama-stack-auth -n llama-stack +kubectl create token llama-stack-auth -n llama-stack > llama-stack-auth-token +``` + +You can validate a request by running: +```bash +curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://127.0.0.1:8321/v1/providers +``` + #### GitHub Token Provider Validates GitHub personal access tokens or OAuth tokens directly: ```yaml diff --git a/llama_stack/apis/common/errors.py b/llama_stack/apis/common/errors.py index ec3d2b1ce..4c9c0a818 100644 --- a/llama_stack/apis/common/errors.py +++ b/llama_stack/apis/common/errors.py @@ -79,3 +79,10 @@ class ConflictError(ValueError): def __init__(self, message: str) -> None: super().__init__(message) + + +class TokenValidationError(ValueError): + """raised when token validation fails during authentication""" + + def __init__(self, message: str) -> None: + super().__init__(message) diff --git a/llama_stack/core/datatypes.py b/llama_stack/core/datatypes.py index c3940fcbd..0f348b067 100644 --- a/llama_stack/core/datatypes.py +++ b/llama_stack/core/datatypes.py @@ -7,6 +7,7 @@ from enum import StrEnum from pathlib import Path from typing import Annotated, Any, Literal, Self +from urllib.parse import urlparse from pydantic import BaseModel, Field, field_validator, model_validator @@ -212,6 +213,7 @@ class AuthProviderType(StrEnum): OAUTH2_TOKEN = "oauth2_token" GITHUB_TOKEN = "github_token" CUSTOM = "custom" + KUBERNETES = "kubernetes" class OAuth2TokenAuthConfig(BaseModel): @@ -282,8 +284,45 @@ class GitHubTokenAuthConfig(BaseModel): ) +class KubernetesAuthProviderConfig(BaseModel): + """Configuration for Kubernetes authentication provider.""" + + type: Literal[AuthProviderType.KUBERNETES] = AuthProviderType.KUBERNETES + api_server_url: str = Field( + default="https://kubernetes.default.svc", + description="Kubernetes API server URL (e.g., https://api.cluster.domain:6443)", + ) + verify_tls: bool = Field(default=True, description="Whether to verify TLS certificates") + tls_cafile: Path | None = Field(default=None, description="Path to CA certificate file for TLS verification") + claims_mapping: dict[str, str] = Field( + default_factory=lambda: { + "username": "roles", + "groups": "roles", + }, + description="Mapping of Kubernetes user claims to access attributes", + ) + + @field_validator("api_server_url") + @classmethod + def validate_api_server_url(cls, v): + parsed = urlparse(v) + if not parsed.scheme or not parsed.netloc: + raise ValueError(f"api_server_url must be a valid URL with scheme and host: {v}") + if parsed.scheme not in ["http", "https"]: + raise ValueError(f"api_server_url scheme must be http or https: {v}") + return v + + @field_validator("claims_mapping") + @classmethod + def validate_claims_mapping(cls, v): + for key, value in v.items(): + if not value: + raise ValueError(f"claims_mapping value cannot be empty: {key}") + return v + + AuthProviderConfig = Annotated[ - OAuth2TokenAuthConfig | GitHubTokenAuthConfig | CustomAuthConfig, + OAuth2TokenAuthConfig | GitHubTokenAuthConfig | CustomAuthConfig | KubernetesAuthProviderConfig, Field(discriminator="type"), ] diff --git a/llama_stack/core/server/auth_providers.py b/llama_stack/core/server/auth_providers.py index a8af6f75a..38188c49a 100644 --- a/llama_stack/core/server/auth_providers.py +++ b/llama_stack/core/server/auth_providers.py @@ -8,16 +8,18 @@ import ssl import time from abc import ABC, abstractmethod from asyncio import Lock -from urllib.parse import parse_qs, urlparse +from urllib.parse import parse_qs, urljoin, urlparse import httpx from jose import jwt from pydantic import BaseModel, Field +from llama_stack.apis.common.errors import TokenValidationError from llama_stack.core.datatypes import ( AuthenticationConfig, CustomAuthConfig, GitHubTokenAuthConfig, + KubernetesAuthProviderConfig, OAuth2TokenAuthConfig, User, ) @@ -162,7 +164,7 @@ class OAuth2TokenAuthProvider(AuthProvider): auth=auth, timeout=10.0, # Add a reasonable timeout ) - if response.status_code != 200: + if response.status_code != httpx.codes.OK: logger.warning(f"Token introspection failed with status code: {response.status_code}") raise ValueError(f"Token introspection failed: {response.status_code}") @@ -272,7 +274,7 @@ class CustomAuthProvider(AuthProvider): json=auth_request.model_dump(), timeout=10.0, # Add a reasonable timeout ) - if response.status_code != 200: + if response.status_code != httpx.codes.OK: logger.warning(f"Authentication failed with status code: {response.status_code}") raise ValueError(f"Authentication failed: {response.status_code}") @@ -374,6 +376,89 @@ async def _get_github_user_info(access_token: str, github_api_base_url: str) -> } +class KubernetesAuthProvider(AuthProvider): + """ + Kubernetes authentication provider that validates tokens using the Kubernetes SelfSubjectReview API. + This provider integrates with Kubernetes API server by using the + /apis/authentication.k8s.io/v1/selfsubjectreviews endpoint to validate tokens and extract user information. + """ + + def __init__(self, config: KubernetesAuthProviderConfig): + self.config = config + + def _httpx_verify_value(self) -> bool | str: + """ + Build the value for httpx's `verify` parameter. + - False disables verification. + - Path string points to a CA bundle. + - True uses system defaults. + """ + if not self.config.verify_tls: + return False + if self.config.tls_cafile: + return self.config.tls_cafile.as_posix() + return True + + async def validate_token(self, token: str, scope: dict | None = None) -> User: + """Validate a token using Kubernetes SelfSubjectReview API endpoint.""" + # Build the Kubernetes SelfSubjectReview API endpoint URL + review_api_url = urljoin(self.config.api_server_url, "/apis/authentication.k8s.io/v1/selfsubjectreviews") + + # Create SelfSubjectReview request body + review_request = {"apiVersion": "authentication.k8s.io/v1", "kind": "SelfSubjectReview"} + verify = self._httpx_verify_value() + + try: + async with httpx.AsyncClient(verify=verify, timeout=10.0) as client: + response = await client.post( + review_api_url, + json=review_request, + headers={ + "Authorization": f"Bearer {token}", + "Content-Type": "application/json", + }, + ) + + if response.status_code == httpx.codes.UNAUTHORIZED: + raise TokenValidationError("Invalid token") + if response.status_code != httpx.codes.CREATED: + logger.warning(f"Kubernetes SelfSubjectReview API failed with status code: {response.status_code}") + raise TokenValidationError(f"Token validation failed: {response.status_code}") + + review_response = response.json() + # Extract user information from SelfSubjectReview response + status = review_response.get("status", {}) + if not status: + raise ValueError("No status found in SelfSubjectReview response") + + user_info = status.get("userInfo", {}) + if not user_info: + raise ValueError("No userInfo found in SelfSubjectReview response") + + username = user_info.get("username") + if not username: + raise ValueError("No username found in SelfSubjectReview response") + + # Build user attributes from Kubernetes user info + user_attributes = get_attributes_from_claims(user_info, self.config.claims_mapping) + + return User( + principal=username, + attributes=user_attributes, + ) + + except httpx.TimeoutException: + logger.warning("Kubernetes SelfSubjectReview API request timed out") + raise ValueError("Token validation timeout") from None + except Exception as e: + logger.warning(f"Error during token validation: {str(e)}") + raise ValueError(f"Token validation error: {str(e)}") from e + + async def close(self): + """Close any resources.""" + pass + + def create_auth_provider(config: AuthenticationConfig) -> AuthProvider: """Factory function to create the appropriate auth provider.""" provider_config = config.provider_config @@ -384,5 +469,7 @@ def create_auth_provider(config: AuthenticationConfig) -> AuthProvider: return OAuth2TokenAuthProvider(provider_config) elif isinstance(provider_config, GitHubTokenAuthConfig): return GitHubTokenAuthProvider(provider_config) + elif isinstance(provider_config, KubernetesAuthProviderConfig): + return KubernetesAuthProvider(provider_config) else: raise ValueError(f"Unknown authentication provider config type: {type(provider_config)}") diff --git a/tests/unit/server/test_auth.py b/tests/unit/server/test_auth.py index 37b543976..205e0ce65 100644 --- a/tests/unit/server/test_auth.py +++ b/tests/unit/server/test_auth.py @@ -774,3 +774,136 @@ def test_has_required_scope_function(): # Test no user (auth disabled) assert _has_required_scope("test.read", None) + + +@pytest.fixture +def mock_kubernetes_api_server(): + return "https://api.cluster.example.com:6443" + + +@pytest.fixture +def kubernetes_auth_app(mock_kubernetes_api_server): + app = FastAPI() + auth_config = AuthenticationConfig( + provider_config={ + "type": "kubernetes", + "api_server_url": mock_kubernetes_api_server, + "verify_tls": False, + "claims_mapping": { + "username": "roles", + "groups": "roles", + "uid": "uid_attr", + }, + }, + ) + app.add_middleware(AuthenticationMiddleware, auth_config=auth_config, impls={}) + + @app.get("/test") + def test_endpoint(): + return {"message": "Authentication successful"} + + return app + + +@pytest.fixture +def kubernetes_auth_client(kubernetes_auth_app): + return TestClient(kubernetes_auth_app) + + +def test_missing_auth_header_kubernetes_auth(kubernetes_auth_client): + response = kubernetes_auth_client.get("/test") + assert response.status_code == 401 + assert "Authentication required" in response.json()["error"]["message"] + + +def test_invalid_auth_header_format_kubernetes_auth(kubernetes_auth_client): + response = kubernetes_auth_client.get("/test", headers={"Authorization": "InvalidFormat token123"}) + assert response.status_code == 401 + assert "Invalid Authorization header format" in response.json()["error"]["message"] + + +async def mock_kubernetes_selfsubjectreview_success(*args, **kwargs): + return MockResponse( + 201, + { + "apiVersion": "authentication.k8s.io/v1", + "kind": "SelfSubjectReview", + "metadata": {"creationTimestamp": "2025-07-15T13:53:56Z"}, + "status": { + "userInfo": { + "username": "alice", + "uid": "alice-uid-123", + "groups": ["system:authenticated", "developers", "admins"], + "extra": {"scopes.authorization.openshift.io": ["user:full"]}, + } + }, + }, + ) + + +async def mock_kubernetes_selfsubjectreview_failure(*args, **kwargs): + return MockResponse(401, {"message": "Unauthorized"}) + + +async def mock_kubernetes_selfsubjectreview_http_error(*args, **kwargs): + return MockResponse(500, {"message": "Internal Server Error"}) + + +@patch("httpx.AsyncClient.post", new=mock_kubernetes_selfsubjectreview_success) +def test_valid_kubernetes_auth_authentication(kubernetes_auth_client, valid_token): + response = kubernetes_auth_client.get("/test", headers={"Authorization": f"Bearer {valid_token}"}) + assert response.status_code == 200 + assert response.json() == {"message": "Authentication successful"} + + +@patch("httpx.AsyncClient.post", new=mock_kubernetes_selfsubjectreview_failure) +def test_invalid_kubernetes_auth_authentication(kubernetes_auth_client, invalid_token): + response = kubernetes_auth_client.get("/test", headers={"Authorization": f"Bearer {invalid_token}"}) + assert response.status_code == 401 + assert "Invalid token" in response.json()["error"]["message"] + + +@patch("httpx.AsyncClient.post", new=mock_kubernetes_selfsubjectreview_http_error) +def test_kubernetes_auth_http_error(kubernetes_auth_client, valid_token): + response = kubernetes_auth_client.get("/test", headers={"Authorization": f"Bearer {valid_token}"}) + assert response.status_code == 401 + assert "Token validation failed" in response.json()["error"]["message"] + + +def test_kubernetes_auth_request_payload(kubernetes_auth_client, valid_token, mock_kubernetes_api_server): + with patch("httpx.AsyncClient.post") as mock_post: + mock_response = MockResponse( + 200, + { + "apiVersion": "authentication.k8s.io/v1", + "kind": "SelfSubjectReview", + "metadata": {"creationTimestamp": "2025-07-15T13:53:56Z"}, + "status": { + "userInfo": { + "username": "test-user", + "uid": "test-uid", + "groups": ["test-group"], + } + }, + }, + ) + mock_post.return_value = mock_response + + kubernetes_auth_client.get("/test", headers={"Authorization": f"Bearer {valid_token}"}) + + # Verify the request was made with correct parameters + mock_post.assert_called_once() + call_args = mock_post.call_args + + # Check URL (passed as positional argument) + assert call_args[0][0] == f"{mock_kubernetes_api_server}/apis/authentication.k8s.io/v1/selfsubjectreviews" + + # Check headers (passed as keyword argument) + headers = call_args[1]["headers"] + assert headers["Authorization"] == f"Bearer {valid_token}" + assert headers["Content-Type"] == "application/json" + + # Check request body (passed as keyword argument) + request_body = call_args[1]["json"] + assert request_body["apiVersion"] == "authentication.k8s.io/v1" + assert request_body["kind"] == "SelfSubjectReview" From 9618adba8990f1404f1c4de48c3ff03780622f76 Mon Sep 17 00:00:00 2001 From: Mohammad Daoud Farooqi Date: Mon, 8 Sep 2025 17:39:13 +0530 Subject: [PATCH 069/124] docs: add MongoDB to external provider list (#3369) The MongoDB integration - Vector search, Full-Text search and Hybrid search have now been added as an external provider offering for Llama Stack: https://github.com/mongodb-partners/mongodb-llama-stack --- docs/source/providers/external/external-providers-list.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/source/providers/external/external-providers-list.md b/docs/source/providers/external/external-providers-list.md index 49f49076b..45fcc50fb 100644 --- a/docs/source/providers/external/external-providers-list.md +++ b/docs/source/providers/external/external-providers-list.md @@ -7,4 +7,5 @@ Here's a list of known external providers that you can use with Llama Stack: | KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) | | KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Inline **and** Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) | | RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) | -| TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) | \ No newline at end of file +| TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) | +| MongoDB | VectorIO with MongoDB | Vector_IO | Remote | [mongodb-llama-stack](https://github.com/mongodb-partners/mongodb-llama-stack) | From ad6ea7fb91f79023f821462286e69da2642e297c Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Mon, 8 Sep 2025 09:05:13 -0600 Subject: [PATCH 070/124] feat: Adding OpenAI Prompts API (#3319) # What does this PR do? This PR adds support for OpenAI Prompts API. Note, OpenAI does not explicitly expose the Prompts API but instead makes it available in the Responses API and in the [Prompts Dashboard](https://platform.openai.com/docs/guides/prompting#create-a-prompt). I have added the following APIs: - CREATE - GET - LIST - UPDATE - Set Default Version The Set Default Version API is made available only in the Prompts Dashboard and configures which prompt version is returned in the GET (the latest version is the default). Overall, the expected functionality in Responses will look like this: ```python from openai import OpenAI client = OpenAI() response = client.responses.create( prompt={ "id": "pmpt_68b0c29740048196bd3a6e6ac3c4d0e20ed9a13f0d15bf5e", "version": "2", "variables": { "city": "San Francisco", "age": 30, } } ) ``` ### Resolves https://github.com/llamastack/llama-stack/issues/3276 ## Test Plan Unit tests added. Integration tests can be added after client generation. ## Next Steps 1. Update Responses API to support Prompt API 2. I'll enhance the UI to implement the Prompt Dashboard. 3. Add cache for lower latency --------- Signed-off-by: Francisco Javier Arceo --- docs/_static/llama-stack-spec.html | 457 ++++++++++++++++++++- docs/_static/llama-stack-spec.yaml | 332 +++++++++++++++ llama_stack/apis/datatypes.py | 2 + llama_stack/apis/prompts/__init__.py | 9 + llama_stack/apis/prompts/prompts.py | 189 +++++++++ llama_stack/apis/resource.py | 1 + llama_stack/core/prompts/__init__.py | 5 + llama_stack/core/prompts/prompts.py | 233 +++++++++++ llama_stack/core/resolver.py | 2 + llama_stack/core/server/server.py | 1 + llama_stack/core/stack.py | 12 + tests/unit/prompts/prompts/__init__.py | 5 + tests/unit/prompts/prompts/conftest.py | 30 ++ tests/unit/prompts/prompts/test_prompts.py | 144 +++++++ 14 files changed, 1414 insertions(+), 8 deletions(-) create mode 100644 llama_stack/apis/prompts/__init__.py create mode 100644 llama_stack/apis/prompts/prompts.py create mode 100644 llama_stack/core/prompts/__init__.py create mode 100644 llama_stack/core/prompts/prompts.py create mode 100644 tests/unit/prompts/prompts/__init__.py create mode 100644 tests/unit/prompts/prompts/conftest.py create mode 100644 tests/unit/prompts/prompts/test_prompts.py diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index 7cb2a73f3..a036e5dc0 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -633,6 +633,80 @@ } } }, + "/v1/prompts": { + "get": { + "responses": { + "200": { + "description": "A ListPromptsResponse containing all prompts.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ListPromptsResponse" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Prompts" + ], + "description": "List all prompts.", + "parameters": [] + }, + "post": { + "responses": { + "200": { + "description": "The created Prompt resource.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Prompt" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Prompts" + ], + "description": "Create a new prompt.", + "parameters": [], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CreatePromptRequest" + } + } + }, + "required": true + } + } + }, "/v1/agents/{agent_id}": { "get": { "responses": { @@ -901,6 +975,143 @@ ] } }, + "/v1/prompts/{prompt_id}": { + "get": { + "responses": { + "200": { + "description": "A Prompt resource.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Prompt" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Prompts" + ], + "description": "Get a prompt by its identifier and optional version.", + "parameters": [ + { + "name": "prompt_id", + "in": "path", + "description": "The identifier of the prompt to get.", + "required": true, + "schema": { + "type": "string" + } + }, + { + "name": "version", + "in": "query", + "description": "The version of the prompt to get (defaults to latest).", + "required": false, + "schema": { + "type": "integer" + } + } + ] + }, + "post": { + "responses": { + "200": { + "description": "The updated Prompt resource with incremented version.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Prompt" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Prompts" + ], + "description": "Update an existing prompt (increments version).", + "parameters": [ + { + "name": "prompt_id", + "in": "path", + "description": "The identifier of the prompt to update.", + "required": true, + "schema": { + "type": "string" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/UpdatePromptRequest" + } + } + }, + "required": true + } + }, + "delete": { + "responses": { + "200": { + "description": "OK" + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Prompts" + ], + "description": "Delete a prompt.", + "parameters": [ + { + "name": "prompt_id", + "in": "path", + "description": "The identifier of the prompt to delete.", + "required": true, + "schema": { + "type": "string" + } + } + ] + } + }, "/v1/inference/embeddings": { "post": { "responses": { @@ -2836,6 +3047,49 @@ ] } }, + "/v1/prompts/{prompt_id}/versions": { + "get": { + "responses": { + "200": { + "description": "A ListPromptsResponse containing all versions of the prompt.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ListPromptsResponse" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Prompts" + ], + "description": "List all versions of a specific prompt.", + "parameters": [ + { + "name": "prompt_id", + "in": "path", + "description": "The identifier of the prompt to list versions for.", + "required": true, + "schema": { + "type": "string" + } + } + ] + } + }, "/v1/providers": { "get": { "responses": { @@ -5007,6 +5261,59 @@ } } }, + "/v1/prompts/{prompt_id}/set-default-version": { + "post": { + "responses": { + "200": { + "description": "The prompt with the specified version now set as default.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Prompt" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Prompts" + ], + "description": "Set which version of a prompt should be the default in get_prompt (latest).", + "parameters": [ + { + "name": "prompt_id", + "in": "path", + "description": "The identifier of the prompt.", + "required": true, + "schema": { + "type": "string" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SetDefaultVersionRequest" + } + } + }, + "required": true + } + } + }, "/v1/post-training/supervised-fine-tune": { "post": { "responses": { @@ -9670,6 +9977,65 @@ ], "title": "OpenAIResponseObjectStreamResponseWebSearchCallSearching" }, + "CreatePromptRequest": { + "type": "object", + "properties": { + "prompt": { + "type": "string", + "description": "The prompt text content with variable placeholders." + }, + "variables": { + "type": "array", + "items": { + "type": "string" + }, + "description": "List of variable names that can be used in the prompt template." + } + }, + "additionalProperties": false, + "required": [ + "prompt" + ], + "title": "CreatePromptRequest" + }, + "Prompt": { + "type": "object", + "properties": { + "prompt": { + "type": "string", + "description": "The system prompt text with variable placeholders. Variables are only supported when using the Responses API." + }, + "version": { + "type": "integer", + "description": "Version (integer starting at 1, incremented on save)" + }, + "prompt_id": { + "type": "string", + "description": "Unique identifier formatted as 'pmpt_<48-digit-hash>'" + }, + "variables": { + "type": "array", + "items": { + "type": "string" + }, + "description": "List of prompt variable names that can be used in the prompt template" + }, + "is_default": { + "type": "boolean", + "default": false, + "description": "Boolean indicating whether this version is the default version for this prompt" + } + }, + "additionalProperties": false, + "required": [ + "version", + "prompt_id", + "variables", + "is_default" + ], + "title": "Prompt", + "description": "A prompt resource representing a stored OpenAI Compatible prompt template in Llama Stack." + }, "OpenAIDeleteResponseObject": { "type": "object", "properties": { @@ -10296,7 +10662,8 @@ "scoring_function", "benchmark", "tool", - "tool_group" + "tool_group", + "prompt" ], "const": "benchmark", "default": "benchmark", @@ -10923,7 +11290,8 @@ "scoring_function", "benchmark", "tool", - "tool_group" + "tool_group", + "prompt" ], "const": "dataset", "default": "dataset", @@ -11073,7 +11441,8 @@ "scoring_function", "benchmark", "tool", - "tool_group" + "tool_group", + "prompt" ], "const": "model", "default": "model", @@ -11338,7 +11707,8 @@ "scoring_function", "benchmark", "tool", - "tool_group" + "tool_group", + "prompt" ], "const": "scoring_function", "default": "scoring_function", @@ -11446,7 +11816,8 @@ "scoring_function", "benchmark", "tool", - "tool_group" + "tool_group", + "prompt" ], "const": "shield", "default": "shield", @@ -11691,7 +12062,8 @@ "scoring_function", "benchmark", "tool", - "tool_group" + "tool_group", + "prompt" ], "const": "tool", "default": "tool", @@ -11773,7 +12145,8 @@ "scoring_function", "benchmark", "tool", - "tool_group" + "tool_group", + "prompt" ], "const": "tool_group", "default": "tool_group", @@ -12067,7 +12440,8 @@ "scoring_function", "benchmark", "tool", - "tool_group" + "tool_group", + "prompt" ], "const": "vector_db", "default": "vector_db", @@ -12882,6 +13256,23 @@ "title": "OpenAIResponseObjectWithInput", "description": "OpenAI response object extended with input context information." }, + "ListPromptsResponse": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "$ref": "#/components/schemas/Prompt" + } + } + }, + "additionalProperties": false, + "required": [ + "data" + ], + "title": "ListPromptsResponse", + "description": "Response model to list prompts." + }, "ListProvidersResponse": { "type": "object", "properties": { @@ -17128,6 +17519,20 @@ "title": "ScoreBatchResponse", "description": "Response from batch scoring operations on datasets." }, + "SetDefaultVersionRequest": { + "type": "object", + "properties": { + "version": { + "type": "integer", + "description": "The version to set as default." + } + }, + "additionalProperties": false, + "required": [ + "version" + ], + "title": "SetDefaultVersionRequest" + }, "AlgorithmConfig": { "oneOf": [ { @@ -17412,6 +17817,37 @@ "title": "SyntheticDataGenerationResponse", "description": "Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold." }, + "UpdatePromptRequest": { + "type": "object", + "properties": { + "prompt": { + "type": "string", + "description": "The updated prompt text content." + }, + "version": { + "type": "integer", + "description": "The current version of the prompt being updated." + }, + "variables": { + "type": "array", + "items": { + "type": "string" + }, + "description": "Updated list of variable names that can be used in the prompt template." + }, + "set_as_default": { + "type": "boolean", + "description": "Set the new version as the default (default=True)." + } + }, + "additionalProperties": false, + "required": [ + "prompt", + "version", + "set_as_default" + ], + "title": "UpdatePromptRequest" + }, "VersionInfo": { "type": "object", "properties": { @@ -17537,6 +17973,10 @@ { "name": "PostTraining (Coming Soon)" }, + { + "name": "Prompts", + "x-displayName": "Protocol for prompt management operations." + }, { "name": "Providers", "x-displayName": "Providers API for inspecting, listing, and modifying providers and their configurations." @@ -17587,6 +18027,7 @@ "Inspect", "Models", "PostTraining (Coming Soon)", + "Prompts", "Providers", "Safety", "Scoring", diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index 25089868c..8ed04c1f8 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -427,6 +427,58 @@ paths: schema: $ref: '#/components/schemas/CreateOpenaiResponseRequest' required: true + /v1/prompts: + get: + responses: + '200': + description: >- + A ListPromptsResponse containing all prompts. + content: + application/json: + schema: + $ref: '#/components/schemas/ListPromptsResponse' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Prompts + description: List all prompts. + parameters: [] + post: + responses: + '200': + description: The created Prompt resource. + content: + application/json: + schema: + $ref: '#/components/schemas/Prompt' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Prompts + description: Create a new prompt. + parameters: [] + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/CreatePromptRequest' + required: true /v1/agents/{agent_id}: get: responses: @@ -616,6 +668,103 @@ paths: required: true schema: type: string + /v1/prompts/{prompt_id}: + get: + responses: + '200': + description: A Prompt resource. + content: + application/json: + schema: + $ref: '#/components/schemas/Prompt' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Prompts + description: >- + Get a prompt by its identifier and optional version. + parameters: + - name: prompt_id + in: path + description: The identifier of the prompt to get. + required: true + schema: + type: string + - name: version + in: query + description: >- + The version of the prompt to get (defaults to latest). + required: false + schema: + type: integer + post: + responses: + '200': + description: >- + The updated Prompt resource with incremented version. + content: + application/json: + schema: + $ref: '#/components/schemas/Prompt' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Prompts + description: >- + Update an existing prompt (increments version). + parameters: + - name: prompt_id + in: path + description: The identifier of the prompt to update. + required: true + schema: + type: string + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/UpdatePromptRequest' + required: true + delete: + responses: + '200': + description: OK + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Prompts + description: Delete a prompt. + parameters: + - name: prompt_id + in: path + description: The identifier of the prompt to delete. + required: true + schema: + type: string /v1/inference/embeddings: post: responses: @@ -1983,6 +2132,37 @@ paths: required: false schema: $ref: '#/components/schemas/Order' + /v1/prompts/{prompt_id}/versions: + get: + responses: + '200': + description: >- + A ListPromptsResponse containing all versions of the prompt. + content: + application/json: + schema: + $ref: '#/components/schemas/ListPromptsResponse' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Prompts + description: List all versions of a specific prompt. + parameters: + - name: prompt_id + in: path + description: >- + The identifier of the prompt to list versions for. + required: true + schema: + type: string /v1/providers: get: responses: @@ -3546,6 +3726,43 @@ paths: schema: $ref: '#/components/schemas/ScoreBatchRequest' required: true + /v1/prompts/{prompt_id}/set-default-version: + post: + responses: + '200': + description: >- + The prompt with the specified version now set as default. + content: + application/json: + schema: + $ref: '#/components/schemas/Prompt' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Prompts + description: >- + Set which version of a prompt should be the default in get_prompt (latest). + parameters: + - name: prompt_id + in: path + description: The identifier of the prompt. + required: true + schema: + type: string + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/SetDefaultVersionRequest' + required: true /v1/post-training/supervised-fine-tune: post: responses: @@ -7148,6 +7365,61 @@ components: - type title: >- OpenAIResponseObjectStreamResponseWebSearchCallSearching + CreatePromptRequest: + type: object + properties: + prompt: + type: string + description: >- + The prompt text content with variable placeholders. + variables: + type: array + items: + type: string + description: >- + List of variable names that can be used in the prompt template. + additionalProperties: false + required: + - prompt + title: CreatePromptRequest + Prompt: + type: object + properties: + prompt: + type: string + description: >- + The system prompt text with variable placeholders. Variables are only + supported when using the Responses API. + version: + type: integer + description: >- + Version (integer starting at 1, incremented on save) + prompt_id: + type: string + description: >- + Unique identifier formatted as 'pmpt_<48-digit-hash>' + variables: + type: array + items: + type: string + description: >- + List of prompt variable names that can be used in the prompt template + is_default: + type: boolean + default: false + description: >- + Boolean indicating whether this version is the default version for this + prompt + additionalProperties: false + required: + - version + - prompt_id + - variables + - is_default + title: Prompt + description: >- + A prompt resource representing a stored OpenAI Compatible prompt template + in Llama Stack. OpenAIDeleteResponseObject: type: object properties: @@ -7621,6 +7893,7 @@ components: - benchmark - tool - tool_group + - prompt const: benchmark default: benchmark description: The resource type, always benchmark @@ -8107,6 +8380,7 @@ components: - benchmark - tool - tool_group + - prompt const: dataset default: dataset description: >- @@ -8219,6 +8493,7 @@ components: - benchmark - tool - tool_group + - prompt const: model default: model description: >- @@ -8410,6 +8685,7 @@ components: - benchmark - tool - tool_group + - prompt const: scoring_function default: scoring_function description: >- @@ -8486,6 +8762,7 @@ components: - benchmark - tool - tool_group + - prompt const: shield default: shield description: The resource type, always shield @@ -8665,6 +8942,7 @@ components: - benchmark - tool - tool_group + - prompt const: tool default: tool description: Type of resource, always 'tool' @@ -8723,6 +9001,7 @@ components: - benchmark - tool - tool_group + - prompt const: tool_group default: tool_group description: Type of resource, always 'tool_group' @@ -8951,6 +9230,7 @@ components: - benchmark - tool - tool_group + - prompt const: vector_db default: vector_db description: >- @@ -9577,6 +9857,18 @@ components: title: OpenAIResponseObjectWithInput description: >- OpenAI response object extended with input context information. + ListPromptsResponse: + type: object + properties: + data: + type: array + items: + $ref: '#/components/schemas/Prompt' + additionalProperties: false + required: + - data + title: ListPromptsResponse + description: Response model to list prompts. ListProvidersResponse: type: object properties: @@ -12722,6 +13014,16 @@ components: title: ScoreBatchResponse description: >- Response from batch scoring operations on datasets. + SetDefaultVersionRequest: + type: object + properties: + version: + type: integer + description: The version to set as default. + additionalProperties: false + required: + - version + title: SetDefaultVersionRequest AlgorithmConfig: oneOf: - $ref: '#/components/schemas/LoraFinetuningConfig' @@ -12918,6 +13220,32 @@ components: description: >- Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold. + UpdatePromptRequest: + type: object + properties: + prompt: + type: string + description: The updated prompt text content. + version: + type: integer + description: >- + The current version of the prompt being updated. + variables: + type: array + items: + type: string + description: >- + Updated list of variable names that can be used in the prompt template. + set_as_default: + type: boolean + description: >- + Set the new version as the default (default=True). + additionalProperties: false + required: + - prompt + - version + - set_as_default + title: UpdatePromptRequest VersionInfo: type: object properties: @@ -13029,6 +13357,9 @@ tags: - name: Inspect - name: Models - name: PostTraining (Coming Soon) + - name: Prompts + x-displayName: >- + Protocol for prompt management operations. - name: Providers x-displayName: >- Providers API for inspecting, listing, and modifying providers and their configurations. @@ -13056,6 +13387,7 @@ x-tagGroups: - Inspect - Models - PostTraining (Coming Soon) + - Prompts - Providers - Safety - Scoring diff --git a/llama_stack/apis/datatypes.py b/llama_stack/apis/datatypes.py index 87fc95917..8d0f2e26d 100644 --- a/llama_stack/apis/datatypes.py +++ b/llama_stack/apis/datatypes.py @@ -102,6 +102,7 @@ class Api(Enum, metaclass=DynamicApiMeta): :cvar benchmarks: Benchmark suite management :cvar tool_groups: Tool group organization :cvar files: File storage and management + :cvar prompts: Prompt versions and management :cvar inspect: Built-in system inspection and introspection """ @@ -127,6 +128,7 @@ class Api(Enum, metaclass=DynamicApiMeta): benchmarks = "benchmarks" tool_groups = "tool_groups" files = "files" + prompts = "prompts" # built-in API inspect = "inspect" diff --git a/llama_stack/apis/prompts/__init__.py b/llama_stack/apis/prompts/__init__.py new file mode 100644 index 000000000..6070f3450 --- /dev/null +++ b/llama_stack/apis/prompts/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .prompts import ListPromptsResponse, Prompt, Prompts + +__all__ = ["Prompt", "Prompts", "ListPromptsResponse"] diff --git a/llama_stack/apis/prompts/prompts.py b/llama_stack/apis/prompts/prompts.py new file mode 100644 index 000000000..e6a376c3f --- /dev/null +++ b/llama_stack/apis/prompts/prompts.py @@ -0,0 +1,189 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import re +import secrets +from typing import Protocol, runtime_checkable + +from pydantic import BaseModel, Field, field_validator, model_validator + +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol +from llama_stack.schema_utils import json_schema_type, webmethod + + +@json_schema_type +class Prompt(BaseModel): + """A prompt resource representing a stored OpenAI Compatible prompt template in Llama Stack. + + :param prompt: The system prompt text with variable placeholders. Variables are only supported when using the Responses API. + :param version: Version (integer starting at 1, incremented on save) + :param prompt_id: Unique identifier formatted as 'pmpt_<48-digit-hash>' + :param variables: List of prompt variable names that can be used in the prompt template + :param is_default: Boolean indicating whether this version is the default version for this prompt + """ + + prompt: str | None = Field(default=None, description="The system prompt with variable placeholders") + version: int = Field(description="Version (integer starting at 1, incremented on save)", ge=1) + prompt_id: str = Field(description="Unique identifier in format 'pmpt_<48-digit-hash>'") + variables: list[str] = Field( + default_factory=list, description="List of variable names that can be used in the prompt template" + ) + is_default: bool = Field( + default=False, description="Boolean indicating whether this version is the default version" + ) + + @field_validator("prompt_id") + @classmethod + def validate_prompt_id(cls, prompt_id: str) -> str: + if not isinstance(prompt_id, str): + raise TypeError("prompt_id must be a string in format 'pmpt_<48-digit-hash>'") + + if not prompt_id.startswith("pmpt_"): + raise ValueError("prompt_id must start with 'pmpt_' prefix") + + hex_part = prompt_id[5:] + if len(hex_part) != 48: + raise ValueError("prompt_id must be in format 'pmpt_<48-digit-hash>' (48 lowercase hex chars)") + + for char in hex_part: + if char not in "0123456789abcdef": + raise ValueError("prompt_id hex part must contain only lowercase hex characters [0-9a-f]") + + return prompt_id + + @field_validator("version") + @classmethod + def validate_version(cls, prompt_version: int) -> int: + if prompt_version < 1: + raise ValueError("version must be >= 1") + return prompt_version + + @model_validator(mode="after") + def validate_prompt_variables(self): + """Validate that all variables used in the prompt are declared in the variables list.""" + if not self.prompt: + return self + + prompt_variables = set(re.findall(r"{{\s*(\w+)\s*}}", self.prompt)) + declared_variables = set(self.variables) + + undeclared = prompt_variables - declared_variables + if undeclared: + raise ValueError(f"Prompt contains undeclared variables: {sorted(undeclared)}") + + return self + + @classmethod + def generate_prompt_id(cls) -> str: + # Generate 48 hex characters (24 bytes) + random_bytes = secrets.token_bytes(24) + hex_string = random_bytes.hex() + return f"pmpt_{hex_string}" + + +class ListPromptsResponse(BaseModel): + """Response model to list prompts.""" + + data: list[Prompt] + + +@runtime_checkable +@trace_protocol +class Prompts(Protocol): + """Protocol for prompt management operations.""" + + @webmethod(route="/prompts", method="GET") + async def list_prompts(self) -> ListPromptsResponse: + """List all prompts. + + :returns: A ListPromptsResponse containing all prompts. + """ + ... + + @webmethod(route="/prompts/{prompt_id}/versions", method="GET") + async def list_prompt_versions( + self, + prompt_id: str, + ) -> ListPromptsResponse: + """List all versions of a specific prompt. + + :param prompt_id: The identifier of the prompt to list versions for. + :returns: A ListPromptsResponse containing all versions of the prompt. + """ + ... + + @webmethod(route="/prompts/{prompt_id}", method="GET") + async def get_prompt( + self, + prompt_id: str, + version: int | None = None, + ) -> Prompt: + """Get a prompt by its identifier and optional version. + + :param prompt_id: The identifier of the prompt to get. + :param version: The version of the prompt to get (defaults to latest). + :returns: A Prompt resource. + """ + ... + + @webmethod(route="/prompts", method="POST") + async def create_prompt( + self, + prompt: str, + variables: list[str] | None = None, + ) -> Prompt: + """Create a new prompt. + + :param prompt: The prompt text content with variable placeholders. + :param variables: List of variable names that can be used in the prompt template. + :returns: The created Prompt resource. + """ + ... + + @webmethod(route="/prompts/{prompt_id}", method="PUT") + async def update_prompt( + self, + prompt_id: str, + prompt: str, + version: int, + variables: list[str] | None = None, + set_as_default: bool = True, + ) -> Prompt: + """Update an existing prompt (increments version). + + :param prompt_id: The identifier of the prompt to update. + :param prompt: The updated prompt text content. + :param version: The current version of the prompt being updated. + :param variables: Updated list of variable names that can be used in the prompt template. + :param set_as_default: Set the new version as the default (default=True). + :returns: The updated Prompt resource with incremented version. + """ + ... + + @webmethod(route="/prompts/{prompt_id}", method="DELETE") + async def delete_prompt( + self, + prompt_id: str, + ) -> None: + """Delete a prompt. + + :param prompt_id: The identifier of the prompt to delete. + """ + ... + + @webmethod(route="/prompts/{prompt_id}/set-default-version", method="PUT") + async def set_default_version( + self, + prompt_id: str, + version: int, + ) -> Prompt: + """Set which version of a prompt should be the default in get_prompt (latest). + + :param prompt_id: The identifier of the prompt. + :param version: The version to set as default. + :returns: The prompt with the specified version now set as default. + """ + ... diff --git a/llama_stack/apis/resource.py b/llama_stack/apis/resource.py index 3731fbf1d..7c4130f7d 100644 --- a/llama_stack/apis/resource.py +++ b/llama_stack/apis/resource.py @@ -19,6 +19,7 @@ class ResourceType(StrEnum): benchmark = "benchmark" tool = "tool" tool_group = "tool_group" + prompt = "prompt" class Resource(BaseModel): diff --git a/llama_stack/core/prompts/__init__.py b/llama_stack/core/prompts/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/core/prompts/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/core/prompts/prompts.py b/llama_stack/core/prompts/prompts.py new file mode 100644 index 000000000..26e8f5cef --- /dev/null +++ b/llama_stack/core/prompts/prompts.py @@ -0,0 +1,233 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json +from typing import Any + +from pydantic import BaseModel + +from llama_stack.apis.prompts import ListPromptsResponse, Prompt, Prompts +from llama_stack.core.datatypes import StackRunConfig +from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR +from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl +from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig + + +class PromptServiceConfig(BaseModel): + """Configuration for the built-in prompt service. + + :param run_config: Stack run configuration containing distribution info + """ + + run_config: StackRunConfig + + +async def get_provider_impl(config: PromptServiceConfig, deps: dict[Any, Any]): + """Get the prompt service implementation.""" + impl = PromptServiceImpl(config, deps) + await impl.initialize() + return impl + + +class PromptServiceImpl(Prompts): + """Built-in prompt service implementation using KVStore.""" + + def __init__(self, config: PromptServiceConfig, deps: dict[Any, Any]): + self.config = config + self.deps = deps + self.kvstore: KVStore + + async def initialize(self) -> None: + kvstore_config = SqliteKVStoreConfig( + db_path=(DISTRIBS_BASE_DIR / self.config.run_config.image_name / "prompts.db").as_posix() + ) + self.kvstore = await kvstore_impl(kvstore_config) + + def _get_default_key(self, prompt_id: str) -> str: + """Get the KVStore key that stores the default version number.""" + return f"prompts:v1:{prompt_id}:default" + + async def _get_prompt_key(self, prompt_id: str, version: int | None = None) -> str: + """Get the KVStore key for prompt data, returning default version if applicable.""" + if version: + return self._get_version_key(prompt_id, str(version)) + + default_key = self._get_default_key(prompt_id) + resolved_version = await self.kvstore.get(default_key) + if resolved_version is None: + raise ValueError(f"Prompt {prompt_id}:default not found") + return self._get_version_key(prompt_id, resolved_version) + + def _get_version_key(self, prompt_id: str, version: str) -> str: + """Get the KVStore key for a specific prompt version.""" + return f"prompts:v1:{prompt_id}:{version}" + + def _get_list_key_prefix(self) -> str: + """Get the key prefix for listing prompts.""" + return "prompts:v1:" + + def _serialize_prompt(self, prompt: Prompt) -> str: + """Serialize a prompt to JSON string for storage.""" + return json.dumps( + { + "prompt_id": prompt.prompt_id, + "prompt": prompt.prompt, + "version": prompt.version, + "variables": prompt.variables or [], + "is_default": prompt.is_default, + } + ) + + def _deserialize_prompt(self, data: str) -> Prompt: + """Deserialize a prompt from JSON string.""" + obj = json.loads(data) + return Prompt( + prompt_id=obj["prompt_id"], + prompt=obj["prompt"], + version=obj["version"], + variables=obj.get("variables", []), + is_default=obj.get("is_default", False), + ) + + async def list_prompts(self) -> ListPromptsResponse: + """List all prompts (default versions only).""" + prefix = self._get_list_key_prefix() + keys = await self.kvstore.keys_in_range(prefix, prefix + "\xff") + + prompts = [] + for key in keys: + if key.endswith(":default"): + try: + default_version = await self.kvstore.get(key) + if default_version: + prompt_id = key.replace(prefix, "").replace(":default", "") + version_key = self._get_version_key(prompt_id, default_version) + data = await self.kvstore.get(version_key) + if data: + prompt = self._deserialize_prompt(data) + prompts.append(prompt) + except (json.JSONDecodeError, KeyError): + continue + + prompts.sort(key=lambda p: p.prompt_id or "", reverse=True) + return ListPromptsResponse(data=prompts) + + async def get_prompt(self, prompt_id: str, version: int | None = None) -> Prompt: + """Get a prompt by its identifier and optional version.""" + key = await self._get_prompt_key(prompt_id, version) + data = await self.kvstore.get(key) + if data is None: + raise ValueError(f"Prompt {prompt_id}:{version if version else 'default'} not found") + return self._deserialize_prompt(data) + + async def create_prompt( + self, + prompt: str, + variables: list[str] | None = None, + ) -> Prompt: + """Create a new prompt.""" + if variables is None: + variables = [] + + prompt_obj = Prompt( + prompt_id=Prompt.generate_prompt_id(), + prompt=prompt, + version=1, + variables=variables, + ) + + version_key = self._get_version_key(prompt_obj.prompt_id, str(prompt_obj.version)) + data = self._serialize_prompt(prompt_obj) + await self.kvstore.set(version_key, data) + + default_key = self._get_default_key(prompt_obj.prompt_id) + await self.kvstore.set(default_key, str(prompt_obj.version)) + + return prompt_obj + + async def update_prompt( + self, + prompt_id: str, + prompt: str, + version: int, + variables: list[str] | None = None, + set_as_default: bool = True, + ) -> Prompt: + """Update an existing prompt (increments version).""" + if version < 1: + raise ValueError("Version must be >= 1") + if variables is None: + variables = [] + + prompt_versions = await self.list_prompt_versions(prompt_id) + latest_prompt = max(prompt_versions.data, key=lambda x: int(x.version)) + + if version and latest_prompt.version != version: + raise ValueError( + f"'{version}' is not the latest prompt version for prompt_id='{prompt_id}'. Use the latest version '{latest_prompt.version}' in request." + ) + + current_version = latest_prompt.version if version is None else version + new_version = current_version + 1 + + updated_prompt = Prompt(prompt_id=prompt_id, prompt=prompt, version=new_version, variables=variables) + + version_key = self._get_version_key(prompt_id, str(new_version)) + data = self._serialize_prompt(updated_prompt) + await self.kvstore.set(version_key, data) + + if set_as_default: + await self.set_default_version(prompt_id, new_version) + + return updated_prompt + + async def delete_prompt(self, prompt_id: str) -> None: + """Delete a prompt and all its versions.""" + await self.get_prompt(prompt_id) + + prefix = f"prompts:v1:{prompt_id}:" + keys = await self.kvstore.keys_in_range(prefix, prefix + "\xff") + + for key in keys: + await self.kvstore.delete(key) + + async def list_prompt_versions(self, prompt_id: str) -> ListPromptsResponse: + """List all versions of a specific prompt.""" + prefix = f"prompts:v1:{prompt_id}:" + keys = await self.kvstore.keys_in_range(prefix, prefix + "\xff") + + default_version = None + prompts = [] + + for key in keys: + data = await self.kvstore.get(key) + if key.endswith(":default"): + default_version = data + else: + if data: + prompt_obj = self._deserialize_prompt(data) + prompts.append(prompt_obj) + + if not prompts: + raise ValueError(f"Prompt {prompt_id} not found") + + for prompt in prompts: + prompt.is_default = str(prompt.version) == default_version + + prompts.sort(key=lambda x: x.version) + return ListPromptsResponse(data=prompts) + + async def set_default_version(self, prompt_id: str, version: int) -> Prompt: + """Set which version of a prompt should be the default, If not set. the default is the latest.""" + version_key = self._get_version_key(prompt_id, str(version)) + data = await self.kvstore.get(version_key) + if data is None: + raise ValueError(f"Prompt {prompt_id} version {version} not found") + + default_key = self._get_default_key(prompt_id) + await self.kvstore.set(default_key, str(version)) + + return self._deserialize_prompt(data) diff --git a/llama_stack/core/resolver.py b/llama_stack/core/resolver.py index a8ad03e1a..373446de6 100644 --- a/llama_stack/core/resolver.py +++ b/llama_stack/core/resolver.py @@ -19,6 +19,7 @@ from llama_stack.apis.inference import Inference, InferenceProvider from llama_stack.apis.inspect import Inspect from llama_stack.apis.models import Models from llama_stack.apis.post_training import PostTraining +from llama_stack.apis.prompts import Prompts from llama_stack.apis.providers import Providers as ProvidersAPI from llama_stack.apis.safety import Safety from llama_stack.apis.scoring import Scoring @@ -93,6 +94,7 @@ def api_protocol_map(external_apis: dict[Api, ExternalApiSpec] | None = None) -> Api.tool_groups: ToolGroups, Api.tool_runtime: ToolRuntime, Api.files: Files, + Api.prompts: Prompts, } if external_apis: diff --git a/llama_stack/core/server/server.py b/llama_stack/core/server/server.py index 288bf46e1..d3e875fec 100644 --- a/llama_stack/core/server/server.py +++ b/llama_stack/core/server/server.py @@ -515,6 +515,7 @@ def main(args: argparse.Namespace | None = None): apis_to_serve.add("inspect") apis_to_serve.add("providers") + apis_to_serve.add("prompts") for api_str in apis_to_serve: api = Api(api_str) diff --git a/llama_stack/core/stack.py b/llama_stack/core/stack.py index bccea48d3..7ab8d2c64 100644 --- a/llama_stack/core/stack.py +++ b/llama_stack/core/stack.py @@ -24,6 +24,7 @@ from llama_stack.apis.inference import Inference from llama_stack.apis.inspect import Inspect from llama_stack.apis.models import Models from llama_stack.apis.post_training import PostTraining +from llama_stack.apis.prompts import Prompts from llama_stack.apis.providers import Providers from llama_stack.apis.safety import Safety from llama_stack.apis.scoring import Scoring @@ -37,6 +38,7 @@ from llama_stack.apis.vector_io import VectorIO from llama_stack.core.datatypes import Provider, StackRunConfig from llama_stack.core.distribution import get_provider_registry from llama_stack.core.inspect import DistributionInspectConfig, DistributionInspectImpl +from llama_stack.core.prompts.prompts import PromptServiceConfig, PromptServiceImpl from llama_stack.core.providers import ProviderImpl, ProviderImplConfig from llama_stack.core.resolver import ProviderRegistry, resolve_impls from llama_stack.core.routing_tables.common import CommonRoutingTableImpl @@ -72,6 +74,7 @@ class LlamaStack( ToolRuntime, RAGToolRuntime, Files, + Prompts, ): pass @@ -305,6 +308,12 @@ def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConf ) impls[Api.providers] = providers_impl + prompts_impl = PromptServiceImpl( + PromptServiceConfig(run_config=run_config), + deps=impls, + ) + impls[Api.prompts] = prompts_impl + # Produces a stack of providers for the given run config. Not all APIs may be # asked for in the run config. @@ -329,6 +338,9 @@ async def construct_stack( # Add internal implementations after all other providers are resolved add_internal_implementations(impls, run_config) + if Api.prompts in impls: + await impls[Api.prompts].initialize() + await register_resources(run_config, impls) await refresh_registry_once(impls) diff --git a/tests/unit/prompts/prompts/__init__.py b/tests/unit/prompts/prompts/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/tests/unit/prompts/prompts/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/tests/unit/prompts/prompts/conftest.py b/tests/unit/prompts/prompts/conftest.py new file mode 100644 index 000000000..b2c619e49 --- /dev/null +++ b/tests/unit/prompts/prompts/conftest.py @@ -0,0 +1,30 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import random + +import pytest + +from llama_stack.core.prompts.prompts import PromptServiceConfig, PromptServiceImpl +from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig + + +@pytest.fixture +async def temp_prompt_store(tmp_path_factory): + unique_id = f"prompt_store_{random.randint(1, 1000000)}" + temp_dir = tmp_path_factory.getbasetemp() + db_path = str(temp_dir / f"{unique_id}.db") + + from llama_stack.core.datatypes import StackRunConfig + from llama_stack.providers.utils.kvstore import kvstore_impl + + mock_run_config = StackRunConfig(image_name="test-distribution", apis=[], providers={}) + config = PromptServiceConfig(run_config=mock_run_config) + store = PromptServiceImpl(config, deps={}) + + store.kvstore = await kvstore_impl(SqliteKVStoreConfig(db_path=db_path)) + + yield store diff --git a/tests/unit/prompts/prompts/test_prompts.py b/tests/unit/prompts/prompts/test_prompts.py new file mode 100644 index 000000000..792e55530 --- /dev/null +++ b/tests/unit/prompts/prompts/test_prompts.py @@ -0,0 +1,144 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +import pytest + + +class TestPrompts: + async def test_create_and_get_prompt(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("Hello world!", ["name"]) + assert prompt.prompt == "Hello world!" + assert prompt.version == 1 + assert prompt.prompt_id.startswith("pmpt_") + assert prompt.variables == ["name"] + + retrieved = await temp_prompt_store.get_prompt(prompt.prompt_id) + assert retrieved.prompt_id == prompt.prompt_id + assert retrieved.prompt == prompt.prompt + + async def test_update_prompt(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("Original") + updated = await temp_prompt_store.update_prompt(prompt.prompt_id, "Updated", 1, ["v"]) + assert updated.version == 2 + assert updated.prompt == "Updated" + + async def test_update_prompt_with_version(self, temp_prompt_store): + version_for_update = 1 + + prompt = await temp_prompt_store.create_prompt("Original") + assert prompt.version == 1 + prompt = await temp_prompt_store.update_prompt(prompt.prompt_id, "Updated", version_for_update, ["v"]) + assert prompt.version == 2 + + with pytest.raises(ValueError): + # now this is a stale version + await temp_prompt_store.update_prompt(prompt.prompt_id, "Another Update", version_for_update, ["v"]) + + with pytest.raises(ValueError): + # this version does not exist + await temp_prompt_store.update_prompt(prompt.prompt_id, "Another Update", 99, ["v"]) + + async def test_delete_prompt(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("to be deleted") + await temp_prompt_store.delete_prompt(prompt.prompt_id) + with pytest.raises(ValueError): + await temp_prompt_store.get_prompt(prompt.prompt_id) + + async def test_list_prompts(self, temp_prompt_store): + response = await temp_prompt_store.list_prompts() + assert response.data == [] + + await temp_prompt_store.create_prompt("first") + await temp_prompt_store.create_prompt("second") + + response = await temp_prompt_store.list_prompts() + assert len(response.data) == 2 + + async def test_version(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("V1") + await temp_prompt_store.update_prompt(prompt.prompt_id, "V2", 1) + + v1 = await temp_prompt_store.get_prompt(prompt.prompt_id, version=1) + assert v1.version == 1 and v1.prompt == "V1" + + latest = await temp_prompt_store.get_prompt(prompt.prompt_id) + assert latest.version == 2 and latest.prompt == "V2" + + async def test_set_default_version(self, temp_prompt_store): + prompt0 = await temp_prompt_store.create_prompt("V1") + prompt1 = await temp_prompt_store.update_prompt(prompt0.prompt_id, "V2", 1) + + assert (await temp_prompt_store.get_prompt(prompt0.prompt_id)).version == 2 + prompt_default = await temp_prompt_store.set_default_version(prompt0.prompt_id, 1) + assert (await temp_prompt_store.get_prompt(prompt0.prompt_id)).version == 1 + assert prompt_default.version == 1 + + prompt2 = await temp_prompt_store.update_prompt(prompt0.prompt_id, "V3", prompt1.version) + assert prompt2.version == 3 + + async def test_prompt_id_generation_and_validation(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("Test") + assert prompt.prompt_id.startswith("pmpt_") + assert len(prompt.prompt_id) == 53 + + with pytest.raises(ValueError): + await temp_prompt_store.get_prompt("invalid_id") + + async def test_list_shows_default_versions(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("V1") + await temp_prompt_store.update_prompt(prompt.prompt_id, "V2", 1) + await temp_prompt_store.update_prompt(prompt.prompt_id, "V3", 2) + + response = await temp_prompt_store.list_prompts() + listed_prompt = response.data[0] + assert listed_prompt.version == 3 and listed_prompt.prompt == "V3" + + await temp_prompt_store.set_default_version(prompt.prompt_id, 1) + + response = await temp_prompt_store.list_prompts() + listed_prompt = response.data[0] + assert listed_prompt.version == 1 and listed_prompt.prompt == "V1" + assert not (await temp_prompt_store.get_prompt(prompt.prompt_id, 3)).is_default + + async def test_get_all_prompt_versions(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("V1") + await temp_prompt_store.update_prompt(prompt.prompt_id, "V2", 1) + await temp_prompt_store.update_prompt(prompt.prompt_id, "V3", 2) + + versions = (await temp_prompt_store.list_prompt_versions(prompt.prompt_id)).data + assert len(versions) == 3 + assert [v.version for v in versions] == [1, 2, 3] + assert [v.is_default for v in versions] == [False, False, True] + + await temp_prompt_store.set_default_version(prompt.prompt_id, 2) + versions = (await temp_prompt_store.list_prompt_versions(prompt.prompt_id)).data + assert [v.is_default for v in versions] == [False, True, False] + + with pytest.raises(ValueError): + await temp_prompt_store.list_prompt_versions("nonexistent") + + async def test_prompt_variable_validation(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("Hello {{ name }}, you live in {{ city }}!", ["name", "city"]) + assert prompt.variables == ["name", "city"] + + prompt_no_vars = await temp_prompt_store.create_prompt("Hello world!", []) + assert prompt_no_vars.variables == [] + + with pytest.raises(ValueError, match="undeclared variables"): + await temp_prompt_store.create_prompt("Hello {{ name }}, invalid {{ unknown }}!", ["name"]) + + async def test_update_prompt_set_as_default_behavior(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("V1") + assert (await temp_prompt_store.get_prompt(prompt.prompt_id)).version == 1 + + prompt_v2 = await temp_prompt_store.update_prompt(prompt.prompt_id, "V2", 1, [], set_as_default=True) + assert prompt_v2.version == 2 + assert (await temp_prompt_store.get_prompt(prompt.prompt_id)).version == 2 + + prompt_v3 = await temp_prompt_store.update_prompt(prompt.prompt_id, "V3", 2, [], set_as_default=False) + assert prompt_v3.version == 3 + assert (await temp_prompt_store.get_prompt(prompt.prompt_id)).version == 2 From ef02b9ea101487ca1829795f18274ad96b211ff6 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Mon, 8 Sep 2025 16:51:38 +0100 Subject: [PATCH 071/124] fix: environment variable typo in inference recorder error message (#3374) The error message was referencing LLAMA_STACK_INFERENCE_MODE instead of the correct LLAMA_STACK_TEST_INFERENCE_MODE environment variable. --- llama_stack/testing/inference_recorder.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 5b64e26d3..298758c92 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -292,7 +292,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint f"No recorded response found for request hash: {request_hash}\n" f"Request: {method} {url} {body}\n" f"Model: {body.get('model', 'unknown')}\n" - f"To record this response, run with LLAMA_STACK_INFERENCE_MODE=record" + f"To record this response, run with LLAMA_STACK_TEST_INFERENCE_MODE=record" ) elif _current_mode == InferenceMode.RECORD: From 09141361fbec54acb25ef0d13c542d6cc41c3b5c Mon Sep 17 00:00:00 2001 From: Swapna Lekkala Date: Mon, 8 Sep 2025 13:22:43 -0700 Subject: [PATCH 072/124] fix: use dataset version 4.0.0 or above --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index fb6d3a330..133b78dbf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -114,7 +114,7 @@ test = [ "psycopg2-binary>=2.9.0", "pypdf", "mcp", - "datasets", + "datasets>=4.0.0", "autoevals", "transformers", "sqlalchemy", From c9268a7a8cf57e72e1facdb0e41e444575cc5170 Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Mon, 8 Sep 2025 14:46:46 -0700 Subject: [PATCH 073/124] fix: pre-commit failing (#3381) # What does this PR do? Fix failing pre-commit, https://github.com/llamastack/llama-stack/actions/workflows/pre-commit.yml ## Test Plan CI --- uv.lock | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/uv.lock b/uv.lock index 43cc59c7a..b38177245 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 2 requires-python = ">=3.12" resolution-markers = [ "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", @@ -895,7 +895,6 @@ dependencies = [ { name = "numpy" }, { name = "packaging" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/5c/f4/7c2136f4660ca504266cc08b38df2aa1db14fea93393b82e099ff34d7290/faiss_cpu-1.11.0.post1.tar.gz", hash = "sha256:06b1ea9ddec9e4d9a41c8ef7478d493b08d770e9a89475056e963081eed757d1", size = 70543, upload-time = "2025-07-15T09:15:02.127Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/30/1e/9980758efa55b4e7a5d6df1ae17c9ddbe5a636bfbf7d22d47c67f7a530f4/faiss_cpu-1.11.0.post1-cp312-cp312-macosx_13_0_x86_64.whl", hash = "sha256:68f6ce2d9c510a5765af2f5711bd76c2c37bd598af747f3300224bdccf45378c", size = 7913676, upload-time = "2025-07-15T09:14:06.077Z" }, { url = "https://files.pythonhosted.org/packages/05/d1/bd785887085faa02916c52320527b8bb54288835b0a3138df89a0e323cc8/faiss_cpu-1.11.0.post1-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:b940c530a8236cc0b9fd9d6e87b3d70b9c6c216bc2baf2649356c908902e52c9", size = 3313952, upload-time = "2025-07-15T09:14:07.584Z" }, @@ -1748,7 +1747,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/5f/e4/f1546746049c99c6b8b247e2f34485b9eae36faa9322b84e2a17262e6712/litellm-1.74.9-py3-none-any.whl", hash = "sha256:ab8f8a6e4d8689d3c7c4f9c3bbc7e46212cc3ebc74ddd0f3c0c921bb459c9874", size = 8740449, upload-time = "2025-07-28T16:42:36.8Z" }, ] - [[package]] name = "llama-stack" version = "0.2.20" @@ -1958,7 +1956,7 @@ test = [ { name = "aiosqlite" }, { name = "autoevals" }, { name = "chardet" }, - { name = "datasets" }, + { name = "datasets", specifier = ">=4.0.0" }, { name = "mcp" }, { name = "milvus-lite", specifier = ">=2.5.0" }, { name = "openai", specifier = ">=1.100.0" }, From 30468d0c43277f066397cc94a6d44c3965b0409f Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Mon, 8 Sep 2025 15:13:42 -0700 Subject: [PATCH 074/124] fix(deps): bump datasets versions for all providers (#3382) Not doing so results in errors of the kind you see in: https://github.com/llamastack/llama-stack-ops/actions/runs/17565092546/job/49890264353 --- llama_stack/providers/registry/datasetio.py | 4 ++-- llama_stack/providers/registry/post_training.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/llama_stack/providers/registry/datasetio.py b/llama_stack/providers/registry/datasetio.py index 43cde83fb..f641b4ce3 100644 --- a/llama_stack/providers/registry/datasetio.py +++ b/llama_stack/providers/registry/datasetio.py @@ -30,7 +30,7 @@ def available_providers() -> list[ProviderSpec]: adapter=AdapterSpec( adapter_type="huggingface", pip_packages=[ - "datasets", + "datasets>=4.0.0", ], module="llama_stack.providers.remote.datasetio.huggingface", config_class="llama_stack.providers.remote.datasetio.huggingface.HuggingfaceDatasetIOConfig", @@ -42,7 +42,7 @@ def available_providers() -> list[ProviderSpec]: adapter=AdapterSpec( adapter_type="nvidia", pip_packages=[ - "datasets", + "datasets>=4.0.0", ], module="llama_stack.providers.remote.datasetio.nvidia", config_class="llama_stack.providers.remote.datasetio.nvidia.NvidiaDatasetIOConfig", diff --git a/llama_stack/providers/registry/post_training.py b/llama_stack/providers/registry/post_training.py index 67238e3fc..47aeb401e 100644 --- a/llama_stack/providers/registry/post_training.py +++ b/llama_stack/providers/registry/post_training.py @@ -48,7 +48,7 @@ def available_providers() -> list[ProviderSpec]: InlineProviderSpec( api=Api.post_training, provider_type="inline::huggingface-gpu", - pip_packages=["trl", "transformers", "peft", "datasets", "torch"], + pip_packages=["trl", "transformers", "peft", "datasets>=4.0.0", "torch"], module="llama_stack.providers.inline.post_training.huggingface", config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig", api_dependencies=[ From 28696c3f30468677fb851b82846acae21deb3e66 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 8 Sep 2025 22:30:03 +0000 Subject: [PATCH 075/124] build: Bump version to 0.2.21 --- llama_stack/ui/package-lock.json | 8 ++++---- llama_stack/ui/package.json | 2 +- pyproject.toml | 6 +++--- uv.lock | 14 +++++++------- 4 files changed, 15 insertions(+), 15 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index cbe8e5557..1db1c61cd 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -18,7 +18,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^12.23.12", - "llama-stack-client": "^0.2.20", + "llama-stack-client": "^0.2.21", "lucide-react": "^0.542.0", "next": "15.3.3", "next-auth": "^4.24.11", @@ -10278,9 +10278,9 @@ "license": "MIT" }, "node_modules/llama-stack-client": { - "version": "0.2.20", - "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.20.tgz", - "integrity": "sha512-1vD5nizTX5JEW8TADxKgy/P1W8YZoPSpdnmfxbdYbWgpQ3BWtbvLS6jmDk7VwVA5fRC4895VfHsRDfS1liHarw==", + "version": "0.2.21", + "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.21.tgz", + "integrity": "sha512-rjU2Vx5xStxDYavU8K1An/SYXiQQjroLcK98B+p0Paz/a7OgRao2S0YwvThJjPUyChY4fO03UIXP9LpmHqlXWQ==", "license": "MIT", "dependencies": { "@types/node": "^18.11.18", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index e817e9ae3..e50401fa6 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -23,7 +23,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^12.23.12", - "llama-stack-client": "^0.2.20", + "llama-stack-client": "^0.2.21", "lucide-react": "^0.542.0", "next": "15.3.3", "next-auth": "^4.24.11", diff --git a/pyproject.toml b/pyproject.toml index 133b78dbf..0414aafb0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ required-version = ">=0.7.0" [project] name = "llama_stack" -version = "0.2.20" +version = "0.2.21" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -31,7 +31,7 @@ dependencies = [ "huggingface-hub>=0.34.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.20", + "llama-stack-client>=0.2.21", "openai>=1.99.6", "prompt-toolkit", "python-dotenv", @@ -55,7 +55,7 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.20", + "llama-stack-client>=0.2.21", "streamlit-option-menu", ] diff --git a/uv.lock b/uv.lock index b38177245..2788c6fef 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.12" resolution-markers = [ "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", @@ -1749,7 +1749,7 @@ wheels = [ [[package]] name = "llama-stack" -version = "0.2.20" +version = "0.2.21" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -1887,8 +1887,8 @@ requires-dist = [ { name = "huggingface-hub", specifier = ">=0.34.0,<1.0" }, { name = "jinja2", specifier = ">=3.1.6" }, { name = "jsonschema" }, - { name = "llama-stack-client", specifier = ">=0.2.20" }, - { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.20" }, + { name = "llama-stack-client", specifier = ">=0.2.21" }, + { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.21" }, { name = "openai", specifier = ">=1.99.6" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, @@ -1997,7 +1997,7 @@ unit = [ [[package]] name = "llama-stack-client" -version = "0.2.20" +version = "0.2.21" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -2016,9 +2016,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/21/91/c5e32219a5192825dd601700e68205c815c5cfee60c64c22172e46a0c83e/llama_stack_client-0.2.20.tar.gz", hash = "sha256:356257f0a4bbb64205f89e113d715925853d5e34ec744e72466da72790ba415b", size = 318311, upload-time = "2025-08-29T21:10:12.854Z" } +sdist = { url = "https://files.pythonhosted.org/packages/f1/d3/8c50561d167f1e9b601b8fffe852b44c1ff97aaa6db6cdedd611d9e02a65/llama_stack_client-0.2.21.tar.gz", hash = "sha256:bd931fdcadedec5ccdbaa3c54d0c17761af1c227711ad6150dc0dd33d7b66ce2", size = 318319, upload-time = "2025-09-08T22:26:57.668Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b0/ba/84914c4eead2fd9251c149fd6a7da28b78acd620793e3c4506116645cb60/llama_stack_client-0.2.20-py3-none-any.whl", hash = "sha256:6e178981d2ce971da2145c79d5b2b123fa50e063ed431494975c2ba01c5b8016", size = 369899, upload-time = "2025-08-29T21:10:11.113Z" }, + { url = "https://files.pythonhosted.org/packages/02/77/dadc682046a2c7ad68be8d2d2afac7007bf4d22efb0d3929d85ab9706ffe/llama_stack_client-0.2.21-py3-none-any.whl", hash = "sha256:adba82fdf18ab3b8ac218cedba4927bd5d26c23c2318e75c8763a44bb6b40693", size = 369902, upload-time = "2025-09-08T22:26:56.308Z" }, ] [[package]] From a8aa815b6a194fad76e4e5f7e73faf588b5d0e01 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Tue, 9 Sep 2025 15:50:56 -0700 Subject: [PATCH 076/124] feat(tests): migrate to global "setups" system for test configuration (#3390) This PR refactors the integration test system to use global "setups" which provides better separation of concerns: **suites = what to test, setups = how to configure.** NOTE: if you naming suggestions, please provide feedback Changes: - New `tests/integration/setups.py` with global, reusable configurations (ollama, vllm, gpt, claude) - Modified `scripts/integration-tests.sh` options to match with the underlying pytest options - Updated documentation to reflect the new global setup system The main benefit is that setups can be reused across multiple suites (e.g., use "gpt" with any suite) even though sometimes they could specifically tailored for a suite (vision <> ollama-vision). It is now easier to add new configurations without modifying existing suites. Usage examples: - `pytest tests/integration --suite=responses --setup=gpt` - `pytest tests/integration --suite=vision` # auto-selects "ollama-vision" setup - `pytest tests/integration --suite=base --setup=vllm` --- .../actions/run-and-record-tests/action.yml | 42 +++--- .github/actions/setup-ollama/action.yml | 4 +- .../actions/setup-test-environment/action.yml | 14 +- .github/workflows/integration-tests.yml | 20 +-- .../workflows/record-integration-tests.yml | 32 ++--- scripts/get_setup_env.py | 71 ++++++++++ scripts/github/schedule-record-workflow.sh | 57 ++++---- scripts/integration-tests.sh | 106 ++++++++------- tests/integration/README.md | 48 ++++--- tests/integration/conftest.py | 71 ++++++---- tests/integration/suites.py | 126 +++++++++++++----- 11 files changed, 385 insertions(+), 206 deletions(-) create mode 100644 scripts/get_setup_env.py diff --git a/.github/actions/run-and-record-tests/action.yml b/.github/actions/run-and-record-tests/action.yml index 7f028b104..a3eb31d9f 100644 --- a/.github/actions/run-and-record-tests/action.yml +++ b/.github/actions/run-and-record-tests/action.yml @@ -5,21 +5,22 @@ inputs: stack-config: description: 'Stack configuration to use' required: true - provider: - description: 'Provider to use for tests' - required: true + setup: + description: 'Setup to use for tests (e.g., ollama, gpt, vllm)' + required: false + default: '' inference-mode: description: 'Inference mode (record or replay)' required: true - test-suite: + suite: description: 'Test suite to use: base, responses, vision, etc.' required: false default: '' - test-subdirs: - description: 'Comma-separated list of test subdirectories to run; overrides test-suite' + subdirs: + description: 'Comma-separated list of test subdirectories to run; overrides suite' required: false default: '' - test-pattern: + pattern: description: 'Regex pattern to pass to pytest -k' required: false default: '' @@ -37,14 +38,23 @@ runs: - name: Run Integration Tests shell: bash run: | - uv run --no-sync ./scripts/integration-tests.sh \ - --stack-config '${{ inputs.stack-config }}' \ - --provider '${{ inputs.provider }}' \ - --test-subdirs '${{ inputs.test-subdirs }}' \ - --test-pattern '${{ inputs.test-pattern }}' \ - --inference-mode '${{ inputs.inference-mode }}' \ - --test-suite '${{ inputs.test-suite }}' \ - | tee pytest-${{ inputs.inference-mode }}.log + SCRIPT_ARGS="--stack-config ${{ inputs.stack-config }} --inference-mode ${{ inputs.inference-mode }}" + + # Add optional arguments only if they are provided + if [ -n '${{ inputs.setup }}' ]; then + SCRIPT_ARGS="$SCRIPT_ARGS --setup ${{ inputs.setup }}" + fi + if [ -n '${{ inputs.suite }}' ]; then + SCRIPT_ARGS="$SCRIPT_ARGS --suite ${{ inputs.suite }}" + fi + if [ -n '${{ inputs.subdirs }}' ]; then + SCRIPT_ARGS="$SCRIPT_ARGS --subdirs ${{ inputs.subdirs }}" + fi + if [ -n '${{ inputs.pattern }}' ]; then + SCRIPT_ARGS="$SCRIPT_ARGS --pattern ${{ inputs.pattern }}" + fi + + uv run --no-sync ./scripts/integration-tests.sh $SCRIPT_ARGS | tee pytest-${{ inputs.inference-mode }}.log - name: Commit and push recordings @@ -58,7 +68,7 @@ runs: echo "New recordings detected, committing and pushing" git add tests/integration/recordings/ - git commit -m "Recordings update from CI (test-suite: ${{ inputs.test-suite }})" + git commit -m "Recordings update from CI (suite: ${{ inputs.suite }})" git fetch origin ${{ github.ref_name }} git rebase origin/${{ github.ref_name }} echo "Rebased successfully" diff --git a/.github/actions/setup-ollama/action.yml b/.github/actions/setup-ollama/action.yml index dc2f87e8c..5c95d131d 100644 --- a/.github/actions/setup-ollama/action.yml +++ b/.github/actions/setup-ollama/action.yml @@ -1,7 +1,7 @@ name: Setup Ollama description: Start Ollama inputs: - test-suite: + suite: description: 'Test suite to use: base, responses, vision, etc.' required: false default: '' @@ -11,7 +11,7 @@ runs: - name: Start Ollama shell: bash run: | - if [ "${{ inputs.test-suite }}" == "vision" ]; then + if [ "${{ inputs.suite }}" == "vision" ]; then image="ollama-with-vision-model" else image="ollama-with-models" diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index 3be76f009..478e8f598 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -8,11 +8,11 @@ inputs: client-version: description: 'Client version (latest or published)' required: true - provider: - description: 'Provider to setup (ollama or vllm)' - required: true + setup: + description: 'Setup to configure (ollama, vllm, gpt, etc.)' + required: false default: 'ollama' - test-suite: + suite: description: 'Test suite to use: base, responses, vision, etc.' required: false default: '' @@ -30,13 +30,13 @@ runs: client-version: ${{ inputs.client-version }} - name: Setup ollama - if: ${{ inputs.provider == 'ollama' && inputs.inference-mode == 'record' }} + if: ${{ (inputs.setup == 'ollama' || inputs.setup == 'ollama-vision') && inputs.inference-mode == 'record' }} uses: ./.github/actions/setup-ollama with: - test-suite: ${{ inputs.test-suite }} + suite: ${{ inputs.suite }} - name: Setup vllm - if: ${{ inputs.provider == 'vllm' && inputs.inference-mode == 'record' }} + if: ${{ inputs.setup == 'vllm' && inputs.inference-mode == 'record' }} uses: ./.github/actions/setup-vllm - name: Build Llama Stack diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index bb53eea2f..711eccd9e 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -28,8 +28,8 @@ on: description: 'Test against both the latest and published versions' type: boolean default: false - test-provider: - description: 'Test against a specific provider' + test-setup: + description: 'Test against a specific setup' type: string default: 'ollama' @@ -42,18 +42,18 @@ jobs: run-replay-mode-tests: runs-on: ubuntu-latest - name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.provider, matrix.python-version, matrix.client-version, matrix.test-suite) }} + name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.setup, matrix.python-version, matrix.client-version, matrix.suite) }} strategy: fail-fast: false matrix: client-type: [library, server] - # Use vllm on weekly schedule, otherwise use test-provider input (defaults to ollama) - provider: ${{ (github.event.schedule == '1 0 * * 0') && fromJSON('["vllm"]') || fromJSON(format('["{0}"]', github.event.inputs.test-provider || 'ollama')) }} + # Use vllm on weekly schedule, otherwise use test-setup input (defaults to ollama) + setup: ${{ (github.event.schedule == '1 0 * * 0') && fromJSON('["vllm"]') || fromJSON(format('["{0}"]', github.event.inputs.test-setup || 'ollama')) }} # Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12 python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }} client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }} - test-suite: [base, vision] + suite: [base, vision] steps: - name: Checkout repository @@ -64,14 +64,14 @@ jobs: with: python-version: ${{ matrix.python-version }} client-version: ${{ matrix.client-version }} - provider: ${{ matrix.provider }} - test-suite: ${{ matrix.test-suite }} + setup: ${{ matrix.setup }} + suite: ${{ matrix.suite }} inference-mode: 'replay' - name: Run tests uses: ./.github/actions/run-and-record-tests with: stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || 'server:ci-tests' }} - provider: ${{ matrix.provider }} + setup: ${{ matrix.setup }} inference-mode: 'replay' - test-suite: ${{ matrix.test-suite }} + suite: ${{ matrix.suite }} diff --git a/.github/workflows/record-integration-tests.yml b/.github/workflows/record-integration-tests.yml index 01797a54b..65a04f125 100644 --- a/.github/workflows/record-integration-tests.yml +++ b/.github/workflows/record-integration-tests.yml @@ -10,19 +10,19 @@ run-name: Run the integration test suite from tests/integration on: workflow_dispatch: inputs: - test-provider: - description: 'Test against a specific provider' + test-setup: + description: 'Test against a specific setup' type: string default: 'ollama' - test-suite: + suite: description: 'Test suite to use: base, responses, vision, etc.' type: string default: '' - test-subdirs: - description: 'Comma-separated list of test subdirectories to run; overrides test-suite' + subdirs: + description: 'Comma-separated list of test subdirectories to run; overrides suite' type: string default: '' - test-pattern: + pattern: description: 'Regex pattern to pass to pytest -k' type: string default: '' @@ -39,10 +39,10 @@ jobs: run: | echo "::group::Workflow Inputs" echo "branch: ${{ github.ref_name }}" - echo "test-provider: ${{ inputs.test-provider }}" - echo "test-suite: ${{ inputs.test-suite }}" - echo "test-subdirs: ${{ inputs.test-subdirs }}" - echo "test-pattern: ${{ inputs.test-pattern }}" + echo "test-setup: ${{ inputs.test-setup }}" + echo "suite: ${{ inputs.suite }}" + echo "subdirs: ${{ inputs.subdirs }}" + echo "pattern: ${{ inputs.pattern }}" echo "::endgroup::" - name: Checkout repository @@ -55,16 +55,16 @@ jobs: with: python-version: "3.12" # Use single Python version for recording client-version: "latest" - provider: ${{ inputs.test-provider || 'ollama' }} - test-suite: ${{ inputs.test-suite }} + setup: ${{ inputs.test-setup || 'ollama' }} + suite: ${{ inputs.suite }} inference-mode: 'record' - name: Run and record tests uses: ./.github/actions/run-and-record-tests with: stack-config: 'server:ci-tests' # recording must be done with server since more tests are run - provider: ${{ inputs.test-provider || 'ollama' }} + setup: ${{ inputs.test-setup || 'ollama' }} inference-mode: 'record' - test-suite: ${{ inputs.test-suite }} - test-subdirs: ${{ inputs.test-subdirs }} - test-pattern: ${{ inputs.test-pattern }} + suite: ${{ inputs.suite }} + subdirs: ${{ inputs.subdirs }} + pattern: ${{ inputs.pattern }} diff --git a/scripts/get_setup_env.py b/scripts/get_setup_env.py new file mode 100644 index 000000000..fad601e76 --- /dev/null +++ b/scripts/get_setup_env.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +""" +Small helper script to extract environment variables from a test setup. +Used by integration-tests.sh to set environment variables before starting the server. +""" + +import argparse +import sys + +from tests.integration.suites import SETUP_DEFINITIONS, SUITE_DEFINITIONS + + +def get_setup_env_vars(setup_name, suite_name=None): + """ + Get environment variables for a setup, with optional suite default fallback. + + Args: + setup_name: Name of the setup (e.g., 'ollama', 'gpt') + suite_name: Optional suite name to get default setup if setup_name is None + + Returns: + Dictionary of environment variables + """ + # If no setup specified, try to get default from suite + if not setup_name and suite_name: + suite = SUITE_DEFINITIONS.get(suite_name) + if suite and suite.default_setup: + setup_name = suite.default_setup + + if not setup_name: + return {} + + setup = SETUP_DEFINITIONS.get(setup_name) + if not setup: + print( + f"Error: Unknown setup '{setup_name}'. Available: {', '.join(sorted(SETUP_DEFINITIONS.keys()))}", + file=sys.stderr, + ) + sys.exit(1) + + return setup.env + + +def main(): + parser = argparse.ArgumentParser(description="Extract environment variables from a test setup") + parser.add_argument("--setup", help="Setup name (e.g., ollama, gpt)") + parser.add_argument("--suite", help="Suite name to get default setup from if --setup not provided") + parser.add_argument("--format", choices=["bash", "json"], default="bash", help="Output format (default: bash)") + + args = parser.parse_args() + + env_vars = get_setup_env_vars(args.setup, args.suite) + + if args.format == "bash": + # Output as bash export statements + for key, value in env_vars.items(): + print(f"export {key}='{value}'") + elif args.format == "json": + import json + + print(json.dumps(env_vars)) + + +if __name__ == "__main__": + main() diff --git a/scripts/github/schedule-record-workflow.sh b/scripts/github/schedule-record-workflow.sh index 09e055611..c292e53e6 100755 --- a/scripts/github/schedule-record-workflow.sh +++ b/scripts/github/schedule-record-workflow.sh @@ -14,7 +14,7 @@ set -euo pipefail # Default values BRANCH="" TEST_SUBDIRS="" -TEST_PROVIDER="ollama" +TEST_SETUP="ollama" TEST_SUITE="base" TEST_PATTERN="" @@ -27,24 +27,24 @@ Trigger the integration test recording workflow remotely. This way you do not ne OPTIONS: -b, --branch BRANCH Branch to run the workflow on (defaults to current branch) - -p, --test-provider PROVIDER Test provider to use: vllm or ollama (default: ollama) - -t, --test-suite SUITE Test suite to use: base, responses, vision, etc. (default: base) - -s, --test-subdirs DIRS Comma-separated list of test subdirectories to run (overrides suite) - -k, --test-pattern PATTERN Regex pattern to pass to pytest -k + -t, --suite SUITE Test suite to use: base, responses, vision, etc. (default: base) + -p, --setup SETUP Test setup to use: vllm, ollama, gpt, etc. (default: ollama) + -s, --subdirs DIRS Comma-separated list of test subdirectories to run (overrides suite) + -k, --pattern PATTERN Regex pattern to pass to pytest -k -h, --help Show this help message EXAMPLES: # Record tests for current branch with agents subdirectory - $0 --test-subdirs "agents" + $0 --subdirs "agents" # Record tests for specific branch with vision tests - $0 -b my-feature-branch --test-suite vision + $0 -b my-feature-branch --suite vision - # Record multiple test subdirectories with specific provider - $0 --test-subdirs "agents,inference" --test-provider vllm + # Record multiple test subdirectories with specific setup + $0 --subdirs "agents,inference" --setup vllm # Record tests matching a specific pattern - $0 --test-subdirs "inference" --test-pattern "test_streaming" + $0 --subdirs "inference" --pattern "test_streaming" EOF } @@ -63,19 +63,19 @@ while [[ $# -gt 0 ]]; do BRANCH="$2" shift 2 ;; - -s|--test-subdirs) + -s|--subdirs) TEST_SUBDIRS="$2" shift 2 ;; - -p|--test-provider) - TEST_PROVIDER="$2" + -p|--setup) + TEST_SETUP="$2" shift 2 ;; - -t|--test-suite) + -t|--suite) TEST_SUITE="$2" shift 2 ;; - -k|--test-pattern) + -k|--pattern) TEST_PATTERN="$2" shift 2 ;; @@ -93,21 +93,16 @@ done # Validate required parameters if [[ -z "$TEST_SUBDIRS" && -z "$TEST_SUITE" ]]; then - echo "Error: --test-subdirs or --test-suite is required" + echo "Error: --subdirs or --suite is required" echo "Please specify which test subdirectories to run or test suite to use, e.g.:" - echo " $0 --test-subdirs \"agents,inference\"" - echo " $0 --test-suite vision" + echo " $0 --subdirs \"agents,inference\"" + echo " $0 --suite vision" echo "" exit 1 fi -# Validate test provider -if [[ "$TEST_PROVIDER" != "vllm" && "$TEST_PROVIDER" != "ollama" ]]; then - echo "❌ Error: Invalid test provider '$TEST_PROVIDER'" - echo " Supported providers: vllm, ollama" - echo " Example: $0 --test-subdirs \"agents\" --test-provider vllm" - exit 1 -fi +# Validate test setup (optional - setups are validated by the workflow itself) +# Common setups: ollama, vllm, gpt, etc. # Check if required tools are installed if ! command -v gh &> /dev/null; then @@ -237,7 +232,7 @@ fi # Build the workflow dispatch command echo "Triggering integration test recording workflow..." echo "Branch: $BRANCH" -echo "Test provider: $TEST_PROVIDER" +echo "Test setup: $TEST_SETUP" echo "Test subdirs: $TEST_SUBDIRS" echo "Test suite: $TEST_SUITE" echo "Test pattern: ${TEST_PATTERN:-"(none)"}" @@ -245,16 +240,16 @@ echo "" # Prepare inputs for gh workflow run if [[ -n "$TEST_SUBDIRS" ]]; then - INPUTS="-f test-subdirs='$TEST_SUBDIRS'" + INPUTS="-f subdirs='$TEST_SUBDIRS'" fi -if [[ -n "$TEST_PROVIDER" ]]; then - INPUTS="$INPUTS -f test-provider='$TEST_PROVIDER'" +if [[ -n "$TEST_SETUP" ]]; then + INPUTS="$INPUTS -f test-setup='$TEST_SETUP'" fi if [[ -n "$TEST_SUITE" ]]; then - INPUTS="$INPUTS -f test-suite='$TEST_SUITE'" + INPUTS="$INPUTS -f suite='$TEST_SUITE'" fi if [[ -n "$TEST_PATTERN" ]]; then - INPUTS="$INPUTS -f test-pattern='$TEST_PATTERN'" + INPUTS="$INPUTS -f pattern='$TEST_PATTERN'" fi # Run the workflow diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index ab7e37579..eee60951d 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -13,10 +13,10 @@ set -euo pipefail # Default values STACK_CONFIG="" -PROVIDER="" +TEST_SUITE="base" +TEST_SETUP="" TEST_SUBDIRS="" TEST_PATTERN="" -TEST_SUITE="base" INFERENCE_MODE="replay" EXTRA_PARAMS="" @@ -27,29 +27,30 @@ Usage: $0 [OPTIONS] Options: --stack-config STRING Stack configuration to use (required) - --provider STRING Provider to use (ollama, vllm, etc.) (required) - --test-suite STRING Comma-separated list of test suites to run (default: 'base') + --suite STRING Test suite to run (default: 'base') + --setup STRING Test setup (models, env) to use (e.g., 'ollama', 'ollama-vision', 'gpt', 'vllm') --inference-mode STRING Inference mode: record or replay (default: replay) - --test-subdirs STRING Comma-separated list of test subdirectories to run (overrides suite) - --test-pattern STRING Regex pattern to pass to pytest -k + --subdirs STRING Comma-separated list of test subdirectories to run (overrides suite) + --pattern STRING Regex pattern to pass to pytest -k --help Show this help message -Suites are defined in tests/integration/suites.py. They are used to narrow the collection of tests and provide default model options. +Suites are defined in tests/integration/suites.py and define which tests to run. +Setups are defined in tests/integration/setups.py and provide global configuration (models, env). You can also specify subdirectories (of tests/integration) to select tests from, which will override the suite. Examples: # Basic inference tests with ollama - $0 --stack-config server:ci-tests --provider ollama + $0 --stack-config server:ci-tests --suite base --setup ollama # Multiple test directories with vllm - $0 --stack-config server:ci-tests --provider vllm --test-subdirs 'inference,agents' + $0 --stack-config server:ci-tests --subdirs 'inference,agents' --setup vllm # Vision tests with ollama - $0 --stack-config server:ci-tests --provider ollama --test-suite vision + $0 --stack-config server:ci-tests --suite vision # default setup for this suite is ollama-vision # Record mode for updating test recordings - $0 --stack-config server:ci-tests --provider ollama --inference-mode record + $0 --stack-config server:ci-tests --suite base --inference-mode record EOF } @@ -60,15 +61,15 @@ while [[ $# -gt 0 ]]; do STACK_CONFIG="$2" shift 2 ;; - --provider) - PROVIDER="$2" + --setup) + TEST_SETUP="$2" shift 2 ;; - --test-subdirs) + --subdirs) TEST_SUBDIRS="$2" shift 2 ;; - --test-suite) + --suite) TEST_SUITE="$2" shift 2 ;; @@ -76,7 +77,7 @@ while [[ $# -gt 0 ]]; do INFERENCE_MODE="$2" shift 2 ;; - --test-pattern) + --pattern) TEST_PATTERN="$2" shift 2 ;; @@ -96,11 +97,13 @@ done # Validate required parameters if [[ -z "$STACK_CONFIG" ]]; then echo "Error: --stack-config is required" + usage exit 1 fi -if [[ -z "$PROVIDER" ]]; then - echo "Error: --provider is required" +if [[ -z "$TEST_SETUP" && -n "$TEST_SUBDIRS" ]]; then + echo "Error: --test-setup is required when --test-subdirs is provided" + usage exit 1 fi @@ -111,7 +114,7 @@ fi echo "=== Llama Stack Integration Test Runner ===" echo "Stack Config: $STACK_CONFIG" -echo "Provider: $PROVIDER" +echo "Setup: $TEST_SETUP" echo "Inference Mode: $INFERENCE_MODE" echo "Test Suite: $TEST_SUITE" echo "Test Subdirs: $TEST_SUBDIRS" @@ -129,21 +132,25 @@ echo "" # Set environment variables export LLAMA_STACK_CLIENT_TIMEOUT=300 -export LLAMA_STACK_TEST_INFERENCE_MODE="$INFERENCE_MODE" - -# Configure provider-specific settings -if [[ "$PROVIDER" == "ollama" ]]; then - export OLLAMA_URL="http://0.0.0.0:11434" - export TEXT_MODEL="ollama/llama3.2:3b-instruct-fp16" - export SAFETY_MODEL="ollama/llama-guard3:1b" - EXTRA_PARAMS="--safety-shield=llama-guard" -else - export VLLM_URL="http://localhost:8000/v1" - export TEXT_MODEL="vllm/meta-llama/Llama-3.2-1B-Instruct" - EXTRA_PARAMS="" -fi THIS_DIR=$(dirname "$0") + +if [[ -n "$TEST_SETUP" ]]; then + EXTRA_PARAMS="--setup=$TEST_SETUP" +fi + +# Apply setup-specific environment variables (needed for server startup and tests) +echo "=== Applying Setup Environment Variables ===" + +# the server needs this +export LLAMA_STACK_TEST_INFERENCE_MODE="$INFERENCE_MODE" + +SETUP_ENV=$(PYTHONPATH=$THIS_DIR/.. python "$THIS_DIR/get_setup_env.py" --suite "$TEST_SUITE" --setup "$TEST_SETUP" --format bash) +echo "Setting up environment variables:" +echo "$SETUP_ENV" +eval "$SETUP_ENV" +echo "" + ROOT_DIR="$THIS_DIR/.." cd $ROOT_DIR @@ -162,6 +169,18 @@ fi # Start Llama Stack Server if needed if [[ "$STACK_CONFIG" == *"server:"* ]]; then + stop_server() { + echo "Stopping Llama Stack Server..." + pids=$(lsof -i :8321 | awk 'NR>1 {print $2}') + if [[ -n "$pids" ]]; then + echo "Killing Llama Stack Server processes: $pids" + kill -9 $pids + else + echo "No Llama Stack Server processes found ?!" + fi + echo "Llama Stack Server stopped" + } + # check if server is already running if curl -s http://localhost:8321/v1/health 2>/dev/null | grep -q "OK"; then echo "Llama Stack Server is already running, skipping start" @@ -185,14 +204,16 @@ if [[ "$STACK_CONFIG" == *"server:"* ]]; then done echo "" fi + + trap stop_server EXIT ERR INT TERM fi # Run tests echo "=== Running Integration Tests ===" EXCLUDE_TESTS="builtin_tool or safety_with_image or code_interpreter or test_rag" -# Additional exclusions for vllm provider -if [[ "$PROVIDER" == "vllm" ]]; then +# Additional exclusions for vllm setup +if [[ "$TEST_SETUP" == "vllm" ]]; then EXCLUDE_TESTS="${EXCLUDE_TESTS} or test_inference_store_tool_calls" fi @@ -229,20 +250,22 @@ if [[ -n "$TEST_SUBDIRS" ]]; then echo "Total test files: $(echo $TEST_FILES | wc -w)" PYTEST_TARGET="$TEST_FILES" - EXTRA_PARAMS="$EXTRA_PARAMS --text-model=$TEXT_MODEL --embedding-model=sentence-transformers/all-MiniLM-L6-v2" else PYTEST_TARGET="tests/integration/" EXTRA_PARAMS="$EXTRA_PARAMS --suite=$TEST_SUITE" fi set +e +set -x pytest -s -v $PYTEST_TARGET \ --stack-config="$STACK_CONFIG" \ + --inference-mode="$INFERENCE_MODE" \ -k "$PYTEST_PATTERN" \ $EXTRA_PARAMS \ --color=yes \ --capture=tee-sys exit_code=$? +set +x set -e if [ $exit_code -eq 0 ]; then @@ -260,18 +283,5 @@ echo "=== System Resources After Tests ===" free -h 2>/dev/null || echo "free command not available" df -h -# stop server -if [[ "$STACK_CONFIG" == *"server:"* ]]; then - echo "Stopping Llama Stack Server..." - pids=$(lsof -i :8321 | awk 'NR>1 {print $2}') - if [[ -n "$pids" ]]; then - echo "Killing Llama Stack Server processes: $pids" - kill -9 $pids - else - echo "No Llama Stack Server processes found ?!" - fi - echo "Llama Stack Server stopped" -fi - echo "" echo "=== Integration Tests Complete ===" diff --git a/tests/integration/README.md b/tests/integration/README.md index b05beeb98..467f97e02 100644 --- a/tests/integration/README.md +++ b/tests/integration/README.md @@ -6,9 +6,7 @@ Integration tests verify complete workflows across different providers using Lla ```bash # Run all integration tests with existing recordings -LLAMA_STACK_TEST_INFERENCE_MODE=replay \ - LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ - uv run --group test \ +uv run --group test \ pytest -sv tests/integration/ --stack-config=starter ``` @@ -42,25 +40,35 @@ Model parameters can be influenced by the following options: Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped if no model is specified. -### Suites (fast selection + sane defaults) +### Suites and Setups -- `--suite`: comma-separated list of named suites that both narrow which tests are collected and prefill common model options (unless you pass them explicitly). +- `--suite`: single named suite that narrows which tests are collected. - Available suites: - - `responses`: collects tests under `tests/integration/responses`; this is a separate suite because it needs a strong tool-calling model. - - `vision`: collects only `tests/integration/inference/test_vision_inference.py`; defaults `--vision-model=ollama/llama3.2-vision:11b`, `--embedding-model=sentence-transformers/all-MiniLM-L6-v2`. -- Explicit flags always win. For example, `--suite=responses --text-model=` overrides the suite’s text model. + - `base`: collects most tests (excludes responses and post_training) + - `responses`: collects tests under `tests/integration/responses` (needs strong tool-calling models) + - `vision`: collects only `tests/integration/inference/test_vision_inference.py` +- `--setup`: global configuration that can be used with any suite. Setups prefill model/env defaults; explicit CLI flags always win. + - Available setups: + - `ollama`: Local Ollama provider with lightweight models (sets OLLAMA_URL, uses llama3.2:3b-instruct-fp16) + - `vllm`: VLLM provider for efficient local inference (sets VLLM_URL, uses Llama-3.2-1B-Instruct) + - `gpt`: OpenAI GPT models for high-quality responses (uses gpt-4o) + - `claude`: Anthropic Claude models for high-quality responses (uses claude-3-5-sonnet) -Examples: +Examples ```bash -# Fast responses run with defaults -pytest -s -v tests/integration --stack-config=server:starter --suite=responses +# Fast responses run with a strong tool-calling model +pytest -s -v tests/integration --stack-config=server:starter --suite=responses --setup=gpt -# Fast single-file vision run with defaults -pytest -s -v tests/integration --stack-config=server:starter --suite=vision +# Fast single-file vision run with Ollama defaults +pytest -s -v tests/integration --stack-config=server:starter --suite=vision --setup=ollama -# Combine suites and override a default -pytest -s -v tests/integration --stack-config=server:starter --suite=responses,vision --embedding-model=text-embedding-3-small +# Base suite with VLLM for performance +pytest -s -v tests/integration --stack-config=server:starter --suite=base --setup=vllm + +# Override a default from setup +pytest -s -v tests/integration --stack-config=server:starter \ + --suite=responses --setup=gpt --embedding-model=text-embedding-3-small ``` ## Examples @@ -127,14 +135,13 @@ pytest tests/integration/ ### RECORD Mode Captures API interactions for later replay: ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=record \ -pytest tests/integration/inference/test_new_feature.py +pytest tests/integration/inference/test_new_feature.py --inference-mode=record ``` ### LIVE Mode Tests make real API calls (but not recorded): ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/ +pytest tests/integration/ --inference-mode=live ``` By default, the recording directory is `tests/integration/recordings`. You can override this by setting the `LLAMA_STACK_TEST_RECORDING_DIR` environment variable. @@ -155,15 +162,14 @@ cat recordings/responses/abc123.json | jq '.' #### Remote Re-recording (Recommended) Use the automated workflow script for easier re-recording: ```bash -./scripts/github/schedule-record-workflow.sh --test-subdirs "inference,agents" +./scripts/github/schedule-record-workflow.sh --subdirs "inference,agents" ``` See the [main testing guide](../README.md#remote-re-recording-recommended) for full details. #### Local Re-recording ```bash # Re-record specific tests -LLAMA_STACK_TEST_INFERENCE_MODE=record \ -pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py +pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py --inference-mode=record ``` Note that when re-recording tests, you must use a Stack pointing to a server (i.e., `server:starter`). This subtlety exists because the set of tests run in server are a superset of the set of tests run in the library client. diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 96260fdb7..4735264c3 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -15,7 +15,7 @@ from dotenv import load_dotenv from llama_stack.log import get_logger -from .suites import SUITE_DEFINITIONS +from .suites import SETUP_DEFINITIONS, SUITE_DEFINITIONS logger = get_logger(__name__, category="tests") @@ -63,19 +63,33 @@ def pytest_configure(config): key, value = env_var.split("=", 1) os.environ[key] = value - suites_raw = config.getoption("--suite") - suites: list[str] = [] - if suites_raw: - suites = [p.strip() for p in str(suites_raw).split(",") if p.strip()] - unknown = [p for p in suites if p not in SUITE_DEFINITIONS] - if unknown: + inference_mode = config.getoption("--inference-mode") + os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = inference_mode + + suite = config.getoption("--suite") + if suite: + if suite not in SUITE_DEFINITIONS: + raise pytest.UsageError(f"Unknown suite: {suite}. Available: {', '.join(sorted(SUITE_DEFINITIONS.keys()))}") + + # Apply setups (global parameterizations): env + defaults + setup = config.getoption("--setup") + if suite and not setup: + setup = SUITE_DEFINITIONS[suite].default_setup + + if setup: + if setup not in SETUP_DEFINITIONS: raise pytest.UsageError( - f"Unknown suite(s): {', '.join(unknown)}. Available: {', '.join(sorted(SUITE_DEFINITIONS.keys()))}" + f"Unknown setup '{setup}'. Available: {', '.join(sorted(SETUP_DEFINITIONS.keys()))}" ) - for suite in suites: - suite_def = SUITE_DEFINITIONS.get(suite, {}) - defaults: dict = suite_def.get("defaults", {}) - for dest, value in defaults.items(): + + setup_obj = SETUP_DEFINITIONS[setup] + logger.info(f"Applying setup '{setup}'{' for suite ' + suite if suite else ''}") + # Apply env first + for k, v in setup_obj.env.items(): + if k not in os.environ: + os.environ[k] = str(v) + # Apply defaults if not provided explicitly + for dest, value in setup_obj.defaults.items(): current = getattr(config.option, dest, None) if not current: setattr(config.option, dest, value) @@ -120,6 +134,13 @@ def pytest_addoption(parser): default=384, help="Output dimensionality of the embedding model to use for testing. Default: 384", ) + + parser.addoption( + "--inference-mode", + help="Inference mode: { record, replay, live } (default: replay)", + choices=["record", "replay", "live"], + default="replay", + ) parser.addoption( "--report", help="Path where the test report should be written, e.g. --report=/path/to/report.md", @@ -127,14 +148,18 @@ def pytest_addoption(parser): available_suites = ", ".join(sorted(SUITE_DEFINITIONS.keys())) suite_help = ( - "Comma-separated integration test suites to narrow collection and prefill defaults. " - "Available: " - f"{available_suites}. " - "Explicit CLI flags (e.g., --text-model) override suite defaults. " - "Examples: --suite=responses or --suite=responses,vision." + f"Single test suite to run (narrows collection). Available: {available_suites}. Example: --suite=responses" ) parser.addoption("--suite", help=suite_help) + # Global setups for any suite + available_setups = ", ".join(sorted(SETUP_DEFINITIONS.keys())) + setup_help = ( + f"Global test setup configuration. Available: {available_setups}. " + "Can be used with any suite. Example: --setup=ollama" + ) + parser.addoption("--setup", help=setup_help) + MODEL_SHORT_IDS = { "meta-llama/Llama-3.2-3B-Instruct": "3B", @@ -221,16 +246,12 @@ pytest_plugins = ["tests.integration.fixtures.common"] def pytest_ignore_collect(path: str, config: pytest.Config) -> bool: """Skip collecting paths outside the selected suite roots for speed.""" - suites_raw = config.getoption("--suite") - if not suites_raw: + suite = config.getoption("--suite") + if not suite: return False - names = [p.strip() for p in str(suites_raw).split(",") if p.strip()] - roots: list[str] = [] - for name in names: - suite_def = SUITE_DEFINITIONS.get(name) - if suite_def: - roots.extend(suite_def.get("roots", [])) + sobj = SUITE_DEFINITIONS.get(suite) + roots: list[str] = sobj.get("roots", []) if isinstance(sobj, dict) else getattr(sobj, "roots", []) if not roots: return False diff --git a/tests/integration/suites.py b/tests/integration/suites.py index 602855055..bacd7ef52 100644 --- a/tests/integration/suites.py +++ b/tests/integration/suites.py @@ -8,46 +8,112 @@ # For example: # # ```bash -# pytest tests/integration/ --suite=vision +# pytest tests/integration/ --suite=vision --setup=ollama # ``` # -# Each suite can: -# - restrict collection to specific roots (dirs or files) -# - provide default CLI option values (e.g. text_model, embedding_model, etc.) +""" +Each suite defines what to run (roots). Suites can be run with different global setups defined in setups.py. +Setups provide environment variables and model defaults that can be reused across multiple suites. + +CLI examples: + pytest tests/integration --suite=responses --setup=gpt + pytest tests/integration --suite=vision --setup=ollama + pytest tests/integration --suite=base --setup=vllm +""" from pathlib import Path +from pydantic import BaseModel, Field + this_dir = Path(__file__).parent -default_roots = [ + + +class Suite(BaseModel): + name: str + roots: list[str] + default_setup: str | None = None + + +class Setup(BaseModel): + """A reusable test configuration with environment and CLI defaults.""" + + name: str + description: str + defaults: dict[str, str] = Field(default_factory=dict) + env: dict[str, str] = Field(default_factory=dict) + + +# Global setups - can be used with any suite "technically" but in reality, some setups might work +# only for specific test suites. +SETUP_DEFINITIONS: dict[str, Setup] = { + "ollama": Setup( + name="ollama", + description="Local Ollama provider with text + safety models", + env={ + "OLLAMA_URL": "http://0.0.0.0:11434", + "SAFETY_MODEL": "ollama/llama-guard3:1b", + }, + defaults={ + "text_model": "ollama/llama3.2:3b-instruct-fp16", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + "safety_model": "ollama/llama-guard3:1b", + "safety_shield": "llama-guard", + }, + ), + "ollama-vision": Setup( + name="ollama", + description="Local Ollama provider with a vision model", + env={ + "OLLAMA_URL": "http://0.0.0.0:11434", + }, + defaults={ + "vision_model": "ollama/llama3.2-vision:11b", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + ), + "vllm": Setup( + name="vllm", + description="vLLM provider with a text model", + env={ + "VLLM_URL": "http://localhost:8000/v1", + }, + defaults={ + "text_model": "vllm/meta-llama/Llama-3.2-1B-Instruct", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + ), + "gpt": Setup( + name="gpt", + description="OpenAI GPT models for high-quality responses and tool calling", + defaults={ + "text_model": "openai/gpt-4o", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + ), +} + + +base_roots = [ str(p) for p in this_dir.glob("*") if p.is_dir() and p.name not in ("__pycache__", "fixtures", "test_cases", "recordings", "responses", "post_training") ] -SUITE_DEFINITIONS: dict[str, dict] = { - "base": { - "description": "Base suite that includes most tests but runs them with a text Ollama model", - "roots": default_roots, - "defaults": { - "text_model": "ollama/llama3.2:3b-instruct-fp16", - "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", - }, - }, - "responses": { - "description": "Suite that includes only the OpenAI Responses tests; needs a strong tool-calling model", - "roots": ["tests/integration/responses"], - "defaults": { - "text_model": "openai/gpt-4o", - "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", - }, - }, - "vision": { - "description": "Suite that includes only the vision tests", - "roots": ["tests/integration/inference/test_vision_inference.py"], - "defaults": { - "vision_model": "ollama/llama3.2-vision:11b", - "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", - }, - }, +SUITE_DEFINITIONS: dict[str, Suite] = { + "base": Suite( + name="base", + roots=base_roots, + default_setup="ollama", + ), + "responses": Suite( + name="responses", + roots=["tests/integration/responses"], + default_setup="gpt", + ), + "vision": Suite( + name="vision", + roots=["tests/integration/inference/test_vision_inference.py"], + default_setup="ollama-vision", + ), } From 9d3a234bf3772083e148d8168a204b9cb2c200ac Mon Sep 17 00:00:00 2001 From: ehhuang Date: Tue, 9 Sep 2025 15:51:20 -0700 Subject: [PATCH 077/124] chore: remove unused variable (#3389) # What does this PR do? ## Test Plan --- llama_stack/core/library_client.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/llama_stack/core/library_client.py b/llama_stack/core/library_client.py index 9e7a8006c..ea5a2ac8e 100644 --- a/llama_stack/core/library_client.py +++ b/llama_stack/core/library_client.py @@ -10,7 +10,6 @@ import json import logging # allow-direct-logging import os import sys -from concurrent.futures import ThreadPoolExecutor from enum import Enum from io import BytesIO from pathlib import Path @@ -148,7 +147,6 @@ class LlamaStackAsLibraryClient(LlamaStackClient): self.async_client = AsyncLlamaStackAsLibraryClient( config_path_or_distro_name, custom_provider_registry, provider_data, skip_logger_removal ) - self.pool_executor = ThreadPoolExecutor(max_workers=4) self.provider_data = provider_data self.loop = asyncio.new_event_loop() From dd1f946b3ee4232dc8e13d3836e7f19e65f5e112 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Tue, 9 Sep 2025 18:54:58 -0400 Subject: [PATCH 078/124] feat: include a default inference store during llama stack build (#3373) # What does this PR do? enables completions storage when using `llama stack build --providers` - - GET /v1/chat/completions - GET /v1/chat/completions/{id} todo: llama stack build and distro codegen should use the same code paths ## Test Plan ci --- llama_stack/cli/stack/_build.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/llama_stack/cli/stack/_build.py b/llama_stack/cli/stack/_build.py index c6e204773..b14e6fe55 100644 --- a/llama_stack/cli/stack/_build.py +++ b/llama_stack/cli/stack/_build.py @@ -45,6 +45,7 @@ from llama_stack.core.utils.dynamic import instantiate_class_type from llama_stack.core.utils.exec import formulate_run_args, run_command from llama_stack.core.utils.image_types import LlamaStackImageType from llama_stack.providers.datatypes import Api +from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig DISTRIBS_PATH = Path(__file__).parent.parent.parent / "distributions" @@ -294,6 +295,12 @@ def _generate_run_config( if build_config.external_providers_dir else EXTERNAL_PROVIDERS_DIR, ) + if not run_config.inference_store: + run_config.inference_store = SqliteSqlStoreConfig( + **SqliteSqlStoreConfig.sample_run_config( + __distro_dir__=(DISTRIBS_BASE_DIR / image_name).as_posix(), db_name="inference_store.db" + ) + ) # build providers dict provider_registry = get_provider_registry(build_config) for api in apis: From 81ad240faa48d2a2d91e5fbfc3dda21443432a6f Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Tue, 9 Sep 2025 23:00:50 -0700 Subject: [PATCH 079/124] fix(k8s): unwedge run.yaml to add files --- .../k8s-benchmark/stack-configmap.yaml | 19 +- .../k8s-benchmark/stack_run_config.yaml | 9 + .../distributions/k8s/stack-configmap.yaml | 182 +++++------------- .../distributions/k8s/stack_run_config.yaml | 9 + 4 files changed, 77 insertions(+), 142 deletions(-) diff --git a/docs/source/distributions/k8s-benchmark/stack-configmap.yaml b/docs/source/distributions/k8s-benchmark/stack-configmap.yaml index edf4ebd75..bf6109b68 100644 --- a/docs/source/distributions/k8s-benchmark/stack-configmap.yaml +++ b/docs/source/distributions/k8s-benchmark/stack-configmap.yaml @@ -6,6 +6,7 @@ data: apis: - agents - inference + - files - safety - telemetry - tool_runtime @@ -19,13 +20,6 @@ data: max_tokens: ${env.VLLM_MAX_TOKENS:=4096} api_token: ${env.VLLM_API_TOKEN:=fake} tls_verify: ${env.VLLM_TLS_VERIFY:=true} - - provider_id: vllm-safety - provider_type: remote::vllm - config: - url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1} - max_tokens: ${env.VLLM_MAX_TOKENS:=4096} - api_token: ${env.VLLM_API_TOKEN:=fake} - tls_verify: ${env.VLLM_TLS_VERIFY:=true} - provider_id: sentence-transformers provider_type: inline::sentence-transformers config: {} @@ -41,6 +35,14 @@ data: db: ${env.POSTGRES_DB:=llamastack} user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard @@ -111,9 +113,6 @@ data: - model_id: ${env.INFERENCE_MODEL} provider_id: vllm-inference model_type: llm - - model_id: ${env.SAFETY_MODEL} - provider_id: vllm-safety - model_type: llm shields: - shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} vector_dbs: [] diff --git a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml b/docs/source/distributions/k8s-benchmark/stack_run_config.yaml index 5a810639e..f8ff7811b 100644 --- a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml +++ b/docs/source/distributions/k8s-benchmark/stack_run_config.yaml @@ -3,6 +3,7 @@ image_name: kubernetes-benchmark-demo apis: - agents - inference +- files - safety - telemetry - tool_runtime @@ -31,6 +32,14 @@ providers: db: ${env.POSTGRES_DB:=llamastack} user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/docs/source/distributions/k8s/stack-configmap.yaml b/docs/source/distributions/k8s/stack-configmap.yaml index 4f95554e3..3dbb0da97 100644 --- a/docs/source/distributions/k8s/stack-configmap.yaml +++ b/docs/source/distributions/k8s/stack-configmap.yaml @@ -1,137 +1,55 @@ apiVersion: v1 data: - stack_run_config.yaml: | - version: '2' - image_name: kubernetes-demo - apis: - - agents - - inference - - safety - - telemetry - - tool_runtime - - vector_io - providers: - inference: - - provider_id: vllm-inference - provider_type: remote::vllm - config: - url: ${env.VLLM_URL:=http://localhost:8000/v1} - max_tokens: ${env.VLLM_MAX_TOKENS:=4096} - api_token: ${env.VLLM_API_TOKEN:=fake} - tls_verify: ${env.VLLM_TLS_VERIFY:=true} - - provider_id: vllm-safety - provider_type: remote::vllm - config: - url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1} - max_tokens: ${env.VLLM_MAX_TOKENS:=4096} - api_token: ${env.VLLM_API_TOKEN:=fake} - tls_verify: ${env.VLLM_TLS_VERIFY:=true} - - provider_id: sentence-transformers - provider_type: inline::sentence-transformers - config: {} - vector_io: - - provider_id: ${env.ENABLE_CHROMADB:+chromadb} - provider_type: remote::chromadb - config: - url: ${env.CHROMADB_URL:=} - kvstore: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - safety: - - provider_id: llama-guard - provider_type: inline::llama-guard - config: - excluded_categories: [] - agents: - - provider_id: meta-reference - provider_type: inline::meta-reference - config: - persistence_store: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - responses_store: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - telemetry: - - provider_id: meta-reference - provider_type: inline::meta-reference - config: - service_name: "${env.OTEL_SERVICE_NAME:=\u200B}" - sinks: ${env.TELEMETRY_SINKS:=console} - tool_runtime: - - provider_id: brave-search - provider_type: remote::brave-search - config: - api_key: ${env.BRAVE_SEARCH_API_KEY:+} - max_results: 3 - - provider_id: tavily-search - provider_type: remote::tavily-search - config: - api_key: ${env.TAVILY_SEARCH_API_KEY:+} - max_results: 3 - - provider_id: rag-runtime - provider_type: inline::rag-runtime - config: {} - - provider_id: model-context-protocol - provider_type: remote::model-context-protocol - config: {} - metadata_store: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - table_name: llamastack_kvstore - inference_store: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - models: - - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 - provider_id: sentence-transformers - model_type: embedding - - metadata: {} - model_id: ${env.INFERENCE_MODEL} - provider_id: vllm-inference - model_type: llm - - metadata: {} - model_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} - provider_id: vllm-safety - model_type: llm - shields: - - shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} - vector_dbs: [] - datasets: [] - scoring_fns: [] - benchmarks: [] - tool_groups: - - toolgroup_id: builtin::websearch - provider_id: tavily-search - - toolgroup_id: builtin::rag - provider_id: rag-runtime - server: - port: 8321 - auth: - provider_config: - type: github_token + stack_run_config.yaml: "version: '2'\nimage_name: kubernetes-demo\napis:\n- agents\n- + inference\n- files\n- safety\n- telemetry\n- tool_runtime\n- vector_io\nproviders:\n + \ inference:\n - provider_id: vllm-inference\n provider_type: remote::vllm\n + \ config:\n url: ${env.VLLM_URL:=http://localhost:8000/v1}\n max_tokens: + ${env.VLLM_MAX_TOKENS:=4096}\n api_token: ${env.VLLM_API_TOKEN:=fake}\n tls_verify: + ${env.VLLM_TLS_VERIFY:=true}\n - provider_id: vllm-safety\n provider_type: + remote::vllm\n config:\n url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1}\n + \ max_tokens: ${env.VLLM_MAX_TOKENS:=4096}\n api_token: ${env.VLLM_API_TOKEN:=fake}\n + \ tls_verify: ${env.VLLM_TLS_VERIFY:=true}\n - provider_id: sentence-transformers\n + \ provider_type: inline::sentence-transformers\n config: {}\n vector_io:\n + \ - provider_id: ${env.ENABLE_CHROMADB:+chromadb}\n provider_type: remote::chromadb\n + \ config:\n url: ${env.CHROMADB_URL:=}\n kvstore:\n type: postgres\n + \ host: ${env.POSTGRES_HOST:=localhost}\n port: ${env.POSTGRES_PORT:=5432}\n + \ db: ${env.POSTGRES_DB:=llamastack}\n user: ${env.POSTGRES_USER:=llamastack}\n + \ password: ${env.POSTGRES_PASSWORD:=llamastack}\n files:\n - provider_id: + meta-reference-files\n provider_type: inline::localfs\n config:\n storage_dir: + ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}\n metadata_store:\n + \ type: sqlite\n db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + \ \n safety:\n - provider_id: llama-guard\n provider_type: inline::llama-guard\n + \ config:\n excluded_categories: []\n agents:\n - provider_id: meta-reference\n + \ provider_type: inline::meta-reference\n config:\n persistence_store:\n + \ type: postgres\n host: ${env.POSTGRES_HOST:=localhost}\n port: + ${env.POSTGRES_PORT:=5432}\n db: ${env.POSTGRES_DB:=llamastack}\n user: + ${env.POSTGRES_USER:=llamastack}\n password: ${env.POSTGRES_PASSWORD:=llamastack}\n + \ responses_store:\n type: postgres\n host: ${env.POSTGRES_HOST:=localhost}\n + \ port: ${env.POSTGRES_PORT:=5432}\n db: ${env.POSTGRES_DB:=llamastack}\n + \ user: ${env.POSTGRES_USER:=llamastack}\n password: ${env.POSTGRES_PASSWORD:=llamastack}\n + \ telemetry:\n - provider_id: meta-reference\n provider_type: inline::meta-reference\n + \ config:\n service_name: \"${env.OTEL_SERVICE_NAME:=\\u200B}\"\n sinks: + ${env.TELEMETRY_SINKS:=console}\n tool_runtime:\n - provider_id: brave-search\n + \ provider_type: remote::brave-search\n config:\n api_key: ${env.BRAVE_SEARCH_API_KEY:+}\n + \ max_results: 3\n - provider_id: tavily-search\n provider_type: remote::tavily-search\n + \ config:\n api_key: ${env.TAVILY_SEARCH_API_KEY:+}\n max_results: + 3\n - provider_id: rag-runtime\n provider_type: inline::rag-runtime\n config: + {}\n - provider_id: model-context-protocol\n provider_type: remote::model-context-protocol\n + \ config: {}\nmetadata_store:\n type: postgres\n host: ${env.POSTGRES_HOST:=localhost}\n + \ port: ${env.POSTGRES_PORT:=5432}\n db: ${env.POSTGRES_DB:=llamastack}\n user: + ${env.POSTGRES_USER:=llamastack}\n password: ${env.POSTGRES_PASSWORD:=llamastack}\n + \ table_name: llamastack_kvstore\ninference_store:\n type: postgres\n host: + ${env.POSTGRES_HOST:=localhost}\n port: ${env.POSTGRES_PORT:=5432}\n db: ${env.POSTGRES_DB:=llamastack}\n + \ user: ${env.POSTGRES_USER:=llamastack}\n password: ${env.POSTGRES_PASSWORD:=llamastack}\nmodels:\n- + metadata:\n embedding_dimension: 384\n model_id: all-MiniLM-L6-v2\n provider_id: + sentence-transformers\n model_type: embedding\n- metadata: {}\n model_id: ${env.INFERENCE_MODEL}\n + \ provider_id: vllm-inference\n model_type: llm\n- metadata: {}\n model_id: + ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}\n provider_id: vllm-safety\n + \ model_type: llm\nshields:\n- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}\nvector_dbs: + []\ndatasets: []\nscoring_fns: []\nbenchmarks: []\ntool_groups:\n- toolgroup_id: + builtin::websearch\n provider_id: tavily-search\n- toolgroup_id: builtin::rag\n + \ provider_id: rag-runtime\nserver:\n port: 8321\n auth:\n provider_config:\n + \ type: github_token\n" kind: ConfigMap metadata: creationTimestamp: null diff --git a/docs/source/distributions/k8s/stack_run_config.yaml b/docs/source/distributions/k8s/stack_run_config.yaml index a2d65e1a9..b841ab977 100644 --- a/docs/source/distributions/k8s/stack_run_config.yaml +++ b/docs/source/distributions/k8s/stack_run_config.yaml @@ -3,6 +3,7 @@ image_name: kubernetes-demo apis: - agents - inference +- files - safety - telemetry - tool_runtime @@ -38,6 +39,14 @@ providers: db: ${env.POSTGRES_DB:=llamastack} user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard From 1c23aeb9372fa3e1286a7d6d8210994000efae6d Mon Sep 17 00:00:00 2001 From: Cesare Pompeiano Date: Wed, 10 Sep 2025 11:19:21 +0200 Subject: [PATCH 080/124] feat: Add vector_db_id to chunk metadata (#3304) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? When running RAG in a multi vector DB setting, it can be difficult to trace where retrieved chunks originate from. This PR adds the `vector_db_id` into each chunk’s metadata, making it easier to understand which database a given chunk came from. This is helpful for debugging and for analyzing retrieval behavior of multiple DBs. Relevant code: ```python for vector_db_id, result in zip(vector_db_ids, results): for chunk, score in zip(result.chunks, result.scores): if not hasattr(chunk, "metadata") or chunk.metadata is None: chunk.metadata = {} chunk.metadata["vector_db_id"] = vector_db_id chunks.append(chunk) scores.append(score) ``` ## Test Plan * Ran Llama Stack in debug mode. * Verified that `vector_db_id` was added to each chunk’s metadata. * Confirmed that the metadata was printed in the console when using the RAG tool. --------- Co-authored-by: are-ces Co-authored-by: Francisco Arceo --- .../inline/tool_runtime/rag/memory.py | 16 +++++- tests/unit/rag/test_rag_query.py | 55 +++++++++++++++++++ 2 files changed, 69 insertions(+), 2 deletions(-) diff --git a/llama_stack/providers/inline/tool_runtime/rag/memory.py b/llama_stack/providers/inline/tool_runtime/rag/memory.py index cb526e8ee..aa629cca8 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/memory.py +++ b/llama_stack/providers/inline/tool_runtime/rag/memory.py @@ -167,8 +167,18 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti for vector_db_id in vector_db_ids ] results: list[QueryChunksResponse] = await asyncio.gather(*tasks) - chunks = [c for r in results for c in r.chunks] - scores = [s for r in results for s in r.scores] + + chunks = [] + scores = [] + + for vector_db_id, result in zip(vector_db_ids, results, strict=False): + for chunk, score in zip(result.chunks, result.scores, strict=False): + if not hasattr(chunk, "metadata") or chunk.metadata is None: + chunk.metadata = {} + chunk.metadata["vector_db_id"] = vector_db_id + + chunks.append(chunk) + scores.append(score) if not chunks: return RAGQueryResult(content=None) @@ -203,6 +213,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti metadata_keys_to_exclude_from_context = [ "token_count", "metadata_token_count", + "vector_db_id", ] metadata_for_context = {} for k in chunk_metadata_keys_to_include_from_context: @@ -227,6 +238,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti "document_ids": [c.metadata["document_id"] for c in chunks[: len(picked)]], "chunks": [c.content for c in chunks[: len(picked)]], "scores": scores[: len(picked)], + "vector_db_ids": [c.metadata["vector_db_id"] for c in chunks[: len(picked)]], }, ) diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index d18d90716..7b897bfe0 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -81,3 +81,58 @@ class TestRagQuery: # Test that invalid mode raises an error with pytest.raises(ValueError): RAGQueryConfig(mode="wrong_mode") + + @pytest.mark.asyncio + async def test_query_adds_vector_db_id_to_chunk_metadata(self): + rag_tool = MemoryToolRuntimeImpl( + config=MagicMock(), + vector_io_api=MagicMock(), + inference_api=MagicMock(), + ) + + vector_db_ids = ["db1", "db2"] + + # Fake chunks from each DB + chunk_metadata1 = ChunkMetadata( + document_id="doc1", + chunk_id="chunk1", + source="test_source1", + metadata_token_count=5, + ) + chunk1 = Chunk( + content="chunk from db1", + metadata={"vector_db_id": "db1", "document_id": "doc1"}, + stored_chunk_id="c1", + chunk_metadata=chunk_metadata1, + ) + + chunk_metadata2 = ChunkMetadata( + document_id="doc2", + chunk_id="chunk2", + source="test_source2", + metadata_token_count=5, + ) + chunk2 = Chunk( + content="chunk from db2", + metadata={"vector_db_id": "db2", "document_id": "doc2"}, + stored_chunk_id="c2", + chunk_metadata=chunk_metadata2, + ) + + rag_tool.vector_io_api.query_chunks = AsyncMock( + side_effect=[ + QueryChunksResponse(chunks=[chunk1], scores=[0.9]), + QueryChunksResponse(chunks=[chunk2], scores=[0.8]), + ] + ) + + result = await rag_tool.query(content="test", vector_db_ids=vector_db_ids) + returned_chunks = result.metadata["chunks"] + returned_scores = result.metadata["scores"] + returned_doc_ids = result.metadata["document_ids"] + returned_vector_db_ids = result.metadata["vector_db_ids"] + + assert returned_chunks == ["chunk from db1", "chunk from db2"] + assert returned_scores == (0.9, 0.8) + assert returned_doc_ids == ["doc1", "doc2"] + assert returned_vector_db_ids == ["db1", "db2"] From 167143131053c8de6ea620a83ebdec41c0b24e50 Mon Sep 17 00:00:00 2001 From: Akram Ben Aissi Date: Wed, 10 Sep 2025 12:55:57 +0200 Subject: [PATCH 081/124] fix: Add missing files_api parameter to MemoryToolRuntimeImpl test (#3394) # What does this PR do? The test_query_adds_vector_db_id_to_chunk_metadata test was failing because MemoryToolRuntimeImpl.__init__() now requires a files_api parameter. Fixes failing unit tests for Python 3.12 and 3.13. ## Test Plan --- tests/unit/rag/test_rag_query.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index 7b897bfe0..183b4d049 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -88,6 +88,7 @@ class TestRagQuery: config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), + files_api=MagicMock(), ) vector_db_ids = ["db1", "db2"] From c836fa29e3b9a587734764cc025551bce68fc349 Mon Sep 17 00:00:00 2001 From: Akram Ben Aissi Date: Wed, 10 Sep 2025 15:27:35 +0200 Subject: [PATCH 082/124] fix: pre-commit issues: non executable shebang file and removal of @pytest.mark.asyncio decorator (#3397) # What does this PR do? Fix pre-commit issues: non executable shebang file, @pytest.mark.asyncio decorator ## Test Plan --- scripts/get_setup_env.py | 0 tests/unit/rag/test_rag_query.py | 1 - 2 files changed, 1 deletion(-) mode change 100644 => 100755 scripts/get_setup_env.py diff --git a/scripts/get_setup_env.py b/scripts/get_setup_env.py old mode 100644 new mode 100755 diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index 183b4d049..a45b66f02 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -82,7 +82,6 @@ class TestRagQuery: with pytest.raises(ValueError): RAGQueryConfig(mode="wrong_mode") - @pytest.mark.asyncio async def test_query_adds_vector_db_id_to_chunk_metadata(self): rag_tool = MemoryToolRuntimeImpl( config=MagicMock(), From 0e27016cf23eca51a0f025897b44109b1b609b71 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Wed, 10 Sep 2025 09:39:29 -0400 Subject: [PATCH 083/124] chore: update the vertexai inference impl to use openai-python for openai-compat functions (#3377) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? update VertexAI inference provider to use openai-python for openai-compat functions ## Test Plan ``` $ VERTEX_AI_PROJECT=... uv run llama stack build --image-type venv --providers inference=remote::vertexai --run ... $ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model vertexai/vertex_ai/gemini-2.5-flash tests/integration/inference/test_openai_completion.py ... ``` i don't have an account to test this. `get_api_key` may also need to be updated per https://cloud.google.com/vertex-ai/generative-ai/docs/start/openai --------- Signed-off-by: Sébastien Han Co-authored-by: Sébastien Han --- llama_stack/providers/registry/inference.py | 2 +- .../remote/inference/vertexai/vertexai.py | 33 ++++++++++++++++--- .../inference/test_openai_completion.py | 3 ++ 3 files changed, 33 insertions(+), 5 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 4176f85a6..541fbb432 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -218,7 +218,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="vertexai", - pip_packages=["litellm", "google-cloud-aiplatform"], + pip_packages=["litellm", "google-cloud-aiplatform", "openai"], module="llama_stack.providers.remote.inference.vertexai", config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig", provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/vertexai/vertexai.py b/llama_stack/providers/remote/inference/vertexai/vertexai.py index 8807fd0e6..27f953ab9 100644 --- a/llama_stack/providers/remote/inference/vertexai/vertexai.py +++ b/llama_stack/providers/remote/inference/vertexai/vertexai.py @@ -6,16 +6,20 @@ from typing import Any +import google.auth.transport.requests +from google.auth import default + from llama_stack.apis.inference import ChatCompletionRequest from llama_stack.providers.utils.inference.litellm_openai_mixin import ( LiteLLMOpenAIMixin, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import VertexAIConfig from .models import MODEL_ENTRIES -class VertexAIInferenceAdapter(LiteLLMOpenAIMixin): +class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): def __init__(self, config: VertexAIConfig) -> None: LiteLLMOpenAIMixin.__init__( self, @@ -27,9 +31,30 @@ class VertexAIInferenceAdapter(LiteLLMOpenAIMixin): self.config = config def get_api_key(self) -> str: - # Vertex AI doesn't use API keys, it uses Application Default Credentials - # Return empty string to let litellm handle authentication via ADC - return "" + """ + Get an access token for Vertex AI using Application Default Credentials. + + Vertex AI uses ADC instead of API keys. This method obtains an access token + from the default credentials and returns it for use with the OpenAI-compatible client. + """ + try: + # Get default credentials - will read from GOOGLE_APPLICATION_CREDENTIALS + credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) + credentials.refresh(google.auth.transport.requests.Request()) + return credentials.token + except Exception: + # If we can't get credentials, return empty string to let LiteLLM handle it + # This allows the LiteLLM mixin to work with ADC directly + return "" + + def get_base_url(self) -> str: + """ + Get the Vertex AI OpenAI-compatible API base URL. + + Returns the Vertex AI OpenAI-compatible endpoint URL. + Source: https://cloud.google.com/vertex-ai/generative-ai/docs/start/openai + """ + return f"https://{self.config.location}-aiplatform.googleapis.com/v1/projects/{self.config.project}/locations/{self.config.location}/endpoints/openapi" async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]: # Get base parameters from parent diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index df1184f1c..f9c837ebd 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -76,6 +76,9 @@ def skip_if_doesnt_support_n(client_with_models, model_id): "remote::gemini", # https://docs.anthropic.com/en/api/openai-sdk#simple-fields "remote::anthropic", + "remote::vertexai", + # Error code: 400 - [{'error': {'code': 400, 'message': 'Unable to submit request because candidateCount must be 1 but + # the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'} ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") From c86e45496e32164286eb8920d2e979a45be31ea3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Han?= Date: Wed, 10 Sep 2025 16:00:46 +0200 Subject: [PATCH 084/124] ci: Re-enable pre-commit to fail (#3399) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit If pre-commit fails, the workflow must fail. --------- Signed-off-by: Sébastien Han --- .github/workflows/pre-commit.yml | 1 - llama_stack/providers/remote/inference/vertexai/vertexai.py | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 792162262..000208043 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -48,7 +48,6 @@ jobs: working-directory: llama_stack/ui - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 - continue-on-error: true env: SKIP: no-commit-to-branch RUFF_OUTPUT_FORMAT: github diff --git a/llama_stack/providers/remote/inference/vertexai/vertexai.py b/llama_stack/providers/remote/inference/vertexai/vertexai.py index 27f953ab9..8996543e7 100644 --- a/llama_stack/providers/remote/inference/vertexai/vertexai.py +++ b/llama_stack/providers/remote/inference/vertexai/vertexai.py @@ -41,7 +41,7 @@ class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): # Get default credentials - will read from GOOGLE_APPLICATION_CREDENTIALS credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) credentials.refresh(google.auth.transport.requests.Request()) - return credentials.token + return str(credentials.token) except Exception: # If we can't get credentials, return empty string to let LiteLLM handle it # This allows the LiteLLM mixin to work with ADC directly From 935b8e28de29400a4b42d8b54169341c5244fec7 Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Wed, 10 Sep 2025 08:48:01 -0700 Subject: [PATCH 085/124] fix: Fireworks chat completion broken due to telemetry (#3392) # What does this PR do? Fix fireworks chat completion broken due to telemetry expecting response.usage Closes https://github.com/llamastack/llama-stack/issues/3391 ## Test Plan 1. `uv run --with llama-stack llama stack build --distro starter --image-type venv --run` Try ``` curl -X POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct", "messages": [{"role": "user", "content": "Hello!"}] }' ``` ``` {"id":"chatcmpl-ee922a08-0df0-4974-b0d3-b322113e8bc0","choices":[{"message":{"role":"assistant","content":"Hello! How can I assist you today?","name":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"object":"chat.completion","created":1757456375,"model":"fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct"}% ``` Without fix fails as mentioned in https://github.com/llamastack/llama-stack/issues/3391 Co-authored-by: Francisco Arceo --- llama_stack/core/routers/inference.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 045093fe0..23972deb5 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -423,7 +423,7 @@ class InferenceRouter(Inference): # response_stream = await provider.openai_completion(**params) response = await provider.openai_completion(**params) - if self.telemetry: + if self.telemetry and getattr(response, "usage", None): metrics = self._construct_metrics( prompt_tokens=response.usage.prompt_tokens, completion_tokens=response.usage.completion_tokens, @@ -529,7 +529,7 @@ class InferenceRouter(Inference): if self.store: asyncio.create_task(self.store.store_chat_completion(response, messages)) - if self.telemetry: + if self.telemetry and getattr(response, "usage", None): metrics = self._construct_metrics( prompt_tokens=response.usage.prompt_tokens, completion_tokens=response.usage.completion_tokens, From f6bf36343df7c69c9f26ae5163cbfb6491ca7247 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 10 Sep 2025 11:52:23 -0700 Subject: [PATCH 086/124] chore: logging perf improvments (#3393) # What does this PR do? - Use BackgroundLogger when logging metric events. - Reuse event loop in BackgroundLogger ## Test Plan ``` cd /docs/source/distributions/k8s-benchmark # start mock server python openai-mock-server.py --port 8000 # start stack server LLAMA_STACK_LOGGING="all=WARNING" uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml # run benchmark script uv run python3 benchmark.py --duration 120 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct ``` ### RPS from 57 -> 62 --- llama_stack/core/routers/inference.py | 14 ++++---- .../providers/utils/telemetry/tracing.py | 34 +++++++++++++------ 2 files changed, 31 insertions(+), 17 deletions(-) diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 23972deb5..9593dd5b9 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -63,7 +63,7 @@ from llama_stack.models.llama.llama3.chat_format import ChatFormat from llama_stack.models.llama.llama3.tokenizer import Tokenizer from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable from llama_stack.providers.utils.inference.inference_store import InferenceStore -from llama_stack.providers.utils.telemetry.tracing import get_current_span +from llama_stack.providers.utils.telemetry.tracing import enqueue_event, get_current_span logger = get_logger(name=__name__, category="core::routers") @@ -160,7 +160,7 @@ class InferenceRouter(Inference): metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model) if self.telemetry: for metric in metrics: - await self.telemetry.log_event(metric) + enqueue_event(metric) return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics] async def _count_tokens( @@ -431,7 +431,7 @@ class InferenceRouter(Inference): model=model_obj, ) for metric in metrics: - await self.telemetry.log_event(metric) + enqueue_event(metric) # these metrics will show up in the client response. response.metrics = ( @@ -537,7 +537,7 @@ class InferenceRouter(Inference): model=model_obj, ) for metric in metrics: - await self.telemetry.log_event(metric) + enqueue_event(metric) # these metrics will show up in the client response. response.metrics = ( metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics @@ -664,7 +664,7 @@ class InferenceRouter(Inference): "completion_tokens", "total_tokens", ]: # Only log completion and total tokens - await self.telemetry.log_event(metric) + enqueue_event(metric) # Return metrics in response async_metrics = [ @@ -710,7 +710,7 @@ class InferenceRouter(Inference): ) for metric in completion_metrics: if metric.metric in ["completion_tokens", "total_tokens"]: # Only log completion and total tokens - await self.telemetry.log_event(metric) + enqueue_event(metric) # Return metrics in response return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics] @@ -806,7 +806,7 @@ class InferenceRouter(Inference): model=model, ) for metric in metrics: - await self.telemetry.log_event(metric) + enqueue_event(metric) yield chunk finally: diff --git a/llama_stack/providers/utils/telemetry/tracing.py b/llama_stack/providers/utils/telemetry/tracing.py index 7694003b5..9969b1055 100644 --- a/llama_stack/providers/utils/telemetry/tracing.py +++ b/llama_stack/providers/utils/telemetry/tracing.py @@ -18,6 +18,7 @@ from functools import wraps from typing import Any from llama_stack.apis.telemetry import ( + Event, LogSeverity, Span, SpanEndPayload, @@ -98,7 +99,7 @@ class BackgroundLogger: def __init__(self, api: Telemetry, capacity: int = 100000): self.api = api self.log_queue: queue.Queue[Any] = queue.Queue(maxsize=capacity) - self.worker_thread = threading.Thread(target=self._process_logs, daemon=True) + self.worker_thread = threading.Thread(target=self._worker, daemon=True) self.worker_thread.start() self._last_queue_full_log_time: float = 0.0 self._dropped_since_last_notice: int = 0 @@ -118,12 +119,16 @@ class BackgroundLogger: self._last_queue_full_log_time = current_time self._dropped_since_last_notice = 0 - def _process_logs(self): + def _worker(self): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + loop.run_until_complete(self._process_logs()) + + async def _process_logs(self): while True: try: event = self.log_queue.get() - # figure out how to use a thread's native loop - asyncio.run(self.api.log_event(event)) + await self.api.log_event(event) except Exception: import traceback @@ -136,6 +141,19 @@ class BackgroundLogger: self.log_queue.join() +def enqueue_event(event: Event) -> None: + """Enqueue a telemetry event to the background logger if available. + + This provides a non-blocking path for routers and other hot paths to + submit telemetry without awaiting the Telemetry API, reducing contention + with the main event loop. + """ + global BACKGROUND_LOGGER + if BACKGROUND_LOGGER is None: + raise RuntimeError("Telemetry API not initialized") + BACKGROUND_LOGGER.log_event(event) + + class TraceContext: spans: list[Span] = [] @@ -256,11 +274,7 @@ class TelemetryHandler(logging.Handler): if record.module in ("asyncio", "selector_events"): return - global CURRENT_TRACE_CONTEXT, BACKGROUND_LOGGER - - if BACKGROUND_LOGGER is None: - raise RuntimeError("Telemetry API not initialized") - + global CURRENT_TRACE_CONTEXT context = CURRENT_TRACE_CONTEXT.get() if context is None: return @@ -269,7 +283,7 @@ class TelemetryHandler(logging.Handler): if span is None: return - BACKGROUND_LOGGER.log_event( + enqueue_event( UnstructuredLogEvent( trace_id=span.trace_id, span_id=span.span_id, From a6b1588dc612df097d4fecce317547515b281ec6 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Wed, 10 Sep 2025 12:53:38 -0600 Subject: [PATCH 087/124] revert: Fireworks chat completion broken due to telemetry (#3402) Reverts llamastack/llama-stack#3392 --- llama_stack/core/routers/inference.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 9593dd5b9..2ed2d0439 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -423,7 +423,7 @@ class InferenceRouter(Inference): # response_stream = await provider.openai_completion(**params) response = await provider.openai_completion(**params) - if self.telemetry and getattr(response, "usage", None): + if self.telemetry: metrics = self._construct_metrics( prompt_tokens=response.usage.prompt_tokens, completion_tokens=response.usage.completion_tokens, @@ -529,7 +529,7 @@ class InferenceRouter(Inference): if self.store: asyncio.create_task(self.store.store_chat_completion(response, messages)) - if self.telemetry and getattr(response, "usage", None): + if self.telemetry: metrics = self._construct_metrics( prompt_tokens=response.usage.prompt_tokens, completion_tokens=response.usage.completion_tokens, From e6edc1f93425032f35f4198a197ba31b5b11d8ee Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Wed, 10 Sep 2025 19:54:10 +0100 Subject: [PATCH 088/124] fix: unbound variable error in schedule-record-workflow.sh (#3401) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Initialize INPUTS variable to prevent 'unbound variable' error Fixes: ./scripts/github/schedule-record-workflow.sh: line 246: INPUTS: unbound variable │ --- scripts/github/schedule-record-workflow.sh | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/scripts/github/schedule-record-workflow.sh b/scripts/github/schedule-record-workflow.sh index c292e53e6..44b0947b6 100755 --- a/scripts/github/schedule-record-workflow.sh +++ b/scripts/github/schedule-record-workflow.sh @@ -239,8 +239,9 @@ echo "Test pattern: ${TEST_PATTERN:-"(none)"}" echo "" # Prepare inputs for gh workflow run +INPUTS= if [[ -n "$TEST_SUBDIRS" ]]; then - INPUTS="-f subdirs='$TEST_SUBDIRS'" + INPUTS="$INPUTS -f subdirs='$TEST_SUBDIRS'" fi if [[ -n "$TEST_SETUP" ]]; then INPUTS="$INPUTS -f test-setup='$TEST_SETUP'" From e980436a2ed98dd725f76dfcec12235ed1d6cc82 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 10 Sep 2025 11:57:42 -0700 Subject: [PATCH 089/124] chore: introduce write queue for inference_store (#3383) # What does this PR do? Adds a write worker queue for writes to inference store. This avoids overwhelming request processing with slow inference writes. ## Test Plan Benchmark: ``` cd /docs/source/distributions/k8s-benchmark # start mock server python openai-mock-server.py --port 8000 # start stack server LLAMA_STACK_LOGGING="all=WARNING" uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml # run benchmark script uv run python3 benchmark.py --duration 120 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct ``` ## RPS from 21 -> 57 --- .../distributions/k8s-benchmark/benchmark.py | 19 ++-- .../k8s-benchmark/stack_run_config.yaml | 9 ++ llama_stack/core/datatypes.py | 13 ++- llama_stack/core/routers/__init__.py | 5 +- llama_stack/core/routers/inference.py | 5 + .../utils/inference/inference_store.py | 98 +++++++++++++++++-- .../utils/inference/test_inference_store.py | 12 +++ 7 files changed, 139 insertions(+), 22 deletions(-) diff --git a/docs/source/distributions/k8s-benchmark/benchmark.py b/docs/source/distributions/k8s-benchmark/benchmark.py index 3d0d18150..83ba9602a 100644 --- a/docs/source/distributions/k8s-benchmark/benchmark.py +++ b/docs/source/distributions/k8s-benchmark/benchmark.py @@ -58,14 +58,6 @@ class BenchmarkStats: print(f"\n{'='*60}") print(f"BENCHMARK RESULTS") - print(f"{'='*60}") - print(f"Total time: {total_time:.2f}s") - print(f"Concurrent users: {self.concurrent_users}") - print(f"Total requests: {self.total_requests}") - print(f"Successful requests: {self.success_count}") - print(f"Failed requests: {len(self.errors)}") - print(f"Success rate: {success_rate:.1f}%") - print(f"Requests per second: {self.success_count / total_time:.2f}") print(f"\nResponse Time Statistics:") print(f" Mean: {statistics.mean(self.response_times):.3f}s") @@ -106,6 +98,15 @@ class BenchmarkStats: print(f" Mean chunks per response: {statistics.mean(self.chunks_received):.1f}") print(f" Total chunks received: {sum(self.chunks_received)}") + print(f"{'='*60}") + print(f"Total time: {total_time:.2f}s") + print(f"Concurrent users: {self.concurrent_users}") + print(f"Total requests: {self.total_requests}") + print(f"Successful requests: {self.success_count}") + print(f"Failed requests: {len(self.errors)}") + print(f"Success rate: {success_rate:.1f}%") + print(f"Requests per second: {self.success_count / total_time:.2f}") + if self.errors: print(f"\nErrors (showing first 5):") for error in self.errors[:5]: @@ -215,7 +216,7 @@ class LlamaStackBenchmark: await asyncio.sleep(1) # Report every second if time.time() >= last_report_time + 10: # Report every 10 seconds elapsed = time.time() - stats.start_time - print(f"Completed: {stats.total_requests} requests in {elapsed:.1f}s") + print(f"Completed: {stats.total_requests} requests in {elapsed:.1f}s, RPS: {stats.total_requests / elapsed:.1f}") last_report_time = time.time() except asyncio.CancelledError: break diff --git a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml b/docs/source/distributions/k8s-benchmark/stack_run_config.yaml index f8ff7811b..5a9e2ae4f 100644 --- a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml +++ b/docs/source/distributions/k8s-benchmark/stack_run_config.yaml @@ -2,6 +2,7 @@ version: '2' image_name: kubernetes-benchmark-demo apis: - agents +- files - inference - files - safety @@ -20,6 +21,14 @@ providers: - provider_id: sentence-transformers provider_type: inline::sentence-transformers config: {} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db vector_io: - provider_id: ${env.ENABLE_CHROMADB:+chromadb} provider_type: remote::chromadb diff --git a/llama_stack/core/datatypes.py b/llama_stack/core/datatypes.py index 0f348b067..faaeefd01 100644 --- a/llama_stack/core/datatypes.py +++ b/llama_stack/core/datatypes.py @@ -431,6 +431,12 @@ class ServerConfig(BaseModel): ) +class InferenceStoreConfig(BaseModel): + sql_store_config: SqlStoreConfig + max_write_queue_size: int = Field(default=10000, description="Max queued writes for inference store") + num_writers: int = Field(default=4, description="Number of concurrent background writers") + + class StackRunConfig(BaseModel): version: int = LLAMA_STACK_RUN_CONFIG_VERSION @@ -464,11 +470,12 @@ Configuration for the persistence store used by the distribution registry. If no a default SQLite store will be used.""", ) - inference_store: SqlStoreConfig | None = Field( + inference_store: InferenceStoreConfig | SqlStoreConfig | None = Field( default=None, description=""" -Configuration for the persistence store used by the inference API. If not specified, -a default SQLite store will be used.""", +Configuration for the persistence store used by the inference API. Can be either a +InferenceStoreConfig (with queue tuning parameters) or a SqlStoreConfig (deprecated). +If not specified, a default SQLite store will be used.""", ) # registry of "resources" in the distribution diff --git a/llama_stack/core/routers/__init__.py b/llama_stack/core/routers/__init__.py index 1faace34a..f129f8ede 100644 --- a/llama_stack/core/routers/__init__.py +++ b/llama_stack/core/routers/__init__.py @@ -78,7 +78,10 @@ async def get_auto_router_impl( # TODO: move pass configs to routers instead if api == Api.inference and run_config.inference_store: - inference_store = InferenceStore(run_config.inference_store, policy) + inference_store = InferenceStore( + config=run_config.inference_store, + policy=policy, + ) await inference_store.initialize() api_to_dep_impl["store"] = inference_store diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 2ed2d0439..762d7073e 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -90,6 +90,11 @@ class InferenceRouter(Inference): async def shutdown(self) -> None: logger.debug("InferenceRouter.shutdown") + if self.store: + try: + await self.store.shutdown() + except Exception as e: + logger.warning(f"Error during InferenceStore shutdown: {e}") async def register_model( self, diff --git a/llama_stack/providers/utils/inference/inference_store.py b/llama_stack/providers/utils/inference/inference_store.py index 43006cfd5..8c69b1683 100644 --- a/llama_stack/providers/utils/inference/inference_store.py +++ b/llama_stack/providers/utils/inference/inference_store.py @@ -3,6 +3,9 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import asyncio +from typing import Any + from llama_stack.apis.inference import ( ListOpenAIChatCompletionResponse, OpenAIChatCompletion, @@ -10,24 +13,43 @@ from llama_stack.apis.inference import ( OpenAIMessageParam, Order, ) -from llama_stack.core.datatypes import AccessRule -from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR +from llama_stack.core.datatypes import AccessRule, InferenceStoreConfig +from llama_stack.log import get_logger from ..sqlstore.api import ColumnDefinition, ColumnType from ..sqlstore.authorized_sqlstore import AuthorizedSqlStore -from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl +from ..sqlstore.sqlstore import SqlStoreConfig, SqlStoreType, sqlstore_impl + +logger = get_logger(name=__name__, category="inference_store") class InferenceStore: - def __init__(self, sql_store_config: SqlStoreConfig, policy: list[AccessRule]): - if not sql_store_config: - sql_store_config = SqliteSqlStoreConfig( - db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(), + def __init__( + self, + config: InferenceStoreConfig | SqlStoreConfig, + policy: list[AccessRule], + ): + # Handle backward compatibility + if not isinstance(config, InferenceStoreConfig): + # Legacy: SqlStoreConfig passed directly as config + config = InferenceStoreConfig( + sql_store_config=config, ) - self.sql_store_config = sql_store_config + + self.config = config + self.sql_store_config = config.sql_store_config self.sql_store = None self.policy = policy + # Disable write queue for SQLite to avoid concurrency issues + self.enable_write_queue = self.sql_store_config.type != SqlStoreType.sqlite + + # Async write queue and worker control + self._queue: asyncio.Queue[tuple[OpenAIChatCompletion, list[OpenAIMessageParam]]] | None = None + self._worker_tasks: list[asyncio.Task[Any]] = [] + self._max_write_queue_size: int = config.max_write_queue_size + self._num_writers: int = max(1, config.num_writers) + async def initialize(self): """Create the necessary tables if they don't exist.""" self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config)) @@ -42,10 +64,68 @@ class InferenceStore: }, ) + if self.enable_write_queue: + self._queue = asyncio.Queue(maxsize=self._max_write_queue_size) + for _ in range(self._num_writers): + self._worker_tasks.append(asyncio.create_task(self._worker_loop())) + else: + logger.info("Write queue disabled for SQLite to avoid concurrency issues") + + async def shutdown(self) -> None: + if not self._worker_tasks: + return + if self._queue is not None: + await self._queue.join() + for t in self._worker_tasks: + if not t.done(): + t.cancel() + for t in self._worker_tasks: + try: + await t + except asyncio.CancelledError: + pass + self._worker_tasks.clear() + + async def flush(self) -> None: + """Wait for all queued writes to complete. Useful for testing.""" + if self.enable_write_queue and self._queue is not None: + await self._queue.join() + async def store_chat_completion( self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam] ) -> None: - if not self.sql_store: + if self.enable_write_queue: + if self._queue is None: + raise ValueError("Inference store is not initialized") + try: + self._queue.put_nowait((chat_completion, input_messages)) + except asyncio.QueueFull: + logger.warning( + f"Write queue full; adding chat completion id={getattr(chat_completion, 'id', '')}" + ) + await self._queue.put((chat_completion, input_messages)) + else: + await self._write_chat_completion(chat_completion, input_messages) + + async def _worker_loop(self) -> None: + assert self._queue is not None + while True: + try: + item = await self._queue.get() + except asyncio.CancelledError: + break + chat_completion, input_messages = item + try: + await self._write_chat_completion(chat_completion, input_messages) + except Exception as e: # noqa: BLE001 + logger.error(f"Error writing chat completion: {e}") + finally: + self._queue.task_done() + + async def _write_chat_completion( + self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam] + ) -> None: + if self.sql_store is None: raise ValueError("Inference store is not initialized") data = chat_completion.model_dump() diff --git a/tests/unit/utils/inference/test_inference_store.py b/tests/unit/utils/inference/test_inference_store.py index 730f54a05..f6d63490a 100644 --- a/tests/unit/utils/inference/test_inference_store.py +++ b/tests/unit/utils/inference/test_inference_store.py @@ -65,6 +65,9 @@ async def test_inference_store_pagination_basic(): input_messages = [OpenAIUserMessageParam(role="user", content=f"Test message for {completion_id}")] await store.store_chat_completion(completion, input_messages) + # Wait for all queued writes to complete + await store.flush() + # Test 1: First page with limit=2, descending order (default) result = await store.list_chat_completions(limit=2, order=Order.desc) assert len(result.data) == 2 @@ -108,6 +111,9 @@ async def test_inference_store_pagination_ascending(): input_messages = [OpenAIUserMessageParam(role="user", content=f"Test message for {completion_id}")] await store.store_chat_completion(completion, input_messages) + # Wait for all queued writes to complete + await store.flush() + # Test ascending order pagination result = await store.list_chat_completions(limit=1, order=Order.asc) assert len(result.data) == 1 @@ -143,6 +149,9 @@ async def test_inference_store_pagination_with_model_filter(): input_messages = [OpenAIUserMessageParam(role="user", content=f"Test message for {completion_id}")] await store.store_chat_completion(completion, input_messages) + # Wait for all queued writes to complete + await store.flush() + # Test pagination with model filter result = await store.list_chat_completions(limit=1, model="model-a", order=Order.desc) assert len(result.data) == 1 @@ -190,6 +199,9 @@ async def test_inference_store_pagination_no_limit(): input_messages = [OpenAIUserMessageParam(role="user", content=f"Test message for {completion_id}")] await store.store_chat_completion(completion, input_messages) + # Wait for all queued writes to complete + await store.flush() + # Test without limit result = await store.list_chat_completions(order=Order.desc) assert len(result.data) == 2 From 7394828c7a84de2c3af0ca37546db17d6a703507 Mon Sep 17 00:00:00 2001 From: Alexey Rybak <50731695+reluctantfuturist@users.noreply.github.com> Date: Wed, 10 Sep 2025 12:43:36 -0700 Subject: [PATCH 090/124] docs: horizontal nav bar (#3407) # What does this PR do? * Adds a horizontal nav bar for easy access to the API reference and the Llama Stack Github repo image ## Test Plan * Built the docs and ran the local HTML server to verify changes --- docs/_static/css/my_theme.css | 101 ++++++++++++++++++++++++++++++ docs/_static/js/horizontal_nav.js | 44 +++++++++++++ docs/source/conf.py | 1 + 3 files changed, 146 insertions(+) create mode 100644 docs/_static/js/horizontal_nav.js diff --git a/docs/_static/css/my_theme.css b/docs/_static/css/my_theme.css index d078ec057..7dcd97c9b 100644 --- a/docs/_static/css/my_theme.css +++ b/docs/_static/css/my_theme.css @@ -1,5 +1,106 @@ @import url("theme.css"); +/* Horizontal Navigation Bar */ +.horizontal-nav { + background-color: #ffffff; + border-bottom: 1px solid #e5e5e5; + padding: 0; + position: fixed; + top: 0; + left: 0; + right: 0; + z-index: 1050; + height: 50px; + box-shadow: 0 2px 4px rgba(0,0,0,0.1); +} + +[data-theme="dark"] .horizontal-nav { + background-color: #1a1a1a; + border-bottom: 1px solid #333; +} + +.horizontal-nav .nav-container { + max-width: 1200px; + margin: 0 auto; + display: flex; + align-items: center; + justify-content: space-between; + padding: 0 20px; + height: 100%; +} + +.horizontal-nav .nav-brand { + font-size: 18px; + font-weight: 600; + color: #333; + text-decoration: none; +} + +[data-theme="dark"] .horizontal-nav .nav-brand { + color: #fff; +} + +.horizontal-nav .nav-links { + display: flex; + align-items: center; + gap: 30px; + list-style: none; + margin: 0; + padding: 0; +} + +.horizontal-nav .nav-links a { + color: #666; + text-decoration: none; + font-size: 14px; + font-weight: 500; + padding: 8px 12px; + border-radius: 6px; + transition: all 0.2s ease; +} + +.horizontal-nav .nav-links a:hover, +.horizontal-nav .nav-links a.active { + color: #333; + background-color: #f5f5f5; +} + +.horizontal-nav .nav-links a.active { + font-weight: 600; +} + +[data-theme="dark"] .horizontal-nav .nav-links a { + color: #ccc; +} + +[data-theme="dark"] .horizontal-nav .nav-links a:hover, +[data-theme="dark"] .horizontal-nav .nav-links a.active { + color: #fff; + background-color: #333; +} + +.horizontal-nav .nav-links .github-link { + display: flex; + align-items: center; + gap: 6px; +} + +.horizontal-nav .nav-links .github-icon { + width: 16px; + height: 16px; + fill: currentColor; +} + +/* Adjust main content to account for fixed nav */ +.wy-nav-side { + top: 50px; + height: calc(100vh - 50px); +} + +.wy-nav-content-wrap { + margin-top: 50px; +} + .wy-nav-content { max-width: 90%; } diff --git a/docs/_static/js/horizontal_nav.js b/docs/_static/js/horizontal_nav.js new file mode 100644 index 000000000..c2384f9d5 --- /dev/null +++ b/docs/_static/js/horizontal_nav.js @@ -0,0 +1,44 @@ +// Horizontal Navigation Bar for Llama Stack Documentation +document.addEventListener('DOMContentLoaded', function() { + // Create the horizontal navigation HTML + const navHTML = ` + + `; + + // Insert the navigation at the beginning of the body + document.body.insertAdjacentHTML('afterbegin', navHTML); + + // Update navigation links based on current page + updateActiveNav(); +}); + +function updateActiveNav() { + const currentPath = window.location.pathname; + const navLinks = document.querySelectorAll('.horizontal-nav .nav-links a'); + + navLinks.forEach(link => { + // Remove any existing active classes + link.classList.remove('active'); + + // Add active class based on current path + if (currentPath === '/' && link.getAttribute('href') === '/') { + link.classList.add('active'); + } else if (currentPath.includes('/references/api_reference/') && link.getAttribute('href').includes('api_reference')) { + link.classList.add('active'); + } + }); +} diff --git a/docs/source/conf.py b/docs/source/conf.py index 3f84d1310..0cbddef31 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -131,6 +131,7 @@ html_static_path = ["../_static"] def setup(app): app.add_css_file("css/my_theme.css") app.add_js_file("js/detect_theme.js") + app.add_js_file("js/horizontal_nav.js") app.add_js_file("js/keyboard_shortcuts.js") def dockerhub_role(name, rawtext, text, lineno, inliner, options={}, content=[]): From a844c4f6e189395f99a6470552876d1ba6b807f1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 10 Sep 2025 13:17:02 -0700 Subject: [PATCH 091/124] chore(python-deps): bump pytest from 8.4.1 to 8.4.2 (#3359) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.4.1 to 8.4.2.
Release notes

Sourced from pytest's releases.

8.4.2

pytest 8.4.2 (2025-09-03)

Bug fixes

  • #13478: Fixed a crash when using console_output_style{.interpreted-text role="confval"} with times and a module is skipped.

  • #13530: Fixed a crash when using pytest.approx{.interpreted-text role="func"} and decimal.Decimal{.interpreted-text role="class"} instances with the decimal.FloatOperation{.interpreted-text role="class"} trap set.

  • #13549: No longer evaluate type annotations in Python 3.14 when inspecting function signatures.

    This prevents crashes during module collection when modules do not explicitly use from __future__ import annotations and import types for annotations within a if TYPE_CHECKING: block.

  • #13559: Added missing [int]{.title-ref} and [float]{.title-ref} variants to the [Literal]{.title-ref} type annotation of the [type]{.title-ref} parameter in pytest.Parser.addini{.interpreted-text role="meth"}.

  • #13563: pytest.approx{.interpreted-text role="func"} now only imports numpy if NumPy is already in sys.modules. This fixes unconditional import behavior introduced in [8.4.0]{.title-ref}.

Improved documentation

  • #13577: Clarify that pytest_generate_tests is discovered in test modules/classes; other hooks must be in conftest.py or plugins.

Contributor-facing changes

  • #13480: Self-testing: fixed a few test failures when run with -Wdefault or a similar override.
  • #13547: Self-testing: corrected expected message for test_doctest_unexpected_exception in Python 3.14.
  • #13684: Make pytest's own testsuite insensitive to the presence of the CI environment variable -- by ogrisel{.interpreted-text role="user"}.
Commits
  • bfae422 Prepare release version 8.4.2
  • 8990538 Fix passenv CI in tox ini and make tests insensitive to the presence of the C...
  • ca676bf Merge pull request #13687 from pytest-dev/patchback/backports/8.4.x/e63f6e51c...
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  • b7f0568 Merge pull request #13685 from CoretexShadow/fix/docs-pytest-generate-tests
  • 2c94c4a add missing colon (#13640) (#13641)
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  • dc6e3be Merge pull request #13605 from The-Compiler/training-update-2025-07
  • f87289c Fix crash with times output style and skipped module (#13573) (#13579)
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- uv.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/uv.lock b/uv.lock index 2788c6fef..6f8ba7ad6 100644 --- a/uv.lock +++ b/uv.lock @@ -3540,7 +3540,7 @@ wheels = [ [[package]] name = "pytest" -version = "8.4.1" +version = "8.4.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, @@ -3549,9 +3549,9 @@ dependencies = [ { name = "pluggy" }, { name = "pygments" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/08/ba/45911d754e8eba3d5a841a5ce61a65a685ff1798421ac054f85aa8747dfb/pytest-8.4.1.tar.gz", hash = "sha256:7c67fd69174877359ed9371ec3af8a3d2b04741818c51e5e99cc1742251fa93c", size = 1517714, upload-time = "2025-06-18T05:48:06.109Z" } +sdist = { url = "https://files.pythonhosted.org/packages/a3/5c/00a0e072241553e1a7496d638deababa67c5058571567b92a7eaa258397c/pytest-8.4.2.tar.gz", hash = "sha256:86c0d0b93306b961d58d62a4db4879f27fe25513d4b969df351abdddb3c30e01", size = 1519618, upload-time = "2025-09-04T14:34:22.711Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl", hash = "sha256:539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7", size = 365474, upload-time = "2025-06-18T05:48:03.955Z" }, + { url = "https://files.pythonhosted.org/packages/a8/a4/20da314d277121d6534b3a980b29035dcd51e6744bd79075a6ce8fa4eb8d/pytest-8.4.2-py3-none-any.whl", hash = "sha256:872f880de3fc3a5bdc88a11b39c9710c3497a547cfa9320bc3c5e62fbf272e79", size = 365750, upload-time = "2025-09-04T14:34:20.226Z" }, ] [[package]] From 369083c0699270d7a3fa4d10f4975a081fcc7acd Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 10 Sep 2025 13:17:28 -0700 Subject: [PATCH 092/124] chore(python-deps): bump locust from 2.39.1 to 2.40.1 (#3358) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [locust](https://github.com/locustio/locust) from 2.39.1 to 2.40.1.
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2.40.1

What's Changed

Full Changelog: https://github.com/locustio/locust/compare/2.40.0...2.40.1

2.40.0

What's Changed

New Contributors

Full Changelog: https://github.com/locustio/locust/compare/2.39.1...2.40.0

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Detailed changelog

The most important changes can also be found in the documentation.

Commits
  • 5df19da Merge pull request #3205 from locustio/move-pytest-plugin-to-own-directory
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  • 6422848 mention that only one locustfile can be distributed
  • aa3da73 Merge pull request #3204 from locustio/delay-imports-in-pytest-plugin-to-avoi...
  • 12050de Pytest plugin: Delay imports to avoid monkey patching until someone actually ...
  • 488d1f8 docs
  • 439b7ab docs fix
  • fcd76a8 docs: rephrase
  • 70c7e9b docs: move pytest further up
  • 06dbf98 docs: fix link
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- uv.lock | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/uv.lock b/uv.lock index 6f8ba7ad6..df3a23e58 100644 --- a/uv.lock +++ b/uv.lock @@ -2023,7 +2023,7 @@ wheels = [ [[package]] name = "locust" -version = "2.39.1" +version = "2.40.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "configargparse" }, @@ -2035,6 +2035,7 @@ dependencies = [ { name = "locust-cloud" }, { name = "msgpack" }, { name = "psutil" }, + { name = "pytest" }, { name = "python-engineio" }, { name = "python-socketio", extra = ["client"] }, { name = "pywin32", marker = "sys_platform == 'win32'" }, @@ -2043,9 +2044,9 @@ dependencies = [ { name = "setuptools" }, { name = "werkzeug" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/95/c8/10aa5445c404eed389b56877e6714c1787190cc09dd70059ce3765979ec5/locust-2.39.1.tar.gz", hash = "sha256:6bdd19e27edf9a1c84391d6cf6e9a737dfb832be7dfbf39053191ae31b9cc498", size = 1409902, upload-time = "2025-08-29T17:41:01.544Z" } +sdist = { url = "https://files.pythonhosted.org/packages/01/22/82f40176473a98c9479bed667d3ad21bb859d2cb67f6880a6b0b6a725e45/locust-2.40.1.tar.gz", hash = "sha256:5bde76c1cf7e412071670f926f34844e119210c93f07a4cf9fc4cb93c60a578a", size = 1411606, upload-time = "2025-09-05T15:57:35.76Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ec/b3/b2f4b2ca88b1e72eba7be2b2982533b887f8b709d222db78eb9602aa5121/locust-2.39.1-py3-none-any.whl", hash = "sha256:fd5148f2f1a4ed34aee968abc4393674e69d1b5e1b54db50a397f6eb09ce0b04", size = 1428155, upload-time = "2025-08-29T17:41:00.245Z" }, + { url = "https://files.pythonhosted.org/packages/3b/e6/9c6335ab16becf4f8ad3da6083ab78793c56ec1ca496d6f7c74660c21c3f/locust-2.40.1-py3-none-any.whl", hash = "sha256:ef0517f9bb5ed0afa7035014faaf944802917e07da8649461aaaf5e5f3ba8a65", size = 1430154, upload-time = "2025-09-05T15:57:33.233Z" }, ] [[package]] From 438c037b1f16ee8123ab71b2aa39529ce32967a5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 10 Sep 2025 13:17:43 -0700 Subject: [PATCH 093/124] chore(python-deps): bump openai from 1.102.0 to 1.106.1 (#3356) Bumps [openai](https://github.com/openai/openai-python) from 1.102.0 to 1.106.1.
Release notes

Sourced from openai's releases.

v1.106.1

1.106.1 (2025-09-04)

Full Changelog: v1.106.0...v1.106.1

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  • internal: move mypy configurations to pyproject.toml file (ca413a2)

v1.106.0

1.106.0 (2025-09-04)

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Features

  • client: support callable api_key (#2588) (e1bad01)
  • improve future compat with pydantic v3 (6645d93)

v1.105.0

1.105.0 (2025-09-03)

Full Changelog: v1.104.2...v1.105.0

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  • api: Add gpt-realtime models (8502041)

v1.104.2

1.104.2 (2025-09-02)

Full Changelog: v1.104.1...v1.104.2

Bug Fixes

  • types: add aliases back for web search tool types (2521cd8)

v1.104.1

1.104.1 (2025-09-02)

Full Changelog: v1.104.0...v1.104.1

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  • api: manual updates for ResponseInputAudio (0db5061)

v1.104.0

1.104.0 (2025-09-02)

Full Changelog: v1.103.0...v1.104.0

... (truncated)

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1.106.1 (2025-09-04)

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1.106.0 (2025-09-04)

Full Changelog: v1.105.0...v1.106.0

Features

  • client: support callable api_key (#2588) (e1bad01)
  • improve future compat with pydantic v3 (6645d93)

1.105.0 (2025-09-03)

Full Changelog: v1.104.2...v1.105.0

Features

  • api: Add gpt-realtime models (8502041)

1.104.2 (2025-09-02)

Full Changelog: v1.104.1...v1.104.2

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  • types: add aliases back for web search tool types (2521cd8)

1.104.1 (2025-09-02)

Full Changelog: v1.104.0...v1.104.1

Chores

  • api: manual updates for ResponseInputAudio (0db5061)

1.104.0 (2025-09-02)

Full Changelog: v1.103.0...v1.104.0

Features

  • types: replace List[str] with SequenceNotStr in params (bc00bda)

... (truncated)

Commits
  • 2adf111 release: 1.106.1
  • c4f9d0b chore(internal): move mypy configurations to pyproject.toml file
  • 2de8d7c release: 1.106.0
  • 2cf4ed5 feat: improve future compat with pydantic v3
  • 25d16be feat(client): support callable api_key (#2588)
  • 8672413 release: 1.105.0
  • 2c60d78 feat(api): Add gpt-realtime models
  • a52463c release: 1.104.2
  • 5a6931d fix(types): add aliases back for web search tool types
  • fb152d9 release: 1.104.1
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=openai&package-manager=uv&previous-version=1.102.0&new-version=1.106.1)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- uv.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/uv.lock b/uv.lock index df3a23e58..3d7713f54 100644 --- a/uv.lock +++ b/uv.lock @@ -2620,7 +2620,7 @@ wheels = [ [[package]] name = "openai" -version = "1.102.0" +version = "1.107.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -2632,9 +2632,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/07/55/da5598ed5c6bdd9939633854049cddc5cbac0da938dfcfcb3c6b119c16c0/openai-1.102.0.tar.gz", hash = "sha256:2e0153bcd64a6523071e90211cbfca1f2bbc5ceedd0993ba932a5869f93b7fc9", size = 519027, upload-time = "2025-08-26T20:50:29.397Z" } +sdist = { url = "https://files.pythonhosted.org/packages/88/67/d6498de300f83ff57a79cb7aa96ef3bef8d6f070c3ded0f1b5b45442a6bc/openai-1.107.0.tar.gz", hash = "sha256:43e04927584e57d0e9e640ee0077c78baf8150098be96ebd5c512539b6c4e9a4", size = 566056, upload-time = "2025-09-08T19:25:47.604Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/bd/0d/c9e7016d82c53c5b5e23e2bad36daebb8921ed44f69c0a985c6529a35106/openai-1.102.0-py3-none-any.whl", hash = "sha256:d751a7e95e222b5325306362ad02a7aa96e1fab3ed05b5888ce1c7ca63451345", size = 812015, upload-time = "2025-08-26T20:50:27.219Z" }, + { url = "https://files.pythonhosted.org/packages/91/ed/e8a4fd20390f2858b95227c288df8fe0c835f7c77625f7583609161684ba/openai-1.107.0-py3-none-any.whl", hash = "sha256:3dcfa3cbb116bd6924b27913b8da28c4a787379ff60049588547a1013e6d6438", size = 950968, upload-time = "2025-09-08T19:25:45.552Z" }, ] [[package]] From d4e45cd5f1e099d9f6ac2d52ad6cd3f74cc4facf Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 10 Sep 2025 13:18:14 -0700 Subject: [PATCH 094/124] chore(ui-deps): bump tailwindcss from 4.1.6 to 4.1.13 in /llama_stack/ui (#3362) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [tailwindcss](https://github.com/tailwindlabs/tailwindcss/tree/HEAD/packages/tailwindcss) from 4.1.6 to 4.1.13.
Release notes

Sourced from tailwindcss's releases.

v4.1.13

Changed

  • Drop warning from browser build (#18731)
  • Drop exact duplicate declarations when emitting CSS (#18809)

Fixed

  • Don't transition visibility when using transition (#18795)
  • Discard matched variants with unknown named values (#18799)
  • Discard matched variants with non-string values (#18799)
  • Show suggestions for known matchVariant values (#18798)
  • Replace deprecated clip with clip-path in sr-only (#18769)
  • Hide internal fields from completions in matchUtilities (#18820)
  • Ignore .vercel folders by default (can be overridden by @source … rules) (#18855)
  • Consider variants starting with @- to be invalid (e.g. @-2xl:flex) (#18869)
  • Do not allow custom variants to start or end with a - or _ (#18867, #18872)
  • Upgrade: Migrate aria theme keys to @custom-variant (#18815)
  • Upgrade: Migrate data theme keys to @custom-variant (#18816)
  • Upgrade: Migrate supports theme keys to @custom-variant (#18817)

v4.1.12

Fixed

  • Don't consider the global important state in @apply (#18404)
  • Add missing suggestions for flex-<number> utilities (#18642)
  • Fix trailing ) from interfering with extraction in Clojure keywords (#18345)
  • Detect classes inside Elixir charlist, word list, and string sigils (#18432)
  • Track source locations through @plugin and @config (#18345)
  • Allow boolean values of process.env.DEBUG in @tailwindcss/node (#18485)
  • Ignore consecutive semicolons in the CSS parser (#18532)
  • Center the dropdown icon added to an input with a paired datalist by default (#18511)
  • Extract candidates in Slang templates (#18565)
  • Improve error messages when encountering invalid functional utility names (#18568)
  • Discard CSS AST objects with false or undefined properties (#18571)
  • Allow users to disable URL rebasing in @tailwindcss/postcss via transformAssetUrls: false (#18321)
  • Fix false-positive migrations in addEventListener and JavaScript variable names (#18718)
  • Fix Standalone CLI showing default Bun help when run via symlink on Windows (#18723)
  • Read from --border-color-* theme keys in divide-* utilities for backwards compatibility (#18704)
  • Don't scan .hdr and .exr files for classes by default (#18734)

v4.1.11

Fixed

  • Add heuristic to skip candidate migrations inside emit(…) (#18330)
  • Extract candidates with variants in Clojure/ClojureScript keywords (#18338)
  • Document --watch=always in the CLI's usage (#18337)
  • Add support for Vite 7 to @tailwindcss/vite (#18384)

v4.1.10

... (truncated)

Changelog

Sourced from tailwindcss's changelog.

[4.1.13] - 2025-09-03

Changed

  • Drop warning from browser build (#18731)
  • Drop exact duplicate declarations when emitting CSS (#18809)

Fixed

  • Don't transition visibility when using transition (#18795)
  • Discard matched variants with unknown named values (#18799)
  • Discard matched variants with non-string values (#18799)
  • Show suggestions for known matchVariant values (#18798)
  • Replace deprecated clip with clip-path in sr-only (#18769)
  • Hide internal fields from completions in matchUtilities (#18820)
  • Ignore .vercel folders by default (can be overridden by @source … rules) (#18855)
  • Consider variants starting with @- to be invalid (e.g. @-2xl:flex) (#18869)
  • Do not allow custom variants to start or end with a - or _ (#18867, #18872)
  • Upgrade: Migrate aria theme keys to @custom-variant (#18815)
  • Upgrade: Migrate data theme keys to @custom-variant (#18816)
  • Upgrade: Migrate supports theme keys to @custom-variant (#18817)

[4.1.12] - 2025-08-13

Fixed

  • Don't consider the global important state in @apply (#18404)
  • Add missing suggestions for flex-<number> utilities (#18642)
  • Fix trailing ) from interfering with extraction in Clojure keywords (#18345)
  • Detect classes inside Elixir charlist, word list, and string sigils (#18432)
  • Track source locations through @plugin and @config (#18345)
  • Allow boolean values of process.env.DEBUG in @tailwindcss/node (#18485)
  • Ignore consecutive semicolons in the CSS parser (#18532)
  • Center the dropdown icon added to an input with a paired datalist by default (#18511)
  • Extract candidates in Slang templates (#18565)
  • Improve error messages when encountering invalid functional utility names (#18568)
  • Discard CSS AST objects with false or undefined properties (#18571)
  • Allow users to disable URL rebasing in @tailwindcss/postcss via transformAssetUrls: false (#18321)
  • Fix false-positive migrations in addEventListener and JavaScript variable names (#18718)
  • Fix Standalone CLI showing default Bun help when run via symlink on Windows (#18723)
  • Read from --border-color-* theme keys in divide-* utilities for backwards compatibility (#18704)
  • Don't scan .hdr and .exr files for classes by default (#18734)

[4.1.11] - 2025-06-26

Fixed

  • Add heuristic to skip candidate migrations inside emit(…) (#18330)
  • Extract candidates with variants in Clojure/ClojureScript keywords (#18338)
  • Document --watch=always in the CLI's usage (#18337)

... (truncated)

Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=tailwindcss&package-manager=npm_and_yarn&previous-version=4.1.6&new-version=4.1.13)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 1db1c61cd..e2c0815fd 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -3578,6 +3578,13 @@ "tailwindcss": "4.1.6" } }, + "node_modules/@tailwindcss/node/node_modules/tailwindcss": { + "version": "4.1.6", + "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.1.6.tgz", + "integrity": "sha512-j0cGLTreM6u4OWzBeLBpycK0WIh8w7kSwcUsQZoGLHZ7xDTdM69lN64AgoIEEwFi0tnhs4wSykUa5YWxAzgFYg==", + "dev": true, + "license": "MIT" + }, "node_modules/@tailwindcss/oxide": { "version": "4.1.6", "resolved": "https://registry.npmjs.org/@tailwindcss/oxide/-/oxide-4.1.6.tgz", @@ -3838,6 +3845,13 @@ "tailwindcss": "4.1.6" } }, + "node_modules/@tailwindcss/postcss/node_modules/tailwindcss": { + "version": "4.1.6", + "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.1.6.tgz", + "integrity": "sha512-j0cGLTreM6u4OWzBeLBpycK0WIh8w7kSwcUsQZoGLHZ7xDTdM69lN64AgoIEEwFi0tnhs4wSykUa5YWxAzgFYg==", + "dev": true, + "license": "MIT" + }, "node_modules/@testing-library/dom": { "version": "10.4.1", "resolved": "https://registry.npmjs.org/@testing-library/dom/-/dom-10.4.1.tgz", @@ -13843,9 +13857,9 @@ } }, "node_modules/tailwindcss": { - "version": "4.1.6", - "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.1.6.tgz", - "integrity": "sha512-j0cGLTreM6u4OWzBeLBpycK0WIh8w7kSwcUsQZoGLHZ7xDTdM69lN64AgoIEEwFi0tnhs4wSykUa5YWxAzgFYg==", + "version": "4.1.13", + "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.1.13.tgz", + "integrity": "sha512-i+zidfmTqtwquj4hMEwdjshYYgMbOrPzb9a0M3ZgNa0JMoZeFC6bxZvO8yr8ozS6ix2SDz0+mvryPeBs2TFE+w==", "dev": true, "license": "MIT" }, From d2f88a10fb0cf366708ec106696c812b8c85629c Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 10 Sep 2025 13:19:36 -0700 Subject: [PATCH 095/124] chore: telemetry test (#3405) # What does this PR do? - removed fixed-duration sleeps ## Test Plan --- .../telemetry/test_openai_telemetry.py | 17 ++++++++--------- tests/integration/telemetry/test_telemetry.py | 5 +---- .../telemetry/test_telemetry_metrics.py | 5 +---- 3 files changed, 10 insertions(+), 17 deletions(-) diff --git a/tests/integration/telemetry/test_openai_telemetry.py b/tests/integration/telemetry/test_openai_telemetry.py index cdd9b6702..b3ffb6b09 100644 --- a/tests/integration/telemetry/test_openai_telemetry.py +++ b/tests/integration/telemetry/test_openai_telemetry.py @@ -49,16 +49,13 @@ def setup_openai_telemetry_data(llama_stack_client, text_model_id): traces = llama_stack_client.telemetry.query_traces(limit=10) if len(traces) >= 5: # 5 OpenAI completion traces break - time.sleep(1) + time.sleep(0.1) if len(traces) < 5: pytest.fail( f"Failed to create sufficient OpenAI completion telemetry data after 30s. Got {len(traces)} traces." ) - # Wait for 5 seconds to ensure traces has completed logging - time.sleep(5) - yield @@ -185,11 +182,13 @@ def test_openai_completion_creates_telemetry(llama_stack_client, text_model_id): assert len(response.choices) > 0, "Response should have at least one choice" # Wait for telemetry to be recorded - time.sleep(3) - - # Check that we have more traces now - final_traces = llama_stack_client.telemetry.query_traces(limit=20) - final_count = len(final_traces) + start_time = time.time() + while time.time() - start_time < 30: + final_traces = llama_stack_client.telemetry.query_traces(limit=20) + final_count = len(final_traces) + if final_count > initial_count: + break + time.sleep(0.1) # Should have at least as many traces as before (might have more due to other activity) assert final_count >= initial_count, "Should have at least as many traces after OpenAI call" diff --git a/tests/integration/telemetry/test_telemetry.py b/tests/integration/telemetry/test_telemetry.py index d363edbc0..e86da954e 100644 --- a/tests/integration/telemetry/test_telemetry.py +++ b/tests/integration/telemetry/test_telemetry.py @@ -42,14 +42,11 @@ def setup_telemetry_data(llama_stack_client, text_model_id): traces = llama_stack_client.telemetry.query_traces(limit=10) if len(traces) >= 4: break - time.sleep(1) + time.sleep(0.1) if len(traces) < 4: pytest.fail(f"Failed to create sufficient telemetry data after 30s. Got {len(traces)} traces.") - # Wait for 5 seconds to ensure traces has completed logging - time.sleep(5) - yield diff --git a/tests/integration/telemetry/test_telemetry_metrics.py b/tests/integration/telemetry/test_telemetry_metrics.py index 4ba2bd2d9..1d8312ae2 100644 --- a/tests/integration/telemetry/test_telemetry_metrics.py +++ b/tests/integration/telemetry/test_telemetry_metrics.py @@ -46,10 +46,7 @@ def setup_telemetry_metrics_data(openai_client, client_with_models, text_model_i break except Exception: pass - time.sleep(1) - - # Wait additional time to ensure all metrics are processed - time.sleep(5) + time.sleep(0.1) # Return the token lists for use in tests return {"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, "total_tokens": total_tokens} From c04f1c1e8c0b8c9df80ab51ce7379476cf218317 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 10 Sep 2025 13:19:44 -0700 Subject: [PATCH 096/124] chore: move benchmarking related code (#3406) # What does this PR do? - moving things and some formatting changes ## Test Plan --- .../k8s-benchmark/README.md | 4 +- .../k8s-benchmark/apply.sh | 0 .../k8s-benchmark/benchmark.py | 129 +++++++------ .../k8s-benchmark/openai-mock-server.py | 170 ++++++++++-------- .../k8s-benchmark/profile_running_server.sh | 0 .../k8s-benchmark/run-benchmark.sh | 0 .../k8s-benchmark/stack-configmap.yaml | 0 .../k8s-benchmark/stack-k8s.yaml.template | 0 .../k8s-benchmark/stack_run_config.yaml | 0 docs/source/contributing/index.md | 2 +- 10 files changed, 156 insertions(+), 149 deletions(-) rename {docs/source/distributions => benchmarking}/k8s-benchmark/README.md (98%) rename {docs/source/distributions => benchmarking}/k8s-benchmark/apply.sh (100%) rename {docs/source/distributions => benchmarking}/k8s-benchmark/benchmark.py (80%) rename {docs/source/distributions => benchmarking}/k8s-benchmark/openai-mock-server.py (60%) rename {docs/source/distributions => benchmarking}/k8s-benchmark/profile_running_server.sh (100%) rename {docs/source/distributions => benchmarking}/k8s-benchmark/run-benchmark.sh (100%) rename {docs/source/distributions => benchmarking}/k8s-benchmark/stack-configmap.yaml (100%) rename {docs/source/distributions => benchmarking}/k8s-benchmark/stack-k8s.yaml.template (100%) rename {docs/source/distributions => benchmarking}/k8s-benchmark/stack_run_config.yaml (100%) diff --git a/docs/source/distributions/k8s-benchmark/README.md b/benchmarking/k8s-benchmark/README.md similarity index 98% rename from docs/source/distributions/k8s-benchmark/README.md rename to benchmarking/k8s-benchmark/README.md index 42da4d466..3b0d0c4db 100644 --- a/docs/source/distributions/k8s-benchmark/README.md +++ b/benchmarking/k8s-benchmark/README.md @@ -34,13 +34,12 @@ This data enables data-driven architectural decisions and performance optimizati **1. Deploy base k8s infrastructure:** ```bash -cd ../k8s +cd ../../docs/source/distributions/k8s ./apply.sh ``` **2. Deploy benchmark components:** ```bash -cd ../k8s-benchmark ./apply.sh ``` @@ -56,7 +55,6 @@ kubectl get pods **Benchmark Llama Stack (default):** ```bash -cd docs/source/distributions/k8s-benchmark/ ./run-benchmark.sh ``` diff --git a/docs/source/distributions/k8s-benchmark/apply.sh b/benchmarking/k8s-benchmark/apply.sh similarity index 100% rename from docs/source/distributions/k8s-benchmark/apply.sh rename to benchmarking/k8s-benchmark/apply.sh diff --git a/docs/source/distributions/k8s-benchmark/benchmark.py b/benchmarking/k8s-benchmark/benchmark.py similarity index 80% rename from docs/source/distributions/k8s-benchmark/benchmark.py rename to benchmarking/k8s-benchmark/benchmark.py index 83ba9602a..d5e34aa23 100644 --- a/docs/source/distributions/k8s-benchmark/benchmark.py +++ b/benchmarking/k8s-benchmark/benchmark.py @@ -14,7 +14,7 @@ import os import random import statistics import time -from typing import Tuple + import aiohttp @@ -55,50 +55,50 @@ class BenchmarkStats: total_time = self.end_time - self.start_time success_rate = (self.success_count / self.total_requests) * 100 - - print(f"\n{'='*60}") - print(f"BENCHMARK RESULTS") - - print(f"\nResponse Time Statistics:") + + print(f"\n{'=' * 60}") + print("BENCHMARK RESULTS") + + print("\nResponse Time Statistics:") print(f" Mean: {statistics.mean(self.response_times):.3f}s") print(f" Median: {statistics.median(self.response_times):.3f}s") print(f" Min: {min(self.response_times):.3f}s") print(f" Max: {max(self.response_times):.3f}s") - + if len(self.response_times) > 1: print(f" Std Dev: {statistics.stdev(self.response_times):.3f}s") - + percentiles = [50, 90, 95, 99] sorted_times = sorted(self.response_times) - print(f"\nPercentiles:") + print("\nPercentiles:") for p in percentiles: idx = int(len(sorted_times) * p / 100) - 1 idx = max(0, min(idx, len(sorted_times) - 1)) print(f" P{p}: {sorted_times[idx]:.3f}s") - + if self.ttft_times: - print(f"\nTime to First Token (TTFT) Statistics:") + print("\nTime to First Token (TTFT) Statistics:") print(f" Mean: {statistics.mean(self.ttft_times):.3f}s") print(f" Median: {statistics.median(self.ttft_times):.3f}s") print(f" Min: {min(self.ttft_times):.3f}s") print(f" Max: {max(self.ttft_times):.3f}s") - + if len(self.ttft_times) > 1: print(f" Std Dev: {statistics.stdev(self.ttft_times):.3f}s") - + sorted_ttft = sorted(self.ttft_times) - print(f"\nTTFT Percentiles:") + print("\nTTFT Percentiles:") for p in percentiles: idx = int(len(sorted_ttft) * p / 100) - 1 idx = max(0, min(idx, len(sorted_ttft) - 1)) print(f" P{p}: {sorted_ttft[idx]:.3f}s") - + if self.chunks_received: - print(f"\nStreaming Statistics:") + print("\nStreaming Statistics:") print(f" Mean chunks per response: {statistics.mean(self.chunks_received):.1f}") print(f" Total chunks received: {sum(self.chunks_received)}") - - print(f"{'='*60}") + + print(f"{'=' * 60}") print(f"Total time: {total_time:.2f}s") print(f"Concurrent users: {self.concurrent_users}") print(f"Total requests: {self.total_requests}") @@ -106,16 +106,16 @@ class BenchmarkStats: print(f"Failed requests: {len(self.errors)}") print(f"Success rate: {success_rate:.1f}%") print(f"Requests per second: {self.success_count / total_time:.2f}") - + if self.errors: - print(f"\nErrors (showing first 5):") + print("\nErrors (showing first 5):") for error in self.errors[:5]: print(f" {error}") class LlamaStackBenchmark: def __init__(self, base_url: str, model_id: str): - self.base_url = base_url.rstrip('/') + self.base_url = base_url.rstrip("/") self.model_id = model_id self.headers = {"Content-Type": "application/json"} self.test_messages = [ @@ -126,74 +126,67 @@ class LlamaStackBenchmark: [ {"role": "user", "content": "What is machine learning?"}, {"role": "assistant", "content": "Machine learning is a subset of AI..."}, - {"role": "user", "content": "Can you give me a practical example?"} - ] + {"role": "user", "content": "Can you give me a practical example?"}, + ], ] - - async def make_async_streaming_request(self) -> Tuple[float, int, float | None, str | None]: + async def make_async_streaming_request(self) -> tuple[float, int, float | None, str | None]: """Make a single async streaming chat completion request.""" messages = random.choice(self.test_messages) - payload = { - "model": self.model_id, - "messages": messages, - "stream": True, - "max_tokens": 100 - } - + payload = {"model": self.model_id, "messages": messages, "stream": True, "max_tokens": 100} + start_time = time.time() chunks_received = 0 ttft = None error = None - + session = aiohttp.ClientSession() - + try: async with session.post( f"{self.base_url}/chat/completions", headers=self.headers, json=payload, - timeout=aiohttp.ClientTimeout(total=30) + timeout=aiohttp.ClientTimeout(total=30), ) as response: if response.status == 200: async for line in response.content: if line: - line_str = line.decode('utf-8').strip() - if line_str.startswith('data: '): + line_str = line.decode("utf-8").strip() + if line_str.startswith("data: "): chunks_received += 1 if ttft is None: ttft = time.time() - start_time - if line_str == 'data: [DONE]': + if line_str == "data: [DONE]": break - + if chunks_received == 0: error = "No streaming chunks received" else: text = await response.text() error = f"HTTP {response.status}: {text[:100]}" - + except Exception as e: error = f"Request error: {str(e)}" finally: await session.close() - + response_time = time.time() - start_time return response_time, chunks_received, ttft, error - async def run_benchmark(self, duration: int, concurrent_users: int) -> BenchmarkStats: """Run benchmark using async requests for specified duration.""" stats = BenchmarkStats() stats.concurrent_users = concurrent_users stats.start_time = time.time() - + print(f"Starting benchmark: {duration}s duration, {concurrent_users} concurrent users") print(f"Target URL: {self.base_url}/chat/completions") print(f"Model: {self.model_id}") - + connector = aiohttp.TCPConnector(limit=concurrent_users) - async with aiohttp.ClientSession(connector=connector) as session: - + async with aiohttp.ClientSession(connector=connector): + async def worker(worker_id: int): """Worker that sends requests sequentially until canceled.""" request_count = 0 @@ -202,12 +195,12 @@ class LlamaStackBenchmark: response_time, chunks, ttft, error = await self.make_async_streaming_request() await stats.add_result(response_time, chunks, ttft, error) request_count += 1 - + except asyncio.CancelledError: break except Exception as e: await stats.add_result(0, 0, None, f"Worker {worker_id} error: {str(e)}") - + # Progress reporting task async def progress_reporter(): last_report_time = time.time() @@ -216,48 +209,52 @@ class LlamaStackBenchmark: await asyncio.sleep(1) # Report every second if time.time() >= last_report_time + 10: # Report every 10 seconds elapsed = time.time() - stats.start_time - print(f"Completed: {stats.total_requests} requests in {elapsed:.1f}s, RPS: {stats.total_requests / elapsed:.1f}") + print( + f"Completed: {stats.total_requests} requests in {elapsed:.1f}s, RPS: {stats.total_requests / elapsed:.1f}" + ) last_report_time = time.time() except asyncio.CancelledError: break - + # Spawn concurrent workers tasks = [asyncio.create_task(worker(i)) for i in range(concurrent_users)] progress_task = asyncio.create_task(progress_reporter()) tasks.append(progress_task) - + # Wait for duration then cancel all tasks await asyncio.sleep(duration) - + for task in tasks: task.cancel() - + # Wait for all tasks to complete await asyncio.gather(*tasks, return_exceptions=True) - + stats.end_time = time.time() return stats def main(): parser = argparse.ArgumentParser(description="Llama Stack Benchmark Tool") - parser.add_argument("--base-url", default=os.getenv("BENCHMARK_BASE_URL", "http://localhost:8000/v1/openai/v1"), - help="Base URL for the API (default: http://localhost:8000/v1/openai/v1)") - parser.add_argument("--model", default=os.getenv("INFERENCE_MODEL", "test-model"), - help="Model ID to use for requests") - parser.add_argument("--duration", type=int, default=60, - help="Duration in seconds to run benchmark (default: 60)") - parser.add_argument("--concurrent", type=int, default=10, - help="Number of concurrent users (default: 10)") - + parser.add_argument( + "--base-url", + default=os.getenv("BENCHMARK_BASE_URL", "http://localhost:8000/v1/openai/v1"), + help="Base URL for the API (default: http://localhost:8000/v1/openai/v1)", + ) + parser.add_argument( + "--model", default=os.getenv("INFERENCE_MODEL", "test-model"), help="Model ID to use for requests" + ) + parser.add_argument("--duration", type=int, default=60, help="Duration in seconds to run benchmark (default: 60)") + parser.add_argument("--concurrent", type=int, default=10, help="Number of concurrent users (default: 10)") + args = parser.parse_args() - + benchmark = LlamaStackBenchmark(args.base_url, args.model) - + try: stats = asyncio.run(benchmark.run_benchmark(args.duration, args.concurrent)) stats.print_summary() - + except KeyboardInterrupt: print("\nBenchmark interrupted by user") except Exception as e: diff --git a/docs/source/distributions/k8s-benchmark/openai-mock-server.py b/benchmarking/k8s-benchmark/openai-mock-server.py similarity index 60% rename from docs/source/distributions/k8s-benchmark/openai-mock-server.py rename to benchmarking/k8s-benchmark/openai-mock-server.py index de0680842..9e898af8e 100755 --- a/docs/source/distributions/k8s-benchmark/openai-mock-server.py +++ b/benchmarking/k8s-benchmark/openai-mock-server.py @@ -11,180 +11,192 @@ OpenAI-compatible mock server that returns: - Valid OpenAI-formatted chat completion responses with dynamic content """ -from flask import Flask, request, jsonify, Response -import time -import random -import uuid -import json import argparse +import json import os +import random +import time +import uuid + +from flask import Flask, Response, jsonify, request app = Flask(__name__) + # Models from environment variables def get_models(): models_str = os.getenv("MOCK_MODELS", "meta-llama/Llama-3.2-3B-Instruct") model_ids = [m.strip() for m in models_str.split(",") if m.strip()] - + return { "object": "list", "data": [ - { - "id": model_id, - "object": "model", - "created": 1234567890, - "owned_by": "vllm" - } - for model_id in model_ids - ] + {"id": model_id, "object": "model", "created": 1234567890, "owned_by": "vllm"} for model_id in model_ids + ], } + def generate_random_text(length=50): """Generate random but coherent text for responses.""" words = [ - "Hello", "there", "I'm", "an", "AI", "assistant", "ready", "to", "help", "you", - "with", "your", "questions", "and", "tasks", "today", "Let", "me","know", "what", - "you'd", "like", "to", "discuss", "or", "explore", "together", "I", "can", "assist", - "with", "various", "topics", "including", "coding", "writing", "analysis", "and", "more" + "Hello", + "there", + "I'm", + "an", + "AI", + "assistant", + "ready", + "to", + "help", + "you", + "with", + "your", + "questions", + "and", + "tasks", + "today", + "Let", + "me", + "know", + "what", + "you'd", + "like", + "to", + "discuss", + "or", + "explore", + "together", + "I", + "can", + "assist", + "with", + "various", + "topics", + "including", + "coding", + "writing", + "analysis", + "and", + "more", ] return " ".join(random.choices(words, k=length)) -@app.route('/v1/models', methods=['GET']) + +@app.route("/v1/models", methods=["GET"]) def list_models(): models = get_models() print(f"[MOCK] Returning models: {[m['id'] for m in models['data']]}") return jsonify(models) -@app.route('/v1/chat/completions', methods=['POST']) + +@app.route("/v1/chat/completions", methods=["POST"]) def chat_completions(): """Return OpenAI-formatted chat completion responses.""" data = request.get_json() - default_model = get_models()['data'][0]['id'] - model = data.get('model', default_model) - messages = data.get('messages', []) - stream = data.get('stream', False) - + default_model = get_models()["data"][0]["id"] + model = data.get("model", default_model) + messages = data.get("messages", []) + stream = data.get("stream", False) + print(f"[MOCK] Chat completion request - model: {model}, stream: {stream}") - + if stream: return handle_streaming_completion(model, messages) else: return handle_non_streaming_completion(model, messages) + def handle_non_streaming_completion(model, messages): response_text = generate_random_text(random.randint(20, 80)) - + # Calculate realistic token counts - prompt_tokens = sum(len(str(msg.get('content', '')).split()) for msg in messages) + prompt_tokens = sum(len(str(msg.get("content", "")).split()) for msg in messages) completion_tokens = len(response_text.split()) - + response = { "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", "object": "chat.completion", "created": int(time.time()), "model": model, - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": response_text - }, - "finish_reason": "stop" - } - ], + "choices": [{"index": 0, "message": {"role": "assistant", "content": response_text}, "finish_reason": "stop"}], "usage": { "prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, - "total_tokens": prompt_tokens + completion_tokens - } + "total_tokens": prompt_tokens + completion_tokens, + }, } - + return jsonify(response) + def handle_streaming_completion(model, messages): def generate_stream(): # Generate response text full_response = generate_random_text(random.randint(30, 100)) words = full_response.split() - + # Send initial chunk initial_chunk = { "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", "object": "chat.completion.chunk", "created": int(time.time()), "model": model, - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": ""} - } - ] + "choices": [{"index": 0, "delta": {"role": "assistant", "content": ""}}], } yield f"data: {json.dumps(initial_chunk)}\n\n" - + # Send word by word for i, word in enumerate(words): chunk = { "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", - "object": "chat.completion.chunk", + "object": "chat.completion.chunk", "created": int(time.time()), "model": model, - "choices": [ - { - "index": 0, - "delta": {"content": f"{word} " if i < len(words) - 1 else word} - } - ] + "choices": [{"index": 0, "delta": {"content": f"{word} " if i < len(words) - 1 else word}}], } yield f"data: {json.dumps(chunk)}\n\n" # Configurable delay to simulate realistic streaming stream_delay = float(os.getenv("STREAM_DELAY_SECONDS", "0.005")) time.sleep(stream_delay) - + # Send final chunk final_chunk = { "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", "object": "chat.completion.chunk", "created": int(time.time()), "model": model, - "choices": [ - { - "index": 0, - "delta": {"content": ""}, - "finish_reason": "stop" - } - ] + "choices": [{"index": 0, "delta": {"content": ""}, "finish_reason": "stop"}], } yield f"data: {json.dumps(final_chunk)}\n\n" yield "data: [DONE]\n\n" - + return Response( generate_stream(), - mimetype='text/event-stream', + mimetype="text/event-stream", headers={ - 'Cache-Control': 'no-cache', - 'Connection': 'keep-alive', - 'Access-Control-Allow-Origin': '*', - } + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "Access-Control-Allow-Origin": "*", + }, ) -@app.route('/health', methods=['GET']) + +@app.route("/health", methods=["GET"]) def health(): return jsonify({"status": "healthy", "type": "openai-mock"}) -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='OpenAI-compatible mock server') - parser.add_argument('--port', type=int, default=8081, - help='Port to run the server on (default: 8081)') + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="OpenAI-compatible mock server") + parser.add_argument("--port", type=int, default=8081, help="Port to run the server on (default: 8081)") args = parser.parse_args() - + port = args.port - + models = get_models() print("Starting OpenAI-compatible mock server...") print(f"- /models endpoint with: {[m['id'] for m in models['data']]}") print("- OpenAI-formatted chat/completion responses with dynamic content") print("- Streaming support with valid SSE format") print(f"- Listening on: http://0.0.0.0:{port}") - app.run(host='0.0.0.0', port=port, debug=False) + app.run(host="0.0.0.0", port=port, debug=False) diff --git a/docs/source/distributions/k8s-benchmark/profile_running_server.sh b/benchmarking/k8s-benchmark/profile_running_server.sh similarity index 100% rename from docs/source/distributions/k8s-benchmark/profile_running_server.sh rename to benchmarking/k8s-benchmark/profile_running_server.sh diff --git a/docs/source/distributions/k8s-benchmark/run-benchmark.sh b/benchmarking/k8s-benchmark/run-benchmark.sh similarity index 100% rename from docs/source/distributions/k8s-benchmark/run-benchmark.sh rename to benchmarking/k8s-benchmark/run-benchmark.sh diff --git a/docs/source/distributions/k8s-benchmark/stack-configmap.yaml b/benchmarking/k8s-benchmark/stack-configmap.yaml similarity index 100% rename from docs/source/distributions/k8s-benchmark/stack-configmap.yaml rename to benchmarking/k8s-benchmark/stack-configmap.yaml diff --git a/docs/source/distributions/k8s-benchmark/stack-k8s.yaml.template b/benchmarking/k8s-benchmark/stack-k8s.yaml.template similarity index 100% rename from docs/source/distributions/k8s-benchmark/stack-k8s.yaml.template rename to benchmarking/k8s-benchmark/stack-k8s.yaml.template diff --git a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml b/benchmarking/k8s-benchmark/stack_run_config.yaml similarity index 100% rename from docs/source/distributions/k8s-benchmark/stack_run_config.yaml rename to benchmarking/k8s-benchmark/stack_run_config.yaml diff --git a/docs/source/contributing/index.md b/docs/source/contributing/index.md index 1846f4d97..71c3bd5a6 100644 --- a/docs/source/contributing/index.md +++ b/docs/source/contributing/index.md @@ -35,5 +35,5 @@ testing/record-replay ### Benchmarking -```{include} ../../../docs/source/distributions/k8s-benchmark/README.md +```{include} ../../../benchmarking/k8s-benchmark/README.md ``` From 0c7f49490cdb6ff757659469d1401b515ac4402c Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 10 Sep 2025 14:34:18 -0700 Subject: [PATCH 097/124] fix(inference_store): on duplicate chat completion IDs, replace (#3408) # What does this PR do? Duplicate chat completion IDs can be generated during tests especially if they are replaying recorded responses across different tests. No need to warn or error under those circumstances. In the wild, this is not likely to happen at all (no evidence) so we aren't really hiding any problem. --- .../utils/inference/inference_store.py | 48 +++++++++++++++---- .../utils/sqlstore/authorized_sqlstore.py | 14 ++++++ 2 files changed, 53 insertions(+), 9 deletions(-) diff --git a/llama_stack/providers/utils/inference/inference_store.py b/llama_stack/providers/utils/inference/inference_store.py index 8c69b1683..17f4c6268 100644 --- a/llama_stack/providers/utils/inference/inference_store.py +++ b/llama_stack/providers/utils/inference/inference_store.py @@ -6,6 +6,8 @@ import asyncio from typing import Any +from sqlalchemy.exc import IntegrityError + from llama_stack.apis.inference import ( ListOpenAIChatCompletionResponse, OpenAIChatCompletion, @@ -129,16 +131,44 @@ class InferenceStore: raise ValueError("Inference store is not initialized") data = chat_completion.model_dump() + record_data = { + "id": data["id"], + "created": data["created"], + "model": data["model"], + "choices": data["choices"], + "input_messages": [message.model_dump() for message in input_messages], + } - await self.sql_store.insert( - table="chat_completions", - data={ - "id": data["id"], - "created": data["created"], - "model": data["model"], - "choices": data["choices"], - "input_messages": [message.model_dump() for message in input_messages], - }, + try: + await self.sql_store.insert( + table="chat_completions", + data=record_data, + ) + except IntegrityError as e: + # Duplicate chat completion IDs can be generated during tests especially if they are replaying + # recorded responses across different tests. No need to warn or error under those circumstances. + # In the wild, this is not likely to happen at all (no evidence) so we aren't really hiding any problem. + + # Check if it's a unique constraint violation + error_message = str(e.orig) if e.orig else str(e) + if self._is_unique_constraint_error(error_message): + # Update the existing record instead + await self.sql_store.update(table="chat_completions", data=record_data, where={"id": data["id"]}) + else: + # Re-raise if it's not a unique constraint error + raise + + def _is_unique_constraint_error(self, error_message: str) -> bool: + """Check if the error is specifically a unique constraint violation.""" + error_lower = error_message.lower() + return any( + indicator in error_lower + for indicator in [ + "unique constraint failed", # SQLite + "duplicate key", # PostgreSQL + "unique violation", # PostgreSQL alternative + "duplicate entry", # MySQL + ] ) async def list_chat_completions( diff --git a/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py b/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py index 867ba2f55..acb688f96 100644 --- a/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py +++ b/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py @@ -172,6 +172,20 @@ class AuthorizedSqlStore: return results.data[0] if results.data else None + async def update(self, table: str, data: Mapping[str, Any], where: Mapping[str, Any]) -> None: + """Update rows with automatic access control attribute capture.""" + enhanced_data = dict(data) + + current_user = get_authenticated_user() + if current_user: + enhanced_data["owner_principal"] = current_user.principal + enhanced_data["access_attributes"] = current_user.attributes + else: + enhanced_data["owner_principal"] = None + enhanced_data["access_attributes"] = None + + await self.sql_store.update(table, enhanced_data, where) + async def delete(self, table: str, where: Mapping[str, Any]) -> None: """Delete rows with automatic access control filtering.""" await self.sql_store.delete(table, where) From 8e05c68d159a40d54768a9473d63b68a5bfbf369 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Han?= Date: Thu, 11 Sep 2025 10:19:59 +0200 Subject: [PATCH 098/124] chore: remove openai dependency from providers (#3398) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? The openai package is already a dependency of the llama-stack project itself, so let's the project dictate which openai version we need and avoid potential breakage with unsatisfiable dependency resolution. Signed-off-by: Sébastien Han --- llama_stack/providers/registry/batches.py | 2 +- llama_stack/providers/registry/inference.py | 20 ++++++++------------ llama_stack/providers/registry/scoring.py | 2 +- pyproject.toml | 4 +--- uv.lock | 8 ++------ 5 files changed, 13 insertions(+), 23 deletions(-) diff --git a/llama_stack/providers/registry/batches.py b/llama_stack/providers/registry/batches.py index de7886efb..a07942486 100644 --- a/llama_stack/providers/registry/batches.py +++ b/llama_stack/providers/registry/batches.py @@ -13,7 +13,7 @@ def available_providers() -> list[ProviderSpec]: InlineProviderSpec( api=Api.batches, provider_type="inline::reference", - pip_packages=["openai"], + pip_packages=[], module="llama_stack.providers.inline.batches.reference", config_class="llama_stack.providers.inline.batches.reference.config.ReferenceBatchesImplConfig", api_dependencies=[ diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 541fbb432..8912560cb 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -75,7 +75,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="vllm", - pip_packages=["openai"], + pip_packages=[], module="llama_stack.providers.remote.inference.vllm", config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig", description="Remote vLLM inference provider for connecting to vLLM servers.", @@ -151,9 +151,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="databricks", - pip_packages=[ - "openai", - ], + pip_packages=[], module="llama_stack.providers.remote.inference.databricks", config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig", description="Databricks inference provider for running models on Databricks' unified analytics platform.", @@ -163,9 +161,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="nvidia", - pip_packages=[ - "openai", - ], + pip_packages=[], module="llama_stack.providers.remote.inference.nvidia", config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig", description="NVIDIA inference provider for accessing NVIDIA NIM models and AI services.", @@ -175,7 +171,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="runpod", - pip_packages=["openai"], + pip_packages=[], module="llama_stack.providers.remote.inference.runpod", config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig", description="RunPod inference provider for running models on RunPod's cloud GPU platform.", @@ -207,7 +203,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="gemini", - pip_packages=["litellm", "openai"], + pip_packages=["litellm"], module="llama_stack.providers.remote.inference.gemini", config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig", provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator", @@ -218,7 +214,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="vertexai", - pip_packages=["litellm", "google-cloud-aiplatform", "openai"], + pip_packages=["litellm", "google-cloud-aiplatform"], module="llama_stack.providers.remote.inference.vertexai", config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig", provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator", @@ -248,7 +244,7 @@ Available Models: api=Api.inference, adapter=AdapterSpec( adapter_type="groq", - pip_packages=["litellm", "openai"], + pip_packages=["litellm"], module="llama_stack.providers.remote.inference.groq", config_class="llama_stack.providers.remote.inference.groq.GroqConfig", provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator", @@ -270,7 +266,7 @@ Available Models: api=Api.inference, adapter=AdapterSpec( adapter_type="sambanova", - pip_packages=["litellm", "openai"], + pip_packages=["litellm"], module="llama_stack.providers.remote.inference.sambanova", config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig", provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator", diff --git a/llama_stack/providers/registry/scoring.py b/llama_stack/providers/registry/scoring.py index 79293d888..a4ec54ed2 100644 --- a/llama_stack/providers/registry/scoring.py +++ b/llama_stack/providers/registry/scoring.py @@ -38,7 +38,7 @@ def available_providers() -> list[ProviderSpec]: InlineProviderSpec( api=Api.scoring, provider_type="inline::braintrust", - pip_packages=["autoevals", "openai"], + pip_packages=["autoevals"], module="llama_stack.providers.inline.scoring.braintrust", config_class="llama_stack.providers.inline.scoring.braintrust.BraintrustScoringConfig", api_dependencies=[ diff --git a/pyproject.toml b/pyproject.toml index 0414aafb0..72c4f6f9e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ dependencies = [ "jinja2>=3.1.6", "jsonschema", "llama-stack-client>=0.2.21", - "openai>=1.99.6", + "openai>=1.100.0", # for expires_after support "prompt-toolkit", "python-dotenv", "python-jose[cryptography]", @@ -80,7 +80,6 @@ dev = [ unit = [ "sqlite-vec", "ollama", - "openai", "aiosqlite", "aiohttp", "psycopg2-binary>=2.9.0", @@ -105,7 +104,6 @@ unit = [ # separately. If you are using "uv" to execute your tests, you can use the "--group" flag to specify extra # dependencies. test = [ - "openai>=1.100.0", # for expires_after support "aiosqlite", "aiohttp", "torch>=2.6.0", diff --git a/uv.lock b/uv.lock index 3d7713f54..065eb3876 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 2 requires-python = ">=3.12" resolution-markers = [ "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", @@ -1839,7 +1839,6 @@ test = [ { name = "datasets" }, { name = "mcp" }, { name = "milvus-lite" }, - { name = "openai" }, { name = "psycopg2-binary" }, { name = "pymilvus" }, { name = "pypdf" }, @@ -1865,7 +1864,6 @@ unit = [ { name = "milvus-lite" }, { name = "moto", extra = ["s3"] }, { name = "ollama" }, - { name = "openai" }, { name = "psycopg2-binary" }, { name = "pymilvus" }, { name = "pypdf" }, @@ -1889,7 +1887,7 @@ requires-dist = [ { name = "jsonschema" }, { name = "llama-stack-client", specifier = ">=0.2.21" }, { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.21" }, - { name = "openai", specifier = ">=1.99.6" }, + { name = "openai", specifier = ">=1.100.0" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, { name = "pandas", marker = "extra == 'ui'" }, @@ -1959,7 +1957,6 @@ test = [ { name = "datasets", specifier = ">=4.0.0" }, { name = "mcp" }, { name = "milvus-lite", specifier = ">=2.5.0" }, - { name = "openai", specifier = ">=1.100.0" }, { name = "psycopg2-binary", specifier = ">=2.9.0" }, { name = "pymilvus", specifier = ">=2.6.1" }, { name = "pypdf" }, @@ -1984,7 +1981,6 @@ unit = [ { name = "milvus-lite", specifier = ">=2.5.0" }, { name = "moto", extras = ["s3"], specifier = ">=5.1.10" }, { name = "ollama" }, - { name = "openai" }, { name = "psycopg2-binary", specifier = ">=2.9.0" }, { name = "pymilvus", specifier = ">=2.6.1" }, { name = "pypdf" }, From 2838d5a20f888c9f8fad666272dd9ca8d3bb4884 Mon Sep 17 00:00:00 2001 From: Sumanth Kamenani Date: Thu, 11 Sep 2025 05:41:53 -0400 Subject: [PATCH 099/124] fix: AWS Bedrock inference profile ID conversion for region-specific endpoints (#3386) Fixes #3370 AWS switched to requiring region-prefixed inference profile IDs instead of foundation model IDs for on-demand throughput. This was causing ValidationException errors. Added auto-detection based on boto3 client region to convert model IDs like meta.llama3-1-70b-instruct-v1:0 to us.meta.llama3-1-70b-instruct-v1:0 depending on the detected region. Also handles edge cases like ARNs, case insensitive regions, and None regions. Tested with this request. ```json { "model_id": "meta.llama3-1-8b-instruct-v1:0", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "tell me a riddle" } ], "sampling_params": { "strategy": { "type": "top_p", "temperature": 0.7, "top_p": 0.9 }, "max_tokens": 512 } } ``` image --- .../remote/inference/bedrock/bedrock.py | 51 +++++++++++++++++- tests/unit/providers/test_bedrock.py | 53 +++++++++++++++++++ 2 files changed, 102 insertions(+), 2 deletions(-) create mode 100644 tests/unit/providers/test_bedrock.py diff --git a/llama_stack/providers/remote/inference/bedrock/bedrock.py b/llama_stack/providers/remote/inference/bedrock/bedrock.py index 63ea196f6..106caed9b 100644 --- a/llama_stack/providers/remote/inference/bedrock/bedrock.py +++ b/llama_stack/providers/remote/inference/bedrock/bedrock.py @@ -53,6 +53,43 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( from .models import MODEL_ENTRIES +REGION_PREFIX_MAP = { + "us": "us.", + "eu": "eu.", + "ap": "ap.", +} + + +def _get_region_prefix(region: str | None) -> str: + # AWS requires region prefixes for inference profiles + if region is None: + return "us." # default to US when we don't know + + # Handle case insensitive region matching + region_lower = region.lower() + for prefix in REGION_PREFIX_MAP: + if region_lower.startswith(f"{prefix}-"): + return REGION_PREFIX_MAP[prefix] + + # Fallback to US for anything we don't recognize + return "us." + + +def _to_inference_profile_id(model_id: str, region: str = None) -> str: + # Return ARNs unchanged + if model_id.startswith("arn:"): + return model_id + + # Return inference profile IDs that already have regional prefixes + if any(model_id.startswith(p) for p in REGION_PREFIX_MAP.values()): + return model_id + + # Default to US East when no region is provided + if region is None: + region = "us-east-1" + + return _get_region_prefix(region) + model_id + class BedrockInferenceAdapter( ModelRegistryHelper, @@ -166,8 +203,13 @@ class BedrockInferenceAdapter( options["repetition_penalty"] = sampling_params.repetition_penalty prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model)) + + # Convert foundation model ID to inference profile ID + region_name = self.client.meta.region_name + inference_profile_id = _to_inference_profile_id(bedrock_model, region_name) + return { - "modelId": bedrock_model, + "modelId": inference_profile_id, "body": json.dumps( { "prompt": prompt, @@ -185,6 +227,11 @@ class BedrockInferenceAdapter( task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) + + # Convert foundation model ID to inference profile ID + region_name = self.client.meta.region_name + inference_profile_id = _to_inference_profile_id(model.provider_resource_id, region_name) + embeddings = [] for content in contents: assert not content_has_media(content), "Bedrock does not support media for embeddings" @@ -193,7 +240,7 @@ class BedrockInferenceAdapter( body = json.dumps(input_body) response = self.client.invoke_model( body=body, - modelId=model.provider_resource_id, + modelId=inference_profile_id, accept="application/json", contentType="application/json", ) diff --git a/tests/unit/providers/test_bedrock.py b/tests/unit/providers/test_bedrock.py new file mode 100644 index 000000000..1ff07bbbe --- /dev/null +++ b/tests/unit/providers/test_bedrock.py @@ -0,0 +1,53 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.providers.remote.inference.bedrock.bedrock import ( + _get_region_prefix, + _to_inference_profile_id, +) + + +def test_region_prefixes(): + assert _get_region_prefix("us-east-1") == "us." + assert _get_region_prefix("eu-west-1") == "eu." + assert _get_region_prefix("ap-south-1") == "ap." + assert _get_region_prefix("ca-central-1") == "us." + + # Test case insensitive + assert _get_region_prefix("US-EAST-1") == "us." + assert _get_region_prefix("EU-WEST-1") == "eu." + assert _get_region_prefix("Ap-South-1") == "ap." + + # Test None region + assert _get_region_prefix(None) == "us." + + +def test_model_id_conversion(): + # Basic conversion + assert ( + _to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0", "us-east-1") == "us.meta.llama3-1-70b-instruct-v1:0" + ) + + # Already has prefix + assert ( + _to_inference_profile_id("us.meta.llama3-1-70b-instruct-v1:0", "us-east-1") + == "us.meta.llama3-1-70b-instruct-v1:0" + ) + + # ARN should be returned unchanged + arn = "arn:aws:bedrock:us-east-1:123456789012:inference-profile/us.meta.llama3-1-70b-instruct-v1:0" + assert _to_inference_profile_id(arn, "us-east-1") == arn + + # ARN should be returned unchanged even without region + assert _to_inference_profile_id(arn) == arn + + # Optional region parameter defaults to us-east-1 + assert _to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0") == "us.meta.llama3-1-70b-instruct-v1:0" + + # Different regions work with optional parameter + assert ( + _to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0", "eu-west-1") == "eu.meta.llama3-1-70b-instruct-v1:0" + ) From c2d281e01b360ba0a2db177b90df6e7ba4df8501 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Thu, 11 Sep 2025 07:48:19 -0400 Subject: [PATCH 100/124] chore(replay): improve replay robustness with un-validated construction (#3414) # What does this PR do? some providers do not produce spec compliant outputs. when this happens the replay infra will fail to construct the proper types and will return a dict to the client. the client likely does not expect a dict. this was discovered with tgi, which returns finish_reason="" when valid values are "stop", "length" or "content_filter" ## Test Plan ci --- llama_stack/testing/inference_recorder.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 298758c92..e78f493a6 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -105,8 +105,12 @@ def _deserialize_response(data: dict[str, Any]) -> Any: return cls.model_validate(data["__data__"]) except (ImportError, AttributeError, TypeError, ValueError) as e: - logger.warning(f"Failed to deserialize object of type {data['__type__']}: {e}") - return data["__data__"] + logger.warning(f"Failed to deserialize object of type {data['__type__']} with model_validate: {e}") + try: + return cls.model_construct(**data["__data__"]) + except Exception as e: + logger.warning(f"Failed to deserialize object of type {data['__type__']} with model_construct: {e}") + return data["__data__"] return data From f31bcc11bc9e4a88ce82dadafea8d4b0cb5f7230 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Han?= Date: Thu, 11 Sep 2025 13:48:38 +0200 Subject: [PATCH 101/124] feat: add Azure OpenAI inference provider support (#3396) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? Llama-stack now supports a new OpenAI compatible endpoint with Azure OpenAI. The starter distro has been updated to add the new remote inference provider. A few tests have been modified and improved. ## Test Plan Deploy a model in the Aure portal then: ``` $ AZURE_API_KEY=... AZURE_API_BASE=... uv run llama stack build --image-type venv --providers inference=remote::azure --run ... $ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model azure/gpt-4.1 tests/integration/inference/test_openai_completion.py ... Results: ``` ============================================= test session starts ============================================== platform darwin -- Python 3.12.8, pytest-8.4.1, pluggy-1.6.0 -- /Users/leseb/Documents/AI/llama-stack/.venv/bin/python3 cachedir: .pytest_cache metadata: {'Python': '3.12.8', 'Platform': 'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '8.4.1', 'pluggy': '1.6.0'}, 'Plugins': {'anyio': '4.9.0', 'html': '4.1.1', 'socket': '0.7.0', 'asyncio': '1.1.0', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'cov': '6.2.1', 'nbval': '0.11.0', 'hydra-core': '1.3.2'}} rootdir: /Users/leseb/Documents/AI/llama-stack configfile: pyproject.toml plugins: anyio-4.9.0, html-4.1.1, socket-0.7.0, asyncio-1.1.0, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, cov-6.2.1, nbval-0.11.0, hydra-core-1.3.2 asyncio: mode=Mode.AUTO, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 27 items tests/integration/inference/test_openai_completion.py::test_openai_completion_non_streaming[txt=azure/gpt-5-mini-inference:completion:sanity] SKIPPED [ 3%] tests/integration/inference/test_openai_completion.py::test_openai_completion_non_streaming_suffix[txt=azure/gpt-5-mini-inference:completion:suffix] SKIPPED [ 7%] tests/integration/inference/test_openai_completion.py::test_openai_completion_streaming[txt=azure/gpt-5-mini-inference:completion:sanity] SKIPPED [ 11%] tests/integration/inference/test_openai_completion.py::test_openai_completion_prompt_logprobs[txt=azure/gpt-5-mini-1] SKIPPED [ 14%] tests/integration/inference/test_openai_completion.py::test_openai_completion_guided_choice[txt=azure/gpt-5-mini] SKIPPED [ 18%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[openai_client-txt=azure/gpt-5-mini-inference:chat_completion:non_streaming_01] PASSED [ 22%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[openai_client-txt=azure/gpt-5-mini-inference:chat_completion:streaming_01] PASSED [ 25%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming_with_n[openai_client-txt=azure/gpt-5-mini-inference:chat_completion:streaming_01] PASSED [ 29%] tests/integration/inference/test_openai_completion.py::test_inference_store[openai_client-txt=azure/gpt-5-mini-True] PASSED [ 33%] tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=azure/gpt-5-mini-True] PASSED [ 37%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming_with_file[txt=azure/gpt-5-mini] SKIPPEDed files.) [ 40%] tests/integration/inference/test_openai_completion.py::test_openai_completion_prompt_logprobs[txt=azure/gpt-5-mini-0] SKIPPED [ 44%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[openai_client-txt=azure/gpt-5-mini-inference:chat_completion:non_streaming_02] PASSED [ 48%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[openai_client-txt=azure/gpt-5-mini-inference:chat_completion:streaming_02] PASSED [ 51%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming_with_n[openai_client-txt=azure/gpt-5-mini-inference:chat_completion:streaming_02] PASSED [ 55%] tests/integration/inference/test_openai_completion.py::test_inference_store[openai_client-txt=azure/gpt-5-mini-False] PASSED [ 59%] tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=azure/gpt-5-mini-False] PASSED [ 62%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[client_with_models-txt=azure/gpt-5-mini-inference:chat_completion:non_streaming_01] PASSED [ 66%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[client_with_models-txt=azure/gpt-5-mini-inference:chat_completion:streaming_01] PASSED [ 70%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming_with_n[client_with_models-txt=azure/gpt-5-mini-inference:chat_completion:streaming_01] PASSED [ 74%] tests/integration/inference/test_openai_completion.py::test_inference_store[client_with_models-txt=azure/gpt-5-mini-True] PASSED [ 77%] tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=azure/gpt-5-mini-True] PASSED [ 81%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[client_with_models-txt=azure/gpt-5-mini-inference:chat_completion:non_streaming_02] PASSED [ 85%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[client_with_models-txt=azure/gpt-5-mini-inference:chat_completion:streaming_02] PASSED [ 88%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming_with_n[client_with_models-txt=azure/gpt-5-mini-inference:chat_completion:streaming_02] PASSED [ 92%] tests/integration/inference/test_openai_completion.py::test_inference_store[client_with_models-txt=azure/gpt-5-mini-False] PASSED [ 96%] tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=azure/gpt-5-mini-False] PASSED [100%] =========================================== short test summary info ============================================ SKIPPED [3] tests/integration/inference/test_openai_completion.py:63: Model azure/gpt-5-mini hosted by remote::azure doesn't support OpenAI completions. SKIPPED [3] tests/integration/inference/test_openai_completion.py:118: Model azure/gpt-5-mini hosted by remote::azure doesn't support vllm extra_body parameters. SKIPPED [1] tests/integration/inference/test_openai_completion.py:124: Model azure/gpt-5-mini hosted by remote::azure doesn't support chat completion calls with base64 encoded files. ================================== 20 passed, 7 skipped, 2 warnings in 51.77s ================================== ``` Signed-off-by: Sébastien Han --- docs/source/providers/inference/index.md | 1 + .../providers/inference/remote_azure.md | 29 + llama_stack/distributions/ci-tests/build.yaml | 1 + llama_stack/distributions/ci-tests/run.yaml | 7 + .../distributions/starter-gpu/build.yaml | 1 + .../distributions/starter-gpu/run.yaml | 7 + llama_stack/distributions/starter/build.yaml | 1 + llama_stack/distributions/starter/run.yaml | 7 + llama_stack/distributions/starter/starter.py | 18 + llama_stack/providers/registry/inference.py | 15 + .../remote/inference/azure/__init__.py | 15 + .../providers/remote/inference/azure/azure.py | 64 + .../remote/inference/azure/config.py | 63 + .../remote/inference/azure/models.py | 28 + .../inference/test_openai_completion.py | 53 +- .../inference/test_text_inference.py | 3 +- .../recordings/responses/0fda25b9241c.json | 71 + .../recordings/responses/2b2ad549510d.json | 448 ++++ .../recordings/responses/57b67d1b1a36.json | 71 + .../recordings/responses/8752115f8d0c.json | 71 + .../recordings/responses/94d11daee205.json | 1178 +++++++++ .../recordings/responses/9f3d749cc1c8.json | 1150 +++++++++ .../recordings/responses/c791119e6359.json | 98 + .../recordings/responses/d3e27b7234e2.json | 2150 +++++++++++++++++ .../recordings/responses/fb785db7fafd.json | 310 +++ .../recordings/responses/ff3271401fb4.json | 556 +++++ 26 files changed, 6403 insertions(+), 13 deletions(-) create mode 100644 docs/source/providers/inference/remote_azure.md create mode 100644 llama_stack/providers/remote/inference/azure/__init__.py create mode 100644 llama_stack/providers/remote/inference/azure/azure.py create mode 100644 llama_stack/providers/remote/inference/azure/config.py create mode 100644 llama_stack/providers/remote/inference/azure/models.py create mode 100644 tests/integration/recordings/responses/0fda25b9241c.json create mode 100644 tests/integration/recordings/responses/2b2ad549510d.json create mode 100644 tests/integration/recordings/responses/57b67d1b1a36.json create mode 100644 tests/integration/recordings/responses/8752115f8d0c.json create mode 100644 tests/integration/recordings/responses/94d11daee205.json create mode 100644 tests/integration/recordings/responses/9f3d749cc1c8.json create mode 100644 tests/integration/recordings/responses/c791119e6359.json create mode 100644 tests/integration/recordings/responses/d3e27b7234e2.json create mode 100644 tests/integration/recordings/responses/fb785db7fafd.json create mode 100644 tests/integration/recordings/responses/ff3271401fb4.json diff --git a/docs/source/providers/inference/index.md b/docs/source/providers/inference/index.md index b6d215474..c5720daef 100644 --- a/docs/source/providers/inference/index.md +++ b/docs/source/providers/inference/index.md @@ -18,6 +18,7 @@ This section contains documentation for all available providers for the **infere inline_meta-reference inline_sentence-transformers remote_anthropic +remote_azure remote_bedrock remote_cerebras remote_databricks diff --git a/docs/source/providers/inference/remote_azure.md b/docs/source/providers/inference/remote_azure.md new file mode 100644 index 000000000..19f8f418b --- /dev/null +++ b/docs/source/providers/inference/remote_azure.md @@ -0,0 +1,29 @@ +# remote::azure + +## Description + + +Azure OpenAI inference provider for accessing GPT models and other Azure services. +Provider documentation +https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview + + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `api_key` | `` | No | | Azure API key for Azure | +| `api_base` | `` | No | | Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com) | +| `api_version` | `str \| None` | No | | Azure API version for Azure (e.g., 2024-12-01-preview) | +| `api_type` | `str \| None` | No | azure | Azure API type for Azure (e.g., azure) | + +## Sample Configuration + +```yaml +api_key: ${env.AZURE_API_KEY:=} +api_base: ${env.AZURE_API_BASE:=} +api_version: ${env.AZURE_API_VERSION:=} +api_type: ${env.AZURE_API_TYPE:=} + +``` + diff --git a/llama_stack/distributions/ci-tests/build.yaml b/llama_stack/distributions/ci-tests/build.yaml index 8e6c0bf67..a4d920cd6 100644 --- a/llama_stack/distributions/ci-tests/build.yaml +++ b/llama_stack/distributions/ci-tests/build.yaml @@ -17,6 +17,7 @@ distribution_spec: - provider_type: remote::vertexai - provider_type: remote::groq - provider_type: remote::sambanova + - provider_type: remote::azure - provider_type: inline::sentence-transformers vector_io: - provider_type: inline::faiss diff --git a/llama_stack/distributions/ci-tests/run.yaml b/llama_stack/distributions/ci-tests/run.yaml index 26a677c7a..a478a3872 100644 --- a/llama_stack/distributions/ci-tests/run.yaml +++ b/llama_stack/distributions/ci-tests/run.yaml @@ -81,6 +81,13 @@ providers: config: url: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} + - provider_id: ${env.AZURE_API_KEY:+azure} + provider_type: remote::azure + config: + api_key: ${env.AZURE_API_KEY:=} + api_base: ${env.AZURE_API_BASE:=} + api_version: ${env.AZURE_API_VERSION:=} + api_type: ${env.AZURE_API_TYPE:=} - provider_id: sentence-transformers provider_type: inline::sentence-transformers vector_io: diff --git a/llama_stack/distributions/starter-gpu/build.yaml b/llama_stack/distributions/starter-gpu/build.yaml index ff7c58e6f..05a2bf180 100644 --- a/llama_stack/distributions/starter-gpu/build.yaml +++ b/llama_stack/distributions/starter-gpu/build.yaml @@ -18,6 +18,7 @@ distribution_spec: - provider_type: remote::vertexai - provider_type: remote::groq - provider_type: remote::sambanova + - provider_type: remote::azure - provider_type: inline::sentence-transformers vector_io: - provider_type: inline::faiss diff --git a/llama_stack/distributions/starter-gpu/run.yaml b/llama_stack/distributions/starter-gpu/run.yaml index 5d9dfcb27..786506706 100644 --- a/llama_stack/distributions/starter-gpu/run.yaml +++ b/llama_stack/distributions/starter-gpu/run.yaml @@ -81,6 +81,13 @@ providers: config: url: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} + - provider_id: ${env.AZURE_API_KEY:+azure} + provider_type: remote::azure + config: + api_key: ${env.AZURE_API_KEY:=} + api_base: ${env.AZURE_API_BASE:=} + api_version: ${env.AZURE_API_VERSION:=} + api_type: ${env.AZURE_API_TYPE:=} - provider_id: sentence-transformers provider_type: inline::sentence-transformers vector_io: diff --git a/llama_stack/distributions/starter/build.yaml b/llama_stack/distributions/starter/build.yaml index e84e528da..2f0cd24fd 100644 --- a/llama_stack/distributions/starter/build.yaml +++ b/llama_stack/distributions/starter/build.yaml @@ -18,6 +18,7 @@ distribution_spec: - provider_type: remote::vertexai - provider_type: remote::groq - provider_type: remote::sambanova + - provider_type: remote::azure - provider_type: inline::sentence-transformers vector_io: - provider_type: inline::faiss diff --git a/llama_stack/distributions/starter/run.yaml b/llama_stack/distributions/starter/run.yaml index a3962b8aa..2814b2ced 100644 --- a/llama_stack/distributions/starter/run.yaml +++ b/llama_stack/distributions/starter/run.yaml @@ -81,6 +81,13 @@ providers: config: url: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} + - provider_id: ${env.AZURE_API_KEY:+azure} + provider_type: remote::azure + config: + api_key: ${env.AZURE_API_KEY:=} + api_base: ${env.AZURE_API_BASE:=} + api_version: ${env.AZURE_API_VERSION:=} + api_type: ${env.AZURE_API_TYPE:=} - provider_id: sentence-transformers provider_type: inline::sentence-transformers vector_io: diff --git a/llama_stack/distributions/starter/starter.py b/llama_stack/distributions/starter/starter.py index 2fca52700..c2dfe95ad 100644 --- a/llama_stack/distributions/starter/starter.py +++ b/llama_stack/distributions/starter/starter.py @@ -59,6 +59,7 @@ ENABLED_INFERENCE_PROVIDERS = [ "cerebras", "nvidia", "bedrock", + "azure", ] INFERENCE_PROVIDER_IDS = { @@ -68,6 +69,7 @@ INFERENCE_PROVIDER_IDS = { "cerebras": "${env.CEREBRAS_API_KEY:+cerebras}", "nvidia": "${env.NVIDIA_API_KEY:+nvidia}", "vertexai": "${env.VERTEX_AI_PROJECT:+vertexai}", + "azure": "${env.AZURE_API_KEY:+azure}", } @@ -277,5 +279,21 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate: "http://localhost:11434", "Ollama URL", ), + "AZURE_API_KEY": ( + "", + "Azure API Key", + ), + "AZURE_API_BASE": ( + "", + "Azure API Base", + ), + "AZURE_API_VERSION": ( + "", + "Azure API Version", + ), + "AZURE_API_TYPE": ( + "azure", + "Azure API Type", + ), }, ) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 8912560cb..64196152b 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -295,4 +295,19 @@ Available Models: description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.", ), ), + remote_provider_spec( + api=Api.inference, + adapter=AdapterSpec( + adapter_type="azure", + pip_packages=["litellm"], + module="llama_stack.providers.remote.inference.azure", + config_class="llama_stack.providers.remote.inference.azure.AzureConfig", + provider_data_validator="llama_stack.providers.remote.inference.azure.config.AzureProviderDataValidator", + description=""" +Azure OpenAI inference provider for accessing GPT models and other Azure services. +Provider documentation +https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview +""", + ), + ), ] diff --git a/llama_stack/providers/remote/inference/azure/__init__.py b/llama_stack/providers/remote/inference/azure/__init__.py new file mode 100644 index 000000000..87bcaf309 --- /dev/null +++ b/llama_stack/providers/remote/inference/azure/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .config import AzureConfig + + +async def get_adapter_impl(config: AzureConfig, _deps): + from .azure import AzureInferenceAdapter + + impl = AzureInferenceAdapter(config) + await impl.initialize() + return impl diff --git a/llama_stack/providers/remote/inference/azure/azure.py b/llama_stack/providers/remote/inference/azure/azure.py new file mode 100644 index 000000000..449bbbb1c --- /dev/null +++ b/llama_stack/providers/remote/inference/azure/azure.py @@ -0,0 +1,64 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any +from urllib.parse import urljoin + +from llama_stack.apis.inference import ChatCompletionRequest +from llama_stack.providers.utils.inference.litellm_openai_mixin import ( + LiteLLMOpenAIMixin, +) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin + +from .config import AzureConfig +from .models import MODEL_ENTRIES + + +class AzureInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): + def __init__(self, config: AzureConfig) -> None: + LiteLLMOpenAIMixin.__init__( + self, + MODEL_ENTRIES, + litellm_provider_name="azure", + api_key_from_config=config.api_key.get_secret_value(), + provider_data_api_key_field="azure_api_key", + openai_compat_api_base=str(config.api_base), + ) + self.config = config + + # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + """ + Get the Azure API base URL. + + Returns the Azure API base URL from the configuration. + """ + return urljoin(str(self.config.api_base), "/openai/v1") + + async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]: + # Get base parameters from parent + params = await super()._get_params(request) + + # Add Azure specific parameters + provider_data = self.get_request_provider_data() + if provider_data: + if getattr(provider_data, "azure_api_key", None): + params["api_key"] = provider_data.azure_api_key + if getattr(provider_data, "azure_api_base", None): + params["api_base"] = provider_data.azure_api_base + if getattr(provider_data, "azure_api_version", None): + params["api_version"] = provider_data.azure_api_version + if getattr(provider_data, "azure_api_type", None): + params["api_type"] = provider_data.azure_api_type + else: + params["api_key"] = self.config.api_key.get_secret_value() + params["api_base"] = str(self.config.api_base) + params["api_version"] = self.config.api_version + params["api_type"] = self.config.api_type + + return params diff --git a/llama_stack/providers/remote/inference/azure/config.py b/llama_stack/providers/remote/inference/azure/config.py new file mode 100644 index 000000000..fe9d61d53 --- /dev/null +++ b/llama_stack/providers/remote/inference/azure/config.py @@ -0,0 +1,63 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +from typing import Any + +from pydantic import BaseModel, Field, HttpUrl, SecretStr + +from llama_stack.schema_utils import json_schema_type + + +class AzureProviderDataValidator(BaseModel): + azure_api_key: SecretStr = Field( + description="Azure API key for Azure", + ) + azure_api_base: HttpUrl = Field( + description="Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com)", + ) + azure_api_version: str | None = Field( + default=None, + description="Azure API version for Azure (e.g., 2024-06-01)", + ) + azure_api_type: str | None = Field( + default="azure", + description="Azure API type for Azure (e.g., azure)", + ) + + +@json_schema_type +class AzureConfig(BaseModel): + api_key: SecretStr = Field( + description="Azure API key for Azure", + ) + api_base: HttpUrl = Field( + description="Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com)", + ) + api_version: str | None = Field( + default_factory=lambda: os.getenv("AZURE_API_VERSION"), + description="Azure API version for Azure (e.g., 2024-12-01-preview)", + ) + api_type: str | None = Field( + default_factory=lambda: os.getenv("AZURE_API_TYPE", "azure"), + description="Azure API type for Azure (e.g., azure)", + ) + + @classmethod + def sample_run_config( + cls, + api_key: str = "${env.AZURE_API_KEY:=}", + api_base: str = "${env.AZURE_API_BASE:=}", + api_version: str = "${env.AZURE_API_VERSION:=}", + api_type: str = "${env.AZURE_API_TYPE:=}", + **kwargs, + ) -> dict[str, Any]: + return { + "api_key": api_key, + "api_base": api_base, + "api_version": api_version, + "api_type": api_type, + } diff --git a/llama_stack/providers/remote/inference/azure/models.py b/llama_stack/providers/remote/inference/azure/models.py new file mode 100644 index 000000000..64c87969b --- /dev/null +++ b/llama_stack/providers/remote/inference/azure/models.py @@ -0,0 +1,28 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.providers.utils.inference.model_registry import ( + ProviderModelEntry, +) + +# https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions +LLM_MODEL_IDS = [ + "gpt-5", + "gpt-5-mini", + "gpt-5-nano", + "gpt-5-chat", + "o1", + "o1-mini", + "o3-mini", + "o4-mini", + "gpt-4.1", + "gpt-4.1-mini", + "gpt-4.1-nano", +] + +SAFETY_MODELS_ENTRIES = list[ProviderModelEntry]() + +MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + SAFETY_MODELS_ENTRIES diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index f9c837ebd..22dec8876 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -6,12 +6,25 @@ import time +import unicodedata import pytest from ..test_cases.test_case import TestCase +def _normalize_text(text: str) -> str: + """ + Normalize Unicode text by removing diacritical marks for comparison. + + The test case streaming_01 expects the answer "Sol" for the question "What's the name of the Sun + in latin?", but the model is returning "sōl" (with a macron over the 'o'), which is the correct + Latin spelling. The test is failing because it's doing a simple case-insensitive string search + for "sol" but the actual response contains the diacritical mark. + """ + return unicodedata.normalize("NFD", text).encode("ascii", "ignore").decode("ascii").lower() + + def provider_from_model(client_with_models, model_id): models = {m.identifier: m for m in client_with_models.models.list()} models.update({m.provider_resource_id: m for m in client_with_models.models.list()}) @@ -42,6 +55,10 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) "remote::groq", "remote::gemini", # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404 "remote::anthropic", # at least claude-3-{5,7}-{haiku,sonnet}-* / claude-{sonnet,opus}-4-* are not supported + "remote::azure", # {'error': {'code': 'OperationNotSupported', 'message': 'The completion operation + # does not work with the specified model, gpt-5-mini. Please choose different model and try + # again. You can learn more about which models can be used with each operation here: + # https://go.microsoft.com/fwlink/?linkid=2197993.'}}"} ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.") @@ -157,7 +174,8 @@ def test_openai_completion_non_streaming_suffix(llama_stack_client, client_with_ assert len(response.choices) > 0 choice = response.choices[0] assert len(choice.text) > 5 - assert "france" in choice.text.lower() + normalized_text = _normalize_text(choice.text) + assert "france" in normalized_text @pytest.mark.parametrize( @@ -248,7 +266,9 @@ def test_openai_chat_completion_non_streaming(compat_client, client_with_models, ) message_content = response.choices[0].message.content.lower().strip() assert len(message_content) > 0 - assert expected.lower() in message_content + normalized_expected = _normalize_text(expected) + normalized_content = _normalize_text(message_content) + assert normalized_expected in normalized_content @pytest.mark.parametrize( @@ -272,10 +292,13 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex ) streamed_content = [] for chunk in response: - if chunk.choices[0].delta.content: + # On some providers like Azure, the choices are empty on the first chunk, so we need to check for that + if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content: streamed_content.append(chunk.choices[0].delta.content.lower().strip()) assert len(streamed_content) > 0 - assert expected.lower() in "".join(streamed_content) + normalized_expected = _normalize_text(expected) + normalized_content = _normalize_text("".join(streamed_content)) + assert normalized_expected in normalized_content @pytest.mark.parametrize( @@ -308,8 +331,12 @@ def test_openai_chat_completion_streaming_with_n(compat_client, client_with_mode streamed_content.get(choice.index, "") + choice.delta.content.lower().strip() ) assert len(streamed_content) == 2 + normalized_expected = _normalize_text(expected) for i, content in streamed_content.items(): - assert expected.lower() in content, f"Choice {i}: Expected {expected.lower()} in {content}" + normalized_content = _normalize_text(content) + assert normalized_expected in normalized_content, ( + f"Choice {i}: Expected {normalized_expected} in {normalized_content}" + ) @pytest.mark.parametrize( @@ -339,9 +366,9 @@ def test_inference_store(compat_client, client_with_models, text_model_id, strea content = "" response_id = None for chunk in response: - if response_id is None: + if response_id is None and chunk.id: response_id = chunk.id - if chunk.choices[0].delta.content: + if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content: content += chunk.choices[0].delta.content else: response_id = response.id @@ -410,11 +437,12 @@ def test_inference_store_tool_calls(compat_client, client_with_models, text_mode content = "" response_id = None for chunk in response: - if response_id is None: + if response_id is None and chunk.id: response_id = chunk.id - if delta := chunk.choices[0].delta: - if delta.content: - content += delta.content + if chunk.choices and len(chunk.choices) > 0: + if delta := chunk.choices[0].delta: + if delta.content: + content += delta.content else: response_id = response.id content = response.choices[0].message.content @@ -484,4 +512,5 @@ def test_openai_chat_completion_non_streaming_with_file(openai_client, client_wi stream=False, ) message_content = response.choices[0].message.content.lower().strip() - assert "hello world" in message_content + normalized_content = _normalize_text(message_content) + assert "hello world" in normalized_content diff --git a/tests/integration/inference/test_text_inference.py b/tests/integration/inference/test_text_inference.py index d7ffe5929..621084231 100644 --- a/tests/integration/inference/test_text_inference.py +++ b/tests/integration/inference/test_text_inference.py @@ -32,6 +32,7 @@ def skip_if_model_doesnt_support_completion(client_with_models, model_id): "remote::vertexai", "remote::groq", "remote::sambanova", + "remote::azure", ) or "openai-compat" in provider.provider_type ): @@ -44,7 +45,7 @@ def skip_if_model_doesnt_support_json_schema_structured_output(client_with_model provider_id = models[model_id].provider_id providers = {p.provider_id: p for p in client_with_models.providers.list()} provider = providers[provider_id] - if provider.provider_type in ("remote::sambanova",): + if provider.provider_type in ("remote::sambanova", "remote::azure"): pytest.skip( f"Model {model_id} hosted by {provider.provider_type} doesn't support json_schema structured output" ) diff --git a/tests/integration/recordings/responses/0fda25b9241c.json b/tests/integration/recordings/responses/0fda25b9241c.json new file mode 100644 index 000000000..b97ee1670 --- /dev/null +++ b/tests/integration/recordings/responses/0fda25b9241c.json @@ -0,0 +1,71 @@ +{ + "request": { + "method": "POST", + "url": "https://shan-mfbb618r-eastus2.cognitiveservices.azure.com/openai/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "gpt-5-mini", + "messages": [ + { + "role": "user", + "content": "Which planet do humans live on?" + } + ], + "stream": false + }, + "endpoint": "/v1/chat/completions", + "model": "gpt-5-mini" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "chatcmpl-CECIXqfvjuluKkZtG3q2QJoSQhBU0", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "Humans live on Earth \u2014 the third planet from the Sun. 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inference_api for context_retriever for RAG ## Test Plan Integration and unit tests added Signed-off-by: Francisco Javier Arceo --- docs/source/building_applications/rag.md | 21 ++ .../tool_runtime/rag/context_retriever.py | 12 +- .../inline/tool_runtime/rag/memory.py | 121 ++++++---- .../integration/tool_runtime/test_rag_tool.py | 208 ++++++++++++++++++ .../utils/memory/test_vector_store.py | 38 ++++ 5 files changed, 355 insertions(+), 45 deletions(-) diff --git a/docs/source/building_applications/rag.md b/docs/source/building_applications/rag.md index 289c38991..802859e87 100644 --- a/docs/source/building_applications/rag.md +++ b/docs/source/building_applications/rag.md @@ -93,10 +93,31 @@ chunks_response = client.vector_io.query( ### Using the RAG Tool +> **⚠️ DEPRECATION NOTICE**: The RAG Tool is being deprecated in favor of directly using the OpenAI-compatible Search +> API. We recommend migrating to the OpenAI APIs for better compatibility and future support. + A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc. and automatically chunks them into smaller pieces. More examples for how to format a RAGDocument can be found in the [appendix](#more-ragdocument-examples). +#### OpenAI API Integration & Migration + +The RAG tool has been updated to use OpenAI-compatible APIs. This provides several benefits: + +- **Files API Integration**: Documents are now uploaded using OpenAI's file upload endpoints +- **Vector Stores API**: Vector storage operations use OpenAI's vector store format with configurable chunking strategies +- **Error Resilience:** When processing multiple documents, individual failures are logged but don't crash the operation. Failed documents are skipped while successful ones continue processing. + +**Migration Path:** +We recommend migrating to the OpenAI-compatible Search API for: +1. **Better OpenAI Ecosystem Integration**: Direct compatibility with OpenAI tools and workflows including the Responses API +2**Future-Proof**: Continued support and feature development +3**Full OpenAI Compatibility**: Vector Stores, Files, and Search APIs are fully compatible with OpenAI's Responses API + +The OpenAI APIs are used under the hood, so you can continue to use your existing RAG Tool code with minimal changes. +However, we recommend updating your code to use the new OpenAI-compatible APIs for better long-term support. If any +documents fail to process, they will be logged in the response but will not cause the entire operation to fail. + ```python from llama_stack_client import RAGDocument diff --git a/llama_stack/providers/inline/tool_runtime/rag/context_retriever.py b/llama_stack/providers/inline/tool_runtime/rag/context_retriever.py index be18430e4..9bc22f979 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/context_retriever.py +++ b/llama_stack/providers/inline/tool_runtime/rag/context_retriever.py @@ -8,7 +8,7 @@ from jinja2 import Template from llama_stack.apis.common.content_types import InterleavedContent -from llama_stack.apis.inference import UserMessage +from llama_stack.apis.inference import OpenAIUserMessageParam from llama_stack.apis.tools.rag_tool import ( DefaultRAGQueryGeneratorConfig, LLMRAGQueryGeneratorConfig, @@ -61,16 +61,16 @@ async def llm_rag_query_generator( messages = [interleaved_content_as_str(content)] template = Template(config.template) - content = template.render({"messages": messages}) + rendered_content: str = template.render({"messages": messages}) model = config.model - message = UserMessage(content=content) - response = await inference_api.chat_completion( - model_id=model, + message = OpenAIUserMessageParam(content=rendered_content) + response = await inference_api.openai_chat_completion( + model=model, messages=[message], stream=False, ) - query = response.completion_message.content + query = response.choices[0].message.content return query diff --git a/llama_stack/providers/inline/tool_runtime/rag/memory.py b/llama_stack/providers/inline/tool_runtime/rag/memory.py index aa629cca8..bc68f198d 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/memory.py +++ b/llama_stack/providers/inline/tool_runtime/rag/memory.py @@ -45,10 +45,7 @@ from llama_stack.apis.vector_io import ( from llama_stack.log import get_logger from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str -from llama_stack.providers.utils.memory.vector_store import ( - content_from_doc, - parse_data_url, -) +from llama_stack.providers.utils.memory.vector_store import parse_data_url from .config import RagToolRuntimeConfig from .context_retriever import generate_rag_query @@ -60,6 +57,47 @@ def make_random_string(length: int = 8): return "".join(secrets.choice(string.ascii_letters + string.digits) for _ in range(length)) +async def raw_data_from_doc(doc: RAGDocument) -> tuple[bytes, str]: + """Get raw binary data and mime type from a RAGDocument for file upload.""" + if isinstance(doc.content, URL): + if doc.content.uri.startswith("data:"): + parts = parse_data_url(doc.content.uri) + mime_type = parts["mimetype"] + data = parts["data"] + + if parts["is_base64"]: + file_data = base64.b64decode(data) + else: + file_data = data.encode("utf-8") + + return file_data, mime_type + else: + async with httpx.AsyncClient() as client: + r = await client.get(doc.content.uri) + r.raise_for_status() + mime_type = r.headers.get("content-type", "application/octet-stream") + return r.content, mime_type + else: + if isinstance(doc.content, str): + content_str = doc.content + else: + content_str = interleaved_content_as_str(doc.content) + + if content_str.startswith("data:"): + parts = parse_data_url(content_str) + mime_type = parts["mimetype"] + data = parts["data"] + + if parts["is_base64"]: + file_data = base64.b64decode(data) + else: + file_data = data.encode("utf-8") + + return file_data, mime_type + else: + return content_str.encode("utf-8"), "text/plain" + + class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRuntime): def __init__( self, @@ -95,46 +133,52 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti return for doc in documents: - if isinstance(doc.content, URL): - if doc.content.uri.startswith("data:"): - parts = parse_data_url(doc.content.uri) - file_data = base64.b64decode(parts["data"]) if parts["is_base64"] else parts["data"].encode() - mime_type = parts["mimetype"] - else: - async with httpx.AsyncClient() as client: - response = await client.get(doc.content.uri) - file_data = response.content - mime_type = doc.mime_type or response.headers.get("content-type", "application/octet-stream") - else: - content_str = await content_from_doc(doc) - file_data = content_str.encode("utf-8") - mime_type = doc.mime_type or "text/plain" + try: + try: + file_data, mime_type = await raw_data_from_doc(doc) + except Exception as e: + log.error(f"Failed to extract content from document {doc.document_id}: {e}") + continue - file_extension = mimetypes.guess_extension(mime_type) or ".txt" - filename = doc.metadata.get("filename", f"{doc.document_id}{file_extension}") + file_extension = mimetypes.guess_extension(mime_type) or ".txt" + filename = doc.metadata.get("filename", f"{doc.document_id}{file_extension}") - file_obj = io.BytesIO(file_data) - file_obj.name = filename + file_obj = io.BytesIO(file_data) + file_obj.name = filename - upload_file = UploadFile(file=file_obj, filename=filename) + upload_file = UploadFile(file=file_obj, filename=filename) - created_file = await self.files_api.openai_upload_file( - file=upload_file, purpose=OpenAIFilePurpose.ASSISTANTS - ) + try: + created_file = await self.files_api.openai_upload_file( + file=upload_file, purpose=OpenAIFilePurpose.ASSISTANTS + ) + except Exception as e: + log.error(f"Failed to upload file for document {doc.document_id}: {e}") + continue - chunking_strategy = VectorStoreChunkingStrategyStatic( - static=VectorStoreChunkingStrategyStaticConfig( - max_chunk_size_tokens=chunk_size_in_tokens, - chunk_overlap_tokens=chunk_size_in_tokens // 4, + chunking_strategy = VectorStoreChunkingStrategyStatic( + static=VectorStoreChunkingStrategyStaticConfig( + max_chunk_size_tokens=chunk_size_in_tokens, + chunk_overlap_tokens=chunk_size_in_tokens // 4, + ) ) - ) - await self.vector_io_api.openai_attach_file_to_vector_store( - vector_store_id=vector_db_id, - file_id=created_file.id, - attributes=doc.metadata, - chunking_strategy=chunking_strategy, - ) + try: + await self.vector_io_api.openai_attach_file_to_vector_store( + vector_store_id=vector_db_id, + file_id=created_file.id, + attributes=doc.metadata, + chunking_strategy=chunking_strategy, + ) + except Exception as e: + log.error( + f"Failed to attach file {created_file.id} to vector store {vector_db_id} for document {doc.document_id}: {e}" + ) + continue + + except Exception as e: + log.error(f"Unexpected error processing document {doc.document_id}: {e}") + continue async def query( self, @@ -274,7 +318,6 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti if query_config: query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config) else: - # handle someone passing an empty dict query_config = RAGQueryConfig() query = kwargs["query"] @@ -285,6 +328,6 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti ) return ToolInvocationResult( - content=result.content, + content=result.content or [], metadata=result.metadata, ) diff --git a/tests/integration/tool_runtime/test_rag_tool.py b/tests/integration/tool_runtime/test_rag_tool.py index b208500d8..b78c39af8 100644 --- a/tests/integration/tool_runtime/test_rag_tool.py +++ b/tests/integration/tool_runtime/test_rag_tool.py @@ -183,6 +183,110 @@ def test_vector_db_insert_from_url_and_query( assert any("llama2" in chunk.content.lower() for chunk in response2.chunks) +def test_rag_tool_openai_apis(client_with_empty_registry, embedding_model_id, embedding_dimension): + vector_db_id = "test_openai_vector_db" + + client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + ) + + available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + actual_vector_db_id = available_vector_dbs[0] + + # different document formats that should work with OpenAI APIs + documents = [ + Document( + document_id="text-doc", + content="This is a plain text document about machine learning algorithms.", + metadata={"type": "text", "category": "AI"}, + ), + Document( + document_id="url-doc", + content="https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/chat.rst", + mime_type="text/plain", + metadata={"type": "url", "source": "pytorch"}, + ), + Document( + document_id="data-url-doc", + content="data:text/plain;base64,VGhpcyBpcyBhIGRhdGEgVVJMIGRvY3VtZW50IGFib3V0IGRlZXAgbGVhcm5pbmcu", # "This is a data URL document about deep learning." + metadata={"type": "data_url", "encoding": "base64"}, + ), + ] + + client_with_empty_registry.tool_runtime.rag_tool.insert( + documents=documents, + vector_db_id=actual_vector_db_id, + chunk_size_in_tokens=256, + ) + + files_list = client_with_empty_registry.files.list() + assert len(files_list.data) >= len(documents), ( + f"Expected at least {len(documents)} files, got {len(files_list.data)}" + ) + + vector_store_files = client_with_empty_registry.vector_io.openai_list_files_in_vector_store( + vector_store_id=actual_vector_db_id + ) + assert len(vector_store_files.data) >= len(documents), f"Expected at least {len(documents)} files in vector store" + + response = client_with_empty_registry.tool_runtime.rag_tool.query( + vector_db_ids=[actual_vector_db_id], + content="Tell me about machine learning and deep learning", + ) + + assert_valid_text_response(response) + content_text = " ".join([chunk.text for chunk in response.content]).lower() + assert "machine learning" in content_text or "deep learning" in content_text + + +def test_rag_tool_exception_handling(client_with_empty_registry, embedding_model_id, embedding_dimension): + vector_db_id = "test_exception_handling" + + client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + ) + + available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + actual_vector_db_id = available_vector_dbs[0] + + documents = [ + Document( + document_id="valid-doc", + content="This is a valid document that should be processed successfully.", + metadata={"status": "valid"}, + ), + Document( + document_id="invalid-url-doc", + content="https://nonexistent-domain-12345.com/invalid.txt", + metadata={"status": "invalid_url"}, + ), + Document( + document_id="another-valid-doc", + content="This is another valid document for testing resilience.", + metadata={"status": "valid"}, + ), + ] + + client_with_empty_registry.tool_runtime.rag_tool.insert( + documents=documents, + vector_db_id=actual_vector_db_id, + chunk_size_in_tokens=256, + ) + + response = client_with_empty_registry.tool_runtime.rag_tool.query( + vector_db_ids=[actual_vector_db_id], + content="valid document", + ) + + assert_valid_text_response(response) + content_text = " ".join([chunk.text for chunk in response.content]).lower() + assert "valid document" in content_text + + def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_id, embedding_dimension): providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"] assert len(providers) > 0 @@ -249,3 +353,107 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i "chunk_template": "This should raise a ValueError because it is missing the proper template variables", }, ) + + +def test_rag_tool_query_generation(client_with_empty_registry, embedding_model_id, embedding_dimension): + vector_db_id = "test_query_generation_db" + + client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + ) + + available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + actual_vector_db_id = available_vector_dbs[0] + + documents = [ + Document( + document_id="ai-doc", + content="Artificial intelligence and machine learning are transforming technology.", + metadata={"category": "AI"}, + ), + Document( + document_id="banana-doc", + content="Don't bring a banana to a knife fight.", + metadata={"category": "wisdom"}, + ), + ] + + client_with_empty_registry.tool_runtime.rag_tool.insert( + documents=documents, + vector_db_id=actual_vector_db_id, + chunk_size_in_tokens=256, + ) + + response = client_with_empty_registry.tool_runtime.rag_tool.query( + vector_db_ids=[actual_vector_db_id], + content="Tell me about AI", + ) + + assert_valid_text_response(response) + content_text = " ".join([chunk.text for chunk in response.content]).lower() + assert "artificial intelligence" in content_text or "machine learning" in content_text + + +def test_rag_tool_pdf_data_url_handling(client_with_empty_registry, embedding_model_id, embedding_dimension): + vector_db_id = "test_pdf_data_url_db" + + client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + ) + + available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + actual_vector_db_id = available_vector_dbs[0] + + sample_pdf = b"%PDF-1.3\n3 0 obj\n<>\nendobj\n4 0 obj\n<>\nstream\nx\x9c\x15\xcc1\x0e\x820\x18@\xe1\x9dS\xbcM]jk$\xd5\xd5(\x83!\x86\xa1\x17\xf8\xa3\xa5`LIh+\xd7W\xc6\xf7\r\xef\xc0\xbd\xd2\xaa\xb6,\xd5\xc5\xb1o\x0c\xa6VZ\xe3znn%\xf3o\xab\xb1\xe7\xa3:Y\xdc\x8bm\xeb\xf3&1\xc8\xd7\xd3\x97\xc82\xe6\x81\x87\xe42\xcb\x87Vb(\x12<\xdd<=}Jc\x0cL\x91\xee\xda$\xb5\xc3\xbd\xd7\xe9\x0f\x8d\x97 $\nendstream\nendobj\n1 0 obj\n<>\nendobj\n5 0 obj\n<>\nendobj\n2 0 obj\n<<\n/ProcSet [/PDF /Text /ImageB /ImageC /ImageI]\n/Font <<\n/F1 5 0 R\n>>\n/XObject <<\n>>\n>>\nendobj\n6 0 obj\n<<\n/Producer (PyFPDF 1.7.2 http://pyfpdf.googlecode.com/)\n/Title (This is a sample title.)\n/Author (Llama Stack Developers)\n/CreationDate (D:20250312165548)\n>>\nendobj\n7 0 obj\n<<\n/Type /Catalog\n/Pages 1 0 R\n/OpenAction [3 0 R /FitH null]\n/PageLayout /OneColumn\n>>\nendobj\nxref\n0 8\n0000000000 65535 f \n0000000272 00000 n \n0000000455 00000 n \n0000000009 00000 n \n0000000087 00000 n \n0000000359 00000 n \n0000000559 00000 n \n0000000734 00000 n \ntrailer\n<<\n/Size 8\n/Root 7 0 R\n/Info 6 0 R\n>>\nstartxref\n837\n%%EOF\n" + + import base64 + + pdf_base64 = base64.b64encode(sample_pdf).decode("utf-8") + pdf_data_url = f"data:application/pdf;base64,{pdf_base64}" + + documents = [ + Document( + document_id="test-pdf-data-url", + content=pdf_data_url, + metadata={"type": "pdf", "source": "data_url"}, + ), + ] + + client_with_empty_registry.tool_runtime.rag_tool.insert( + documents=documents, + vector_db_id=actual_vector_db_id, + chunk_size_in_tokens=256, + ) + + files_list = client_with_empty_registry.files.list() + assert len(files_list.data) >= 1, "PDF should have been uploaded to Files API" + + pdf_file = None + for file in files_list.data: + if file.filename and "test-pdf-data-url" in file.filename: + pdf_file = file + break + + assert pdf_file is not None, "PDF file should be found in Files API" + assert pdf_file.bytes == len(sample_pdf), f"File size should match original PDF ({len(sample_pdf)} bytes)" + + file_content = client_with_empty_registry.files.retrieve_content(pdf_file.id) + assert file_content.startswith(b"%PDF-"), "Retrieved file should be a valid PDF" + + vector_store_files = client_with_empty_registry.vector_io.openai_list_files_in_vector_store( + vector_store_id=actual_vector_db_id + ) + assert len(vector_store_files.data) >= 1, "PDF should be attached to vector store" + + response = client_with_empty_registry.tool_runtime.rag_tool.query( + vector_db_ids=[actual_vector_db_id], + content="sample title", + ) + + assert_valid_text_response(response) + content_text = " ".join([chunk.text for chunk in response.content]).lower() + assert "sample title" in content_text or "title" in content_text diff --git a/tests/unit/providers/utils/memory/test_vector_store.py b/tests/unit/providers/utils/memory/test_vector_store.py index 90b229262..590bdd1d2 100644 --- a/tests/unit/providers/utils/memory/test_vector_store.py +++ b/tests/unit/providers/utils/memory/test_vector_store.py @@ -178,3 +178,41 @@ def test_content_from_data_and_mime_type_both_encodings_fail(): # Should raise an exception instead of returning empty string with pytest.raises(UnicodeDecodeError): content_from_data_and_mime_type(data, mime_type) + + +async def test_memory_tool_error_handling(): + """Test that memory tool handles various failures gracefully without crashing.""" + from llama_stack.providers.inline.tool_runtime.rag.config import RagToolRuntimeConfig + from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRuntimeImpl + + config = RagToolRuntimeConfig() + memory_tool = MemoryToolRuntimeImpl( + config=config, + vector_io_api=AsyncMock(), + inference_api=AsyncMock(), + files_api=AsyncMock(), + ) + + docs = [ + RAGDocument(document_id="good_doc", content="Good content", metadata={}), + RAGDocument(document_id="bad_url_doc", content=URL(uri="https://bad.url"), metadata={}), + RAGDocument(document_id="another_good_doc", content="Another good content", metadata={}), + ] + + mock_file1 = MagicMock() + mock_file1.id = "file_good1" + mock_file2 = MagicMock() + mock_file2.id = "file_good2" + memory_tool.files_api.openai_upload_file.side_effect = [mock_file1, mock_file2] + + with patch("httpx.AsyncClient") as mock_client: + mock_instance = AsyncMock() + mock_instance.get.side_effect = Exception("Bad URL") + mock_client.return_value.__aenter__.return_value = mock_instance + + # won't raise exception despite one document failing + await memory_tool.insert(docs, "vector_store_123") + + # processed 2 documents successfully, skipped 1 + assert memory_tool.files_api.openai_upload_file.call_count == 2 + assert memory_tool.vector_io_api.openai_attach_file_to_vector_store.call_count == 2 From 8ef1189be7c6ea6e9fb2e3cf3f502123e0e4635a Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Thu, 11 Sep 2025 09:04:38 -0400 Subject: [PATCH 103/124] chore: update the vLLM inference impl to use OpenAIMixin for openai-compat functions (#3404) # What does this PR do? update vLLM inference provider to use OpenAIMixin for openai-compat functions inference recordings from Qwen3-0.6B and vLLM 0.8.3 - ``` docker run --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host \ vllm/vllm-openai:latest \ --model Qwen/Qwen3-0.6B --enable-auto-tool-choice --tool-call-parser hermes ``` ## Test Plan ``` ./scripts/integration-tests.sh --stack-config server:ci-tests --setup vllm --subdirs inference ``` --- .../providers/remote/inference/vllm/vllm.py | 197 +----------------- .../providers/utils/inference/openai_mixin.py | 28 ++- .../providers/inference/test_remote_vllm.py | 21 +- 3 files changed, 44 insertions(+), 202 deletions(-) diff --git a/llama_stack/providers/remote/inference/vllm/vllm.py b/llama_stack/providers/remote/inference/vllm/vllm.py index 9e9a80ca5..77f5d82af 100644 --- a/llama_stack/providers/remote/inference/vllm/vllm.py +++ b/llama_stack/providers/remote/inference/vllm/vllm.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. import json -from collections.abc import AsyncGenerator, AsyncIterator +from collections.abc import AsyncGenerator from typing import Any import httpx @@ -38,13 +38,6 @@ from llama_stack.apis.inference import ( LogProbConfig, Message, ModelStore, - OpenAIChatCompletion, - OpenAICompletion, - OpenAIEmbeddingData, - OpenAIEmbeddingsResponse, - OpenAIEmbeddingUsage, - OpenAIMessageParam, - OpenAIResponseFormatParam, ResponseFormat, SamplingParams, TextTruncation, @@ -71,11 +64,11 @@ from llama_stack.providers.utils.inference.openai_compat import ( convert_message_to_openai_dict, convert_tool_call, get_sampling_options, - prepare_openai_completion_params, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( completion_request_to_prompt, content_has_media, @@ -288,7 +281,7 @@ async def _process_vllm_chat_completion_stream_response( yield c -class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): +class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate): # automatically set by the resolver when instantiating the provider __provider_id__: str model_store: ModelStore | None = None @@ -296,7 +289,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): def __init__(self, config: VLLMInferenceAdapterConfig) -> None: self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries()) self.config = config - self.client = None async def initialize(self) -> None: if not self.config.url: @@ -308,8 +300,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): return self.config.refresh_models async def list_models(self) -> list[Model] | None: - self._lazy_initialize_client() - assert self.client is not None # mypy models = [] async for m in self.client.models.list(): model_type = ModelType.llm # unclear how to determine embedding vs. llm models @@ -340,8 +330,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): HealthResponse: A dictionary containing the health status. """ try: - client = self._create_client() if self.client is None else self.client - _ = [m async for m in client.models.list()] # Ensure the client is initialized + _ = [m async for m in self.client.models.list()] # Ensure the client is initialized return HealthResponse(status=HealthStatus.OK) except Exception as e: return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}") @@ -351,19 +340,14 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): raise ValueError("Model store not set") return await self.model_store.get_model(model_id) - def _lazy_initialize_client(self): - if self.client is not None: - return + def get_api_key(self): + return self.config.api_token - log.info(f"Initializing vLLM client with base_url={self.config.url}") - self.client = self._create_client() + def get_base_url(self): + return self.config.url - def _create_client(self): - return AsyncOpenAI( - base_url=self.config.url, - api_key=self.config.api_token, - http_client=httpx.AsyncClient(verify=self.config.tls_verify), - ) + def get_extra_client_params(self): + return {"http_client": httpx.AsyncClient(verify=self.config.tls_verify)} async def completion( self, @@ -374,7 +358,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): stream: bool | None = False, logprobs: LogProbConfig | None = None, ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]: - self._lazy_initialize_client() if sampling_params is None: sampling_params = SamplingParams() model = await self._get_model(model_id) @@ -406,7 +389,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): logprobs: LogProbConfig | None = None, tool_config: ToolConfig | None = None, ) -> ChatCompletionResponse | AsyncGenerator[ChatCompletionResponseStreamChunk, None]: - self._lazy_initialize_client() if sampling_params is None: sampling_params = SamplingParams() model = await self._get_model(model_id) @@ -479,16 +461,12 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): yield chunk async def register_model(self, model: Model) -> Model: - # register_model is called during Llama Stack initialization, hence we cannot init self.client if not initialized yet. - # self.client should only be created after the initialization is complete to avoid asyncio cross-context errors. - # Changing this may lead to unpredictable behavior. - client = self._create_client() if self.client is None else self.client try: model = await self.register_helper.register_model(model) except ValueError: pass # Ignore statically unknown model, will check live listing try: - res = await client.models.list() + res = await self.client.models.list() except APIConnectionError as e: raise ValueError( f"Failed to connect to vLLM at {self.config.url}. Please check if vLLM is running and accessible at that URL." @@ -543,8 +521,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): output_dimension: int | None = None, task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: - self._lazy_initialize_client() - assert self.client is not None model = await self._get_model(model_id) kwargs = {} @@ -560,154 +536,3 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): embeddings = [data.embedding for data in response.data] return EmbeddingsResponse(embeddings=embeddings) - - async def openai_embeddings( - self, - model: str, - input: str | list[str], - encoding_format: str | None = "float", - dimensions: int | None = None, - user: str | None = None, - ) -> OpenAIEmbeddingsResponse: - self._lazy_initialize_client() - assert self.client is not None - model_obj = await self._get_model(model) - assert model_obj.model_type == ModelType.embedding - - # Convert input to list if it's a string - input_list = [input] if isinstance(input, str) else input - - # Call vLLM embeddings endpoint with encoding_format - response = await self.client.embeddings.create( - model=model_obj.provider_resource_id, - input=input_list, - dimensions=dimensions, - encoding_format=encoding_format, - ) - - # Convert response to OpenAI format - data = [ - OpenAIEmbeddingData( - embedding=embedding_data.embedding, - index=i, - ) - for i, embedding_data in enumerate(response.data) - ] - - # Not returning actual token usage since vLLM doesn't provide it - usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1) - - return OpenAIEmbeddingsResponse( - data=data, - model=model_obj.provider_resource_id, - usage=usage, - ) - - async def openai_completion( - self, - model: str, - prompt: str | list[str] | list[int] | list[list[int]], - best_of: int | None = None, - echo: bool | None = None, - frequency_penalty: float | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_tokens: int | None = None, - n: int | None = None, - presence_penalty: float | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - top_p: float | None = None, - user: str | None = None, - guided_choice: list[str] | None = None, - prompt_logprobs: int | None = None, - suffix: str | None = None, - ) -> OpenAICompletion: - self._lazy_initialize_client() - model_obj = await self._get_model(model) - - extra_body: dict[str, Any] = {} - if prompt_logprobs is not None and prompt_logprobs >= 0: - extra_body["prompt_logprobs"] = prompt_logprobs - if guided_choice: - extra_body["guided_choice"] = guided_choice - - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - prompt=prompt, - best_of=best_of, - echo=echo, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - logprobs=logprobs, - max_tokens=max_tokens, - n=n, - presence_penalty=presence_penalty, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - top_p=top_p, - user=user, - extra_body=extra_body, - ) - return await self.client.completions.create(**params) # type: ignore - - async def openai_chat_completion( - self, - model: str, - messages: list[OpenAIMessageParam], - frequency_penalty: float | None = None, - function_call: str | dict[str, Any] | None = None, - functions: list[dict[str, Any]] | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_completion_tokens: int | None = None, - max_tokens: int | None = None, - n: int | None = None, - parallel_tool_calls: bool | None = None, - presence_penalty: float | None = None, - response_format: OpenAIResponseFormatParam | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - tool_choice: str | dict[str, Any] | None = None, - tools: list[dict[str, Any]] | None = None, - top_logprobs: int | None = None, - top_p: float | None = None, - user: str | None = None, - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - self._lazy_initialize_client() - model_obj = await self._get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - return await self.client.chat.completions.create(**params) # type: ignore diff --git a/llama_stack/providers/utils/inference/openai_mixin.py b/llama_stack/providers/utils/inference/openai_mixin.py index f60deee6e..a3c0ffadc 100644 --- a/llama_stack/providers/utils/inference/openai_mixin.py +++ b/llama_stack/providers/utils/inference/openai_mixin.py @@ -67,6 +67,17 @@ class OpenAIMixin(ABC): """ pass + def get_extra_client_params(self) -> dict[str, Any]: + """ + Get any extra parameters to pass to the AsyncOpenAI client. + + Child classes can override this method to provide additional parameters + such as timeout settings, proxies, etc. + + :return: A dictionary of extra parameters + """ + return {} + @property def client(self) -> AsyncOpenAI: """ @@ -78,6 +89,7 @@ class OpenAIMixin(ABC): return AsyncOpenAI( api_key=self.get_api_key(), base_url=self.get_base_url(), + **self.get_extra_client_params(), ) async def _get_provider_model_id(self, model: str) -> str: @@ -124,10 +136,15 @@ class OpenAIMixin(ABC): """ Direct OpenAI completion API call. """ - if guided_choice is not None: - logger.warning("guided_choice is not supported by the OpenAI API. Ignoring.") - if prompt_logprobs is not None: - logger.warning("prompt_logprobs is not supported by the OpenAI API. Ignoring.") + # Handle parameters that are not supported by OpenAI API, but may be by the provider + # prompt_logprobs is supported by vLLM + # guided_choice is supported by vLLM + # TODO: test coverage + extra_body: dict[str, Any] = {} + if prompt_logprobs is not None and prompt_logprobs >= 0: + extra_body["prompt_logprobs"] = prompt_logprobs + if guided_choice: + extra_body["guided_choice"] = guided_choice # TODO: fix openai_completion to return type compatible with OpenAI's API response return await self.client.completions.create( # type: ignore[no-any-return] @@ -150,7 +167,8 @@ class OpenAIMixin(ABC): top_p=top_p, user=user, suffix=suffix, - ) + ), + extra_body=extra_body, ) async def openai_chat_completion( diff --git a/tests/unit/providers/inference/test_remote_vllm.py b/tests/unit/providers/inference/test_remote_vllm.py index ce0e930b1..a48af2a1d 100644 --- a/tests/unit/providers/inference/test_remote_vllm.py +++ b/tests/unit/providers/inference/test_remote_vllm.py @@ -11,7 +11,7 @@ import threading import time from http.server import BaseHTTPRequestHandler, HTTPServer from typing import Any -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import AsyncMock, MagicMock, PropertyMock, patch import pytest from openai.types.chat.chat_completion_chunk import ( @@ -150,10 +150,12 @@ async def test_tool_call_response(vllm_inference_adapter): """Verify that tool call arguments from a CompletionMessage are correctly converted into the expected JSON format.""" - # Patch the call to vllm so we can inspect the arguments sent were correct - with patch.object( - vllm_inference_adapter.client.chat.completions, "create", new_callable=AsyncMock - ) as mock_nonstream_completion: + # Patch the client property to avoid instantiating a real AsyncOpenAI client + with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client: + mock_client = MagicMock() + mock_client.chat.completions.create = AsyncMock() + mock_create_client.return_value = mock_client + messages = [ SystemMessage(content="You are a helpful assistant"), UserMessage(content="How many?"), @@ -179,7 +181,7 @@ async def test_tool_call_response(vllm_inference_adapter): tool_config=ToolConfig(tool_choice=ToolChoice.auto), ) - assert mock_nonstream_completion.call_args.kwargs["messages"][2]["tool_calls"] == [ + assert mock_client.chat.completions.create.call_args.kwargs["messages"][2]["tool_calls"] == [ { "id": "foo", "type": "function", @@ -641,9 +643,7 @@ async def test_health_status_success(vllm_inference_adapter): This test verifies that the health method returns a HealthResponse with status OK, only when the connection to the vLLM server is successful. """ - # Set vllm_inference_adapter.client to None to ensure _create_client is called - vllm_inference_adapter.client = None - with patch.object(vllm_inference_adapter, "_create_client") as mock_create_client: + with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client: # Create mock client and models mock_client = MagicMock() mock_models = MagicMock() @@ -674,8 +674,7 @@ async def test_health_status_failure(vllm_inference_adapter): This test verifies that the health method returns a HealthResponse with status ERROR and an appropriate error message when the connection to the vLLM server fails. """ - vllm_inference_adapter.client = None - with patch.object(vllm_inference_adapter, "_create_client") as mock_create_client: + with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client: # Create mock client and models mock_client = MagicMock() mock_models = MagicMock() From 72387b4bd229bba60b43f95679da62630fc0f3c7 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Thu, 11 Sep 2025 11:45:16 -0400 Subject: [PATCH 104/124] chore(unit tests): remove network use, update async test (#3418) # What does this PR do? update the async detection test for vllm - remove a network access from unit tests - remove direct logging use the idea behind the test is to mock inference w/ a sleep, initiate concurrent inference calls, verify the total execution time is close to the sleep time. in a non-async env the total time would be closer to sleep * num concurrent calls. ## Test Plan ci --- .../providers/inference/test_remote_vllm.py | 160 +++++++----------- 1 file changed, 60 insertions(+), 100 deletions(-) diff --git a/tests/unit/providers/inference/test_remote_vllm.py b/tests/unit/providers/inference/test_remote_vllm.py index a48af2a1d..61b16b5d1 100644 --- a/tests/unit/providers/inference/test_remote_vllm.py +++ b/tests/unit/providers/inference/test_remote_vllm.py @@ -6,11 +6,7 @@ import asyncio import json -import logging # allow-direct-logging -import threading import time -from http.server import BaseHTTPRequestHandler, HTTPServer -from typing import Any from unittest.mock import AsyncMock, MagicMock, PropertyMock, patch import pytest @@ -18,7 +14,7 @@ from openai.types.chat.chat_completion_chunk import ( ChatCompletionChunk as OpenAIChatCompletionChunk, ) from openai.types.chat.chat_completion_chunk import ( - Choice as OpenAIChoice, + Choice as OpenAIChoiceChunk, ) from openai.types.chat.chat_completion_chunk import ( ChoiceDelta as OpenAIChoiceDelta, @@ -35,6 +31,9 @@ from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponseEventType, CompletionMessage, + OpenAIAssistantMessageParam, + OpenAIChatCompletion, + OpenAIChoice, SystemMessage, ToolChoice, ToolConfig, @@ -61,41 +60,6 @@ from llama_stack.providers.remote.inference.vllm.vllm import ( # -v -s --tb=short --disable-warnings -class MockInferenceAdapterWithSleep: - def __init__(self, sleep_time: int, response: dict[str, Any]): - self.httpd = None - - class DelayedRequestHandler(BaseHTTPRequestHandler): - # ruff: noqa: N802 - def do_POST(self): - time.sleep(sleep_time) - response_body = json.dumps(response).encode("utf-8") - self.send_response(code=200) - self.send_header("Content-Type", "application/json") - self.send_header("Content-Length", len(response_body)) - self.end_headers() - self.wfile.write(response_body) - - self.request_handler = DelayedRequestHandler - - def __enter__(self): - httpd = HTTPServer(("", 0), self.request_handler) - self.httpd = httpd - host, port = httpd.server_address - httpd_thread = threading.Thread(target=httpd.serve_forever) - httpd_thread.daemon = True # stop server if this thread terminates - httpd_thread.start() - - config = VLLMInferenceAdapterConfig(url=f"http://{host}:{port}") - inference_adapter = VLLMInferenceAdapter(config) - return inference_adapter - - def __exit__(self, _exc_type, _exc_value, _traceback): - if self.httpd: - self.httpd.shutdown() - self.httpd.server_close() - - @pytest.fixture(scope="module") def mock_openai_models_list(): with patch("openai.resources.models.AsyncModels.list", new_callable=AsyncMock) as mock_list: @@ -201,7 +165,7 @@ async def test_tool_call_delta_empty_tool_call_buf(): async def mock_stream(): delta = OpenAIChoiceDelta(content="", tool_calls=None) - choices = [OpenAIChoice(delta=delta, finish_reason="stop", index=0)] + choices = [OpenAIChoiceChunk(delta=delta, finish_reason="stop", index=0)] mock_chunk = OpenAIChatCompletionChunk( id="chunk-1", created=1, @@ -227,7 +191,7 @@ async def test_tool_call_delta_streaming_arguments_dict(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice( + OpenAIChoiceChunk( delta=OpenAIChoiceDelta( content="", tool_calls=[ @@ -252,7 +216,7 @@ async def test_tool_call_delta_streaming_arguments_dict(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice( + OpenAIChoiceChunk( delta=OpenAIChoiceDelta( content="", tool_calls=[ @@ -277,7 +241,9 @@ async def test_tool_call_delta_streaming_arguments_dict(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice(delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0) + OpenAIChoiceChunk( + delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0 + ) ], ) for chunk in [mock_chunk_1, mock_chunk_2, mock_chunk_3]: @@ -301,7 +267,7 @@ async def test_multiple_tool_calls(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice( + OpenAIChoiceChunk( delta=OpenAIChoiceDelta( content="", tool_calls=[ @@ -326,7 +292,7 @@ async def test_multiple_tool_calls(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice( + OpenAIChoiceChunk( delta=OpenAIChoiceDelta( content="", tool_calls=[ @@ -351,7 +317,9 @@ async def test_multiple_tool_calls(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice(delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0) + OpenAIChoiceChunk( + delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0 + ) ], ) for chunk in [mock_chunk_1, mock_chunk_2, mock_chunk_3]: @@ -395,59 +363,6 @@ async def test_process_vllm_chat_completion_stream_response_no_choices(): assert chunks[0].event.event_type.value == "start" -@pytest.mark.allow_network -def test_chat_completion_doesnt_block_event_loop(caplog): - loop = asyncio.new_event_loop() - loop.set_debug(True) - caplog.set_level(logging.WARNING) - - # Log when event loop is blocked for more than 200ms - loop.slow_callback_duration = 0.5 - # Sleep for 500ms in our delayed http response - sleep_time = 0.5 - - mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm-inference") - mock_response = { - "id": "chatcmpl-abc123", - "object": "chat.completion", - "created": 1, - "modle": "mock-model", - "choices": [ - { - "message": {"content": ""}, - "logprobs": None, - "finish_reason": "stop", - "index": 0, - } - ], - } - - async def do_chat_completion(): - await inference_adapter.chat_completion( - "mock-model", - [], - stream=False, - tools=None, - tool_config=ToolConfig(tool_choice=ToolChoice.auto), - ) - - with MockInferenceAdapterWithSleep(sleep_time, mock_response) as inference_adapter: - inference_adapter.model_store = AsyncMock() - inference_adapter.model_store.get_model.return_value = mock_model - loop.run_until_complete(inference_adapter.initialize()) - - # Clear the logs so far and run the actual chat completion we care about - caplog.clear() - loop.run_until_complete(do_chat_completion()) - - # Ensure we don't have any asyncio warnings in the captured log - # records from our chat completion call. A message gets logged - # here any time we exceed the slow_callback_duration configured - # above. - asyncio_warnings = [record.message for record in caplog.records if record.name == "asyncio"] - assert not asyncio_warnings - - async def test_get_params_empty_tools(vllm_inference_adapter): request = ChatCompletionRequest( tools=[], @@ -696,3 +611,48 @@ async def test_health_status_failure(vllm_inference_adapter): assert "Health check failed: Connection failed" in health_response["message"] mock_models.list.assert_called_once() + + +async def test_openai_chat_completion_is_async(vllm_inference_adapter): + """ + Verify that openai_chat_completion is async and doesn't block the event loop. + + To do this we mock the underlying inference with a sleep, start multiple + inference calls in parallel, and ensure the total time taken is less + than the sum of the individual sleep times. + """ + sleep_time = 0.5 + + async def mock_create(*args, **kwargs): + await asyncio.sleep(sleep_time) + return OpenAIChatCompletion( + id="chatcmpl-abc123", + created=1, + model="mock-model", + choices=[ + OpenAIChoice( + message=OpenAIAssistantMessageParam( + content="nothing interesting", + ), + finish_reason="stop", + index=0, + ) + ], + ) + + async def do_inference(): + await vllm_inference_adapter.openai_chat_completion( + "mock-model", messages=["one fish", "two fish"], stream=False + ) + + with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client: + mock_client = MagicMock() + mock_client.chat.completions.create = AsyncMock(side_effect=mock_create) + mock_create_client.return_value = mock_client + + start_time = time.time() + await asyncio.gather(do_inference(), do_inference(), do_inference(), do_inference()) + total_time = time.time() - start_time + + assert mock_create_client.call_count == 4 # no cheating + assert total_time < (sleep_time * 2), f"Total time taken: {total_time}s exceeded expected max" From c7ef1f13df981622216833578c70d98f702d9cc6 Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Thu, 11 Sep 2025 11:10:41 -0700 Subject: [PATCH 105/124] feat: Add langchain llamastack Integration example notebook (#3314) # What does this PR do? The notebook was reverted(https://github.com/llamastack/llama-stack/pull/3259) as it had some local paths, I missed correcting. Trying with corrections now ## Test Plan Ran the Jupyter notebook --- .../langchain/Llama_Stack_LangChain.ipynb | 701 ++++++++++++++++++ 1 file changed, 701 insertions(+) create mode 100644 docs/notebooks/langchain/Llama_Stack_LangChain.ipynb diff --git a/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb b/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb new file mode 100644 index 000000000..d44ac6994 --- /dev/null +++ b/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb @@ -0,0 +1,701 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ztegmwm4sp", + "metadata": {}, + "source": [ + "## LlamaStack + LangChain Integration Tutorial\n", + "\n", + "This notebook demonstrates how to integrate **LlamaStack** with **LangChain** to build a complete RAG (Retrieval-Augmented Generation) system.\n", + "\n", + "### Overview\n", + "\n", + "- **LlamaStack**: Provides the infrastructure for running LLMs and Open AI Compatible Vector Stores\n", + "- **LangChain**: Provides the framework for chaining operations and prompt templates\n", + "- **Integration**: Uses LlamaStack's OpenAI-compatible API with LangChain\n", + "\n", + "### What You'll See\n", + "\n", + "1. Setting up LlamaStack server with Fireworks AI provider\n", + "2. Creating and Querying Vector Stores\n", + "3. Building RAG chains with LangChain + LLAMAStack\n", + "4. Querying the chain for relevant information\n", + "\n", + "### Prerequisites\n", + "\n", + "- Fireworks API key\n", + "\n", + "---\n", + "\n", + "### 1. Installation and Setup" + ] + }, + { + "cell_type": "markdown", + "id": "2ktr5ls2cas", + "metadata": {}, + "source": [ + "#### Install Required Dependencies\n", + "\n", + "First, we install all the necessary packages for LangChain and FastAPI integration." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5b6a6a17-b931-4bea-8273-0d6e5563637a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uv in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.7.20)\n", + "\u001b[2mUsing Python 3.12.11 environment at: /Users/swapna942/miniconda3\u001b[0m\n", + "\u001b[2mAudited \u001b[1m7 packages\u001b[0m \u001b[2min 42ms\u001b[0m\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install uv\n", + "!uv pip install fastapi uvicorn \"langchain>=0.2\" langchain-openai \\\n", + " langchain-community langchain-text-splitters \\\n", + " faiss-cpu" + ] + }, + { + "cell_type": "markdown", + "id": "wmt9jvqzh7n", + "metadata": {}, + "source": [ + "### 2. LlamaStack Server Setup\n", + "\n", + "#### Build and Start LlamaStack Server\n", + "\n", + "This section sets up the LlamaStack server with:\n", + "- **Fireworks AI** as the inference provider\n", + "- **Sentence Transformers** for embeddings\n", + "\n", + "The server runs on `localhost:8321` and provides OpenAI-compatible endpoints." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd2dacf3-ec8b-4cc7-8ff4-b5b6ea4a6e9e", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import os\n", + "import subprocess\n", + "import time\n", + "\n", + "# Remove UV_SYSTEM_PYTHON to ensure uv creates a proper virtual environment\n", + "# instead of trying to use system Python globally, which could cause permission issues\n", + "# and package conflicts with the system's Python installation\n", + "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", + " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", + "\n", + "def run_llama_stack_server_background():\n", + " \"\"\"Build and run LlamaStack server in one step using --run flag\"\"\"\n", + " log_file = open(\"llama_stack_server.log\", \"w\")\n", + " process = subprocess.Popen(\n", + " \"uv run --with llama-stack llama stack build --distro starter --image-type venv --run\",\n", + " shell=True,\n", + " stdout=log_file,\n", + " stderr=log_file,\n", + " text=True,\n", + " )\n", + "\n", + " print(f\"Building and starting Llama Stack server with PID: {process.pid}\")\n", + " return process\n", + "\n", + "\n", + "def wait_for_server_to_start():\n", + " import requests\n", + " from requests.exceptions import ConnectionError\n", + "\n", + " url = \"http://0.0.0.0:8321/v1/health\"\n", + " max_retries = 30\n", + " retry_interval = 1\n", + "\n", + " print(\"Waiting for server to start\", end=\"\")\n", + " for _ in range(max_retries):\n", + " try:\n", + " response = requests.get(url)\n", + " if response.status_code == 200:\n", + " print(\"\\nServer is ready!\")\n", + " return True\n", + " except ConnectionError:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(retry_interval)\n", + "\n", + " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", + " return False\n", + "\n", + "\n", + "def kill_llama_stack_server():\n", + " # Kill any existing llama stack server processes using pkill command\n", + " os.system(\"pkill -f llama_stack.core.server.server\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "28bd8dbd-4576-4e76-813f-21ab94db44a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building and starting Llama Stack server with PID: 19747\n", + "Waiting for server to start....\n", + "Server is ready!\n" + ] + } + ], + "source": [ + "server_process = run_llama_stack_server_background()\n", + "assert wait_for_server_to_start()" + ] + }, + { + "cell_type": "markdown", + "id": "gr9cdcg4r7n", + "metadata": {}, + "source": [ + "#### Install LlamaStack Client\n", + "\n", + "Install the client library to interact with the LlamaStack server." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "487d2dbc-d071-400e-b4f0-dcee58f8dc95", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2mUsing Python 3.12.11 environment at: /Users/swapna942/miniconda3\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 27ms\u001b[0m\u001b[0m\n" + ] + } + ], + "source": [ + "!uv pip install llama_stack_client" + ] + }, + { + "cell_type": "markdown", + "id": "0j5hag7l9x89", + "metadata": {}, + "source": [ + "### 3. Initialize LlamaStack Client\n", + "\n", + "Create a client connection to the LlamaStack server with API keys for different providers:\n", + "\n", + "- **Fireworks API Key**: For Fireworks models\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ab4eff97-4565-4c73-b1b3-0020a4c7e2a5", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_stack_client import LlamaStackClient\n", + "\n", + "client = LlamaStackClient(\n", + " base_url=\"http://0.0.0.0:8321\",\n", + " provider_data={\"fireworks_api_key\": \"***\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "vwhexjy1e8o", + "metadata": {}, + "source": [ + "#### Explore Available Models and Safety Features\n", + "\n", + "Check what models and safety shields are available through your LlamaStack instance." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "880443ef-ac3c-48b1-a80a-7dab5b25ac61", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/models \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/shields \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available Fireworks models:\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-405b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-3b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-11b-vision-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-90b-vision-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p3-70b-instruct\n", + "- fireworks/accounts/fireworks/models/llama4-scout-instruct-basic\n", + "- fireworks/accounts/fireworks/models/llama4-maverick-instruct-basic\n", + "- fireworks/nomic-ai/nomic-embed-text-v1.5\n", + "- fireworks/accounts/fireworks/models/llama-guard-3-8b\n", + "- fireworks/accounts/fireworks/models/llama-guard-3-11b-vision\n", + "----\n", + "Available shields (safety models):\n", + "code-scanner\n", + "llama-guard\n", + "nemo-guardrail\n", + "----\n" + ] + } + ], + "source": [ + "print(\"Available Fireworks models:\")\n", + "for m in client.models.list():\n", + " if m.identifier.startswith(\"fireworks/\"):\n", + " print(f\"- {m.identifier}\")\n", + "\n", + "print(\"----\")\n", + "print(\"Available shields (safety models):\")\n", + "for s in client.shields.list():\n", + " print(s.identifier)\n", + "print(\"----\")" + ] + }, + { + "cell_type": "markdown", + "id": "gojp7at31ht", + "metadata": {}, + "source": [ + "### 4. Vector Store Setup\n", + "\n", + "#### Create a Vector Store with File Upload\n", + "\n", + "Create a vector store using the OpenAI-compatible vector stores API:\n", + "\n", + "- **Vector Store**: OpenAI-compatible vector store for document storage\n", + "- **File Upload**: Automatic chunking and embedding of uploaded files \n", + "- **Embedding Model**: Sentence Transformers model for text embeddings\n", + "- **Dimensions**: 384-dimensional embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "be2c2899-ea53-4e5f-b6b8-ed425f5d6572", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File(id='file-54652c95c56c4c34918a97d7ff8a4320', bytes=41, created_at=1757442621, expires_at=1788978621, filename='shipping_policy.txt', object='file', purpose='assistants')\n", + "File(id='file-fb1227c1d1854da1bd774d21e5b7e41c', bytes=48, created_at=1757442621, expires_at=1788978621, filename='returns_policy.txt', object='file', purpose='assistants')\n", + "File(id='file-673f874852fe42798675a13d06a256e2', bytes=45, created_at=1757442621, expires_at=1788978621, filename='support.txt', object='file', purpose='assistants')\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "from io import BytesIO\n", + "\n", + "docs = [\n", + " (\"Acme ships globally in 3-5 business days.\", {\"title\": \"Shipping Policy\"}),\n", + " (\"Returns are accepted within 30 days of purchase.\", {\"title\": \"Returns Policy\"}),\n", + " (\"Support is available 24/7 via chat and email.\", {\"title\": \"Support\"}),\n", + "]\n", + "\n", + "file_ids = []\n", + "for content, metadata in docs:\n", + " with BytesIO(content.encode()) as file_buffer:\n", + " file_buffer.name = f\"{metadata['title'].replace(' ', '_').lower()}.txt\"\n", + " create_file_response = client.files.create(file=file_buffer, purpose=\"assistants\")\n", + " print(create_file_response)\n", + " file_ids.append(create_file_response.id)\n", + "\n", + "# Create vector store with files\n", + "vector_store = client.vector_stores.create(\n", + " name=\"acme_docs\",\n", + " file_ids=file_ids,\n", + " embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n", + " embedding_dimension=384,\n", + " provider_id=\"faiss\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9061tmi1zpq", + "metadata": {}, + "source": [ + "#### Test Vector Store Search\n", + "\n", + "Query the vector store. This performs semantic search to find relevant documents based on the query." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ba9d1901-bd5e-4216-b3e6-19dc74551cc6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Acme ships globally in 3-5 business days.\n", + "Returns are accepted within 30 days of purchase.\n" + ] + } + ], + "source": [ + "search_response = client.vector_stores.search(\n", + " vector_store_id=vector_store.id,\n", + " query=\"How long does shipping take?\",\n", + " max_num_results=2\n", + ")\n", + "for result in search_response.data:\n", + " content = result.content[0].text\n", + " print(content)" + ] + }, + { + "cell_type": "markdown", + "id": "usne6mbspms", + "metadata": {}, + "source": [ + "### 5. LangChain Integration\n", + "\n", + "#### Configure LangChain with LlamaStack\n", + "\n", + "Set up LangChain to use LlamaStack's OpenAI-compatible API:\n", + "\n", + "- **Base URL**: Points to LlamaStack's OpenAI endpoint\n", + "- **Headers**: Include Fireworks API key for model access\n", + "- **Model**: Use Meta Llama v3p1 8b instruct model for inference" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c378bd10-09c2-417c-bdfc-1e0a2dd19084", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# Point LangChain to Llamastack Server\n", + "llm = ChatOpenAI(\n", + " base_url=\"http://0.0.0.0:8321/v1/openai/v1\",\n", + " api_key=\"dummy\",\n", + " model=\"fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\",\n", + " default_headers={\"X-LlamaStack-Provider-Data\": '{\"fireworks_api_key\": \"***\"}'},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5a4ddpcuk3l", + "metadata": {}, + "source": [ + "#### Test LLM Connection\n", + "\n", + "Verify that LangChain can successfully communicate with the LlamaStack server." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f88ffb5a-657b-4916-9375-c6ddc156c25e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "data": { + "text/plain": [ + "AIMessage(content=\"A llama's gentle eyes shine bright,\\nIn the Andes, it roams through morning light.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': None, 'model_name': 'fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct', 'system_fingerprint': None, 'id': 'chatcmpl-602b5967-82a3-476b-9cd2-7d3b29b76ee8', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--0933c465-ff4d-4a7b-b7fb-fd97dd8244f3-0')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test llm with simple message\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n", + "]\n", + "llm.invoke(messages)" + ] + }, + { + "cell_type": "markdown", + "id": "0xh0jg6a0l4a", + "metadata": {}, + "source": [ + "### 6. Building the RAG Chain\n", + "\n", + "#### Create a Complete RAG Pipeline\n", + "\n", + "Build a LangChain pipeline that combines:\n", + "\n", + "1. **Vector Search**: Query LlamaStack's Open AI compatible Vector Store\n", + "2. **Context Assembly**: Format retrieved documents\n", + "3. **Prompt Template**: Structure the input for the LLM\n", + "4. **LLM Generation**: Generate answers using context\n", + "5. **Output Parsing**: Extract the final response\n", + "\n", + "**Chain Flow**: `Query → Vector Search → Context + Question → LLM → Response`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9684427d-dcc7-4544-9af5-8b110d014c42", + "metadata": {}, + "outputs": [], + "source": [ + "# LangChain for prompt template and chaining + LLAMA Stack Client Vector DB and LLM chat completion\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n", + "\n", + "\n", + "def join_docs(docs):\n", + " return \"\\n\\n\".join([f\"[{d.filename}] {d.content[0].text}\" for d in docs.data])\n", + "\n", + "PROMPT = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\"system\", \"You are a helpful assistant. Use the following context to answer.\"),\n", + " (\"user\", \"Question: {question}\\n\\nContext:\\n{context}\"),\n", + " ]\n", + ")\n", + "\n", + "vector_step = RunnableLambda(\n", + " lambda x: client.vector_stores.search(\n", + " vector_store_id=vector_store.id,\n", + " query=x,\n", + " max_num_results=2\n", + " )\n", + " )\n", + "\n", + "chain = (\n", + " {\"context\": vector_step | RunnableLambda(join_docs), \"question\": RunnablePassthrough()}\n", + " | PROMPT\n", + " | llm\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0onu6rhphlra", + "metadata": {}, + "source": [ + "### 7. Testing the RAG System\n", + "\n", + "#### Example 1: Shipping Query" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "03322188-9509-446a-a4a8-ce3bb83ec87c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "❓ How long does shipping take?\n", + "💡 Acme ships globally in 3-5 business days. This means that shipping typically takes between 3 to 5 working days from the date of dispatch or order fulfillment.\n" + ] + } + ], + "source": [ + "query = \"How long does shipping take?\"\n", + "response = chain.invoke(query)\n", + "print(\"❓\", query)\n", + "print(\"💡\", response)" + ] + }, + { + "cell_type": "markdown", + "id": "b7krhqj88ku", + "metadata": {}, + "source": [ + "#### Example 2: Returns Policy Query" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "61995550-bb0b-46a8-a5d0-023207475d60", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "❓ Can I return a product after 40 days?\n", + "💡 Based on the provided context, you cannot return a product after 40 days. The return window is limited to 30 days from the date of purchase.\n" + ] + } + ], + "source": [ + "query = \"Can I return a product after 40 days?\"\n", + "response = chain.invoke(query)\n", + "print(\"❓\", query)\n", + "print(\"💡\", response)" + ] + }, + { + "cell_type": "markdown", + "id": "h4w24fadvjs", + "metadata": {}, + "source": [ + "---\n", + "We have successfully built a RAG system that combines:\n", + "\n", + "- **LlamaStack** for infrastructure (LLM serving + Vector Store)\n", + "- **LangChain** for orchestration (prompts + chains)\n", + "- **Fireworks** for high-quality language models\n", + "\n", + "### Key Benefits\n", + "\n", + "1. **Unified Infrastructure**: Single server for LLMs and Vector Store\n", + "2. **OpenAI Compatibility**: Easy integration with existing LangChain code\n", + "3. **Multi-Provider Support**: Switch between different LLM providers\n", + "4. **Production Ready**: Built-in safety shields and monitoring\n", + "\n", + "### Next Steps\n", + "\n", + "- Add more sophisticated document processing\n", + "- Implement conversation memory\n", + "- Add safety filtering and monitoring\n", + "- Scale to larger document collections\n", + "- Integrate with web frameworks like FastAPI or Streamlit\n", + "\n", + "---\n", + "\n", + "##### 🔧 Cleanup\n", + "\n", + "Don't forget to stop the LlamaStack server when you're done:\n", + "\n", + "```python\n", + "kill_llama_stack_server()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "15647c46-22ce-4698-af3f-8161329d8e3a", + "metadata": {}, + "outputs": [], + "source": [ + "kill_llama_stack_server()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 69a52213a190bddcf118bb13206353ff4b30d33d Mon Sep 17 00:00:00 2001 From: Charlie Doern Date: Thu, 11 Sep 2025 16:30:09 -0400 Subject: [PATCH 106/124] fix: oasdiff enhancements and stability (#3419) # What does this PR do? only run conformance tests when the spec is changed. Also, cache oasdiff such that it is not installed every time the test is run Signed-off-by: Charlie Doern --- .github/workflows/conformance.yml | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/.github/workflows/conformance.yml b/.github/workflows/conformance.yml index c0a7795a3..c7962c93d 100644 --- a/.github/workflows/conformance.yml +++ b/.github/workflows/conformance.yml @@ -13,11 +13,8 @@ on: branches: [ main ] types: [opened, synchronize, reopened] paths: - - 'llama_stack/**' - - '!llama_stack/ui/**' - - 'tests/**' - - 'uv.lock' - - 'pyproject.toml' + - 'docs/_static/llama-stack-spec.yaml' + - 'docs/_static/llama-stack-spec.html' - '.github/workflows/conformance.yml' # This workflow itself concurrency: @@ -43,10 +40,27 @@ jobs: ref: ${{ github.event.pull_request.base.ref }} path: 'base' + # Cache oasdiff to avoid checksum failures and speed up builds + - name: Cache oasdiff + id: cache-oasdiff + uses: actions/cache@0400d5f644dc74513175e3cd8d07132dd4860809 + with: + path: ~/oasdiff + key: oasdiff-${{ runner.os }} + # Install oasdiff: https://github.com/oasdiff/oasdiff, a tool for detecting breaking changes in OpenAPI specs. - name: Install oasdiff + if: steps.cache-oasdiff.outputs.cache-hit != 'true' run: | curl -fsSL https://raw.githubusercontent.com/oasdiff/oasdiff/main/install.sh | sh + cp /usr/local/bin/oasdiff ~/oasdiff + + # Setup cached oasdiff + - name: Setup cached oasdiff + if: steps.cache-oasdiff.outputs.cache-hit == 'true' + run: | + sudo cp ~/oasdiff /usr/local/bin/oasdiff + sudo chmod +x /usr/local/bin/oasdiff # Run oasdiff to detect breaking changes in the API specification # This step will fail if incompatible changes are detected, preventing breaking changes from being merged From d31e641d6902dd1f43a3cc034af31a58ac135425 Mon Sep 17 00:00:00 2001 From: Akram Ben Aissi Date: Fri, 12 Sep 2025 10:10:59 +0100 Subject: [PATCH 107/124] fix: Improve pre-commit workflow error handling and feedback (#3400) # What does this PR do? fix: Improve pre-commit workflow error handling and feedback - Add explicit step to check pre-commit results and provide clear error messages - Improve verification steps with better error messages and file listings - Use GitHub Actions annotations (::error:: and ::warning::) for better visibility - Maintain continue-on-error for pre-commit step but add proper failure handling This addresses the issue where pre-commit failures were silent but still caused workflow failures later, making it difficult to understand what needed to be fixed. ## Test Plan Signed-off-by: Akram Ben Aissi --- .github/workflows/pre-commit.yml | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 000208043..b5845be53 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -47,11 +47,21 @@ jobs: run: npm ci working-directory: llama_stack/ui - - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 + - name: Run pre-commit + id: precommit + uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 + continue-on-error: true env: SKIP: no-commit-to-branch RUFF_OUTPUT_FORMAT: github + - name: Check pre-commit results + if: steps.precommit.outcome == 'failure' + run: | + echo "::error::Pre-commit hooks failed. Please run 'pre-commit run --all-files' locally and commit the fixes." + echo "::warning::Some pre-commit hooks failed. Check the output above for details." + exit 1 + - name: Debug run: | echo "github.ref: ${{ github.ref }}" @@ -79,17 +89,23 @@ jobs: echo "No changes to commit" fi - - name: Verify if there are any diff files after pre-commit + - name: Verify no uncommitted changes if: github.actor != 'dependabot[bot]' run: | - git diff --exit-code || (echo "There are uncommitted changes, run pre-commit locally and commit again" && exit 1) + if ! git diff --exit-code; then + echo "::error::There are uncommitted changes after pre-commit. Please run 'pre-commit run --all-files' locally and commit the fixes." + echo "::warning::Files with changes:" + git diff --name-status + exit 1 + fi - name: Verify if there are any new files after pre-commit if: github.actor != 'dependabot[bot]' run: | unstaged_files=$(git ls-files --others --exclude-standard) if [ -n "$unstaged_files" ]; then - echo "There are uncommitted new files, run pre-commit locally and commit again" + echo "::error::There are new untracked files after pre-commit. Please run 'pre-commit run --all-files' locally and commit the fixes." + echo "::warning::New files:" echo "$unstaged_files" exit 1 fi From f67081d2d6088a0d3175baffad94977ddf8f6483 Mon Sep 17 00:00:00 2001 From: Doug Edgar Date: Fri, 12 Sep 2025 02:18:19 -0700 Subject: [PATCH 108/124] feat: migrate to FIPS-validated cryptographic algorithms (#3423) # What does this PR do? Migrates MD5 and SHA-1 hash algorithms to SHA-256. In particular, replaces: - MD5 in chunk ID generation. - MD5 in file verification. - SHA-1 in model identifier digests. And updates all related test expectations. Original discussion: https://github.com/llamastack/llama-stack/discussions/3413 Closes #3424. ## Test Plan Unit tests from scripts/unit-tests.sh were updated to match the new hash output, and ran to verify the tests pass. Signed-off-by: Doug Edgar --- llama_stack/cli/verify_download.py | 17 +++++++---------- .../providers/utils/vector_io/vector_utils.py | 6 ++---- llama_stack/testing/inference_recorder.py | 2 +- .../providers/vector_io/test_vector_utils.py | 12 ++++++------ 4 files changed, 16 insertions(+), 21 deletions(-) diff --git a/llama_stack/cli/verify_download.py b/llama_stack/cli/verify_download.py index b7f4cfdb5..e738abb4f 100644 --- a/llama_stack/cli/verify_download.py +++ b/llama_stack/cli/verify_download.py @@ -48,15 +48,12 @@ def setup_verify_download_parser(parser: argparse.ArgumentParser) -> None: parser.set_defaults(func=partial(run_verify_cmd, parser=parser)) -def calculate_md5(filepath: Path, chunk_size: int = 8192) -> str: - # NOTE: MD5 is used here only for download integrity verification, - # not for security purposes - # TODO: switch to SHA256 - md5_hash = hashlib.md5(usedforsecurity=False) +def calculate_sha256(filepath: Path, chunk_size: int = 8192) -> str: + sha256_hash = hashlib.sha256() with open(filepath, "rb") as f: for chunk in iter(lambda: f.read(chunk_size), b""): - md5_hash.update(chunk) - return md5_hash.hexdigest() + sha256_hash.update(chunk) + return sha256_hash.hexdigest() def load_checksums(checklist_path: Path) -> dict[str, str]: @@ -64,10 +61,10 @@ def load_checksums(checklist_path: Path) -> dict[str, str]: with open(checklist_path) as f: for line in f: if line.strip(): - md5sum, filepath = line.strip().split(" ", 1) + sha256sum, filepath = line.strip().split(" ", 1) # Remove leading './' if present filepath = filepath.lstrip("./") - checksums[filepath] = md5sum + checksums[filepath] = sha256sum return checksums @@ -88,7 +85,7 @@ def verify_files(model_dir: Path, checksums: dict[str, str], console: Console) - matches = False if exists: - actual_hash = calculate_md5(full_path) + actual_hash = calculate_sha256(full_path) matches = actual_hash == expected_hash results.append( diff --git a/llama_stack/providers/utils/vector_io/vector_utils.py b/llama_stack/providers/utils/vector_io/vector_utils.py index e55ac75ae..324f35405 100644 --- a/llama_stack/providers/utils/vector_io/vector_utils.py +++ b/llama_stack/providers/utils/vector_io/vector_utils.py @@ -12,14 +12,12 @@ import uuid def generate_chunk_id(document_id: str, chunk_text: str, chunk_window: str | None = None) -> str: """ Generate a unique chunk ID using a hash of the document ID and chunk text. - - Note: MD5 is used only to calculate an identifier, not for security purposes. - Adding usedforsecurity=False for compatibility with FIPS environments. + Then use the first 32 characters of the hash to create a UUID. """ hash_input = f"{document_id}:{chunk_text}".encode() if chunk_window: hash_input += f":{chunk_window}".encode() - return str(uuid.UUID(hashlib.md5(hash_input, usedforsecurity=False).hexdigest())) + return str(uuid.UUID(hashlib.sha256(hash_input).hexdigest()[:32])) def proper_case(s: str) -> str: diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index e78f493a6..6f017c51d 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -211,7 +211,7 @@ def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str: return sorted(set(idents)) identifiers = _extract_model_identifiers() - return hashlib.sha1(("|".join(identifiers)).encode("utf-8")).hexdigest()[:8] + return hashlib.sha256(("|".join(identifiers)).encode("utf-8")).hexdigest()[:8] def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]]) -> dict[str, Any] | None: diff --git a/tests/unit/providers/vector_io/test_vector_utils.py b/tests/unit/providers/vector_io/test_vector_utils.py index a5d803a82..10ebe5bfb 100644 --- a/tests/unit/providers/vector_io/test_vector_utils.py +++ b/tests/unit/providers/vector_io/test_vector_utils.py @@ -26,9 +26,9 @@ def test_generate_chunk_id(): chunk_ids = sorted([chunk.chunk_id for chunk in chunks]) assert chunk_ids == [ - "177a1368-f6a8-0c50-6e92-18677f2c3de3", - "bc744db3-1b25-0a9c-cdff-b6ba3df73c36", - "f68df25d-d9aa-ab4d-5684-64a233add20d", + "31d1f9a3-c8d2-66e7-3c37-af2acd329778", + "d07dade7-29c0-cda7-df29-0249a1dcbc3e", + "d14f75a1-5855-7f72-2c78-d9fc4275a346", ] @@ -36,14 +36,14 @@ def test_generate_chunk_id_with_window(): chunk = Chunk(content="test", metadata={"document_id": "doc-1"}) chunk_id1 = generate_chunk_id("doc-1", chunk, chunk_window="0-1") chunk_id2 = generate_chunk_id("doc-1", chunk, chunk_window="1-2") - assert chunk_id1 == "149018fe-d0eb-0f8d-5f7f-726bdd2aeedb" - assert chunk_id2 == "4562c1ee-9971-1f3b-51a6-7d05e5211154" + assert chunk_id1 == "8630321a-d9cb-2bb6-cd28-ebf68dafd866" + assert chunk_id2 == "13a1c09a-cbda-b61a-2d1a-7baa90888685" def test_chunk_id(): # Test with existing chunk ID chunk_with_id = Chunk(content="test", metadata={"document_id": "existing-id"}) - assert chunk_with_id.chunk_id == "84ededcc-b80b-a83e-1a20-ca6515a11350" + assert chunk_with_id.chunk_id == "11704f92-42b6-61df-bf85-6473e7708fbd" # Test with document ID in metadata chunk_with_doc_id = Chunk(content="test", metadata={"document_id": "doc-1"}) From 3de9ad0a87d7bfad50ab23c859cebcaf06b6911b Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 12 Sep 2025 17:59:56 -0400 Subject: [PATCH 109/124] chore(recorder, tests): add test for openai /v1/models (#3426) # What does this PR do? - [x] adds a test for the recorder's handling of /v1/models - [x] adds a fix for /v1/models handling ## Test Plan ci --- llama_stack/testing/inference_recorder.py | 60 ++++++++++--------- .../distribution/test_inference_recordings.py | 51 ++++++++++++++-- 2 files changed, 79 insertions(+), 32 deletions(-) diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 6f017c51d..745160976 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -7,6 +7,7 @@ from __future__ import annotations # for forward references import hashlib +import inspect import json import os from collections.abc import Generator @@ -198,16 +199,11 @@ def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str: Supported endpoints: - '/api/tags' (Ollama): response body has 'models': [ { name/model/digest/id/... }, ... ] - - '/v1/models' (OpenAI): response body has 'data': [ { id: ... }, ... ] + - '/v1/models' (OpenAI): response body is: [ { id: ... }, ... ] Returns a list of unique identifiers or None if structure doesn't match. """ - body = response["body"] - if endpoint == "/api/tags": - items = body.get("models") - idents = [m.model for m in items] - else: - items = body.get("data") - idents = [m.id for m in items] + items = response["body"] + idents = [m.model if endpoint == "/api/tags" else m.id for m in items] return sorted(set(idents)) identifiers = _extract_model_identifiers() @@ -219,28 +215,22 @@ def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]]) seen: dict[str, dict[str, Any]] = {} for rec in records: body = rec["response"]["body"] - if endpoint == "/api/tags": - items = body.models - elif endpoint == "/v1/models": - items = body.data - else: - items = [] - - for m in items: - if endpoint == "/v1/models": + if endpoint == "/v1/models": + for m in body: key = m.id - else: + seen[key] = m + elif endpoint == "/api/tags": + for m in body.models: key = m.model - seen[key] = m + seen[key] = m ordered = [seen[k] for k in sorted(seen.keys())] canonical = records[0] canonical_req = canonical.get("request", {}) if isinstance(canonical_req, dict): canonical_req["endpoint"] = endpoint - if endpoint == "/v1/models": - body = {"data": ordered, "object": "list"} - else: + body = ordered + if endpoint == "/api/tags": from ollama import ListResponse body = ListResponse(models=ordered) @@ -252,7 +242,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint if _current_mode == InferenceMode.LIVE or _current_storage is None: # Normal operation - return await original_method(self, *args, **kwargs) + if inspect.iscoroutinefunction(original_method): + return await original_method(self, *args, **kwargs) + else: + return original_method(self, *args, **kwargs) # Get base URL based on client type if client_type == "openai": @@ -300,7 +293,14 @@ async def _patched_inference_method(original_method, self, client_type, endpoint ) elif _current_mode == InferenceMode.RECORD: - response = await original_method(self, *args, **kwargs) + if inspect.iscoroutinefunction(original_method): + response = await original_method(self, *args, **kwargs) + else: + response = original_method(self, *args, **kwargs) + + # we want to store the result of the iterator, not the iterator itself + if endpoint == "/v1/models": + response = [m async for m in response] request_data = { "method": method, @@ -380,10 +380,14 @@ def patch_inference_clients(): _original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs ) - async def patched_models_list(self, *args, **kwargs): - return await _patched_inference_method( - _original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs - ) + def patched_models_list(self, *args, **kwargs): + async def _iter(): + for item in await _patched_inference_method( + _original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs + ): + yield item + + return _iter() # Apply OpenAI patches AsyncChatCompletions.create = patched_chat_completions_create diff --git a/tests/unit/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py index c69cf319b..94fd2536e 100644 --- a/tests/unit/distribution/test_inference_recordings.py +++ b/tests/unit/distribution/test_inference_recordings.py @@ -6,10 +6,11 @@ import tempfile from pathlib import Path -from unittest.mock import patch +from unittest.mock import AsyncMock, Mock, patch import pytest from openai import AsyncOpenAI +from openai.types.model import Model as OpenAIModel # Import the real Pydantic response types instead of using Mocks from llama_stack.apis.inference import ( @@ -158,7 +159,9 @@ class TestInferenceRecording: return real_openai_chat_response temp_storage_dir = temp_storage_dir / "test_recording_mode" - with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): + with patch( + "openai.resources.chat.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create + ): with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") @@ -184,7 +187,9 @@ class TestInferenceRecording: temp_storage_dir = temp_storage_dir / "test_replay_mode" # First, record a response - with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): + with patch( + "openai.resources.chat.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create + ): with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") @@ -213,6 +218,42 @@ class TestInferenceRecording: # Verify the original method was NOT called mock_create_patch.assert_not_called() + async def test_replay_mode_models(self, temp_storage_dir): + """Test that replay mode returns stored responses without making real model listing calls.""" + + async def _async_iterator(models): + for model in models: + yield model + + models = [ + OpenAIModel(id="foo", created=1, object="model", owned_by="test"), + OpenAIModel(id="bar", created=2, object="model", owned_by="test"), + ] + + expected_ids = {m.id for m in models} + + temp_storage_dir = temp_storage_dir / "test_replay_mode_models" + + # baseline - mock works without recording + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.models._get_api_list = Mock(return_value=_async_iterator(models)) + assert {m.id async for m in client.models.list()} == expected_ids + client.models._get_api_list.assert_called_once() + + # record the call + with inference_recording(mode=InferenceMode.RECORD, storage_dir=temp_storage_dir): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.models._get_api_list = Mock(return_value=_async_iterator(models)) + assert {m.id async for m in client.models.list()} == expected_ids + client.models._get_api_list.assert_called_once() + + # replay the call + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=temp_storage_dir): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.models._get_api_list = Mock(return_value=_async_iterator(models)) + assert {m.id async for m in client.models.list()} == expected_ids + client.models._get_api_list.assert_not_called() + async def test_replay_missing_recording(self, temp_storage_dir): """Test that replay mode fails when no recording is found.""" temp_storage_dir = temp_storage_dir / "test_replay_missing_recording" @@ -233,7 +274,9 @@ class TestInferenceRecording: temp_storage_dir = temp_storage_dir / "test_embeddings_recording" # Record - with patch("openai.resources.embeddings.AsyncEmbeddings.create", side_effect=mock_create): + with patch( + "openai.resources.embeddings.AsyncEmbeddings.create", new_callable=AsyncMock, side_effect=mock_create + ): with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") From 8cf2128b40195634c4024e3c797eceaaa4da19bc Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 13 Sep 2025 12:28:04 -0400 Subject: [PATCH 110/124] chore(tests): always show slowest tests (#3431) # What does this PR do? help developers identify slow tests by always passing --duration to pytest ## Test Plan n/a --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 72c4f6f9e..ce95b758f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -354,6 +354,7 @@ warn_required_dynamic_aliases = true classmethod-decorators = ["classmethod", "pydantic.field_validator"] [tool.pytest.ini_options] +addopts = ["--durations=10"] asyncio_mode = "auto" markers = [ "allow_network: Allow network access for specific unit tests", From 6787755c0c8af6b59322352f985cffb224aadd3b Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 13 Sep 2025 14:11:38 -0400 Subject: [PATCH 111/124] chore(recorder): add support for NOT_GIVEN (#3430) # What does this PR do? the recorder mocks the openai-python interface. the openai-python interface allows NOT_GIVEN as an input option. this change properly handles NOT_GIVEN. ## Test Plan ci (coverage for chat, completions, embeddings) --- llama_stack/testing/inference_recorder.py | 5 ++ .../distribution/test_inference_recordings.py | 65 ++++++++++++++++++- 2 files changed, 67 insertions(+), 3 deletions(-) diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 745160976..f899d73d3 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -16,6 +16,8 @@ from enum import StrEnum from pathlib import Path from typing import Any, Literal, cast +from openai import NOT_GIVEN + from llama_stack.log import get_logger logger = get_logger(__name__, category="testing") @@ -250,6 +252,9 @@ async def _patched_inference_method(original_method, self, client_type, endpoint # Get base URL based on client type if client_type == "openai": base_url = str(self._client.base_url) + + # the OpenAI client methods may pass NOT_GIVEN for unset parameters; filter these out + kwargs = {k: v for k, v in kwargs.items() if v is not NOT_GIVEN} elif client_type == "ollama": # Get base URL from the client (Ollama client uses host attribute) base_url = getattr(self, "host", "http://localhost:11434") diff --git a/tests/unit/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py index 94fd2536e..4909bbe1e 100644 --- a/tests/unit/distribution/test_inference_recordings.py +++ b/tests/unit/distribution/test_inference_recordings.py @@ -9,7 +9,7 @@ from pathlib import Path from unittest.mock import AsyncMock, Mock, patch import pytest -from openai import AsyncOpenAI +from openai import NOT_GIVEN, AsyncOpenAI from openai.types.model import Model as OpenAIModel # Import the real Pydantic response types instead of using Mocks @@ -17,6 +17,7 @@ from llama_stack.apis.inference import ( OpenAIAssistantMessageParam, OpenAIChatCompletion, OpenAIChoice, + OpenAICompletion, OpenAIEmbeddingData, OpenAIEmbeddingsResponse, OpenAIEmbeddingUsage, @@ -170,6 +171,7 @@ class TestInferenceRecording: messages=[{"role": "user", "content": "Hello, how are you?"}], temperature=0.7, max_tokens=50, + user=NOT_GIVEN, ) # Verify the response was returned correctly @@ -198,6 +200,7 @@ class TestInferenceRecording: messages=[{"role": "user", "content": "Hello, how are you?"}], temperature=0.7, max_tokens=50, + user=NOT_GIVEN, ) # Now test replay mode - should not call the original method @@ -281,7 +284,11 @@ class TestInferenceRecording: client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") response = await client.embeddings.create( - model="nomic-embed-text", input=["Hello world", "Test embedding"] + model=real_embeddings_response.model, + input=["Hello world", "Test embedding"], + encoding_format=NOT_GIVEN, + dimensions=NOT_GIVEN, + user=NOT_GIVEN, ) assert len(response.data) == 2 @@ -292,7 +299,8 @@ class TestInferenceRecording: client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") response = await client.embeddings.create( - model="nomic-embed-text", input=["Hello world", "Test embedding"] + model=real_embeddings_response.model, + input=["Hello world", "Test embedding"], ) # Verify we got the recorded response @@ -302,6 +310,57 @@ class TestInferenceRecording: # Verify original method was not called mock_create_patch.assert_not_called() + async def test_completions_recording(self, temp_storage_dir): + real_completions_response = OpenAICompletion( + id="test_completion", + object="text_completion", + created=1234567890, + model="llama3.2:3b", + choices=[ + { + "text": "Hello! I'm doing well, thank you for asking.", + "index": 0, + "logprobs": None, + "finish_reason": "stop", + } + ], + ) + + async def mock_create(*args, **kwargs): + return real_completions_response + + temp_storage_dir = temp_storage_dir / "test_completions_recording" + + # Record + with patch( + "openai.resources.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create + ): + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + + response = await client.completions.create( + model=real_completions_response.model, + prompt="Hello, how are you?", + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) + + assert response.choices[0].text == real_completions_response.choices[0].text + + # Replay + with patch("openai.resources.completions.AsyncCompletions.create") as mock_create_patch: + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + response = await client.completions.create( + model=real_completions_response.model, + prompt="Hello, how are you?", + temperature=0.7, + max_tokens=50, + ) + assert response.choices[0].text == real_completions_response.choices[0].text + mock_create_patch.assert_not_called() + async def test_live_mode(self, real_openai_chat_response): """Test that live mode passes through to original methods.""" From 36fd97e306d14cbb5eba7c18ce93dcb05bdf9206 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Sep 2025 09:46:05 +0200 Subject: [PATCH 112/124] chore(ui-deps): bump next from 15.3.3 to 15.5.3 in /llama_stack/ui (#3438) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [next](https://github.com/vercel/next.js) from 15.3.3 to 15.5.3.
Release notes

Sourced from next's releases.

v15.5.3

[!NOTE]
This release is backporting bug fixes. It does not include all pending features/changes on canary.

Core Changes

  • fix: validation return types of pages API routes (#83069)
  • fix: relative paths in dev in validator.ts (#83073)
  • fix: remove satisfies keyword from type validation to preserve old TS compatibility (#83071)

Credits

Huge thanks to @​bgub for helping!

v15.5.2

[!NOTE]
This release is backporting bug fixes. It does not include all pending features/changes on canary.

Core Changes

  • fix: disable unknownatrules lint rule entirely (#83059)
  • revert: add ?dpl to fonts in /_next/static/media (#83062)

Credits

Huge thanks to @​bgub and @​ztanner for helping!

v15.5.1

[!NOTE]
This release is backporting bug fixes. It does not include all pending features/changes on canary.

Core Changes

  • fix: aliased navigations should apply scroll handling (#82900)
  • Turbopack: fix invalid NFT entry with file behind symlink (#82887)
  • fix: typesafe linking to route handlers and pages API routes (#82858)
  • fix: change "noUnknownAtRules" to "warn" for Biome (#82974)
  • fix: add path normalization to getRelativePath for Windows (#82918)
  • feat: add typesafety with config.typedRoutes to redirect() and permanentRedirect() (#82860)
  • fix: avoid importing types that will be unused (#82856)
  • fix: update the config.api.responseLimit type (#82852)
  • fix: update validation return types (#82854)

Credits

Huge thanks to @​bgub, @​mischnic, and @​ztanner for helping!

v15.5.1-canary.39

Core Changes

  • [metadata] change the metadata routes params to promises: #83560

... (truncated)

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 370 +++++++++++++++++-------------- llama_stack/ui/package.json | 2 +- 2 files changed, 199 insertions(+), 173 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index e2c0815fd..ff73fa2e8 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -20,7 +20,7 @@ "framer-motion": "^12.23.12", "llama-stack-client": "^0.2.21", "lucide-react": "^0.542.0", - "next": "15.3.3", + "next": "15.5.3", "next-auth": "^4.24.11", "next-themes": "^0.4.6", "react": "^19.0.0", @@ -664,9 +664,9 @@ } }, "node_modules/@emnapi/runtime": { - "version": "1.4.3", - "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.4.3.tgz", - "integrity": "sha512-pBPWdu6MLKROBX05wSNKcNb++m5Er+KQ9QkB+WVM+pW2Kx9hoSrVTnu3BdkI5eBLZoKu/J6mW/B6i6bJB2ytXQ==", + "version": "1.5.0", + "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.5.0.tgz", + "integrity": "sha512-97/BJ3iXHww3djw6hYIfErCZFee7qCtrneuLa20UXFCOTCfBM2cvQHjWJ2EG0s0MtdNwInarqCTz35i4wWXHsQ==", "license": "MIT", "optional": true, "dependencies": { @@ -927,9 +927,9 @@ } }, "node_modules/@img/sharp-darwin-arm64": { - "version": "0.34.1", - "resolved": "https://registry.npmjs.org/@img/sharp-darwin-arm64/-/sharp-darwin-arm64-0.34.1.tgz", - "integrity": "sha512-pn44xgBtgpEbZsu+lWf2KNb6OAf70X68k+yk69Ic2Xz11zHR/w24/U49XT7AeRwJ0Px+mhALhU5LPci1Aymk7A==", + "version": "0.34.3", + "resolved": "https://registry.npmjs.org/@img/sharp-darwin-arm64/-/sharp-darwin-arm64-0.34.3.tgz", + "integrity": "sha512-ryFMfvxxpQRsgZJqBd4wsttYQbCxsJksrv9Lw/v798JcQ8+w84mBWuXwl+TT0WJ/WrYOLaYpwQXi3sA9nTIaIg==", "cpu": [ "arm64" ], @@ -945,13 +945,13 @@ "url": "https://opencollective.com/libvips" }, "optionalDependencies": { - "@img/sharp-libvips-darwin-arm64": "1.1.0" + "@img/sharp-libvips-darwin-arm64": "1.2.0" } }, "node_modules/@img/sharp-darwin-x64": { - "version": "0.34.1", - "resolved": "https://registry.npmjs.org/@img/sharp-darwin-x64/-/sharp-darwin-x64-0.34.1.tgz", - "integrity": "sha512-VfuYgG2r8BpYiOUN+BfYeFo69nP/MIwAtSJ7/Zpxc5QF3KS22z8Pvg3FkrSFJBPNQ7mmcUcYQFBmEQp7eu1F8Q==", + "version": "0.34.3", + "resolved": "https://registry.npmjs.org/@img/sharp-darwin-x64/-/sharp-darwin-x64-0.34.3.tgz", + "integrity": "sha512-yHpJYynROAj12TA6qil58hmPmAwxKKC7reUqtGLzsOHfP7/rniNGTL8tjWX6L3CTV4+5P4ypcS7Pp+7OB+8ihA==", "cpu": [ "x64" ], @@ -967,13 +967,13 @@ "url": "https://opencollective.com/libvips" }, "optionalDependencies": { - "@img/sharp-libvips-darwin-x64": "1.1.0" + "@img/sharp-libvips-darwin-x64": "1.2.0" } }, "node_modules/@img/sharp-libvips-darwin-arm64": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-arm64/-/sharp-libvips-darwin-arm64-1.1.0.tgz", - "integrity": "sha512-HZ/JUmPwrJSoM4DIQPv/BfNh9yrOA8tlBbqbLz4JZ5uew2+o22Ik+tHQJcih7QJuSa0zo5coHTfD5J8inqj9DA==", + "version": "1.2.0", + "resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-arm64/-/sharp-libvips-darwin-arm64-1.2.0.tgz", + "integrity": "sha512-sBZmpwmxqwlqG9ueWFXtockhsxefaV6O84BMOrhtg/YqbTaRdqDE7hxraVE3y6gVM4eExmfzW4a8el9ArLeEiQ==", "cpu": [ "arm64" ], @@ -987,9 +987,9 @@ } }, "node_modules/@img/sharp-libvips-darwin-x64": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-x64/-/sharp-libvips-darwin-x64-1.1.0.tgz", - "integrity": "sha512-Xzc2ToEmHN+hfvsl9wja0RlnXEgpKNmftriQp6XzY/RaSfwD9th+MSh0WQKzUreLKKINb3afirxW7A0fz2YWuQ==", + "version": "1.2.0", + "resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-x64/-/sharp-libvips-darwin-x64-1.2.0.tgz", + "integrity": "sha512-M64XVuL94OgiNHa5/m2YvEQI5q2cl9d/wk0qFTDVXcYzi43lxuiFTftMR1tOnFQovVXNZJ5TURSDK2pNe9Yzqg==", "cpu": [ "x64" ], @@ -1003,9 +1003,9 @@ } }, "node_modules/@img/sharp-libvips-linux-arm": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm/-/sharp-libvips-linux-arm-1.1.0.tgz", - "integrity": "sha512-s8BAd0lwUIvYCJyRdFqvsj+BJIpDBSxs6ivrOPm/R7piTs5UIwY5OjXrP2bqXC9/moGsyRa37eYWYCOGVXxVrA==", + "version": "1.2.0", + "resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm/-/sharp-libvips-linux-arm-1.2.0.tgz", + "integrity": "sha512-mWd2uWvDtL/nvIzThLq3fr2nnGfyr/XMXlq8ZJ9WMR6PXijHlC3ksp0IpuhK6bougvQrchUAfzRLnbsen0Cqvw==", "cpu": [ "arm" ], @@ -1019,9 +1019,9 @@ } }, "node_modules/@img/sharp-libvips-linux-arm64": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm64/-/sharp-libvips-linux-arm64-1.1.0.tgz", - "integrity": "sha512-IVfGJa7gjChDET1dK9SekxFFdflarnUB8PwW8aGwEoF3oAsSDuNUTYS+SKDOyOJxQyDC1aPFMuRYLoDInyV9Ew==", + "version": "1.2.0", + "resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm64/-/sharp-libvips-linux-arm64-1.2.0.tgz", + "integrity": "sha512-RXwd0CgG+uPRX5YYrkzKyalt2OJYRiJQ8ED/fi1tq9WQW2jsQIn0tqrlR5l5dr/rjqq6AHAxURhj2DVjyQWSOA==", "cpu": [ "arm64" ], @@ -1035,9 +1035,9 @@ } }, "node_modules/@img/sharp-libvips-linux-ppc64": { - 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"node_modules/@swc/counter": { - "version": "0.1.3", - "resolved": "https://registry.npmjs.org/@swc/counter/-/counter-0.1.3.tgz", - "integrity": "sha512-e2BR4lsJkkRlKZ/qCHPw9ZaSxc0MVUd7gtbtaB7aMvHeJVYe8sOB8DBZkP2DtISHGSku9sCK6T6cnY0CtXrOCQ==", - "license": "Apache-2.0" - }, "node_modules/@swc/helpers": { "version": "0.5.15", "resolved": "https://registry.npmjs.org/@swc/helpers/-/helpers-0.5.15.tgz", @@ -5475,17 +5519,6 @@ "dev": true, "license": "MIT" }, - "node_modules/busboy": { - "version": "1.6.0", - "resolved": "https://registry.npmjs.org/busboy/-/busboy-1.6.0.tgz", - "integrity": "sha512-8SFQbg/0hQ9xy3UNTB0YEnsNBbWfhf7RtnzpL7TkBiTBRfrQ9Fxcnz7VJsleJpyp6rVLvXiuORqjlHi5q+PYuA==", - "dependencies": { - "streamsearch": "^1.1.0" - }, - "engines": { - "node": ">=10.16.0" - } - }, "node_modules/bytes": { "version": "3.1.2", "resolved": "https://registry.npmjs.org/bytes/-/bytes-3.1.2.tgz", @@ -8295,9 +8328,9 @@ } }, "node_modules/is-arrayish": { - "version": "0.3.2", - "resolved": "https://registry.npmjs.org/is-arrayish/-/is-arrayish-0.3.2.tgz", - "integrity": "sha512-eVRqCvVlZbuw3GrM63ovNSNAeA1K16kaR/LRY/92w0zxQ5/1YzwblUX652i4Xs9RwAGjW9d9y6X88t8OaAJfWQ==", + "version": "0.3.4", + "resolved": "https://registry.npmjs.org/is-arrayish/-/is-arrayish-0.3.4.tgz", + "integrity": "sha512-m6UrgzFVUYawGBh1dUsWR5M2Clqic9RVXC/9f8ceNlv2IcO9j9J/z8UoCLPqtsPBFNzEpfR3xftohbfqDx8EQA==", "license": "MIT", "optional": true }, @@ -11542,14 +11575,13 @@ } }, "node_modules/next": { - "version": "15.3.3", - "resolved": "https://registry.npmjs.org/next/-/next-15.3.3.tgz", - "integrity": "sha512-JqNj29hHNmCLtNvd090SyRbXJiivQ+58XjCcrC50Crb5g5u2zi7Y2YivbsEfzk6AtVI80akdOQbaMZwWB1Hthw==", + "version": "15.5.3", + "resolved": "https://registry.npmjs.org/next/-/next-15.5.3.tgz", + "integrity": "sha512-r/liNAx16SQj4D+XH/oI1dlpv9tdKJ6cONYPwwcCC46f2NjpaRWY+EKCzULfgQYV6YKXjHBchff2IZBSlZmJNw==", + "license": "MIT", "dependencies": { - "@next/env": "15.3.3", - "@swc/counter": "0.1.3", + "@next/env": "15.5.3", "@swc/helpers": "0.5.15", - "busboy": "1.6.0", "caniuse-lite": "^1.0.30001579", "postcss": "8.4.31", "styled-jsx": "5.1.6" @@ -11561,19 +11593,19 @@ "node": "^18.18.0 || ^19.8.0 || >= 20.0.0" }, "optionalDependencies": { - "@next/swc-darwin-arm64": "15.3.3", - "@next/swc-darwin-x64": "15.3.3", - "@next/swc-linux-arm64-gnu": "15.3.3", - "@next/swc-linux-arm64-musl": "15.3.3", - "@next/swc-linux-x64-gnu": "15.3.3", - "@next/swc-linux-x64-musl": "15.3.3", - "@next/swc-win32-arm64-msvc": "15.3.3", - "@next/swc-win32-x64-msvc": "15.3.3", - "sharp": "^0.34.1" + "@next/swc-darwin-arm64": "15.5.3", + "@next/swc-darwin-x64": "15.5.3", + "@next/swc-linux-arm64-gnu": "15.5.3", + "@next/swc-linux-arm64-musl": "15.5.3", + "@next/swc-linux-x64-gnu": "15.5.3", + "@next/swc-linux-x64-musl": "15.5.3", + "@next/swc-win32-arm64-msvc": "15.5.3", + "@next/swc-win32-x64-msvc": "15.5.3", + "sharp": "^0.34.3" }, "peerDependencies": { "@opentelemetry/api": "^1.1.0", - "@playwright/test": "^1.41.2", + "@playwright/test": "^1.51.1", "babel-plugin-react-compiler": "*", "react": "^18.2.0 || 19.0.0-rc-de68d2f4-20241204 || ^19.0.0", "react-dom": "^18.2.0 || 19.0.0-rc-de68d2f4-20241204 || ^19.0.0", @@ -13240,16 +13272,16 @@ "license": "ISC" }, "node_modules/sharp": { - "version": "0.34.1", - "resolved": "https://registry.npmjs.org/sharp/-/sharp-0.34.1.tgz", - "integrity": "sha512-1j0w61+eVxu7DawFJtnfYcvSv6qPFvfTaqzTQ2BLknVhHTwGS8sc63ZBF4rzkWMBVKybo4S5OBtDdZahh2A1xg==", + "version": "0.34.3", + "resolved": "https://registry.npmjs.org/sharp/-/sharp-0.34.3.tgz", + "integrity": "sha512-eX2IQ6nFohW4DbvHIOLRB3MHFpYqaqvXd3Tp5e/T/dSH83fxaNJQRvDMhASmkNTsNTVF2/OOopzRCt7xokgPfg==", "hasInstallScript": true, "license": "Apache-2.0", "optional": true, "dependencies": { "color": "^4.2.3", - "detect-libc": "^2.0.3", - "semver": "^7.7.1" + "detect-libc": "^2.0.4", + "semver": "^7.7.2" }, "engines": { "node": "^18.17.0 || ^20.3.0 || >=21.0.0" @@ -13258,26 +13290,28 @@ "url": "https://opencollective.com/libvips" }, "optionalDependencies": { - "@img/sharp-darwin-arm64": "0.34.1", - "@img/sharp-darwin-x64": "0.34.1", - "@img/sharp-libvips-darwin-arm64": "1.1.0", - "@img/sharp-libvips-darwin-x64": "1.1.0", - "@img/sharp-libvips-linux-arm": "1.1.0", - "@img/sharp-libvips-linux-arm64": "1.1.0", - "@img/sharp-libvips-linux-ppc64": "1.1.0", - "@img/sharp-libvips-linux-s390x": "1.1.0", - "@img/sharp-libvips-linux-x64": "1.1.0", - "@img/sharp-libvips-linuxmusl-arm64": "1.1.0", - "@img/sharp-libvips-linuxmusl-x64": "1.1.0", - "@img/sharp-linux-arm": "0.34.1", - "@img/sharp-linux-arm64": "0.34.1", - "@img/sharp-linux-s390x": "0.34.1", - "@img/sharp-linux-x64": "0.34.1", - "@img/sharp-linuxmusl-arm64": "0.34.1", - "@img/sharp-linuxmusl-x64": "0.34.1", - "@img/sharp-wasm32": "0.34.1", - "@img/sharp-win32-ia32": "0.34.1", - "@img/sharp-win32-x64": "0.34.1" + "@img/sharp-darwin-arm64": "0.34.3", + "@img/sharp-darwin-x64": "0.34.3", + "@img/sharp-libvips-darwin-arm64": "1.2.0", + "@img/sharp-libvips-darwin-x64": "1.2.0", + "@img/sharp-libvips-linux-arm": "1.2.0", + "@img/sharp-libvips-linux-arm64": "1.2.0", + "@img/sharp-libvips-linux-ppc64": "1.2.0", + "@img/sharp-libvips-linux-s390x": "1.2.0", + "@img/sharp-libvips-linux-x64": "1.2.0", + "@img/sharp-libvips-linuxmusl-arm64": "1.2.0", + "@img/sharp-libvips-linuxmusl-x64": "1.2.0", + "@img/sharp-linux-arm": "0.34.3", + "@img/sharp-linux-arm64": "0.34.3", + "@img/sharp-linux-ppc64": "0.34.3", + "@img/sharp-linux-s390x": "0.34.3", + "@img/sharp-linux-x64": "0.34.3", + "@img/sharp-linuxmusl-arm64": "0.34.3", + "@img/sharp-linuxmusl-x64": "0.34.3", + "@img/sharp-wasm32": "0.34.3", + "@img/sharp-win32-arm64": "0.34.3", + "@img/sharp-win32-ia32": "0.34.3", + "@img/sharp-win32-x64": "0.34.3" } }, "node_modules/shebang-command": { @@ -13403,9 +13437,9 @@ "license": "ISC" }, "node_modules/simple-swizzle": { - "version": "0.2.2", - "resolved": "https://registry.npmjs.org/simple-swizzle/-/simple-swizzle-0.2.2.tgz", - "integrity": "sha512-JA//kQgZtbuY83m+xT+tXJkmJncGMTFT+C+g2h2R9uxkYIrE2yy9sgmcLhCnw57/WSD+Eh3J97FPEDFnbXnDUg==", + "version": "0.2.4", + "resolved": "https://registry.npmjs.org/simple-swizzle/-/simple-swizzle-0.2.4.tgz", + "integrity": "sha512-nAu1WFPQSMNr2Zn9PGSZK9AGn4t/y97lEm+MXTtUDwfP0ksAIX4nO+6ruD9Jwut4C49SB1Ws+fbXsm/yScWOHw==", "license": "MIT", "optional": true, "dependencies": { @@ -13526,14 +13560,6 @@ "node": ">= 0.8" } }, - "node_modules/streamsearch": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/streamsearch/-/streamsearch-1.1.0.tgz", - "integrity": "sha512-Mcc5wHehp9aXz1ax6bZUyY5afg9u2rv5cqQI3mRrYkGC8rW2hM02jWuwjtL++LS5qinSyhj2QfLyNsuc+VsExg==", - "engines": { - "node": ">=10.0.0" - } - }, "node_modules/string-length": { "version": "4.0.2", "resolved": "https://registry.npmjs.org/string-length/-/string-length-4.0.2.tgz", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index e50401fa6..a0a8b2c7c 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -25,7 +25,7 @@ "framer-motion": "^12.23.12", "llama-stack-client": "^0.2.21", "lucide-react": "^0.542.0", - "next": "15.3.3", + "next": "15.5.3", "next-auth": "^4.24.11", "next-themes": "^0.4.6", "react": "^19.0.0", From b6cb8178976b941a1fdb3894b00bd13eaca91561 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Sep 2025 09:46:14 +0200 Subject: [PATCH 113/124] chore(ui-deps): bump @radix-ui/react-select from 2.2.5 to 2.2.6 in /llama_stack/ui (#3437) Bumps [@radix-ui/react-select](https://github.com/radix-ui/primitives) from 2.2.5 to 2.2.6.
Commits

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 77 ++++++++++++++------------------ llama_stack/ui/package.json | 2 +- 2 files changed, 34 insertions(+), 45 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index ff73fa2e8..f333aa809 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -11,7 +11,7 @@ "@radix-ui/react-collapsible": "^1.1.12", "@radix-ui/react-dialog": "^1.1.13", "@radix-ui/react-dropdown-menu": "^2.1.16", - "@radix-ui/react-select": "^2.2.5", + "@radix-ui/react-select": "^2.2.6", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", "@radix-ui/react-tooltip": "^1.2.8", @@ -2924,22 +2924,22 @@ } }, "node_modules/@radix-ui/react-select": { - "version": "2.2.5", - "resolved": "https://registry.npmjs.org/@radix-ui/react-select/-/react-select-2.2.5.tgz", - "integrity": "sha512-HnMTdXEVuuyzx63ME0ut4+sEMYW6oouHWNGUZc7ddvUWIcfCva/AMoqEW/3wnEllriMWBa0RHspCYnfCWJQYmA==", + "version": "2.2.6", + "resolved": "https://registry.npmjs.org/@radix-ui/react-select/-/react-select-2.2.6.tgz", + "integrity": "sha512-I30RydO+bnn2PQztvo25tswPH+wFBjehVGtmagkU78yMdwTwVf12wnAOF+AeP8S2N8xD+5UPbGhkUfPyvT+mwQ==", "license": "MIT", "dependencies": { "@radix-ui/number": "1.1.1", - "@radix-ui/primitive": "1.1.2", + "@radix-ui/primitive": "1.1.3", "@radix-ui/react-collection": "1.1.7", "@radix-ui/react-compose-refs": "1.1.2", "@radix-ui/react-context": "1.1.2", "@radix-ui/react-direction": "1.1.1", - "@radix-ui/react-dismissable-layer": "1.1.10", - "@radix-ui/react-focus-guards": "1.1.2", + "@radix-ui/react-dismissable-layer": "1.1.11", + "@radix-ui/react-focus-guards": "1.1.3", "@radix-ui/react-focus-scope": "1.1.7", "@radix-ui/react-id": "1.1.1", - "@radix-ui/react-popper": "1.2.7", + "@radix-ui/react-popper": "1.2.8", "@radix-ui/react-portal": "1.1.9", "@radix-ui/react-primitive": "2.1.3", "@radix-ui/react-slot": "1.2.3", @@ -2966,13 +2966,19 @@ } } }, + "node_modules/@radix-ui/react-select/node_modules/@radix-ui/primitive": { + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/@radix-ui/primitive/-/primitive-1.1.3.tgz", + "integrity": "sha512-JTF99U/6XIjCBo0wqkU5sK10glYe27MRRsfwoiq5zzOEZLHU3A3KCMa5X/azekYRCJ0HlwI0crAXS/5dEHTzDg==", + "license": "MIT" + }, "node_modules/@radix-ui/react-select/node_modules/@radix-ui/react-dismissable-layer": { - "version": "1.1.10", - "resolved": "https://registry.npmjs.org/@radix-ui/react-dismissable-layer/-/react-dismissable-layer-1.1.10.tgz", - "integrity": "sha512-IM1zzRV4W3HtVgftdQiiOmA0AdJlCtMLe00FXaHwgt3rAnNsIyDqshvkIW3hj/iu5hu8ERP7KIYki6NkqDxAwQ==", + "version": "1.1.11", + "resolved": "https://registry.npmjs.org/@radix-ui/react-dismissable-layer/-/react-dismissable-layer-1.1.11.tgz", + "integrity": "sha512-Nqcp+t5cTB8BinFkZgXiMJniQH0PsUt2k51FUhbdfeKvc4ACcG2uQniY/8+h1Yv6Kza4Q7lD7PQV0z0oicE0Mg==", "license": "MIT", "dependencies": { - "@radix-ui/primitive": "1.1.2", + "@radix-ui/primitive": "1.1.3", "@radix-ui/react-compose-refs": "1.1.2", "@radix-ui/react-primitive": "2.1.3", "@radix-ui/react-use-callback-ref": "1.1.1", @@ -2993,6 +2999,21 @@ } } }, + "node_modules/@radix-ui/react-select/node_modules/@radix-ui/react-focus-guards": { + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-focus-guards/-/react-focus-guards-1.1.3.tgz", + "integrity": "sha512-0rFg/Rj2Q62NCm62jZw0QX7a3sz6QCQU0LpZdNrJX8byRGaGVTqbrW9jAoIAHyMQqsNpeZ81YgSizOt5WXq0Pw==", + "license": "MIT", + "peerDependencies": { + "@types/react": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-select/node_modules/@radix-ui/react-focus-scope": { "version": "1.1.7", "resolved": "https://registry.npmjs.org/@radix-ui/react-focus-scope/-/react-focus-scope-1.1.7.tgz", @@ -3018,38 +3039,6 @@ } } }, - "node_modules/@radix-ui/react-select/node_modules/@radix-ui/react-popper": { - "version": "1.2.7", - "resolved": "https://registry.npmjs.org/@radix-ui/react-popper/-/react-popper-1.2.7.tgz", - "integrity": "sha512-IUFAccz1JyKcf/RjB552PlWwxjeCJB8/4KxT7EhBHOJM+mN7LdW+B3kacJXILm32xawcMMjb2i0cIZpo+f9kiQ==", - "license": "MIT", - "dependencies": { - "@floating-ui/react-dom": "^2.0.0", - "@radix-ui/react-arrow": "1.1.7", - "@radix-ui/react-compose-refs": "1.1.2", - "@radix-ui/react-context": "1.1.2", - "@radix-ui/react-primitive": "2.1.3", - "@radix-ui/react-use-callback-ref": "1.1.1", - "@radix-ui/react-use-layout-effect": "1.1.1", - "@radix-ui/react-use-rect": "1.1.1", - "@radix-ui/react-use-size": "1.1.1", - "@radix-ui/rect": "1.1.1" - }, - "peerDependencies": { - "@types/react": "*", - "@types/react-dom": "*", - "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", - "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" - }, - "peerDependenciesMeta": { - "@types/react": { - "optional": true - }, - "@types/react-dom": { - "optional": true - } - } - }, "node_modules/@radix-ui/react-select/node_modules/@radix-ui/react-portal": { "version": "1.1.9", "resolved": "https://registry.npmjs.org/@radix-ui/react-portal/-/react-portal-1.1.9.tgz", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index a0a8b2c7c..ccbc2a4c2 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -16,7 +16,7 @@ "@radix-ui/react-collapsible": "^1.1.12", "@radix-ui/react-dialog": "^1.1.13", "@radix-ui/react-dropdown-menu": "^2.1.16", - "@radix-ui/react-select": "^2.2.5", + "@radix-ui/react-select": "^2.2.6", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", "@radix-ui/react-tooltip": "^1.2.8", From 01bdcce4d2218754acfe960de58598bc50e32d21 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Mon, 15 Sep 2025 15:25:53 -0400 Subject: [PATCH 114/124] chore(recorder): update mocks to be closer to non-mock environment (#3442) # What does this PR do? the @required_args decorator in openai-python is masking the async nature of the {AsyncCompletions,chat.AsyncCompletions}.create method. see https://github.com/openai/openai-python/issues/996 this means two things - 0. we cannot use iscoroutine in the recorder to detect async vs non 1. our mocks are inappropriately introducing identifiable async for (0), we update the iscoroutine check w/ detection of /v1/models, which is the only non-async function we mock & record. for (1), we could leave everything as is and assume (0) will catch errors. to be defensive, we update the unit tests to mock below create methods, allowing the true openai-python create() methods to be tested. --- llama_stack/testing/inference_recorder.py | 14 +- .../distribution/test_inference_recordings.py | 208 +++++++++--------- 2 files changed, 113 insertions(+), 109 deletions(-) diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index f899d73d3..674016fb1 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -7,7 +7,6 @@ from __future__ import annotations # for forward references import hashlib -import inspect import json import os from collections.abc import Generator @@ -243,11 +242,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint global _current_mode, _current_storage if _current_mode == InferenceMode.LIVE or _current_storage is None: - # Normal operation - if inspect.iscoroutinefunction(original_method): - return await original_method(self, *args, **kwargs) - else: + if endpoint == "/v1/models": return original_method(self, *args, **kwargs) + else: + return await original_method(self, *args, **kwargs) # Get base URL based on client type if client_type == "openai": @@ -298,10 +296,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint ) elif _current_mode == InferenceMode.RECORD: - if inspect.iscoroutinefunction(original_method): - response = await original_method(self, *args, **kwargs) - else: + if endpoint == "/v1/models": response = original_method(self, *args, **kwargs) + else: + response = await original_method(self, *args, **kwargs) # we want to store the result of the iterator, not the iterator itself if endpoint == "/v1/models": diff --git a/tests/unit/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py index 4909bbe1e..5740357c1 100644 --- a/tests/unit/distribution/test_inference_recordings.py +++ b/tests/unit/distribution/test_inference_recordings.py @@ -155,27 +155,22 @@ class TestInferenceRecording: async def test_recording_mode(self, temp_storage_dir, real_openai_chat_response): """Test that recording mode captures and stores responses.""" - - async def mock_create(*args, **kwargs): - return real_openai_chat_response - temp_storage_dir = temp_storage_dir / "test_recording_mode" - with patch( - "openai.resources.chat.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create - ): - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - response = await client.chat.completions.create( - model="llama3.2:3b", - messages=[{"role": "user", "content": "Hello, how are you?"}], - temperature=0.7, - max_tokens=50, - user=NOT_GIVEN, - ) + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) - # Verify the response was returned correctly - assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." + # Verify the response was returned correctly + assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." + client.chat.completions._post.assert_called_once() # Verify recording was stored storage = ResponseStorage(temp_storage_dir) @@ -183,43 +178,38 @@ class TestInferenceRecording: async def test_replay_mode(self, temp_storage_dir, real_openai_chat_response): """Test that replay mode returns stored responses without making real calls.""" - - async def mock_create(*args, **kwargs): - return real_openai_chat_response - temp_storage_dir = temp_storage_dir / "test_replay_mode" # First, record a response - with patch( - "openai.resources.chat.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create - ): - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - response = await client.chat.completions.create( - model="llama3.2:3b", - messages=[{"role": "user", "content": "Hello, how are you?"}], - temperature=0.7, - max_tokens=50, - user=NOT_GIVEN, - ) + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) + client.chat.completions._post.assert_called_once() # Now test replay mode - should not call the original method - with patch("openai.resources.chat.completions.AsyncCompletions.create") as mock_create_patch: - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - response = await client.chat.completions.create( - model="llama3.2:3b", - messages=[{"role": "user", "content": "Hello, how are you?"}], - temperature=0.7, - max_tokens=50, - ) + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + ) - # Verify we got the recorded response - assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." + # Verify we got the recorded response + assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." - # Verify the original method was NOT called - mock_create_patch.assert_not_called() + # Verify the original method was NOT called + client.chat.completions._post.assert_not_called() async def test_replay_mode_models(self, temp_storage_dir): """Test that replay mode returns stored responses without making real model listing calls.""" @@ -272,43 +262,50 @@ class TestInferenceRecording: async def test_embeddings_recording(self, temp_storage_dir, real_embeddings_response): """Test recording and replay of embeddings calls.""" - async def mock_create(*args, **kwargs): - return real_embeddings_response + # baseline - mock works without recording + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.embeddings._post = AsyncMock(return_value=real_embeddings_response) + response = await client.embeddings.create( + model=real_embeddings_response.model, + input=["Hello world", "Test embedding"], + encoding_format=NOT_GIVEN, + ) + assert len(response.data) == 2 + assert response.data[0].embedding == [0.1, 0.2, 0.3] + client.embeddings._post.assert_called_once() temp_storage_dir = temp_storage_dir / "test_embeddings_recording" # Record - with patch( - "openai.resources.embeddings.AsyncEmbeddings.create", new_callable=AsyncMock, side_effect=mock_create - ): - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.embeddings._post = AsyncMock(return_value=real_embeddings_response) - response = await client.embeddings.create( - model=real_embeddings_response.model, - input=["Hello world", "Test embedding"], - encoding_format=NOT_GIVEN, - dimensions=NOT_GIVEN, - user=NOT_GIVEN, - ) + response = await client.embeddings.create( + model=real_embeddings_response.model, + input=["Hello world", "Test embedding"], + encoding_format=NOT_GIVEN, + dimensions=NOT_GIVEN, + user=NOT_GIVEN, + ) - assert len(response.data) == 2 + assert len(response.data) == 2 # Replay - with patch("openai.resources.embeddings.AsyncEmbeddings.create") as mock_create_patch: - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.embeddings._post = AsyncMock(return_value=real_embeddings_response) - response = await client.embeddings.create( - model=real_embeddings_response.model, - input=["Hello world", "Test embedding"], - ) + response = await client.embeddings.create( + model=real_embeddings_response.model, + input=["Hello world", "Test embedding"], + ) - # Verify we got the recorded response - assert len(response.data) == 2 - assert response.data[0].embedding == [0.1, 0.2, 0.3] + # Verify we got the recorded response + assert len(response.data) == 2 + assert response.data[0].embedding == [0.1, 0.2, 0.3] - # Verify original method was not called - mock_create_patch.assert_not_called() + # Verify original method was not called + client.embeddings._post.assert_not_called() async def test_completions_recording(self, temp_storage_dir): real_completions_response = OpenAICompletion( @@ -326,40 +323,49 @@ class TestInferenceRecording: ], ) - async def mock_create(*args, **kwargs): - return real_completions_response - temp_storage_dir = temp_storage_dir / "test_completions_recording" + # baseline - mock works without recording + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.completions._post = AsyncMock(return_value=real_completions_response) + response = await client.completions.create( + model=real_completions_response.model, + prompt="Hello, how are you?", + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) + assert response.choices[0].text == real_completions_response.choices[0].text + client.completions._post.assert_called_once() + # Record - with patch( - "openai.resources.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create - ): - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.completions._post = AsyncMock(return_value=real_completions_response) - response = await client.completions.create( - model=real_completions_response.model, - prompt="Hello, how are you?", - temperature=0.7, - max_tokens=50, - user=NOT_GIVEN, - ) + response = await client.completions.create( + model=real_completions_response.model, + prompt="Hello, how are you?", + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) - assert response.choices[0].text == real_completions_response.choices[0].text + assert response.choices[0].text == real_completions_response.choices[0].text + client.completions._post.assert_called_once() # Replay - with patch("openai.resources.completions.AsyncCompletions.create") as mock_create_patch: - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - response = await client.completions.create( - model=real_completions_response.model, - prompt="Hello, how are you?", - temperature=0.7, - max_tokens=50, - ) - assert response.choices[0].text == real_completions_response.choices[0].text - mock_create_patch.assert_not_called() + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.completions._post = AsyncMock(return_value=real_completions_response) + response = await client.completions.create( + model=real_completions_response.model, + prompt="Hello, how are you?", + temperature=0.7, + max_tokens=50, + ) + assert response.choices[0].text == real_completions_response.choices[0].text + client.completions._post.assert_not_called() async def test_live_mode(self, real_openai_chat_response): """Test that live mode passes through to original methods.""" From ab321739f2e2a328765b13c874d3cd408b530887 Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Mon, 15 Sep 2025 20:43:38 +0100 Subject: [PATCH 115/124] feat: create HTTP DELETE API endpoints to unregister ScoringFn and Benchmark resources in Llama Stack (#3371) # What does this PR do? This PR provides functionality for users to unregister ScoringFn and Benchmark resources for `scoring` and `eval` APIs. Closes #3051 ## Test Plan Updated integration and unit tests via CI workflow --- docs/_static/llama-stack-spec.html | 68 +++++++++++++++++++ docs/_static/llama-stack-spec.yaml | 49 +++++++++++++ llama_stack/apis/benchmarks/benchmarks.py | 8 +++ .../scoring_functions/scoring_functions.py | 8 +++ llama_stack/core/routing_tables/benchmarks.py | 4 ++ llama_stack/core/routing_tables/common.py | 4 ++ .../core/routing_tables/scoring_functions.py | 4 ++ .../inline/eval/meta_reference/eval.py | 7 ++ .../inline/scoring/llm_as_judge/scoring.py | 3 + .../providers/remote/eval/nvidia/eval.py | 13 +++- tests/integration/scoring/test_scoring.py | 42 +++++++++++- .../routers/test_routing_tables.py | 22 ++++++ tests/unit/providers/nvidia/test_eval.py | 12 ++++ 13 files changed, 241 insertions(+), 3 deletions(-) diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index a036e5dc0..9ddb070d7 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -1380,6 +1380,40 @@ } } ] + }, + "delete": { + "responses": { + "200": { + "description": "OK" + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Benchmarks" + ], + "description": "Unregister a benchmark.", + "parameters": [ + { + "name": "benchmark_id", + "in": "path", + "description": "The ID of the benchmark to unregister.", + "required": true, + "schema": { + "type": "string" + } + } + ] } }, "/v1/openai/v1/chat/completions/{completion_id}": { @@ -1620,6 +1654,40 @@ } } ] + }, + "delete": { + "responses": { + "200": { + "description": "OK" + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "ScoringFunctions" + ], + "description": "Unregister a scoring function.", + "parameters": [ + { + "name": "scoring_fn_id", + "in": "path", + "description": "The ID of the scoring function to unregister.", + "required": true, + "schema": { + "type": "string" + } + } + ] } }, "/v1/shields/{identifier}": { diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index 8ed04c1f8..94dc5c0f9 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -954,6 +954,30 @@ paths: required: true schema: type: string + delete: + responses: + '200': + description: OK + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Benchmarks + description: Unregister a benchmark. + parameters: + - name: benchmark_id + in: path + description: The ID of the benchmark to unregister. + required: true + schema: + type: string /v1/openai/v1/chat/completions/{completion_id}: get: responses: @@ -1119,6 +1143,31 @@ paths: required: true schema: type: string + delete: + responses: + '200': + description: OK + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - ScoringFunctions + description: Unregister a scoring function. + parameters: + - name: scoring_fn_id + in: path + description: >- + The ID of the scoring function to unregister. + required: true + schema: + type: string /v1/shields/{identifier}: get: responses: diff --git a/llama_stack/apis/benchmarks/benchmarks.py b/llama_stack/apis/benchmarks/benchmarks.py index 706eaed6c..8d0a25e7b 100644 --- a/llama_stack/apis/benchmarks/benchmarks.py +++ b/llama_stack/apis/benchmarks/benchmarks.py @@ -93,3 +93,11 @@ class Benchmarks(Protocol): :param metadata: The metadata to use for the benchmark. """ ... + + @webmethod(route="/eval/benchmarks/{benchmark_id}", method="DELETE") + async def unregister_benchmark(self, benchmark_id: str) -> None: + """Unregister a benchmark. + + :param benchmark_id: The ID of the benchmark to unregister. + """ + ... diff --git a/llama_stack/apis/scoring_functions/scoring_functions.py b/llama_stack/apis/scoring_functions/scoring_functions.py index 05b6325b7..541067766 100644 --- a/llama_stack/apis/scoring_functions/scoring_functions.py +++ b/llama_stack/apis/scoring_functions/scoring_functions.py @@ -197,3 +197,11 @@ class ScoringFunctions(Protocol): :param params: The parameters for the scoring function for benchmark eval, these can be overridden for app eval. """ ... + + @webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="DELETE") + async def unregister_scoring_function(self, scoring_fn_id: str) -> None: + """Unregister a scoring function. + + :param scoring_fn_id: The ID of the scoring function to unregister. + """ + ... diff --git a/llama_stack/core/routing_tables/benchmarks.py b/llama_stack/core/routing_tables/benchmarks.py index c875dee5b..8c87d395d 100644 --- a/llama_stack/core/routing_tables/benchmarks.py +++ b/llama_stack/core/routing_tables/benchmarks.py @@ -56,3 +56,7 @@ class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks): provider_resource_id=provider_benchmark_id, ) await self.register_object(benchmark) + + async def unregister_benchmark(self, benchmark_id: str) -> None: + existing_benchmark = await self.get_benchmark(benchmark_id) + await self.unregister_object(existing_benchmark) diff --git a/llama_stack/core/routing_tables/common.py b/llama_stack/core/routing_tables/common.py index e523746d8..ca2f3af42 100644 --- a/llama_stack/core/routing_tables/common.py +++ b/llama_stack/core/routing_tables/common.py @@ -64,6 +64,10 @@ async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None: return await p.unregister_shield(obj.identifier) elif api == Api.datasetio: return await p.unregister_dataset(obj.identifier) + elif api == Api.eval: + return await p.unregister_benchmark(obj.identifier) + elif api == Api.scoring: + return await p.unregister_scoring_function(obj.identifier) elif api == Api.tool_runtime: return await p.unregister_toolgroup(obj.identifier) else: diff --git a/llama_stack/core/routing_tables/scoring_functions.py b/llama_stack/core/routing_tables/scoring_functions.py index 71e5bed63..520f07014 100644 --- a/llama_stack/core/routing_tables/scoring_functions.py +++ b/llama_stack/core/routing_tables/scoring_functions.py @@ -60,3 +60,7 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions): ) scoring_fn.provider_id = provider_id await self.register_object(scoring_fn) + + async def unregister_scoring_function(self, scoring_fn_id: str) -> None: + existing_scoring_fn = await self.get_scoring_function(scoring_fn_id) + await self.unregister_object(existing_scoring_fn) diff --git a/llama_stack/providers/inline/eval/meta_reference/eval.py b/llama_stack/providers/inline/eval/meta_reference/eval.py index 9ae2018c4..a03e8951c 100644 --- a/llama_stack/providers/inline/eval/meta_reference/eval.py +++ b/llama_stack/providers/inline/eval/meta_reference/eval.py @@ -75,6 +75,13 @@ class MetaReferenceEvalImpl( ) self.benchmarks[task_def.identifier] = task_def + async def unregister_benchmark(self, benchmark_id: str) -> None: + if benchmark_id in self.benchmarks: + del self.benchmarks[benchmark_id] + + key = f"{EVAL_TASKS_PREFIX}{benchmark_id}" + await self.kvstore.delete(key) + async def run_eval( self, benchmark_id: str, diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py b/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py index fd651877c..9b7628524 100644 --- a/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py +++ b/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py @@ -63,6 +63,9 @@ class LlmAsJudgeScoringImpl( async def register_scoring_function(self, function_def: ScoringFn) -> None: self.llm_as_judge_fn.register_scoring_fn_def(function_def) + async def unregister_scoring_function(self, scoring_fn_id: str) -> None: + self.llm_as_judge_fn.unregister_scoring_fn_def(scoring_fn_id) + async def score_batch( self, dataset_id: str, diff --git a/llama_stack/providers/remote/eval/nvidia/eval.py b/llama_stack/providers/remote/eval/nvidia/eval.py index 3572de0ef..a474e78e3 100644 --- a/llama_stack/providers/remote/eval/nvidia/eval.py +++ b/llama_stack/providers/remote/eval/nvidia/eval.py @@ -51,18 +51,23 @@ class NVIDIAEvalImpl( async def shutdown(self) -> None: ... - async def _evaluator_get(self, path): + async def _evaluator_get(self, path: str): """Helper for making GET requests to the evaluator service.""" response = requests.get(url=f"{self.config.evaluator_url}{path}") response.raise_for_status() return response.json() - async def _evaluator_post(self, path, data): + async def _evaluator_post(self, path: str, data: dict[str, Any]): """Helper for making POST requests to the evaluator service.""" response = requests.post(url=f"{self.config.evaluator_url}{path}", json=data) response.raise_for_status() return response.json() + async def _evaluator_delete(self, path: str) -> None: + """Helper for making DELETE requests to the evaluator service.""" + response = requests.delete(url=f"{self.config.evaluator_url}{path}") + response.raise_for_status() + async def register_benchmark(self, task_def: Benchmark) -> None: """Register a benchmark as an evaluation configuration.""" await self._evaluator_post( @@ -75,6 +80,10 @@ class NVIDIAEvalImpl( }, ) + async def unregister_benchmark(self, benchmark_id: str) -> None: + """Unregister a benchmark evaluation configuration from NeMo Evaluator.""" + await self._evaluator_delete(f"/v1/evaluation/configs/{DEFAULT_NAMESPACE}/{benchmark_id}") + async def run_eval( self, benchmark_id: str, diff --git a/tests/integration/scoring/test_scoring.py b/tests/integration/scoring/test_scoring.py index 315ff050c..1112f9164 100644 --- a/tests/integration/scoring/test_scoring.py +++ b/tests/integration/scoring/test_scoring.py @@ -9,6 +9,7 @@ from pathlib import Path import pandas as pd import pytest +import requests @pytest.fixture @@ -77,7 +78,46 @@ def test_scoring_functions_register( assert len(list_response) > 0 assert any(x.identifier == sample_scoring_fn_id for x in list_response) - # TODO: add unregister api for scoring functions + +def test_scoring_functions_unregister( + llama_stack_client, + sample_scoring_fn_id, + judge_model_id, + sample_judge_prompt_template, +): + llm_as_judge_provider = [ + x + for x in llama_stack_client.providers.list() + if x.api == "scoring" and x.provider_type == "inline::llm-as-judge" + ] + if len(llm_as_judge_provider) == 0: + pytest.skip("No llm-as-judge provider found, cannot test unregister") + + llm_as_judge_provider_id = llm_as_judge_provider[0].provider_id + + # Register first + register_scoring_function( + llama_stack_client, + llm_as_judge_provider_id, + sample_scoring_fn_id, + judge_model_id, + sample_judge_prompt_template, + ) + + # Ensure it is present + list_response = llama_stack_client.scoring_functions.list() + assert any(x.identifier == sample_scoring_fn_id for x in list_response) + + # Unregister scoring fn + try: + base_url = llama_stack_client.base_url + except AttributeError: + pytest.skip("No server base_url available; cannot test HTTP unregister in library mode") + + resp = requests.delete(f"{base_url}/v1/scoring-functions/{sample_scoring_fn_id}", timeout=30) + assert resp.status_code in (200, 204) + list_after = llama_stack_client.scoring_functions.list() + assert all(x.identifier != sample_scoring_fn_id for x in list_after) @pytest.mark.parametrize("scoring_fn_id", ["basic::equality"]) diff --git a/tests/unit/distribution/routers/test_routing_tables.py b/tests/unit/distribution/routers/test_routing_tables.py index 1ceee81c6..bbfea3f46 100644 --- a/tests/unit/distribution/routers/test_routing_tables.py +++ b/tests/unit/distribution/routers/test_routing_tables.py @@ -105,6 +105,9 @@ class ScoringFunctionsImpl(Impl): async def register_scoring_function(self, scoring_fn): return scoring_fn + async def unregister_scoring_function(self, scoring_fn_id: str): + return scoring_fn_id + class BenchmarksImpl(Impl): def __init__(self): @@ -113,6 +116,9 @@ class BenchmarksImpl(Impl): async def register_benchmark(self, benchmark): return benchmark + async def unregister_benchmark(self, benchmark_id: str): + return benchmark_id + class ToolGroupsImpl(Impl): def __init__(self): @@ -330,6 +336,13 @@ async def test_scoring_functions_routing_table(cached_disk_dist_registry): assert "test-scoring-fn" in scoring_fn_ids assert "test-scoring-fn-2" in scoring_fn_ids + # Unregister scoring functions and verify listing + for i in range(len(scoring_functions.data)): + await table.unregister_scoring_function(scoring_functions.data[i].scoring_fn_id) + + scoring_functions_list_after_deletion = await table.list_scoring_functions() + assert len(scoring_functions_list_after_deletion.data) == 0 + async def test_benchmarks_routing_table(cached_disk_dist_registry): table = BenchmarksRoutingTable({"test_provider": BenchmarksImpl()}, cached_disk_dist_registry, {}) @@ -347,6 +360,15 @@ async def test_benchmarks_routing_table(cached_disk_dist_registry): benchmark_ids = {b.identifier for b in benchmarks.data} assert "test-benchmark" in benchmark_ids + # Unregister the benchmark and verify removal + await table.unregister_benchmark(benchmark_id="test-benchmark") + benchmarks_after = await table.list_benchmarks() + assert len(benchmarks_after.data) == 0 + + # Unregistering a non-existent benchmark should raise a clear error + with pytest.raises(ValueError, match="Benchmark 'dummy_benchmark' not found"): + await table.unregister_benchmark(benchmark_id="dummy_benchmark") + async def test_tool_groups_routing_table(cached_disk_dist_registry): table = ToolGroupsRoutingTable({"test_provider": ToolGroupsImpl()}, cached_disk_dist_registry, {}) diff --git a/tests/unit/providers/nvidia/test_eval.py b/tests/unit/providers/nvidia/test_eval.py index 584ca2101..2bdcbbeba 100644 --- a/tests/unit/providers/nvidia/test_eval.py +++ b/tests/unit/providers/nvidia/test_eval.py @@ -52,14 +52,19 @@ class TestNVIDIAEvalImpl(unittest.TestCase): self.evaluator_post_patcher = patch( "llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_post" ) + self.evaluator_delete_patcher = patch( + "llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_delete" + ) self.mock_evaluator_get = self.evaluator_get_patcher.start() self.mock_evaluator_post = self.evaluator_post_patcher.start() + self.mock_evaluator_delete = self.evaluator_delete_patcher.start() def tearDown(self): """Clean up after each test.""" self.evaluator_get_patcher.stop() self.evaluator_post_patcher.stop() + self.evaluator_delete_patcher.stop() def _assert_request_body(self, expected_json): """Helper method to verify request body in Evaluator POST request is correct""" @@ -115,6 +120,13 @@ class TestNVIDIAEvalImpl(unittest.TestCase): self.mock_evaluator_post.assert_called_once() self._assert_request_body({"namespace": benchmark.provider_id, "name": benchmark.identifier, **eval_config}) + def test_unregister_benchmark(self): + # Unregister the benchmark + self.run_async(self.eval_impl.unregister_benchmark(benchmark_id=MOCK_BENCHMARK_ID)) + + # Verify the Evaluator API was called correctly + self.mock_evaluator_delete.assert_called_once_with(f"/v1/evaluation/configs/nvidia/{MOCK_BENCHMARK_ID}") + def test_run_eval(self): benchmark_config = BenchmarkConfig( eval_candidate=ModelCandidate( From f4ab154ade646cc153f1f2840d6596fc6d5b24af Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Mon, 15 Sep 2025 15:52:40 -0400 Subject: [PATCH 116/124] feat: add dynamic model registration support to TGI inference (#3417) # What does this PR do? adds dynamic model support to TGI add new overwrite_completion_id feature to OpenAIMixin to deal with TGI always returning id="" ## Test Plan tgi: `docker run --gpus all --shm-size 1g -p 8080:80 -v /data:/data ghcr.io/huggingface/text-generation-inference --model-id Qwen/Qwen3-0.6B` stack: `TGI_URL=http://localhost:8080 uv run llama stack build --image-type venv --distro ci-tests --run` test: `./scripts/integration-tests.sh --stack-config http://localhost:8321 --setup tgi --subdirs inference --pattern openai` --- .../providers/remote/inference/tgi/tgi.py | 58 +- .../providers/utils/inference/openai_mixin.py | 33 +- .../inference/test_openai_completion.py | 3 +- .../recordings/responses/27463384d1a3.json | 56 + .../recordings/responses/4d4440c8641b.json | 42 + .../recordings/responses/7ef63231b9f8.json | 56 + .../recordings/responses/89b141855b81.json | 3820 ++++++++++++++++ .../recordings/responses/a98eecadddc8.json | 366 ++ .../recordings/responses/aacf9abc51d4.json | 2624 +++++++++++ .../recordings/responses/b9f6e724ae06.json | 976 ++++ .../recordings/responses/cf55f983d1ff.json | 84 + .../recordings/responses/e08e01e5652a.json | 56 + .../recordings/responses/f518ea4fde7d.json | 4054 +++++++++++++++++ tests/integration/suites.py | 10 + 14 files changed, 12218 insertions(+), 20 deletions(-) create mode 100644 tests/integration/recordings/responses/27463384d1a3.json create mode 100644 tests/integration/recordings/responses/4d4440c8641b.json create mode 100644 tests/integration/recordings/responses/7ef63231b9f8.json create mode 100644 tests/integration/recordings/responses/89b141855b81.json create mode 100644 tests/integration/recordings/responses/a98eecadddc8.json create mode 100644 tests/integration/recordings/responses/aacf9abc51d4.json create mode 100644 tests/integration/recordings/responses/b9f6e724ae06.json create mode 100644 tests/integration/recordings/responses/cf55f983d1ff.json create mode 100644 tests/integration/recordings/responses/e08e01e5652a.json create mode 100644 tests/integration/recordings/responses/f518ea4fde7d.json diff --git a/llama_stack/providers/remote/inference/tgi/tgi.py b/llama_stack/providers/remote/inference/tgi/tgi.py index 97c72d14c..27597900f 100644 --- a/llama_stack/providers/remote/inference/tgi/tgi.py +++ b/llama_stack/providers/remote/inference/tgi/tgi.py @@ -8,6 +8,7 @@ from collections.abc import AsyncGenerator from huggingface_hub import AsyncInferenceClient, HfApi +from pydantic import SecretStr from llama_stack.apis.common.content_types import ( InterleavedContent, @@ -33,6 +34,7 @@ from llama_stack.apis.inference import ( ToolPromptFormat, ) from llama_stack.apis.models import Model +from llama_stack.apis.models.models import ModelType from llama_stack.log import get_logger from llama_stack.models.llama.sku_list import all_registered_models from llama_stack.providers.datatypes import ModelsProtocolPrivate @@ -41,16 +43,15 @@ from llama_stack.providers.utils.inference.model_registry import ( build_hf_repo_model_entry, ) from llama_stack.providers.utils.inference.openai_compat import ( - OpenAIChatCompletionToLlamaStackMixin, OpenAICompatCompletionChoice, OpenAICompatCompletionResponse, - OpenAICompletionToLlamaStackMixin, get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_model_input_info, completion_request_to_prompt_model_input_info, @@ -73,26 +74,49 @@ def build_hf_repo_model_entries(): class _HfAdapter( + OpenAIMixin, Inference, - OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ModelsProtocolPrivate, ): - client: AsyncInferenceClient + url: str + api_key: SecretStr + + hf_client: AsyncInferenceClient max_tokens: int model_id: str + overwrite_completion_id = True # TGI always returns id="" + def __init__(self) -> None: self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries()) self.huggingface_repo_to_llama_model_id = { model.huggingface_repo: model.descriptor() for model in all_registered_models() if model.huggingface_repo } + def get_api_key(self): + return self.api_key.get_secret_value() + + def get_base_url(self): + return self.url + async def shutdown(self) -> None: pass + async def list_models(self) -> list[Model] | None: + models = [] + async for model in self.client.models.list(): + models.append( + Model( + identifier=model.id, + provider_resource_id=model.id, + provider_id=self.__provider_id__, + metadata={}, + model_type=ModelType.llm, + ) + ) + return models + async def register_model(self, model: Model) -> Model: - model = await self.register_helper.register_model(model) if model.provider_resource_id != self.model_id: raise ValueError( f"Model {model.provider_resource_id} does not match the model {self.model_id} served by TGI." @@ -176,7 +200,7 @@ class _HfAdapter( params = await self._get_params_for_completion(request) async def _generate_and_convert_to_openai_compat(): - s = await self.client.text_generation(**params) + s = await self.hf_client.text_generation(**params) async for chunk in s: token_result = chunk.token finish_reason = None @@ -194,7 +218,7 @@ class _HfAdapter( async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator: params = await self._get_params_for_completion(request) - r = await self.client.text_generation(**params) + r = await self.hf_client.text_generation(**params) choice = OpenAICompatCompletionChoice( finish_reason=r.details.finish_reason, @@ -241,7 +265,7 @@ class _HfAdapter( async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse: params = await self._get_params(request) - r = await self.client.text_generation(**params) + r = await self.hf_client.text_generation(**params) choice = OpenAICompatCompletionChoice( finish_reason=r.details.finish_reason, @@ -256,7 +280,7 @@ class _HfAdapter( params = await self._get_params(request) async def _generate_and_convert_to_openai_compat(): - s = await self.client.text_generation(**params) + s = await self.hf_client.text_generation(**params) async for chunk in s: token_result = chunk.token @@ -308,18 +332,21 @@ class TGIAdapter(_HfAdapter): if not config.url: raise ValueError("You must provide a URL in run.yaml (or via the TGI_URL environment variable) to use TGI.") log.info(f"Initializing TGI client with url={config.url}") - self.client = AsyncInferenceClient(model=config.url, provider="hf-inference") - endpoint_info = await self.client.get_endpoint_info() + self.hf_client = AsyncInferenceClient(model=config.url, provider="hf-inference") + endpoint_info = await self.hf_client.get_endpoint_info() self.max_tokens = endpoint_info["max_total_tokens"] self.model_id = endpoint_info["model_id"] + self.url = f"{config.url.rstrip('/')}/v1" + self.api_key = SecretStr("NO_KEY") class InferenceAPIAdapter(_HfAdapter): async def initialize(self, config: InferenceAPIImplConfig) -> None: - self.client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value()) - endpoint_info = await self.client.get_endpoint_info() + self.hf_client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value()) + endpoint_info = await self.hf_client.get_endpoint_info() self.max_tokens = endpoint_info["max_total_tokens"] self.model_id = endpoint_info["model_id"] + # TODO: how do we set url for this? class InferenceEndpointAdapter(_HfAdapter): @@ -331,6 +358,7 @@ class InferenceEndpointAdapter(_HfAdapter): endpoint.wait(timeout=60) # Initialize the adapter - self.client = endpoint.async_client + self.hf_client = endpoint.async_client self.model_id = endpoint.repository self.max_tokens = int(endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]) + # TODO: how do we set url for this? diff --git a/llama_stack/providers/utils/inference/openai_mixin.py b/llama_stack/providers/utils/inference/openai_mixin.py index a3c0ffadc..938927d21 100644 --- a/llama_stack/providers/utils/inference/openai_mixin.py +++ b/llama_stack/providers/utils/inference/openai_mixin.py @@ -4,6 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import uuid from abc import ABC, abstractmethod from collections.abc import AsyncIterator from typing import Any @@ -43,6 +44,12 @@ class OpenAIMixin(ABC): The model_store is set in routing_tables/common.py during provider initialization. """ + # Allow subclasses to control whether to overwrite the 'id' field in OpenAI responses + # is overwritten with a client-side generated id. + # + # This is useful for providers that do not return a unique id in the response. + overwrite_completion_id: bool = False + @abstractmethod def get_api_key(self) -> str: """ @@ -110,6 +117,23 @@ class OpenAIMixin(ABC): raise ValueError(f"Model {model} has no provider_resource_id") return model_obj.provider_resource_id + async def _maybe_overwrite_id(self, resp: Any, stream: bool | None) -> Any: + if not self.overwrite_completion_id: + return resp + + new_id = f"cltsd-{uuid.uuid4()}" + if stream: + + async def _gen(): + async for chunk in resp: + chunk.id = new_id + yield chunk + + return _gen() + else: + resp.id = new_id + return resp + async def openai_completion( self, model: str, @@ -147,7 +171,7 @@ class OpenAIMixin(ABC): extra_body["guided_choice"] = guided_choice # TODO: fix openai_completion to return type compatible with OpenAI's API response - return await self.client.completions.create( # type: ignore[no-any-return] + resp = await self.client.completions.create( **await prepare_openai_completion_params( model=await self._get_provider_model_id(model), prompt=prompt, @@ -171,6 +195,8 @@ class OpenAIMixin(ABC): extra_body=extra_body, ) + return await self._maybe_overwrite_id(resp, stream) # type: ignore[no-any-return] + async def openai_chat_completion( self, model: str, @@ -200,8 +226,7 @@ class OpenAIMixin(ABC): """ Direct OpenAI chat completion API call. """ - # Type ignore because return types are compatible - return await self.client.chat.completions.create( # type: ignore[no-any-return] + resp = await self.client.chat.completions.create( **await prepare_openai_completion_params( model=await self._get_provider_model_id(model), messages=messages, @@ -229,6 +254,8 @@ class OpenAIMixin(ABC): ) ) + return await self._maybe_overwrite_id(resp, stream) # type: ignore[no-any-return] + async def openai_embeddings( self, model: str, diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 22dec8876..35869276b 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -48,7 +48,6 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) "remote::nvidia", "remote::runpod", "remote::sambanova", - "remote::tgi", "remote::vertexai", # {"error":{"message":"Unknown request URL: GET /openai/v1/completions. Please check the URL for typos, # or see the docs at https://console.groq.com/docs/","type":"invalid_request_error","code":"unknown_url"}} @@ -96,6 +95,7 @@ def skip_if_doesnt_support_n(client_with_models, model_id): "remote::vertexai", # Error code: 400 - [{'error': {'code': 400, 'message': 'Unable to submit request because candidateCount must be 1 but # the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'} + "remote::tgi", # TGI ignores n param silently ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") @@ -110,7 +110,6 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode "remote::cerebras", "remote::databricks", "remote::runpod", - "remote::tgi", ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.") diff --git a/tests/integration/recordings/responses/27463384d1a3.json b/tests/integration/recordings/responses/27463384d1a3.json new file mode 100644 index 000000000..fcdf3a0e3 --- /dev/null +++ b/tests/integration/recordings/responses/27463384d1a3.json @@ -0,0 +1,56 @@ +{ + "request": { + "method": "POST", + "url": "http://localhost:8080/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "Qwen/Qwen3-0.6B", + "messages": [ + { + "role": "user", + "content": "Hello, world!" + } + ], + "stream": false + }, + "endpoint": "/v1/chat/completions", + "model": "Qwen/Qwen3-0.6B" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "\nOkay, the user just said \"Hello, world!\" so I need to respond in a friendly way. My prompt says to respond in the same style, so I should start with \"Hello, world!\" but maybe add some helpful information. Let me think. Since the user is probably testing or just sharing, a simple \"Hello, world!\" with a question would be best for user interaction. I'll make sure to keep it positive and open-ended.\n\n\nHello, world! \ud83d\ude0a What do you need today?", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1757550395, + "model": "Qwen/Qwen3-0.6B", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "3.3.5-dev0-sha-1b90c50", + "usage": { + "completion_tokens": 108, + "prompt_tokens": 12, + "total_tokens": 120, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/4d4440c8641b.json b/tests/integration/recordings/responses/4d4440c8641b.json new file mode 100644 index 000000000..2fd9bf13b --- /dev/null +++ b/tests/integration/recordings/responses/4d4440c8641b.json @@ -0,0 +1,42 @@ +{ + "request": { + "method": "POST", + "url": "http://localhost:8080/v1/v1/completions", + "headers": {}, + "body": { + "model": "Qwen/Qwen3-0.6B", + "prompt": "Respond to this question and explain your answer. Complete the sentence using one word: Roses are red, violets are ", + "stream": false + }, + "endpoint": "/v1/completions", + "model": "Qwen/Qwen3-0.6B" + }, + "response": { + "body": { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "", + "choices": [ + { + "finish_reason": "length", + "index": 0, + "logprobs": null, + "text": " ______.\nA. yellow \nB. red \nC. blue \nD. green \nAnswer:\nThe word is **green**.\n\nAnswer:\nD\n\nThe answer is green because when comparing a rose and a violet, the red hue of roses and the color green of violets are different.\n\nAnswer:\nD\nAnswer:\nD\n\nRoses are red, violets are **green**.\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\n\nRoses are red, violets are **green**.\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\n\nRoses are red, violets are **green**.\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\n\nRoses are red, violets are **green**.\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\n\nRoses are red, violets are **green**.\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\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er:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\nD\nAnswer:\n" + } + ], + "created": 1757550347, + "model": "Qwen/Qwen3-0.6B", + "object": "text_completion", + "system_fingerprint": "3.3.5-dev0-sha-1b90c50", + "usage": { + "completion_tokens": 4071, + "prompt_tokens": 25, + "total_tokens": 4096, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/7ef63231b9f8.json b/tests/integration/recordings/responses/7ef63231b9f8.json new file mode 100644 index 000000000..60f3e3c36 --- /dev/null +++ b/tests/integration/recordings/responses/7ef63231b9f8.json @@ -0,0 +1,56 @@ +{ + "request": { + "method": "POST", + "url": "http://localhost:8080/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "Qwen/Qwen3-0.6B", + "messages": [ + { + "role": "user", + "content": "Which planet has rings around it with a name starting with letter S?" + } + ], + "stream": false + }, + "endpoint": "/v1/chat/completions", + "model": "Qwen/Qwen3-0.6B" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "\nOkay, so the user is asking which planet has rings around it and its name starts with the letter S. Let me think... I know that the Sun is a star, not a planet. So the Moon is a natural satellite, which has the Moon's name and rings. But the Moon's name starts with M, not S. The Earth has the name Earth, but the rings aren't really around the Earth in any real sense. Mars has a thin ring of dust. Venus and Mercury don't have rings in the sense of planetary rings as we know. Wait, maybe the answer is the Moon, even though it's not the same as the name starting with S. But the question says a planet, so if there's a planet named S, that would be it. But actually, the only planet with rings is Jupiter. Wait, Jupiter has a famous system of rings. But why does the question mention a planet with a name starting with S? Maybe there's a trick. Let me double-check. Jupiter's name starts with J, so maybe the answer is Venus? But Venus doesn't have rings. Mercury, too, doesn't. The Moon, as a planet, a dwarf planet, and has rings. Despite the name, the rings are around it. So the answer would be the Moon. Therefore, the planet with rings and name starting with S is the Moon.\n\n\nThe planet with rings around it and a name starting with the letter **S** is the **Moon**. Though its name doesn't start with an **S**, it is technically a dwarf planet and has the rings in its orbit. 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"text_model": "tgi/Qwen/Qwen3-0.6B", + }, + ), } From 65d45c731849ec0324d45066d38923c921326791 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Han?= Date: Tue, 16 Sep 2025 13:55:10 +0200 Subject: [PATCH 117/124] chore: various watsonx fixes (#3428) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? use a logger * update the distro to add the Files API otherwise it won't start since it is a dependency of vector * clarify project_id and api_key requirements * disable openai compatible calls since the endpoint returns 404 * disable text_inference structured format tests * fixed openai client initialization ## Test Plan Execute text_inference: ``` WATSONX_API_KEY=... WATSONX_PROJECT_ID=... python -m llama_stack.core.server.server llama_stack/distributions/watsonx/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -vvvv -ra --text-model watsonx/meta-llama/llama-3-3-70b-instruct tests/integration/inference/test_text_inference.py ============================================= test session starts ============================================== platform darwin -- Python 3.12.8, pytest-8.4.2, pluggy-1.6.0 -- /Users/leseb/Documents/AI/llama-stack/.venv/bin/python3 cachedir: .pytest_cache metadata: {'Python': '3.12.8', 'Platform': 'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '8.4.2', 'pluggy': '1.6.0'}, 'Plugins': {'anyio': '4.9.0', 'html': '4.1.1', 'socket': '0.7.0', 'asyncio': '1.1.0', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'cov': '6.2.1', 'nbval': '0.11.0', 'hydra-core': '1.3.2'}} rootdir: /Users/leseb/Documents/AI/llama-stack configfile: pyproject.toml plugins: anyio-4.9.0, html-4.1.1, socket-0.7.0, asyncio-1.1.0, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, cov-6.2.1, nbval-0.11.0, hydra-core-1.3.2 asyncio: mode=Mode.AUTO, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 20 items tests/integration/inference/test_text_inference.py::test_text_completion_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:sanity] PASSED [ 5%] tests/integration/inference/test_text_inference.py::test_text_completion_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:sanity] PASSED [ 10%] tests/integration/inference/test_text_inference.py::test_text_completion_stop_sequence[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:stop_sequence] XFAIL [ 15%] tests/integration/inference/test_text_inference.py::test_text_completion_log_probs_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:log_probs] XFAIL [ 20%] tests/integration/inference/test_text_inference.py::test_text_completion_log_probs_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:log_probs] XFAIL [ 25%] tests/integration/inference/test_text_inference.py::test_text_completion_structured_output[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:structured_output] SKIPPED structured output) [ 30%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:non_streaming_01] PASSED [ 35%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:streaming_01] PASSED [ 40%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling] PASSED [ 45%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling] PASSED [ 50%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_tool_choice_required[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling] PASSED [ 55%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_tool_choice_none[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling] PASSED [ 60%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_structured_output[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:structured_output] SKIPPEDstructured output) [ 65%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling_tools_absent-True] PASSED [ 70%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:text_then_tool] XFAIL [ 75%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:non_streaming_02] PASSED [ 80%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:streaming_02] PASSED [ 85%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling_tools_absent-False] PASSED [ 90%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_then_answer] XFAIL [ 95%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:array_parameter] XFAIL [100%] =========================================== short test summary info ============================================ SKIPPED [2] tests/integration/inference/test_text_inference.py:49: Model watsonx/meta-llama/llama-3-3-70b-instruct hosted by remote::watsonx doesn't support json_schema structured output XFAIL tests/integration/inference/test_text_inference.py::test_text_completion_stop_sequence[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:stop_sequence] - remote::watsonx doesn't support 'stop' parameter yet XFAIL tests/integration/inference/test_text_inference.py::test_text_completion_log_probs_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:log_probs] - remote::watsonx doesn't support log probs yet XFAIL tests/integration/inference/test_text_inference.py::test_text_completion_log_probs_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:log_probs] - remote::watsonx doesn't support log probs yet XFAIL tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:text_then_tool] - Not tested for non-llama4 models yet XFAIL tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_then_answer] - Not tested for non-llama4 models yet XFAIL tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:array_parameter] - Not tested for non-llama4 models yet ============================ 12 passed, 2 skipped, 6 xfailed, 14 warnings in 36.88s ============================ ``` --------- Signed-off-by: Sébastien Han --- .../providers/inference/remote_watsonx.md | 4 ++-- llama_stack/distributions/watsonx/run.yaml | 9 +++++++++ llama_stack/distributions/watsonx/watsonx.py | 12 +++++++++-- .../remote/inference/watsonx/config.py | 4 ++-- .../remote/inference/watsonx/watsonx.py | 20 +++++++++++++++++-- .../inference/test_openai_completion.py | 2 ++ .../inference/test_text_inference.py | 3 ++- 7 files changed, 45 insertions(+), 9 deletions(-) diff --git a/docs/source/providers/inference/remote_watsonx.md b/docs/source/providers/inference/remote_watsonx.md index 0eb8a6fc4..e885a07fc 100644 --- a/docs/source/providers/inference/remote_watsonx.md +++ b/docs/source/providers/inference/remote_watsonx.md @@ -9,8 +9,8 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `url` | `` | No | https://us-south.ml.cloud.ibm.com | A base url for accessing the watsonx.ai | -| `api_key` | `pydantic.types.SecretStr \| None` | No | | The watsonx API key, only needed of using the hosted service | -| `project_id` | `str \| None` | No | | The Project ID key, only needed of using the hosted service | +| `api_key` | `pydantic.types.SecretStr \| None` | No | | The watsonx API key | +| `project_id` | `str \| None` | No | | The Project ID key | | `timeout` | `` | No | 60 | Timeout for the HTTP requests | ## Sample Configuration diff --git a/llama_stack/distributions/watsonx/run.yaml b/llama_stack/distributions/watsonx/run.yaml index f5fe31bef..92f367910 100644 --- a/llama_stack/distributions/watsonx/run.yaml +++ b/llama_stack/distributions/watsonx/run.yaml @@ -10,6 +10,7 @@ apis: - telemetry - tool_runtime - vector_io +- files providers: inference: - provider_id: watsonx @@ -94,6 +95,14 @@ providers: provider_type: inline::rag-runtime - provider_id: model-context-protocol provider_type: remote::model-context-protocol + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/watsonx/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/files_metadata.db metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/registry.db diff --git a/llama_stack/distributions/watsonx/watsonx.py b/llama_stack/distributions/watsonx/watsonx.py index 1ef2ef339..c3cab5d1b 100644 --- a/llama_stack/distributions/watsonx/watsonx.py +++ b/llama_stack/distributions/watsonx/watsonx.py @@ -9,6 +9,7 @@ from pathlib import Path from llama_stack.apis.models import ModelType from llama_stack.core.datatypes import BuildProvider, ModelInput, Provider, ToolGroupInput from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings, get_model_registry +from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig from llama_stack.providers.inline.inference.sentence_transformers import ( SentenceTransformersInferenceConfig, ) @@ -16,7 +17,7 @@ from llama_stack.providers.remote.inference.watsonx import WatsonXConfig from llama_stack.providers.remote.inference.watsonx.models import MODEL_ENTRIES -def get_distribution_template() -> DistributionTemplate: +def get_distribution_template(name: str = "watsonx") -> DistributionTemplate: providers = { "inference": [ BuildProvider(provider_type="remote::watsonx"), @@ -42,6 +43,7 @@ def get_distribution_template() -> DistributionTemplate: BuildProvider(provider_type="inline::rag-runtime"), BuildProvider(provider_type="remote::model-context-protocol"), ], + "files": [BuildProvider(provider_type="inline::localfs")], } inference_provider = Provider( @@ -79,9 +81,14 @@ def get_distribution_template() -> DistributionTemplate: }, ) + files_provider = Provider( + provider_id="meta-reference-files", + provider_type="inline::localfs", + config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"), + ) default_models, _ = get_model_registry(available_models) return DistributionTemplate( - name="watsonx", + name=name, distro_type="remote_hosted", description="Use watsonx for running LLM inference", container_image=None, @@ -92,6 +99,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider, embedding_provider], + "files": [files_provider], }, default_models=default_models + [embedding_model], default_tool_groups=default_tool_groups, diff --git a/llama_stack/providers/remote/inference/watsonx/config.py b/llama_stack/providers/remote/inference/watsonx/config.py index ae4bd55c1..42c25d93e 100644 --- a/llama_stack/providers/remote/inference/watsonx/config.py +++ b/llama_stack/providers/remote/inference/watsonx/config.py @@ -26,11 +26,11 @@ class WatsonXConfig(BaseModel): ) api_key: SecretStr | None = Field( default_factory=lambda: os.getenv("WATSONX_API_KEY"), - description="The watsonx API key, only needed of using the hosted service", + description="The watsonx API key", ) project_id: str | None = Field( default_factory=lambda: os.getenv("WATSONX_PROJECT_ID"), - description="The Project ID key, only needed of using the hosted service", + description="The Project ID key", ) timeout: int = Field( default=60, diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py index cb7fc175f..ab5ca76db 100644 --- a/llama_stack/providers/remote/inference/watsonx/watsonx.py +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -38,6 +38,7 @@ from llama_stack.apis.inference import ( TopKSamplingStrategy, TopPSamplingStrategy, ) +from llama_stack.log import get_logger from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper from llama_stack.providers.utils.inference.openai_compat import ( OpenAICompatCompletionChoice, @@ -57,14 +58,29 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( from . import WatsonXConfig from .models import MODEL_ENTRIES +logger = get_logger(name=__name__, category="inference::watsonx") + + +# Note on structured output +# WatsonX returns responses with a json embedded into a string. +# Examples: + +# ChatCompletionResponse(completion_message=CompletionMessage(content='```json\n{\n +# "first_name": "Michael",\n "last_name": "Jordan",\n'...) +# Not even a valid JSON, but we can still extract the JSON from the content + +# CompletionResponse(content=' \nThe best answer is $\\boxed{\\{"name": "Michael Jordan", +# "year_born": "1963", "year_retired": "2003"\\}}$') +# Find the start of the boxed content + class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): def __init__(self, config: WatsonXConfig) -> None: ModelRegistryHelper.__init__(self, MODEL_ENTRIES) - print(f"Initializing watsonx InferenceAdapter({config.url})...") - + logger.info(f"Initializing watsonx InferenceAdapter({config.url})...") self._config = config + self._openai_client: AsyncOpenAI | None = None self._project_id = self._config.project_id diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 35869276b..051529719 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -58,6 +58,7 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) # does not work with the specified model, gpt-5-mini. Please choose different model and try # again. You can learn more about which models can be used with each operation here: # https://go.microsoft.com/fwlink/?linkid=2197993.'}}"} + "remote::watsonx", # return 404 when hitting the /openai/v1 endpoint ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.") @@ -110,6 +111,7 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode "remote::cerebras", "remote::databricks", "remote::runpod", + "remote::watsonx", # watsonx returns 404 when hitting the /openai/v1 endpoint ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.") diff --git a/tests/integration/inference/test_text_inference.py b/tests/integration/inference/test_text_inference.py index 621084231..a5f95a963 100644 --- a/tests/integration/inference/test_text_inference.py +++ b/tests/integration/inference/test_text_inference.py @@ -45,7 +45,7 @@ def skip_if_model_doesnt_support_json_schema_structured_output(client_with_model provider_id = models[model_id].provider_id providers = {p.provider_id: p for p in client_with_models.providers.list()} provider = providers[provider_id] - if provider.provider_type in ("remote::sambanova", "remote::azure"): + if provider.provider_type in ("remote::sambanova", "remote::azure", "remote::watsonx"): pytest.skip( f"Model {model_id} hosted by {provider.provider_type} doesn't support json_schema structured output" ) @@ -211,6 +211,7 @@ def test_text_completion_log_probs_streaming(client_with_models, text_model_id, ) def test_text_completion_structured_output(client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_completion(client_with_models, text_model_id) + skip_if_model_doesnt_support_json_schema_structured_output(client_with_models, text_model_id) class AnswerFormat(BaseModel): name: str From 6b855af96fe2c344a2e13fbfa43bc1836d4136f6 Mon Sep 17 00:00:00 2001 From: Charlie Doern Date: Tue, 16 Sep 2025 12:18:36 -0400 Subject: [PATCH 118/124] feat: introduce api leveling proposal (#3317) # What does this PR do? this document outlines different API stability levels, how to enforce them, and next steps ## Next Steps Following the adoption of this document, all existing APIs should follow the enforcement protocol. relates to #3237 Signed-off-by: Charlie Doern --- docs/source/apis/api_leveling.md | 94 ++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 docs/source/apis/api_leveling.md diff --git a/docs/source/apis/api_leveling.md b/docs/source/apis/api_leveling.md new file mode 100644 index 000000000..bb012030f --- /dev/null +++ b/docs/source/apis/api_leveling.md @@ -0,0 +1,94 @@ +# Llama Stack API Stability Leveling + +In order to provide a stable experience in Llama Stack, the various APIs need different stability levels indicating the level of support, backwards compatability, and overall production readiness. + +## Different Levels + +### v1alpha + +- Little to no expectation of support between versions +- Breaking changes are permitted +- Datatypes and parameters can break +- Routes can be added and removed + +#### Graduation Criteria + +- an API can graduate from `v1alpha` to `v1beta` if the team has identified the extent of the non-optional routes and the shape of their parameters/return types for the API eg. `/v1/openai/chat/completions`. Optional types can change. +- CRUD must stay stable once in `v1beta`. This is a commitment to backward compatibility, guaranteeing that most code you write against the v1beta version will not break during future updates. We may make additive changes (like adding a new, optional field to a response), but we will not make breaking changes (like renaming an existing "modelName" field to "name", changing an ID's data type from an integer to a string, or altering an endpoint URL). +- for OpenAI APIs, a comparison to the OpenAI spec for the specific API can be done to ensure completeness. + +### v1beta + +- API routes remain consistent between versions +- Parameters and return types are not ensured between versions +- API, besides minor fixes and adjustments, should be _almost_ v1. Changes should not be drastic. + +#### Graduation Criteria + +- an API can graduate from `v1beta` to `v1` if the API surface and datatypes are complete as identified by the team. The parameters and return types that are mandatory for each route are stable. All aspects of graduating from `v1alpha1` to `v1beta` apply as well. +- Optional parameters, routes, or parts of the return type can be added after graduating to `v1` + +### v1 (stable) + +- Considered stable +- Backwards compatible between Z-streams + - Y-stream breaking changes must go through the proper approval and announcement process. +- Datatypes for a route and its return types cannot change between Z-streams + - Y-stream datatype changes should be sparing, unless the changes are additional net-new parameters +- Must have proper conformance testing as outlined in https://github.com/llamastack/llama-stack/issues/3237 + +### v2+ (Major Versions) + +Introducing a new major version like `/v2` is a significant and disruptive event that should be treated as a last resort. It is reserved for essential changes to a stable `/v1` API that are fundamentally backward-incompatible and cannot be implemented through additive, non-breaking changes or breaking changes across X/Y-Stream releases (x.y.z). + +If a `/v2` version is deemed absolutely necessary, it must adhere to the following protocol to ensure a sane and predictable transition for users: + +#### Lifecycle Progression + + A new major version must follow the same stability lifecycle as `/v1`. It will be introduced as `/v2alpha`, mature to `/v2beta`, and finally become stable as `/v2`. + +#### Coexistence: + +The new `/v2` API must be introduced alongside the existing `/v1` API and run in parallel. It must not replace the `/v1` API immediately. + +#### Deprecation Policy: + +When a `/v2` API is introduced, a clear and generous deprecation policy for the `/v1` API must be published simultaneously. This policy must outline the timeline for the eventual removal of the `/v1` API, giving users ample time to migrate. + +### API Stability vs. Provider Stability + +The leveling introduced in this document relates to the stability of the API and not specifically the providers within the API. + +Providers can iterate as much as they want on functionality as long as they work within the bounds of an API. If they need to change the API, then the API should not be `/v1`, or those breaking changes can only happen on a y-stream release basis. + +### Approval and Announcement Process for Breaking Changes + +- **PR Labeling**: Any pull request that introduces a breaking API change must be clearly labeled with `breaking-change`. +- **PR Title/Commit**: Any pull request that introduces a breaking API change must contain `BREAKING CHANGE` in the title and commit footer. Alternatively, the commit can include `!`, eg. `feat(api)!: title goes here` This is outlined in the [conventional commits documentation](https://www.conventionalcommits.org/en/v1.0.0/#specification) +- **Maintainer Review**: At least one maintainer must explicitly acknowledge the breaking change during review by applying the `breaking-change` label. An approval must come with this label or the acknowledgement this label has already been applied. +- **Announcement**: Breaking changes require inclusion in release notes and, if applicable, a separate communication (e.g., Discord, Github Issues, or GitHub Discussions) prior to release. + +If a PR has proper approvals, labels, and commit/title hygiene, the failing API conformance tests will be bypassed. + + +## Enforcement + +### Migration of API routes under `/v1alpha`, `/v1beta`, and `/v1` + +Instead of placing every API under `/v1`, any API that is not fully stable or complete should go under `/v1alpha` or `/v1beta`. For example, at the time of this writing, `post_training` belongs here, as well as any OpenAI-compatible API whose surface does not exactly match the upstream OpenAI API it mimics. + +This migration is crucial as we get Llama Stack in the hands of users who intend to productize various APIs. A clear view of what is stable and what is actively being developed will enable users to pick and choose various APIs to build their products on. + +This migration will be a breaking change for any API moving out of `/v1`. Ideally, this should happen before 0.3.0 and especially 1.0.0. + +### `x-stability` tags in the OpenAPI spec for oasdiff + +`x-stability` tags allow tools like oasdiff to enforce different rules for different stability levels; these tags should match the routes: [oasdiff stability](https://github.com/oasdiff/oasdiff/blob/main/docs/STABILITY.md) + +### Testing + +The testing of each stable API is already outlined in [issue #3237](https://github.com/llamastack/llama-stack/issues/3237) and is being worked on. These sorts of conformance tests should apply primarily to `/v1` APIs only, with `/v1alpha` and `/v1beta` having any tests the maintainers see fit as well as basic testing to ensure the routing works properly. + +### New APIs going forward + +Any subsequently introduced APIs should be introduced as `/v1alpha` \ No newline at end of file From 3defdf7d3a58e9c6e66666d4121fd7a7ddf45e9e Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Tue, 16 Sep 2025 11:33:43 -0700 Subject: [PATCH 119/124] fix: docker failing to start container[pydantic] (#3460) # What does this PR do? Pinning to latest pydantic version 2.11.9 as sometime we are picking older version and failing to start container in github actions : https://github.com/llamastack/llama-stack-ops/actions/runs/17750263127 Closes https://github.com/llamastack/llama-stack/issues/3461 ## Test Plan Tested locally with the following commands to start a container Build container `llama stack build --distro starter --image-type container` start container `docker run -d -p 8321:8321 --name llama-stack-test distribution-starter:0.2.21` check health http://localhost:8321/v1/health Couldnt repro with older version(`2.8.2`), but `2.11.9` pydantic is able to start the container https://pypi.org/project/pydantic/#history , 2.11.9 is the latest version --- pyproject.toml | 4 ++-- uv.lock | 10 +++++----- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index ce95b758f..0950c0dc2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,7 @@ dependencies = [ "prompt-toolkit", "python-dotenv", "python-jose[cryptography]", - "pydantic>=2", + "pydantic>=2.11.9", "rich", "starlette", "termcolor", @@ -141,7 +141,7 @@ docs = [ "sphinxcontrib.openapi", "requests", ] -codegen = ["rich", "pydantic", "jinja2>=3.1.6"] +codegen = ["rich", "pydantic>=2.11.9", "jinja2>=3.1.6"] benchmark = [ "locust>=2.39.1", ] diff --git a/uv.lock b/uv.lock index 065eb3876..14848d2d4 100644 --- a/uv.lock +++ b/uv.lock @@ -1893,7 +1893,7 @@ requires-dist = [ { name = "pandas", marker = "extra == 'ui'" }, { name = "pillow" }, { name = "prompt-toolkit" }, - { name = "pydantic", specifier = ">=2" }, + { name = "pydantic", specifier = ">=2.11.9" }, { name = "python-dotenv" }, { name = "python-jose", extras = ["cryptography"] }, { name = "python-multipart", specifier = ">=0.0.20" }, @@ -1911,7 +1911,7 @@ provides-extras = ["ui"] benchmark = [{ name = "locust", specifier = ">=2.39.1" }] codegen = [ { name = "jinja2", specifier = ">=3.1.6" }, - { name = "pydantic" }, + { name = "pydantic", specifier = ">=2.11.9" }, { name = "rich" }, ] dev = [ @@ -3393,7 +3393,7 @@ wheels = [ [[package]] name = "pydantic" -version = "2.11.7" +version = "2.11.9" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "annotated-types" }, @@ -3401,9 +3401,9 @@ dependencies = [ { name = "typing-extensions" }, { name = "typing-inspection" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/00/dd/4325abf92c39ba8623b5af936ddb36ffcfe0beae70405d456ab1fb2f5b8c/pydantic-2.11.7.tar.gz", hash = "sha256:d989c3c6cb79469287b1569f7447a17848c998458d49ebe294e975b9baf0f0db", size = 788350, upload-time = "2025-06-14T08:33:17.137Z" } +sdist = { url = "https://files.pythonhosted.org/packages/ff/5d/09a551ba512d7ca404d785072700d3f6727a02f6f3c24ecfd081c7cf0aa8/pydantic-2.11.9.tar.gz", hash = "sha256:6b8ffda597a14812a7975c90b82a8a2e777d9257aba3453f973acd3c032a18e2", size = 788495, upload-time = "2025-09-13T11:26:39.325Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/6a/c0/ec2b1c8712ca690e5d61979dee872603e92b8a32f94cc1b72d53beab008a/pydantic-2.11.7-py3-none-any.whl", hash = "sha256:dde5df002701f6de26248661f6835bbe296a47bf73990135c7d07ce741b9623b", size = 444782, upload-time = "2025-06-14T08:33:14.905Z" }, + { url = "https://files.pythonhosted.org/packages/3e/d3/108f2006987c58e76691d5ae5d200dd3e0f532cb4e5fa3560751c3a1feba/pydantic-2.11.9-py3-none-any.whl", hash = "sha256:c42dd626f5cfc1c6950ce6205ea58c93efa406da65f479dcb4029d5934857da2", size = 444855, upload-time = "2025-09-13T11:26:36.909Z" }, ] [[package]] From 49d4a5cc8456ddb7dfd06c4a07ad871cfd7b2e66 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Tue, 16 Sep 2025 14:53:41 -0400 Subject: [PATCH 120/124] feat: add embedding and dynamic model support to Together inference adapter (#3458) # What does this PR do? adds embedding and dynamic model support to Together inference adapter - updated to use OpenAIMixin - workarounds for Together api quirks - recordings for together suite when subdirs=inference,pattern=openai ## Test Plan ``` $ TOGETHER_API_KEY=_NONE_ ./scripts/integration-tests.sh --stack-config server:ci-tests --setup together --subdirs inference --pattern openai ... tests/integration/inference/test_openai_completion.py::test_openai_completion_non_streaming[txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:completion:sanity] instantiating llama_stack_client Port 8321 is already in use, assuming server is already running... llama_stack_client instantiated in 0.121s PASSED [ 2%] tests/integration/inference/test_openai_completion.py::test_openai_completion_non_streaming_suffix[txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:completion:suffix] SKIPPED [ 4%] tests/integration/inference/test_openai_completion.py::test_openai_completion_streaming[txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:completion:sanity] PASSED [ 6%] tests/integration/inference/test_openai_completion.py::test_openai_completion_prompt_logprobs[txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-1] SKIPPED [ 8%] tests/integration/inference/test_openai_completion.py::test_openai_completion_guided_choice[txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free] SKIPPED [ 10%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:non_streaming_01] PASSED [ 12%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:streaming_01] PASSED [ 14%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming_with_n[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:streaming_01] SKIPPED [ 17%] tests/integration/inference/test_openai_completion.py::test_inference_store[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-True] PASSED [ 19%] tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-True] PASSED [ 21%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming_with_file[txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free] SKIPPED [ 23%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_single_string[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 25%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_multiple_strings[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 27%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_encoding_format_float[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 29%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_dimensions[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] SKIPPED [ 31%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_user_parameter[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] SKIPPED [ 34%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_empty_list_error[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 36%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_invalid_model_error[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 38%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_different_inputs_different_outputs[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 40%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_encoding_format_base64[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] SKIPPED [ 42%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_base64_batch_processing[openai_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] SKIPPED [ 44%] tests/integration/inference/test_openai_completion.py::test_openai_completion_prompt_logprobs[txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-0] SKIPPED [ 46%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:non_streaming_02] PASSED [ 48%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:streaming_02] PASSED [ 51%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming_with_n[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:streaming_02] SKIPPED [ 53%] tests/integration/inference/test_openai_completion.py::test_inference_store[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-False] PASSED [ 55%] tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-False] PASSED [ 57%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_single_string[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 59%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_multiple_strings[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 61%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_encoding_format_float[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 63%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_dimensions[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] SKIPPED [ 65%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_user_parameter[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] SKIPPED [ 68%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_empty_list_error[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 70%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_invalid_model_error[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 72%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_different_inputs_different_outputs[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] PASSED [ 74%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_encoding_format_base64[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] SKIPPED [ 76%] tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_base64_batch_processing[llama_stack_client-emb=together/togethercomputer/m2-bert-80M-32k-retrieval] SKIPPED [ 78%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:non_streaming_01] PASSED [ 80%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:streaming_01] PASSED [ 82%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming_with_n[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:streaming_01] SKIPPED [ 85%] tests/integration/inference/test_openai_completion.py::test_inference_store[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-True] PASSED [ 87%] tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-True] PASSED [ 89%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:non_streaming_02] PASSED [ 91%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:streaming_02] PASSED [ 93%] tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming_with_n[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-inference:chat_completion:streaming_02] SKIPPED [ 95%] tests/integration/inference/test_openai_completion.py::test_inference_store[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-False] PASSED [ 97%] tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free-False] PASSED [100%] ============================================ 30 passed, 17 skipped, 50 deselected, 4 warnings in 21.96s ============================================= ``` --- .../remote/inference/together/models.py | 120 +- .../remote/inference/together/together.py | 205 +- .../inference/test_openai_completion.py | 1 + .../inference/test_openai_embeddings.py | 42 +- .../recordings/responses/07c5fa34d9ca.json | 800 ++++++ .../recordings/responses/0c1f45455d3b.json | 59 + .../recordings/responses/17030e75309f.json | 800 ++++++ .../recordings/responses/432a346b2ed8.json | 2352 +++++++++++++++++ .../recordings/responses/4ca6152a0eb8.json | 59 + .../recordings/responses/511eb1b92e34.json | 1278 +++++++++ .../recordings/responses/565b1072cb9d.json | 46 + .../recordings/responses/6730dcde0b73.json | 756 ++++++ .../recordings/responses/6857b19d3f0a.json | 87 + .../recordings/responses/6c4e2e207e8a.json | 59 + .../recordings/responses/72e075bf28e8.json | 800 ++++++ .../recordings/responses/894fdacb1cfa.json | 176 ++ .../recordings/responses/bce560cbf1c6.json | 800 ++++++ .../recordings/responses/d85689907fec.json | 350 +++ .../recordings/responses/f0bbea34c5cc.json | 611 +++++ tests/integration/suites.py | 8 + 20 files changed, 9229 insertions(+), 180 deletions(-) create mode 100644 tests/integration/recordings/responses/07c5fa34d9ca.json create mode 100644 tests/integration/recordings/responses/0c1f45455d3b.json create mode 100644 tests/integration/recordings/responses/17030e75309f.json create mode 100644 tests/integration/recordings/responses/432a346b2ed8.json create mode 100644 tests/integration/recordings/responses/4ca6152a0eb8.json create mode 100644 tests/integration/recordings/responses/511eb1b92e34.json create mode 100644 tests/integration/recordings/responses/565b1072cb9d.json create mode 100644 tests/integration/recordings/responses/6730dcde0b73.json create mode 100644 tests/integration/recordings/responses/6857b19d3f0a.json create mode 100644 tests/integration/recordings/responses/6c4e2e207e8a.json create mode 100644 tests/integration/recordings/responses/72e075bf28e8.json create mode 100644 tests/integration/recordings/responses/894fdacb1cfa.json create mode 100644 tests/integration/recordings/responses/bce560cbf1c6.json create mode 100644 tests/integration/recordings/responses/d85689907fec.json create mode 100644 tests/integration/recordings/responses/f0bbea34c5cc.json diff --git a/llama_stack/providers/remote/inference/together/models.py b/llama_stack/providers/remote/inference/together/models.py index 575ec1f3d..2aba614cb 100644 --- a/llama_stack/providers/remote/inference/together/models.py +++ b/llama_stack/providers/remote/inference/together/models.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from llama_stack.apis.models import ModelType from llama_stack.models.llama.sku_types import CoreModelId from llama_stack.providers.utils.inference.model_registry import ( ProviderModelEntry, @@ -21,57 +20,84 @@ SAFETY_MODELS_ENTRIES = [ CoreModelId.llama_guard_3_11b_vision.value, ), ] -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", - CoreModelId.llama3_1_405b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-3.2-3B-Instruct-Turbo", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo", - CoreModelId.llama3_2_11b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo", - CoreModelId.llama3_2_90b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-3.3-70B-Instruct-Turbo", - CoreModelId.llama3_3_70b_instruct.value, - ), - ProviderModelEntry( - provider_model_id="togethercomputer/m2-bert-80M-8k-retrieval", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 768, - "context_length": 8192, - }, - ), - ProviderModelEntry( + +# source: https://docs.together.ai/docs/serverless-models#embedding-models +EMBEDDING_MODEL_ENTRIES = { + "togethercomputer/m2-bert-80M-32k-retrieval": ProviderModelEntry( provider_model_id="togethercomputer/m2-bert-80M-32k-retrieval", - model_type=ModelType.embedding, metadata={ "embedding_dimension": 768, "context_length": 32768, }, ), - build_hf_repo_model_entry( - "meta-llama/Llama-4-Scout-17B-16E-Instruct", - CoreModelId.llama4_scout_17b_16e_instruct.value, + "BAAI/bge-large-en-v1.5": ProviderModelEntry( + provider_model_id="BAAI/bge-large-en-v1.5", + metadata={ + "embedding_dimension": 1024, + "context_length": 512, + }, ), - build_hf_repo_model_entry( - "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", - CoreModelId.llama4_maverick_17b_128e_instruct.value, + "BAAI/bge-base-en-v1.5": ProviderModelEntry( + provider_model_id="BAAI/bge-base-en-v1.5", + metadata={ + "embedding_dimension": 768, + "context_length": 512, + }, ), -] + SAFETY_MODELS_ENTRIES + "Alibaba-NLP/gte-modernbert-base": ProviderModelEntry( + provider_model_id="Alibaba-NLP/gte-modernbert-base", + metadata={ + "embedding_dimension": 768, + "context_length": 8192, + }, + ), + "intfloat/multilingual-e5-large-instruct": ProviderModelEntry( + provider_model_id="intfloat/multilingual-e5-large-instruct", + metadata={ + "embedding_dimension": 1024, + "context_length": 512, + }, + ), +} +MODEL_ENTRIES = ( + [ + build_hf_repo_model_entry( + "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", + CoreModelId.llama3_1_8b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", + CoreModelId.llama3_1_70b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", + CoreModelId.llama3_1_405b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/Llama-3.2-3B-Instruct-Turbo", + CoreModelId.llama3_2_3b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo", + CoreModelId.llama3_2_11b_vision_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo", + CoreModelId.llama3_2_90b_vision_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/Llama-3.3-70B-Instruct-Turbo", + CoreModelId.llama3_3_70b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/Llama-4-Scout-17B-16E-Instruct", + CoreModelId.llama4_scout_17b_16e_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + CoreModelId.llama4_maverick_17b_128e_instruct.value, + ), + ] + + SAFETY_MODELS_ENTRIES + + list(EMBEDDING_MODEL_ENTRIES.values()) +) diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py index 54c76607f..d45bd489f 100644 --- a/llama_stack/providers/remote/inference/together/together.py +++ b/llama_stack/providers/remote/inference/together/together.py @@ -4,11 +4,11 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from collections.abc import AsyncGenerator, AsyncIterator -from typing import Any +from collections.abc import AsyncGenerator -from openai import AsyncOpenAI +from openai import NOT_GIVEN, AsyncOpenAI from together import AsyncTogether +from together.constants import BASE_URL from llama_stack.apis.common.content_types import ( InterleavedContent, @@ -23,12 +23,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, - OpenAIChatCompletion, - OpenAIChatCompletionChunk, - OpenAICompletion, OpenAIEmbeddingsResponse, - OpenAIMessageParam, - OpenAIResponseFormatParam, ResponseFormat, ResponseFormatType, SamplingParams, @@ -38,18 +33,20 @@ from llama_stack.apis.inference import ( ToolDefinition, ToolPromptFormat, ) +from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage +from llama_stack.apis.models import Model, ModelType from llama_stack.core.request_headers import NeedsRequestProviderData from llama_stack.log import get_logger from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper from llama_stack.providers.utils.inference.openai_compat import ( convert_message_to_openai_dict, get_sampling_options, - prepare_openai_completion_params, process_chat_completion_response, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, completion_request_to_prompt, @@ -59,15 +56,22 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( ) from .config import TogetherImplConfig -from .models import MODEL_ENTRIES +from .models import EMBEDDING_MODEL_ENTRIES, MODEL_ENTRIES logger = get_logger(name=__name__, category="inference::together") -class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData): +class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, NeedsRequestProviderData): def __init__(self, config: TogetherImplConfig) -> None: ModelRegistryHelper.__init__(self, MODEL_ENTRIES, config.allowed_models) self.config = config + self._model_cache: dict[str, Model] = {} + + def get_api_key(self): + return self.config.api_key.get_secret_value() + + def get_base_url(self): + return BASE_URL async def initialize(self) -> None: pass @@ -255,6 +259,37 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi embeddings = [item.embedding for item in r.data] return EmbeddingsResponse(embeddings=embeddings) + async def list_models(self) -> list[Model] | None: + self._model_cache = {} + # Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client + for m in await self._get_client().models.list(): + if m.type == "embedding": + if m.id not in EMBEDDING_MODEL_ENTRIES: + logger.warning(f"Unknown embedding dimension for model {m.id}, skipping.") + continue + self._model_cache[m.id] = Model( + provider_id=self.__provider_id__, + provider_resource_id=EMBEDDING_MODEL_ENTRIES[m.id].provider_model_id, + identifier=m.id, + model_type=ModelType.embedding, + metadata=EMBEDDING_MODEL_ENTRIES[m.id].metadata, + ) + else: + self._model_cache[m.id] = Model( + provider_id=self.__provider_id__, + provider_resource_id=m.id, + identifier=m.id, + model_type=ModelType.llm, + ) + + return self._model_cache.values() + + async def should_refresh_models(self) -> bool: + return True + + async def check_model_availability(self, model): + return model in self._model_cache + async def openai_embeddings( self, model: str, @@ -263,125 +298,39 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi dimensions: int | None = None, user: str | None = None, ) -> OpenAIEmbeddingsResponse: - raise NotImplementedError() + """ + Together's OpenAI-compatible embeddings endpoint is not compatible with + the standard OpenAI embeddings endpoint. - async def openai_completion( - self, - model: str, - prompt: str | list[str] | list[int] | list[list[int]], - best_of: int | None = None, - echo: bool | None = None, - frequency_penalty: float | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_tokens: int | None = None, - n: int | None = None, - presence_penalty: float | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - top_p: float | None = None, - user: str | None = None, - guided_choice: list[str] | None = None, - prompt_logprobs: int | None = None, - suffix: str | None = None, - ) -> OpenAICompletion: - model_obj = await self.model_store.get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - prompt=prompt, - best_of=best_of, - echo=echo, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - logprobs=logprobs, - max_tokens=max_tokens, - n=n, - presence_penalty=presence_penalty, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - top_p=top_p, - user=user, + The endpoint - + - does not return usage information + - does not support user param, returns 400 Unrecognized request arguments supplied: user + - does not support dimensions param, returns 400 Unrecognized request arguments supplied: dimensions + - does not support encoding_format param, always returns floats, never base64 + """ + # Together support ticket #13332 -> will not fix + if user is not None: + raise ValueError("Together's embeddings endpoint does not support user param.") + # Together support ticket #13333 -> escalated + if dimensions is not None: + raise ValueError("Together's embeddings endpoint does not support dimensions param.") + # Together support ticket #13331 -> will not fix, compute client side + if encoding_format not in (None, NOT_GIVEN, "float"): + raise ValueError("Together's embeddings endpoint only supports encoding_format='float'.") + + response = await self.client.embeddings.create( + model=await self._get_provider_model_id(model), + input=input, ) - return await self._get_openai_client().completions.create(**params) # type: ignore - async def openai_chat_completion( - self, - model: str, - messages: list[OpenAIMessageParam], - frequency_penalty: float | None = None, - function_call: str | dict[str, Any] | None = None, - functions: list[dict[str, Any]] | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_completion_tokens: int | None = None, - max_tokens: int | None = None, - n: int | None = None, - parallel_tool_calls: bool | None = None, - presence_penalty: float | None = None, - response_format: OpenAIResponseFormatParam | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - tool_choice: str | dict[str, Any] | None = None, - tools: list[dict[str, Any]] | None = None, - top_logprobs: int | None = None, - top_p: float | None = None, - user: str | None = None, - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - model_obj = await self.model_store.get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - if params.get("stream", False): - return self._stream_openai_chat_completion(params) - return await self._get_openai_client().chat.completions.create(**params) # type: ignore + response.model = model # return the user the same model id they provided, avoid exposing the provider model id - async def _stream_openai_chat_completion(self, params: dict) -> AsyncGenerator: - # together.ai sometimes adds usage data to the stream, even if include_usage is False - # This causes an unexpected final chunk with empty choices array to be sent - # to clients that may not handle it gracefully. - include_usage = False - if params.get("stream_options", None): - include_usage = params["stream_options"].get("include_usage", False) - stream = await self._get_openai_client().chat.completions.create(**params) + # Together support ticket #13330 -> escalated + # - togethercomputer/m2-bert-80M-32k-retrieval *does not* return usage information + if not hasattr(response, "usage") or response.usage is None: + logger.warning( + f"Together's embedding endpoint for {model} did not return usage information, substituting -1s." + ) + response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1) - seen_finish_reason = False - async for chunk in stream: - # Final usage chunk with no choices that the user didn't request, so discard - if not include_usage and seen_finish_reason and len(chunk.choices) == 0: - break - yield chunk - for choice in chunk.choices: - if choice.finish_reason: - seen_finish_reason = True - break + return response diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 051529719..b232f8658 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -97,6 +97,7 @@ def skip_if_doesnt_support_n(client_with_models, model_id): # Error code: 400 - [{'error': {'code': 400, 'message': 'Unable to submit request because candidateCount must be 1 but # the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'} "remote::tgi", # TGI ignores n param silently + "remote::together", # `n` > 1 is not supported when streaming tokens. Please disable `stream` ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") diff --git a/tests/integration/inference/test_openai_embeddings.py b/tests/integration/inference/test_openai_embeddings.py index 2c545cc43..622b97287 100644 --- a/tests/integration/inference/test_openai_embeddings.py +++ b/tests/integration/inference/test_openai_embeddings.py @@ -29,9 +29,35 @@ def provider_from_model(client_with_models, model_id): return providers[provider_id] -def skip_if_model_doesnt_support_variable_dimensions(model_id): - if "text-embedding-3" not in model_id: - pytest.skip("{model_id} does not support variable output embedding dimensions") +def skip_if_model_doesnt_support_user_param(client, model_id): + provider = provider_from_model(client, model_id) + if provider.provider_type in ( + "remote::together", # service returns 400 + ): + pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support user param.") + + +def skip_if_model_doesnt_support_encoding_format_base64(client, model_id): + provider = provider_from_model(client, model_id) + if provider.provider_type in ( + "remote::together", # param silently ignored, always returns floats + ): + pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.") + + +def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_id): + provider = provider_from_model(client_with_models, model_id) + if provider.provider_type in ( + "remote::together", # returns 400 + "inline::sentence-transformers", + ): + pytest.skip( + f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions." + ) + if provider.provider_type == "remote::openai" and "text-embedding-3" not in model_id: + pytest.skip( + f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions." + ) @pytest.fixture(params=["openai_client", "llama_stack_client"]) @@ -92,6 +118,7 @@ def test_openai_embeddings_multiple_strings(compat_client, client_with_models, e response = compat_client.embeddings.create( model=embedding_model_id, input=input_texts, + encoding_format="float", ) assert response.object == "list" @@ -127,7 +154,7 @@ def test_openai_embeddings_with_encoding_format_float(compat_client, client_with def test_openai_embeddings_with_dimensions(compat_client, client_with_models, embedding_model_id): """Test OpenAI embeddings endpoint with custom dimensions parameter.""" skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id) - skip_if_model_doesnt_support_variable_dimensions(embedding_model_id) + skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id) input_text = "Test dimensions parameter" dimensions = 16 @@ -148,6 +175,7 @@ def test_openai_embeddings_with_dimensions(compat_client, client_with_models, em def test_openai_embeddings_with_user_parameter(compat_client, client_with_models, embedding_model_id): """Test OpenAI embeddings endpoint with user parameter.""" skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id) + skip_if_model_doesnt_support_user_param(client_with_models, embedding_model_id) input_text = "Test user parameter" user_id = "test-user-123" @@ -196,11 +224,13 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli response1 = compat_client.embeddings.create( model=embedding_model_id, input=input_text1, + encoding_format="float", ) response2 = compat_client.embeddings.create( model=embedding_model_id, input=input_text2, + encoding_format="float", ) embedding1 = response1.data[0].embedding @@ -214,7 +244,8 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli def test_openai_embeddings_with_encoding_format_base64(compat_client, client_with_models, embedding_model_id): """Test OpenAI embeddings endpoint with base64 encoding format.""" skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id) - skip_if_model_doesnt_support_variable_dimensions(embedding_model_id) + skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id) + skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id) input_text = "Test base64 encoding format" dimensions = 12 @@ -247,6 +278,7 @@ def test_openai_embeddings_with_encoding_format_base64(compat_client, client_wit def test_openai_embeddings_base64_batch_processing(compat_client, client_with_models, embedding_model_id): """Test OpenAI embeddings endpoint with base64 encoding for batch processing.""" skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id) + skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id) input_texts = ["First text for base64", "Second text for base64", "Third text for base64"] diff --git a/tests/integration/recordings/responses/07c5fa34d9ca.json b/tests/integration/recordings/responses/07c5fa34d9ca.json new file mode 100644 index 000000000..af1460120 --- /dev/null +++ b/tests/integration/recordings/responses/07c5fa34d9ca.json @@ -0,0 +1,800 @@ +{ + "request": { + "method": "POST", + "url": "https://api.together.xyz/v1/v1/embeddings", + "headers": {}, + "body": { + "model": "togethercomputer/m2-bert-80M-32k-retrieval", + "input": "Test encoding format" + }, + "endpoint": "/v1/embeddings", + "model": "togethercomputer/m2-bert-80M-32k-retrieval" + }, + "response": { + "body": { + "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse", + "__data__": { + "data": [ + { + "embedding": [ + -0.011256923, + 0.0037174695, + 0.047607094, + -0.03605117, + 0.022678856, + 0.0022196341, + 0.008172763, + -0.07876377, + -0.012652523, + -0.124776885, + -0.07201225, + 0.011470616, + 0.020233244, + -0.03953407, + 0.017867543, + 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"embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval", + }, + ), } From ececc323d360891eafb15bd2f06039e13edbcbfd Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 16 Sep 2025 19:44:03 +0000 Subject: [PATCH 121/124] build: Bump version to 0.2.22 --- llama_stack/ui/package-lock.json | 8 ++++---- llama_stack/ui/package.json | 2 +- pyproject.toml | 6 +++--- uv.lock | 14 +++++++------- 4 files changed, 15 insertions(+), 15 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index f333aa809..3f22716c6 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -18,7 +18,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^12.23.12", - "llama-stack-client": "^0.2.21", + "llama-stack-client": "^0.2.22", "lucide-react": "^0.542.0", "next": "15.5.3", "next-auth": "^4.24.11", @@ -10314,9 +10314,9 @@ "license": "MIT" }, "node_modules/llama-stack-client": { - "version": "0.2.21", - "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.21.tgz", - "integrity": "sha512-rjU2Vx5xStxDYavU8K1An/SYXiQQjroLcK98B+p0Paz/a7OgRao2S0YwvThJjPUyChY4fO03UIXP9LpmHqlXWQ==", + "version": "0.2.22", + "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.22.tgz", + "integrity": "sha512-7aW3UQj5MwjV73Brd+yQ1e4W1W33nhozyeHM5tzOgbsVZ88tL78JNiNvyFqDR5w6V9XO4/uSGGiQVG6v83yR4w==", "license": "MIT", "dependencies": { "@types/node": "^18.11.18", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index ccbc2a4c2..2b0322b28 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -23,7 +23,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^12.23.12", - "llama-stack-client": "^0.2.21", + "llama-stack-client": "^0.2.22", "lucide-react": "^0.542.0", "next": "15.5.3", "next-auth": "^4.24.11", diff --git a/pyproject.toml b/pyproject.toml index 0950c0dc2..ecbd8991a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ required-version = ">=0.7.0" [project] name = "llama_stack" -version = "0.2.21" +version = "0.2.22" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -31,7 +31,7 @@ dependencies = [ "huggingface-hub>=0.34.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.21", + "llama-stack-client>=0.2.22", "openai>=1.100.0", # for expires_after support "prompt-toolkit", "python-dotenv", @@ -55,7 +55,7 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.21", + "llama-stack-client>=0.2.22", "streamlit-option-menu", ] diff --git a/uv.lock b/uv.lock index 14848d2d4..0833a9d77 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.12" resolution-markers = [ "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", @@ -1749,7 +1749,7 @@ wheels = [ [[package]] name = "llama-stack" -version = "0.2.21" +version = "0.2.22" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -1885,8 +1885,8 @@ requires-dist = [ { name = "huggingface-hub", specifier = ">=0.34.0,<1.0" }, { name = "jinja2", specifier = ">=3.1.6" }, { name = "jsonschema" }, - { name = "llama-stack-client", specifier = ">=0.2.21" }, - { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.21" }, + { name = "llama-stack-client", specifier = ">=0.2.22" }, + { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.22" }, { name = "openai", specifier = ">=1.100.0" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, @@ -1993,7 +1993,7 @@ unit = [ [[package]] name = "llama-stack-client" -version = "0.2.21" +version = "0.2.22" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -2012,9 +2012,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/f1/d3/8c50561d167f1e9b601b8fffe852b44c1ff97aaa6db6cdedd611d9e02a65/llama_stack_client-0.2.21.tar.gz", hash = "sha256:bd931fdcadedec5ccdbaa3c54d0c17761af1c227711ad6150dc0dd33d7b66ce2", size = 318319, upload-time = "2025-09-08T22:26:57.668Z" } +sdist = { url = "https://files.pythonhosted.org/packages/60/80/4260816bfaaa889d515206c9df4906d08d405bf94c9b4d1be399b1923e46/llama_stack_client-0.2.22.tar.gz", hash = "sha256:9a0bc756b91ebd539858eeaf1f231c5e5c6900e1ea4fcced726c6717f3d27ca7", size = 318309, upload-time = "2025-09-16T19:43:33.212Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/02/77/dadc682046a2c7ad68be8d2d2afac7007bf4d22efb0d3929d85ab9706ffe/llama_stack_client-0.2.21-py3-none-any.whl", hash = "sha256:adba82fdf18ab3b8ac218cedba4927bd5d26c23c2318e75c8763a44bb6b40693", size = 369902, upload-time = "2025-09-08T22:26:56.308Z" }, + { url = "https://files.pythonhosted.org/packages/d1/8e/1ebf6ac0dbb62b81038e856ed00768e283d927b14fcd614e3018a227092b/llama_stack_client-0.2.22-py3-none-any.whl", hash = "sha256:b260d73aec56fcfd8fa601b3b34c2f83c4fbcfb7261a246b02bbdf6c2da184fe", size = 369901, upload-time = "2025-09-16T19:43:32.089Z" }, ] [[package]] From e0e2b1bd0e683d6457af55bb65809423bb65b37b Mon Sep 17 00:00:00 2001 From: Omar Abdelwahab Date: Tue, 16 Sep 2025 19:09:06 -0700 Subject: [PATCH 122/124] fix: Added a bug fix when registering new models (#3453) # What does this PR do? Modified the code in registry.py. The key changes are: 1. Removed the `return False` statement 2. Added a warning log message that includes the object type, identifier, and provider_id for better debugging. 3. The method now continues with the registration process instead of early returning. --------- Co-authored-by: Omar Abdelwahab --- llama_stack/core/store/registry.py | 8 +++++--- tests/unit/registry/test_registry.py | 13 +++++++++---- 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/llama_stack/core/store/registry.py b/llama_stack/core/store/registry.py index 5f4abe9aa..a764d692a 100644 --- a/llama_stack/core/store/registry.py +++ b/llama_stack/core/store/registry.py @@ -96,9 +96,11 @@ class DiskDistributionRegistry(DistributionRegistry): async def register(self, obj: RoutableObjectWithProvider) -> bool: existing_obj = await self.get(obj.type, obj.identifier) - # dont register if the object's providerid already exists - if existing_obj and existing_obj.provider_id == obj.provider_id: - return False + # warn if the object's providerid is different but proceed with registration + if existing_obj and existing_obj.provider_id != obj.provider_id: + logger.warning( + f"Object {existing_obj.type}:{existing_obj.identifier}'s {existing_obj.provider_id} provider is being replaced with {obj.provider_id}" + ) await self.kvstore.set( KEY_FORMAT.format(type=obj.type, identifier=obj.identifier), diff --git a/tests/unit/registry/test_registry.py b/tests/unit/registry/test_registry.py index 4ea4a20b9..9873bec5b 100644 --- a/tests/unit/registry/test_registry.py +++ b/tests/unit/registry/test_registry.py @@ -129,7 +129,7 @@ async def test_duplicate_provider_registration(cached_disk_dist_registry): result = await cached_disk_dist_registry.get("vector_db", "test_vector_db_2") assert result is not None - assert result.embedding_model == original_vector_db.embedding_model # Original values preserved + assert result.embedding_model == duplicate_vector_db.embedding_model # Original values preserved async def test_get_all_objects(cached_disk_dist_registry): @@ -174,10 +174,14 @@ async def test_parse_registry_values_error_handling(sqlite_kvstore): ) await sqlite_kvstore.set( - KEY_FORMAT.format(type="vector_db", identifier="valid_vector_db"), valid_db.model_dump_json() + KEY_FORMAT.format(type="vector_db", identifier="valid_vector_db"), + valid_db.model_dump_json(), ) - await sqlite_kvstore.set(KEY_FORMAT.format(type="vector_db", identifier="corrupted_json"), "{not valid json") + await sqlite_kvstore.set( + KEY_FORMAT.format(type="vector_db", identifier="corrupted_json"), + "{not valid json", + ) await sqlite_kvstore.set( KEY_FORMAT.format(type="vector_db", identifier="missing_fields"), @@ -212,7 +216,8 @@ async def test_cached_registry_error_handling(sqlite_kvstore): ) await sqlite_kvstore.set( - KEY_FORMAT.format(type="vector_db", identifier="valid_cached_db"), valid_db.model_dump_json() + KEY_FORMAT.format(type="vector_db", identifier="valid_cached_db"), + valid_db.model_dump_json(), ) await sqlite_kvstore.set( From fad48435484a82fae7f4c7686db3104523a37889 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Wed, 17 Sep 2025 09:18:43 +0100 Subject: [PATCH 123/124] fix: unbound variable PR_HEAD_REPO (#3469) Add default value for PR_HEAD_REPO to prevent 'unbound variable' error when no PR exists for a branch. Signed-off-by: Derek Higgins --- scripts/github/schedule-record-workflow.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/scripts/github/schedule-record-workflow.sh b/scripts/github/schedule-record-workflow.sh index 44b0947b6..afe664f80 100755 --- a/scripts/github/schedule-record-workflow.sh +++ b/scripts/github/schedule-record-workflow.sh @@ -13,6 +13,7 @@ set -euo pipefail # Default values BRANCH="" +PR_HEAD_REPO="" TEST_SUBDIRS="" TEST_SETUP="ollama" TEST_SUITE="base" From 9acf49753e58d611195c03af44fb42d8ebf5c13b Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Wed, 17 Sep 2025 02:24:58 -0600 Subject: [PATCH 124/124] fix: Fixing prompts import warning (#3455) # What does this PR do? Fixes this warning in llama stack build: ```bash WARNING 2025-09-15 15:29:02,197 llama_stack.core.distribution:149 core: Failed to import module prompts: No module named 'llama_stack.providers.registry.prompts'" ``` ## Test Plan Test added --------- Signed-off-by: Francisco Javier Arceo --- llama_stack/core/distribution.py | 5 ++++- tests/unit/distribution/test_distribution.py | 20 +++++++++++++++++++- 2 files changed, 23 insertions(+), 2 deletions(-) diff --git a/llama_stack/core/distribution.py b/llama_stack/core/distribution.py index 977eb5393..c104b6764 100644 --- a/llama_stack/core/distribution.py +++ b/llama_stack/core/distribution.py @@ -26,6 +26,9 @@ from llama_stack.providers.datatypes import ( logger = get_logger(name=__name__, category="core") +INTERNAL_APIS = {Api.inspect, Api.providers, Api.prompts} + + def stack_apis() -> list[Api]: return list(Api) @@ -70,7 +73,7 @@ def builtin_automatically_routed_apis() -> list[AutoRoutedApiInfo]: def providable_apis() -> list[Api]: routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()} - return [api for api in Api if api not in routing_table_apis and api != Api.inspect and api != Api.providers] + return [api for api in Api if api not in routing_table_apis and api not in INTERNAL_APIS] def _load_remote_provider_spec(spec_data: dict[str, Any], api: Api) -> ProviderSpec: diff --git a/tests/unit/distribution/test_distribution.py b/tests/unit/distribution/test_distribution.py index c72106e46..c6c2eb2c7 100644 --- a/tests/unit/distribution/test_distribution.py +++ b/tests/unit/distribution/test_distribution.py @@ -12,7 +12,7 @@ import yaml from pydantic import BaseModel, Field, ValidationError from llama_stack.core.datatypes import Api, Provider, StackRunConfig -from llama_stack.core.distribution import get_provider_registry +from llama_stack.core.distribution import INTERNAL_APIS, get_provider_registry, providable_apis from llama_stack.providers.datatypes import ProviderSpec @@ -152,6 +152,24 @@ class TestProviderRegistry: assert registry[Api.inference]["test_provider"].provider_type == "test_provider" assert registry[Api.inference]["test_provider"].api == Api.inference + def test_internal_apis_excluded(self): + """Test that internal APIs are excluded and APIs without provider registries are marked as internal.""" + import importlib + + apis = providable_apis() + + for internal_api in INTERNAL_APIS: + assert internal_api not in apis, f"Internal API {internal_api} should not be in providable_apis" + + for api in apis: + module_name = f"llama_stack.providers.registry.{api.name.lower()}" + try: + importlib.import_module(module_name) + except ImportError as err: + raise AssertionError( + f"API {api} is in providable_apis but has no provider registry module ({module_name})" + ) from err + def test_external_remote_providers(self, api_directories, mock_providers, base_config, provider_spec_yaml): """Test loading external remote providers from YAML files.""" remote_dir, _ = api_directories