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/_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/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/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) 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/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. 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/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/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/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/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/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/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/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/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/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] 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] diff --git a/llama_stack/core/stack.py b/llama_stack/core/stack.py index 87a3978c1..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, @@ -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/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/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 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/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/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/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/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) 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) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 1801cdcad..fb841afdf 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", @@ -115,7 +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", @@ -291,7 +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/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, 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/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..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 @@ -15,14 +15,17 @@ 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, 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 @@ -83,22 +86,85 @@ 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) -> 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 + + 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) - 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 +187,7 @@ 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 @@ -129,27 +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( @@ -163,14 +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, @@ -183,29 +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", - where=where_conditions if where_conditions else None, + policy=self.policy, + 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, @@ -216,41 +286,20 @@ class S3FilesImpl(Files): ) async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject: - row = await self.sql_store.fetch_one("openai_files", 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", 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", 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( @@ -261,7 +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/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 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/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 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/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/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/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/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 4a6958399..5b64e26d3 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 @@ -31,6 +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" @@ -52,7 +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(): @@ -61,28 +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) @@ -125,33 +117,18 @@ 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 - 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) @@ -163,58 +140,107 @@ 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) 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(): 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): @@ -236,8 +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 @@ -245,7 +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"] @@ -315,12 +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, @@ -345,10 +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): @@ -402,11 +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"] @@ -420,16 +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 @@ -438,7 +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/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); + } +}; diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 58888e586..7873cdfd5 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -14,11 +14,11 @@ "@radix-ui/react-select": "^2.2.5", "@radix-ui/react-separator": "^1.1.7", 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"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": { @@ -10021,9 +10147,9 @@ "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": "0.2.20", + "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", @@ -10066,13 +10192,6 @@ "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", @@ -11206,18 +11325,18 @@ } }, "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": { @@ -12105,9 +12224,9 @@ } }, "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": { @@ -14008,9 +14127,9 @@ } }, "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 4e29e8a5c..b37ff233f 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -19,11 +19,11 @@ "@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": "^11.18.2", - "llama-stack-client": "^0.2.18", + "framer-motion": "^12.23.12", + "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", @@ -41,19 +41,19 @@ "@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", + "@types/node": "^24", "@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", "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", diff --git a/pyproject.toml b/pyproject.toml index 6c76da895..fb6d3a330 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 = "0.2.20" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -31,9 +31,8 @@ dependencies = [ "huggingface-hub>=0.34.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.18", - "llama-api-client>=0.1.2", - "openai>=1.99.6,<1.100.0", + "llama-stack-client>=0.2.20", + "openai>=1.99.6", "prompt-toolkit", "python-dotenv", "python-jose[cryptography]", @@ -56,7 +55,7 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.18", + "llama-stack-client>=0.2.20", "streamlit-option-menu", ] @@ -84,6 +83,7 @@ unit = [ "openai", "aiosqlite", "aiohttp", + "psycopg2-binary>=2.9.0", "pypdf", "mcp", "chardet", @@ -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", @@ -105,12 +105,13 @@ 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", "torchvision>=0.21.0", "chardet", + "psycopg2-binary>=2.9.0", "pypdf", "mcp", "datasets", @@ -119,7 +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", ] @@ -144,7 +145,7 @@ docs = [ ] codegen = ["rich", "pydantic", "jinja2>=3.1.6"] benchmark = [ - "locust>=2.37.14", + "locust>=2.39.1", ] [project.urls] 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/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): 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/integration/recordings/index.sqlite b/tests/integration/recordings/index.sqlite deleted file mode 100644 index 0c88416f1..000000000 Binary files a/tests/integration/recordings/index.sqlite and /dev/null differ 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, - 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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 new file mode 100644 index 000000000..6131b1d5e --- /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": [ + 0.042460807, + -0.06189971, + -0.0784711, + 0.0064329687, + 0.03129365, + 0.00807445, + 0.05801836, + 0.025447326, + 0.016402787, + 0.045995634, + -0.028924342, + 0.04451832, + 0.05686613, + -0.015340794, 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"2025-09-03T17:37:50.436472Z", "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": "2025-08-01T23:14:01.942369Z", + "created_at": "2025-09-03T17:37:50.478138Z", "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": "2025-08-01T23:14:01.99605Z", + "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": "2025-09-03T17:37:50.645851Z", "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": "2025-08-01T23:14:02.211753Z", + "created_at": "2025-09-03T17:37:50.688403Z", "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": "2025-08-01T23:14:02.265564Z", + "created_at": "2025-09-03T17:37:50.72991Z", "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": "2025-08-01T23:14:02.31618Z", + "created_at": "2025-09-03T17:37:50.771635Z", "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": "2025-08-01T23:14:02.370325Z", + "created_at": "2025-09-03T17:37:50.813711Z", "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": "2025-08-01T23:14:02.424667Z", + "created_at": "2025-09-03T17:37:50.856201Z", "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": "2025-08-01T23:14:02.47913Z", + "created_at": 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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 @@ 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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__": { - 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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, - 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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 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b/tests/integration/recordings/responses/545d86510a80.json index 8126fd241..7cd718d56 100644 --- a/tests/integration/recordings/responses/545d86510a80.json +++ b/tests/integration/recordings/responses/545d86510a80.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.59711Z", + "created_at": "2025-09-03T17:42:32.625862Z", "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": "2025-08-01T23:14:38.671294Z", + "created_at": "2025-09-03T17:42:32.668885Z", "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": "2025-08-01T23:14:38.736161Z", + "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__": 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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__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-15T20:24:49.557000029Z", + 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+ } + } + }, + "is_streaming": false + } +} 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__": 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"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, + 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"__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__": 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"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__": 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"total_tokens": 2 + } + } + }, + "is_streaming": false + } +} 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 new file mode 100644 index 000000000..29a978e07 --- /dev/null +++ b/tests/integration/recordings/responses/7b25b702ea18.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": [ + "test query" + ], + "encoding_format": "float" + }, + "endpoint": "/v1/embeddings", + "model": "all-minilm:l6-v2" + }, + "response": { + "body": { + "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse", + "__data__": { + "data": [ + { + "embedding": [ 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+ "total_tokens": 2 + } + } + }, + "is_streaming": false + } +} 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 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.273466Z", + "created_at": "2025-09-03T17:37:48.883619Z", "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": "2025-08-01T23:13:59.325562Z", + "created_at": "2025-09-03T17:37:48.92504Z", "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": "2025-08-01T23:13:59.379223Z", + "created_at": "2025-09-03T17:37:48.966274Z", "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": "2025-08-01T23:13:59.436435Z", + "created_at": "2025-09-03T17:37:49.007525Z", "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, @@ 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index 7b1d5261e..e2e32da73 100644 --- a/tests/integration/recordings/responses/7e6806cba34a.json +++ b/tests/integration/recordings/responses/7e6806cba34a.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:14.382398152Z", + "created_at": "2025-09-03T17:41:43.22891Z", "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": "2025-07-31T17:59:14.561084788Z", + "created_at": "2025-09-03T17:41:43.268911Z", "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": "2025-07-31T17:59:14.743154167Z", + "created_at": "2025-09-03T17:41:43.310121Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ 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b/tests/integration/recordings/responses/80e4404d8987.json index 8cfe1836d..7eabfc363 100644 --- a/tests/integration/recordings/responses/80e4404d8987.json +++ b/tests/integration/recordings/responses/80e4404d8987.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "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", - 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"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__": 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+ "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__": 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"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__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.157953Z", + "created_at": "2025-09-03T17:37:55.384951Z", "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": "2025-08-01T23:14:10.210869Z", + "created_at": "2025-09-03T17:37:55.426563Z", "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": "2025-08-01T23:14:10.263387Z", + "created_at": "2025-09-03T17:37:55.467648Z", "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": "2025-08-01T23:14:10.317794Z", + "created_at": "2025-09-03T17:37:55.509469Z", "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": "2025-08-01T23:14:10.373978Z", + "created_at": "2025-09-03T17:37:55.552302Z", "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": "2025-08-01T23:14:10.429702Z", + "created_at": "2025-09-03T17:37:55.596236Z", "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": "2025-08-01T23:14:10.483762Z", + "created_at": "2025-09-03T17:37:55.637816Z", "done": true, "done_reason": "stop", - "total_duration": 1041142084, - "load_duration": 110407459, + "total_duration": 726849208, + "load_duration": 147625750, "prompt_eval_count": 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"done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.401272Z", + "created_at": "2025-09-03T17:34:23.520282Z", "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": "2025-07-29T23:46:36.444321Z", + "created_at": "2025-09-03T17:34:23.561947Z", "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": "2025-07-29T23:46:36.48795Z", + "created_at": "2025-09-03T17:34:23.603986Z", "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": 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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, - 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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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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" + } + ], + "model": "all-minilm:l6-v2", + "object": "list", + "usage": { + "prompt_tokens": 6, + "total_tokens": 6 + } + } + }, + "is_streaming": false + } +} 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, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.39284Z", + "created_at": "2025-09-03T17:36:21.588896Z", "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": "2025-08-04T22:55:15.44438Z", + "created_at": "2025-09-03T17:36:21.629146Z", "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": "2025-08-04T22:55:15.497561Z", + "created_at": "2025-09-03T17:36:21.669722Z", "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": "2025-08-04T22:55:15.550461Z", + "created_at": "2025-09-03T17:36:21.710707Z", "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": "2025-08-04T22:55:15.599866Z", + "created_at": "2025-09-03T17:36:21.751267Z", "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": "2025-08-04T22:55:15.651899Z", + "created_at": "2025-09-03T17:36:21.791565Z", "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": "2025-08-04T22:55:15.702896Z", + "created_at": "2025-09-03T17:36:21.83176Z", "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": "2025-08-04T22:55:15.75492Z", + "created_at": "2025-09-03T17:36:21.872029Z", "done": false, "done_reason": null, "total_duration": null, @@ -363,7 +363,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.805824Z", + "created_at": "2025-09-03T17:36:21.914066Z", "done": false, "done_reason": null, "total_duration": null, @@ -381,7 +381,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.8564Z", + "created_at": "2025-09-03T17:36:21.955317Z", "done": false, "done_reason": null, "total_duration": null, @@ -399,7 +399,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.907374Z", + "created_at": "2025-09-03T17:36:21.995588Z", "done": false, "done_reason": null, "total_duration": null, @@ -417,7 +417,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.959599Z", + "created_at": "2025-09-03T17:36:22.03605Z", "done": false, "done_reason": null, "total_duration": null, @@ -435,7 +435,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.012545Z", + "created_at": "2025-09-03T17:36:22.076924Z", "done": false, "done_reason": null, "total_duration": null, @@ -453,7 +453,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.065508Z", + "created_at": "2025-09-03T17:36:22.117922Z", "done": false, "done_reason": null, "total_duration": null, @@ -471,7 +471,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.122471Z", + "created_at": "2025-09-03T17:36:22.158925Z", "done": false, "done_reason": null, "total_duration": null, @@ -489,7 +489,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.175606Z", + "created_at": "2025-09-03T17:36:22.199113Z", "done": false, "done_reason": null, "total_duration": null, @@ -507,7 +507,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.227171Z", + "created_at": "2025-09-03T17:36:22.239797Z", "done": false, "done_reason": null, "total_duration": null, @@ -525,7 +525,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.278522Z", + "created_at": "2025-09-03T17:36:22.280592Z", "done": false, "done_reason": null, "total_duration": null, @@ -543,7 +543,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.329492Z", + "created_at": "2025-09-03T17:36:22.321607Z", "done": false, "done_reason": null, "total_duration": null, @@ -561,7 +561,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.381232Z", + "created_at": "2025-09-03T17:36:22.36237Z", "done": false, "done_reason": null, "total_duration": null, @@ -579,7 +579,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.43463Z", + "created_at": "2025-09-03T17:36:22.402735Z", "done": false, "done_reason": null, "total_duration": null, @@ -597,7 +597,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.483135Z", + "created_at": "2025-09-03T17:36:22.44328Z", "done": false, "done_reason": null, "total_duration": null, @@ -615,7 +615,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.544729Z", + "created_at": "2025-09-03T17:36:22.48369Z", "done": false, "done_reason": null, "total_duration": null, @@ -633,7 +633,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.605218Z", + "created_at": "2025-09-03T17:36:22.524383Z", "done": false, "done_reason": null, "total_duration": null, @@ -651,7 +651,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.660652Z", + "created_at": "2025-09-03T17:36:22.564975Z", "done": false, "done_reason": null, "total_duration": null, @@ -669,7 +669,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.718606Z", + "created_at": 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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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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.142748Z", + "created_at": "2025-09-03T17:36:23.661884Z", "done": false, "done_reason": null, "total_duration": null, @@ -1155,7 +1155,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.198201Z", + "created_at": "2025-09-03T17:36:23.703234Z", "done": false, "done_reason": null, "total_duration": null, @@ -1173,7 +1173,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.247029Z", + "created_at": "2025-09-03T17:36:23.743994Z", "done": false, "done_reason": null, "total_duration": null, @@ -1191,7 +1191,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.298673Z", + "created_at": "2025-09-03T17:36:23.784238Z", "done": false, "done_reason": 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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__": 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"done": false, "done_reason": null, "total_duration": null, @@ -1407,7 +1407,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.922652Z", + "created_at": "2025-09-03T17:36:24.275212Z", "done": false, "done_reason": null, "total_duration": null, @@ -1425,7 +1425,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.974032Z", + "created_at": "2025-09-03T17:36:24.315722Z", "done": false, "done_reason": null, "total_duration": null, @@ -1443,7 +1443,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.025272Z", + "created_at": "2025-09-03T17:36:24.355996Z", "done": false, "done_reason": null, "total_duration": null, @@ -1461,7 +1461,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "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": null, @@ -1533,7 +1533,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.280582Z", + "created_at": "2025-09-03T17:36:24.562228Z", "done": false, "done_reason": null, "total_duration": null, @@ -1551,7 +1551,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.330127Z", + "created_at": "2025-09-03T17:36:24.604366Z", "done": false, "done_reason": null, "total_duration": null, @@ -1569,7 +1569,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.379656Z", + "created_at": "2025-09-03T17:36:24.645258Z", "done": false, "done_reason": null, "total_duration": null, @@ -1587,7 +1587,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.430197Z", + 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.630956Z", + "created_at": "2025-09-03T17:36:24.846655Z", "done": false, "done_reason": null, "total_duration": null, @@ -1677,7 +1677,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.682434Z", + "created_at": "2025-09-03T17:36:24.886602Z", "done": false, "done_reason": null, "total_duration": null, @@ -1695,7 +1695,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.731714Z", + "created_at": "2025-09-03T17:36:24.926582Z", "done": false, "done_reason": null, "total_duration": null, @@ -1713,7 +1713,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.780871Z", + "created_at": "2025-09-03T17:36:24.966301Z", "done": false, "done_reason": 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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__": 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"done": false, "done_reason": null, "total_duration": null, @@ -2775,7 +2775,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.55017Z", + "created_at": "2025-09-03T17:36:27.413166Z", "done": false, "done_reason": null, "total_duration": null, @@ -2793,7 +2793,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.600579Z", + "created_at": "2025-09-03T17:36:27.453878Z", "done": false, "done_reason": null, "total_duration": null, @@ -2811,7 +2811,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.654506Z", + "created_at": "2025-09-03T17:36:27.495693Z", "done": false, "done_reason": null, "total_duration": null, @@ -2829,7 +2829,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.709135Z", + "created_at": "2025-09-03T17:36:27.536879Z", "done": false, "done_reason": null, "total_duration": null, @@ -2847,7 +2847,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.760466Z", + "created_at": "2025-09-03T17:36:27.578071Z", "done": false, "done_reason": null, "total_duration": null, @@ -2865,7 +2865,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.813218Z", + "created_at": "2025-09-03T17:36:27.619459Z", "done": false, "done_reason": null, "total_duration": null, @@ -2883,7 +2883,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.865353Z", + "created_at": "2025-09-03T17:36:27.660329Z", "done": false, "done_reason": null, "total_duration": null, @@ -2901,7 +2901,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.922629Z", + "created_at": "2025-09-03T17:36:27.701195Z", "done": false, "done_reason": null, "total_duration": null, @@ -2919,7 +2919,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.975942Z", + "created_at": "2025-09-03T17:36:27.74184Z", "done": false, "done_reason": null, "total_duration": null, @@ -2937,7 +2937,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.028952Z", + "created_at": "2025-09-03T17:36:27.782435Z", "done": false, "done_reason": null, "total_duration": null, @@ -2955,7 +2955,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.086171Z", + "created_at": 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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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- "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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"2025-08-04T22:55:27.806748Z", + "created_at": "2025-09-03T17:36:30.646752Z", "done": false, "done_reason": null, "total_duration": null, @@ -4215,7 +4215,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.859134Z", + "created_at": "2025-09-03T17:36:30.68766Z", "done": false, "done_reason": null, "total_duration": null, @@ -4233,7 +4233,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.911671Z", + "created_at": "2025-09-03T17:36:30.728603Z", "done": false, "done_reason": null, "total_duration": null, @@ -4251,7 +4251,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.964185Z", + "created_at": "2025-09-03T17:36:30.769336Z", "done": false, "done_reason": null, "total_duration": null, @@ -4269,7 +4269,7 @@ "__type__": 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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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"2025-08-04T22:55:30.314187Z", + "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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"__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__": 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"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": 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"__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": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.078626Z", + "created_at": "2025-09-03T17:36:35.45891Z", "done": false, "done_reason": null, "total_duration": null, @@ -6303,7 +6303,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.132072Z", + "created_at": "2025-09-03T17:36:35.501211Z", "done": false, "done_reason": null, "total_duration": null, @@ -6321,7 +6321,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.185534Z", + "created_at": "2025-09-03T17:36:35.543696Z", "done": false, "done_reason": null, "total_duration": null, @@ -6339,7 +6339,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.237811Z", + "created_at": "2025-09-03T17:36:35.584233Z", "done": false, "done_reason": null, "total_duration": null, @@ -6357,7 +6357,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.289202Z", + "created_at": "2025-09-03T17:36:35.626596Z", "done": false, "done_reason": null, "total_duration": null, @@ -6375,7 +6375,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.341588Z", + "created_at": "2025-09-03T17:36:35.667752Z", "done": false, "done_reason": null, "total_duration": null, @@ -6393,7 +6393,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.393213Z", + "created_at": "2025-09-03T17:36:35.70907Z", "done": false, "done_reason": null, "total_duration": null, @@ -6411,7 +6411,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.444819Z", + 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.66848Z", + "created_at": "2025-09-03T17:36:35.915661Z", "done": false, "done_reason": null, "total_duration": null, @@ -6501,7 +6501,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.721915Z", + "created_at": "2025-09-03T17:36:35.95745Z", "done": false, "done_reason": null, "total_duration": null, @@ -6519,7 +6519,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.776127Z", + "created_at": "2025-09-03T17:36:35.998856Z", "done": false, "done_reason": null, "total_duration": null, @@ -6537,7 +6537,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.833308Z", + "created_at": "2025-09-03T17:36:36.040666Z", "done": false, "done_reason": 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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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"2025-09-03T17:36:36.501868Z", "done": false, "done_reason": null, "total_duration": null, @@ -6753,7 +6753,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.464886Z", + "created_at": "2025-09-03T17:36:36.544291Z", "done": false, "done_reason": null, "total_duration": null, @@ -6771,7 +6771,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.515495Z", + "created_at": "2025-09-03T17:36:36.58593Z", "done": false, "done_reason": null, "total_duration": null, @@ -6789,7 +6789,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.566584Z", + "created_at": "2025-09-03T17:36:36.627055Z", "done": false, "done_reason": null, "total_duration": null, @@ -6807,7 +6807,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.616019Z", + "created_at": "2025-09-03T17:36:36.668404Z", "done": false, "done_reason": null, "total_duration": null, @@ -6825,7 +6825,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.669824Z", + "created_at": "2025-09-03T17:36:36.709546Z", "done": false, "done_reason": null, "total_duration": null, @@ -6843,7 +6843,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.724262Z", + "created_at": "2025-09-03T17:36:36.750533Z", "done": false, "done_reason": null, "total_duration": null, @@ -6861,7 +6861,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.779373Z", + "created_at": "2025-09-03T17:36:36.792039Z", "done": false, "done_reason": null, "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__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.229749Z", + "created_at": "2025-09-03T17:36:37.125478Z", "done": false, "done_reason": null, "total_duration": null, @@ -7023,7 +7023,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.287566Z", + "created_at": "2025-09-03T17:36:37.166749Z", "done": false, "done_reason": null, "total_duration": null, @@ -7041,7 +7041,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.343992Z", + "created_at": "2025-09-03T17:36:37.207713Z", "done": false, "done_reason": null, "total_duration": null, @@ -7059,7 +7059,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:36.402701Z", + "created_at": "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 new file mode 100644 index 000000000..b22354c20 --- /dev/null +++ b/tests/integration/recordings/responses/d86d4fc1eaca.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": [ + "machine learning and artificial intelligence" + ], + "encoding_format": "float" + }, + "endpoint": "/v1/embeddings", + "model": "all-minilm:l6-v2" + }, + "response": { + "body": { + "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse", + "__data__": { + "data": [ + { + "embedding": [ + -0.04308226, + 0.008707138, + 0.06876158, + 0.018115537, + 0.04603657, + 0.0026118131, + -0.0032358477, + -0.041284926, + -0.09074888, + -0.033087812, + -0.026611822, + 0.0077352105, + 0.020191023, + -0.03254043, + -0.035847843, + 0.031108031, + -0.039247137, + 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b/tests/integration/recordings/responses/dac7a32e5db9.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:50:00.921192644Z", + "created_at": "2025-09-03T17:39:36.919474Z", "done": true, "done_reason": "stop", - "total_duration": 2073152067, - "load_duration": 42902450, + "total_duration": 470635833, + "load_duration": 113755958, "prompt_eval_count": 23, - "prompt_eval_duration": 795517987, + "prompt_eval_duration": 67480542, "eval_count": 8, - "eval_duration": 1234259942, + "eval_duration": 288746541, "response": "The capital of France is Paris.", "thinking": null, "context": null diff --git 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", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/e0a6dce1d94b.json b/tests/integration/recordings/responses/e0a6dce1d94b.json new file mode 100644 index 000000000..4a285b30b --- /dev/null +++ b/tests/integration/recordings/responses/e0a6dce1d94b.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": [ + "This is a test file 2" + ], + "encoding_format": "float" + }, + "endpoint": "/v1/embeddings", + "model": "all-minilm:l6-v2" + }, + "response": { + "body": { + "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse", + "__data__": { + "data": [ + { + "embedding": [ + -0.028407024, + 0.08176727, + -0.07856116, + 0.027924549, + 0.05008439, + -0.035268802, + -0.0040619136, + 0.029315198, + -0.05775003, + 0.013769637, + 0.14610882, + -0.012019041, + -0.024392882, + -0.05509032, + -0.02661779, + -0.013253934, + -0.109151706, + -0.037233494, + -0.0036058167, + 0.04766495, + 0.06212885, + 0.0070259646, + -0.015513743, + -0.008010851, + 0.037648663, + 0.01587603, + -0.041856695, + 0.09732178, + -0.025641596, + -0.11368298, + 0.03550726, + 0.07043342, + 0.016779423, + 0.02220752, + 0.123395406, + 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-0.036182683, + 0.029673496, + -0.017278075, + -0.09458994, + -0.057882637, + -0.06579892, + -0.06124832, + -0.10455079, + -0.02925637, + 0.0013624659, + 0.0060532107, + 0.04077331, + -0.036694046, + 0.016800206, + 0.005279432, + 0.030968234, + -0.05446385, + 0.0048696757, + 0.070877954, + 0.06684445, + 0.017715273, + -0.029237686 + ], + "index": 0, + "object": "embedding" + } + ], + "model": "all-minilm:l6-v2", + "object": "list", + "usage": { + "prompt_tokens": 6, + "total_tokens": 6 + } + } + }, + "is_streaming": false + } +} 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. The background is out of focus, but it appears to be a grassy field.", "thinking": null, "images": null, "tool_calls": null diff --git a/tests/integration/recordings/responses/f477c2fe1332.json b/tests/integration/recordings/responses/f477c2fe1332.json index 2e29690ee..d3c8e7176 100644 --- a/tests/integration/recordings/responses/f477c2fe1332.json +++ b/tests/integration/recordings/responses/f477c2fe1332.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.046199Z", + "created_at": "2025-09-03T17:42:31.583665Z", "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": "2025-08-01T23:14:37.097228Z", + "created_at": "2025-09-03T17:42:31.625653Z", "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": "2025-08-01T23:14:37.147575Z", + "created_at": "2025-09-03T17:42:31.667189Z", "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": "2025-08-01T23:14:37.199038Z", + "created_at": "2025-09-03T17:42:31.708905Z", "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": "2025-08-01T23:14:37.25106Z", + "created_at": "2025-09-03T17:42:31.751003Z", "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": "2025-08-01T23:14:37.302712Z", + "created_at": "2025-09-03T17:42:31.792516Z", "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": "2025-08-01T23:14:37.355658Z", + "created_at": "2025-09-03T17:42:31.834194Z", "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": "2025-08-01T23:14:37.407436Z", + "created_at": "2025-09-03T17:42:31.878321Z", "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": "2025-08-01T23:14:37.459062Z", + "created_at": "2025-09-03T17:42:31.921552Z", "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": "2025-08-01T23:14:37.511804Z", + "created_at": "2025-09-03T17:42:31.963105Z", "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": "2025-08-01T23:14:37.562406Z", + "created_at": "2025-09-03T17:42:32.005494Z", "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": "2025-08-01T23:14:37.614648Z", + "created_at": "2025-09-03T17:42:32.047231Z", "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": "2025-08-01T23:14:37.665414Z", + "created_at": "2025-09-03T17:42:32.089031Z", "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": "2025-08-01T23:14:37.71826Z", + "created_at": "2025-09-03T17:42:32.130704Z", "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": "2025-08-01T23:14:37.769822Z", + "created_at": "2025-09-03T17:42:32.172183Z", "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": "2025-08-01T23:14:37.821049Z", + "created_at": "2025-09-03T17:42:32.21392Z", "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": "2025-08-01T23:14:37.872903Z", + "created_at": "2025-09-03T17:42:32.255392Z", "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": "2025-08-01T23:14:37.924976Z", + "created_at": "2025-09-03T17:42:32.297249Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,7 +346,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.976776Z", + "created_at": "2025-09-03T17:42:32.341358Z", "done": false, "done_reason": null, "total_duration": null, @@ -364,7 +364,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.029285Z", + "created_at": "2025-09-03T17:42:32.384155Z", "done": false, "done_reason": null, "total_duration": null, @@ -382,15 +382,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.084154Z", + "created_at": "2025-09-03T17:42:32.426441Z", "done": true, "done_reason": "stop", - "total_duration": 1782717042, - "load_duration": 78612834, + "total_duration": 1659557917, + "load_duration": 75341875, "prompt_eval_count": 375, - "prompt_eval_duration": 658000000, + "prompt_eval_duration": 740178250, "eval_count": 21, - "eval_duration": 1044000000, + "eval_duration": 843394541, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/f6d655e91ac3.json b/tests/integration/recordings/responses/f6d655e91ac3.json new file mode 100644 index 000000000..185fff181 --- /dev/null +++ b/tests/integration/recordings/responses/f6d655e91ac3.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": [ + "This is a test file" + ], + "encoding_format": "float" + }, + "endpoint": 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0.07488009, + 0.06460031, + 0.044803377, + -0.028485151 + ], + "index": 0, + "object": "embedding" + } + ], + "model": "all-minilm:l6-v2", + "object": "list", + "usage": { + "prompt_tokens": 5, + "total_tokens": 5 + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/f70f30f54211.json b/tests/integration/recordings/responses/f70f30f54211.json index e0ea9c016..c4dd90e68 100644 --- a/tests/integration/recordings/responses/f70f30f54211.json +++ b/tests/integration/recordings/responses/f70f30f54211.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", @@ -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", 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], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-971", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "I'm happy to help you with testing the test API for OpenAI's Model 0, but I need to clarify a few things.\n\nOpenAI's Model 0 is an early version of their AI model, and it's not publicly available. However, I can simulate some interactions with a hypothetical API that might be similar to what they provide.\n\nHere's an example test:\n```\nPOST /test HTTP/1.1\nHost: 0 api.openai.com\n\nContent-Type: application/json\n\n{\n \"text\": \"This is a prompt for testing the Model 0 API\"\n}\n```\n\nPlease note that this is not an official API, and you should not try to interact with it directly. However, I can simulate a response for you:\n\n```\nHTTP/1.1 200 OK\nContent-Type: application/json\n\n{\n \"complete\": false,\n \"error\": null\n}\n```\n\nIn a real-world scenario, the Model 0 API would likely respond with much more complex and accurate results. For example:\n\n```\nHTTP/1.1 200 OK\nContent-Type: application/json\n\n{\n \"id\": \"\",\n \"text\": {\n \"parent_id\": \"\",\n \"text\": \"I can generate text similar to human writing.\"\n }\n}\n```", - "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": 272, - "prompt_tokens": 31, - "total_tokens": 303, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/4a3a4447b16b.json b/tests/integration/recordings/vision/responses/4a3a4447b16b.json deleted file mode 100644 index a99e1fcc3..000000000 --- a/tests/integration/recordings/vision/responses/4a3a4447b16b.json +++ /dev/null @@ -1,68 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/tags", - "headers": {}, - "body": {}, - "endpoint": "/api/tags", - "model": "" - }, - "response": { - "body": { - "__type__": "ollama._types.ListResponse", - "__data__": { - "models": [ - { - "model": "nomic-embed-text:latest", - "modified_at": "2025-07-31T23:55:40.635067Z", - "digest": "0a109f422b47e3a30ba2b10eca18548e944e8a23073ee3f3e947efcf3c45e59f", - "size": 274302450, - "details": { - "parent_model": "", - "format": "gguf", - "family": "nomic-bert", - "families": [ - "nomic-bert" - ], - "parameter_size": "137M", - "quantization_level": "F16" - } - }, - { - "model": "all-minilm:l6-v2", - "modified_at": "2025-07-30T17:18:31Z", - "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef", - "size": 45960996, - "details": { - "parent_model": "", - "format": "gguf", - "family": "bert", - "families": [ - "bert" - ], - "parameter_size": "23M", - "quantization_level": "F16" - } - }, - { - "model": "llama3.2-vision:11b", - "modified_at": "2025-07-30T17:18:21Z", - "digest": "6f2f9757ae97e8a3f8ea33d6adb2b11d93d9a35bef277cd2c0b1b5af8e8d0b1e", - "size": 7816589186, - "details": { - "parent_model": "", - "format": "gguf", - "family": "mllama", - "families": [ - "mllama" - ], - "parameter_size": "10.7B", - "quantization_level": "Q4_K_M" - } - } - ] - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/67198cbad48f.json b/tests/integration/recordings/vision/responses/67198cbad48f.json deleted file mode 100644 index 8326d5329..000000000 --- a/tests/integration/recordings/vision/responses/67198cbad48f.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 OpenAI telemetry creation" - } - ], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__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/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/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py index 1dbd14540..c69cf319b 100644 --- a/tests/unit/distribution/test_inference_recordings.py +++ b/tests/unit/distribution/test_inference_recordings.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. -import sqlite3 import tempfile from pathlib import Path from unittest.mock import patch @@ -133,7 +132,6 @@ class TestInferenceRecording: # Test directory creation assert storage.test_dir.exists() assert storage.responses_dir.exists() - assert storage.db_path.exists() # Test storing and retrieving a recording request_hash = "test_hash_123" @@ -147,15 +145,6 @@ class TestInferenceRecording: storage.store_recording(request_hash, request_data, response_data) - # Verify SQLite record - with sqlite3.connect(storage.db_path) as conn: - result = conn.execute("SELECT * FROM recordings WHERE request_hash = ?", (request_hash,)).fetchone() - - assert result is not None - assert result[0] == request_hash # request_hash - assert result[2] == "/v1/chat/completions" # endpoint - assert result[3] == "llama3.2:3b" # model - # Verify file storage and retrieval retrieved = storage.find_recording(request_hash) assert retrieved is not None @@ -185,10 +174,7 @@ class TestInferenceRecording: # Verify recording was stored storage = ResponseStorage(temp_storage_dir) - with sqlite3.connect(storage.db_path) as conn: - recordings = conn.execute("SELECT COUNT(*) FROM recordings").fetchone()[0] - - assert recordings == 1 + assert storage.responses_dir.exists() async def test_replay_mode(self, temp_storage_dir, real_openai_chat_response): """Test that replay mode returns stored responses without making real calls.""" @@ -280,7 +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( 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..c665bf124 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, ) @@ -249,3 +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/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) 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 a/tests/unit/providers/vector_io/test_qdrant.py b/tests/unit/providers/vector_io/test_qdrant.py index 4207cbee3..aab5b6f45 100644 --- a/tests/unit/providers/vector_io/test_qdrant.py +++ 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, @@ -53,7 +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 @@ -68,7 +71,13 @@ 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() 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 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": 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