Merge branch 'main' into HuggingfacePostTrainingConfig-branch

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
 

docs/source/advanced_apis/post_training/inline_huggingface.md

@@ -35,3 +35,6 @@ device: cpu
 
 ```
 
+[Find more detailed information here!](huggingface.md)
+
+

docs/source/advanced_apis/post_training/inline_torchtune.md

@@ -22,3 +22,4 @@ checkpoint_format: meta
 
 ```
 
+[Find more detailed information here!](torchtune.md)

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 `/<resources>/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
 

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)
+- **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](https://llama-stack.readthedocs.io/en/latest/building_applications/agent_execution_loop.html)
+- **Agent Execution Loop**: For understanding how agents process turns and steps in their execution, see the [Agent Execution Loop documentation](agent_execution_loop.md)

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)

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)

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
 

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)
 

docs/source/distributions/self_hosted_distro/meta-reference-gpu.md

@@ -2,7 +2,7 @@
 orphan: true
 ---
 <!-- This file was auto-generated by distro_codegen.py, please edit source -->
-# 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

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

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"

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)

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:

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_id: torchtune-cpu
-    provider_type: inline::huggingface-cpu
+    provider_type: inline::torchtune-cpu
     config:
-      checkpoint_format: huggingface
+      checkpoint_format: meta
-      distributed_backend: null
-      device: cpu
-      dpo_output_dir: ~/.llama/distributions/ci-tests/dpo_output
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference

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

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:

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_id: huggingface-gpu
-    provider_type: inline::torchtune-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

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

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:

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_id: torchtune-cpu
-    provider_type: inline::huggingface-cpu
+    provider_type: inline::torchtune-cpu
     config:
-      checkpoint_format: huggingface
+      checkpoint_format: meta
-      distributed_backend: null
-      device: cpu
-      dpo_output_dir: ~/.llama/distributions/starter/dpo_output
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference

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"),

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",

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(
-            **{
+            **{  # type: ignore
-                **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(
-            **{
                 **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(
-            **{
+            api=Api.post_training,
-                **huggingface_def,
+            provider_type="inline::huggingface-gpu",
-                "provider_type": "inline::huggingface-gpu",
+            pip_packages=["trl", "transformers", "peft", "datasets", "torch"],
-                "pip_packages": (cast(list[str], huggingface_def["pip_packages"]) + ["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,

llama_stack/testing/inference_recorder.py

@@ -9,7 +9,6 @@ from __future__ import annotations  # for forward references
 import hashlib
 import json
 import os
-import sqlite3
 from collections.abc import Generator
 from contextlib import contextmanager
 from enum import StrEnum
@@ -125,28 +124,13 @@ class ResponseStorage:
     def __init__(self, test_dir: Path):
         self.test_dir = test_dir
         self.responses_dir = self.test_dir / "responses"
-        self.db_path = self.test_dir / "index.sqlite"
 
         self._ensure_directories()
-        self._init_database()
 
     def _ensure_directories(self):
         self.test_dir.mkdir(parents=True, exist_ok=True)
         self.responses_dir.mkdir(exist_ok=True)
 
-    def _init_database(self):
-        with sqlite3.connect(self.db_path) as conn:
-            conn.execute("""
-                CREATE TABLE IF NOT EXISTS recordings (
-                    request_hash TEXT PRIMARY KEY,
-                    response_file TEXT,
-                    endpoint TEXT,
-                    model TEXT,
-                    timestamp TEXT,
-                    is_streaming BOOLEAN
-                )
-            """)
-
     def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]):
         """Store a request/response pair."""
         # Generate unique response filename
@@ -169,34 +153,9 @@ class ResponseStorage:
             f.write("\n")
             f.flush()
 
-        # Update SQLite index
-        with sqlite3.connect(self.db_path) as conn:
-            conn.execute(
-                """
-                INSERT OR REPLACE INTO recordings
-                (request_hash, response_file, endpoint, model, timestamp, is_streaming)
-                VALUES (?, ?, ?, ?, datetime('now'), ?)
-            """,
-                (
-                    request_hash,
-                    response_file,
-                    request.get("endpoint", ""),
-                    request.get("model", ""),
-                    response.get("is_streaming", False),
-                ),
-            )
-
     def find_recording(self, request_hash: str) -> dict[str, Any] | None:
         """Find a recorded response by request hash."""
-        with sqlite3.connect(self.db_path) as conn:
+        response_file = f"{request_hash[:12]}.json"
-            result = conn.execute(
-                "SELECT response_file FROM recordings WHERE request_hash = ?", (request_hash,)
-            ).fetchone()
-
-        if not result:
-            return None
-
-        response_file = result[0]
         response_path = self.responses_dir / response_file
 
         if not response_path.exists():

llama_stack/ui/package-lock.json

@@ -18,7 +18,7 @@
         "class-variance-authority": "^0.7.1",
         "clsx": "^2.1.1",
         "framer-motion": "^11.18.2",
-        "llama-stack-client": "^0.2.18",
+        "llama-stack-client": "^0.2.19",
         "lucide-react": "^0.510.0",
         "next": "15.3.3",
         "next-auth": "^4.24.11",
@@ -36,7 +36,7 @@
         "@eslint/eslintrc": "^3",
         "@tailwindcss/postcss": "^4",
         "@testing-library/dom": "^10.4.1",
-        "@testing-library/jest-dom": "^6.6.3",
+        "@testing-library/jest-dom": "^6.8.0",
         "@testing-library/react": "^16.3.0",
         "@types/jest": "^29.5.14",
         "@types/node": "^20",
@@ -3597,18 +3597,17 @@
       }
     },
     "node_modules/@testing-library/jest-dom": {
-      "version": "6.6.3",
+      "version": "6.8.0",
-      "resolved": "https://registry.npmjs.org/@testing-library/jest-dom/-/jest-dom-6.6.3.tgz",
+      "resolved": "https://registry.npmjs.org/@testing-library/jest-dom/-/jest-dom-6.8.0.tgz",
-      "integrity": "sha512-IteBhl4XqYNkM54f4ejhLRJiZNqcSCoXUOG2CPK7qbD322KjQozM4kHQOfkG2oln9b9HTYqs+Sae8vBATubxxA==",
+      "integrity": "sha512-WgXcWzVM6idy5JaftTVC8Vs83NKRmGJz4Hqs4oyOuO2J4r/y79vvKZsb+CaGyCSEbUPI6OsewfPd0G1A0/TUZQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@adobe/css-tools": "^4.4.0",
         "aria-query": "^5.0.0",
-        "chalk": "^3.0.0",
         "css.escape": "^1.5.1",
         "dom-accessibility-api": "^0.6.3",
-        "lodash": "^4.17.21",
+        "picocolors": "^1.1.1",
         "redent": "^3.0.0"
       },
       "engines": {
@@ -3617,20 +3616,6 @@
         "yarn": ">=1"
       }
     },
-    "node_modules/@testing-library/jest-dom/node_modules/chalk": {
-      "version": "3.0.0",
-      "resolved": "https://registry.npmjs.org/chalk/-/chalk-3.0.0.tgz",
-      "integrity": "sha512-4D3B6Wf41KOYRFdszmDqMCGq5VV/uMAB273JILmO+3jAlh8X4qDtdtgCR3fxtbLEMzSx22QdhnDcJvu2u1fVwg==",
-      "dev": true,
-      "license": "MIT",
-      "dependencies": {
-        "ansi-styles": "^4.1.0",
-        "supports-color": "^7.1.0"
-      },
-      "engines": {
-        "node": ">=8"
-      }
-    },
     "node_modules/@testing-library/jest-dom/node_modules/dom-accessibility-api": {
       "version": "0.6.3",
       "resolved": "https://registry.npmjs.org/dom-accessibility-api/-/dom-accessibility-api-0.6.3.tgz",
@@ -10021,9 +10006,9 @@
       "license": "MIT"
     },
     "node_modules/llama-stack-client": {
-      "version": "0.2.18",
+      "version": "0.2.19",
-      "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.18.tgz",
+      "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.19.tgz",
-      "integrity": "sha512-k+xQOz/TIU0cINP4Aih8q6xs7f/6qs0fLDMXTTKQr5C0F1jtCjRiwsas7bTsDfpKfYhg/7Xy/wPw/uZgi6aIVg==",
+      "integrity": "sha512-sDuAhUdEGlERZ3jlMUzPXcQTgMv/pGbDrPX0ifbE5S+gr7Q+7ohuQYrIXe+hXgIipFjq+y4b2c5laZ76tmAyEA==",
       "license": "MIT",
       "dependencies": {
         "@types/node": "^18.11.18",
@@ -10066,13 +10051,6 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
-    "node_modules/lodash": {
-      "version": "4.17.21",
-      "resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.21.tgz",
-      "integrity": "sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==",
-      "dev": true,
-      "license": "MIT"
-    },
     "node_modules/lodash.merge": {
       "version": "4.6.2",
       "resolved": "https://registry.npmjs.org/lodash.merge/-/lodash.merge-4.6.2.tgz",

llama_stack/ui/package.json

@@ -23,7 +23,7 @@
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",
     "framer-motion": "^11.18.2",
-    "llama-stack-client": "^0.2.18",
+    "llama-stack-client": "^0.2.19",
     "lucide-react": "^0.510.0",
     "next": "15.3.3",
     "next-auth": "^4.24.11",
@@ -41,7 +41,7 @@
     "@eslint/eslintrc": "^3",
     "@tailwindcss/postcss": "^4",
     "@testing-library/dom": "^10.4.1",
-    "@testing-library/jest-dom": "^6.6.3",
+    "@testing-library/jest-dom": "^6.8.0",
     "@testing-library/react": "^16.3.0",
     "@types/jest": "^29.5.14",
     "@types/node": "^20",

pyproject.toml

@@ -7,7 +7,7 @@ required-version = ">=0.7.0"
 
 [project]
 name = "llama_stack"
-version = "0.2.18"
+version = "0.2.19"
 authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }]
 description = "Llama Stack"
 readme = "README.md"
@@ -31,7 +31,7 @@ dependencies = [
     "huggingface-hub>=0.34.0,<1.0",
     "jinja2>=3.1.6",
     "jsonschema",
-    "llama-stack-client>=0.2.18",
+    "llama-stack-client>=0.2.19",
     "llama-api-client>=0.1.2",
     "openai>=1.99.6,<1.100.0",
     "prompt-toolkit",
@@ -56,7 +56,7 @@ dependencies = [
 ui = [
     "streamlit",
     "pandas",
-    "llama-stack-client>=0.2.18",
+    "llama-stack-client>=0.2.19",
     "streamlit-option-menu",
 ]
 

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:
+        assert storage.responses_dir.exists()
-            recordings = conn.execute("SELECT COUNT(*) FROM recordings").fetchone()[0]
-
-        assert recordings == 1
 
     async def test_replay_mode(self, temp_storage_dir, real_openai_chat_response):
         """Test that replay mode returns stored responses without making real calls."""

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