Merge branch 'main' into feat/litellm_sambanova_usage

.github/TRIAGERS.md

@@ -0,0 +1,2 @@
+# This file documents Triage members in the Llama Stack community
+@franciscojavierarceo @leseb

.github/workflows/changelog.yml

@@ -0,0 +1,29 @@
+name: Update Changelog
+
+on:
+  release:
+    types: [published, unpublished, created, edited, deleted, released]
+
+permissions:
+  contents: read
+
+jobs:
+  generate_changelog:
+    name: Generate changelog
+    permissions:
+      contents: write  # for peter-evans/create-pull-request to create branch
+      pull-requests: write  # for peter-evans/create-pull-request to create a PR
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: main
+          fetch-depth: 0
+      - run: |
+          python ./scripts/gen-changelog.py
+      - uses: peter-evans/create-pull-request@v7
+        with:
+          title: 'docs: update CHANGELOG.md for ${{ github.ref_name }}'
+          commit-message: 'docs: update CHANGELOG.md for ${{ github.ref_name }}'
+          branch: create-pull-request/changelog
+          signoff: true

.github/workflows/integration-tests.yml

@@ -1,4 +1,4 @@
-name: Integration tests
+name: Integration Tests
 
 on:
   push:

CONTRIBUTING.md

@@ -86,7 +86,7 @@ LLAMA_STACK_CONFIG=
 
 And then use this dotenv file when running client SDK tests via the following:
 ```bash
-uv run --env-file .env -- pytest -v tests/api/inference/test_text_inference.py
+uv run --env-file .env -- pytest -v tests/integration/inference/test_text_inference.py
 ```
 
 ## Pre-commit Hooks

README.md

@@ -4,7 +4,8 @@
 [![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-stack)](https://pypi.org/project/llama-stack/)
 [![License](https://img.shields.io/pypi/l/llama_stack.svg)](https://github.com/meta-llama/llama-stack/blob/main/LICENSE)
 [![Discord](https://img.shields.io/discord/1257833999603335178)](https://discord.gg/llama-stack)
-![Unit](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml/badge.svg?branch=main)
+[![Unit Tests](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml/badge.svg?branch=main)](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml?query=branch%3Amain)
+[![Integration Tests](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml/badge.svg?branch=main)](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml?query=branch%3Amain)
 
 [**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb)
 
@@ -72,26 +73,6 @@ A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider
 |                   Fireworks                   |                    [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general)                    |          [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/fireworks.html)           |
 | vLLM |                  [llamastack/distribution-remote-vllm](https://hub.docker.com/repository/docker/llamastack/distribution-remote-vllm/general)                  |         [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/remote-vllm.html)          |
 
-### Installation
-
-You have two ways to install this repository:
-
-* **Install as a package**:
-   You can install the repository directly from [PyPI](https://pypi.org/project/llama-stack/) by running the following command:
-   ```bash
-   pip install llama-stack
-   ```
-
-* **Install from source**:
-   If you prefer to install from the source code, we recommend using [uv](https://github.com/astral-sh/uv).
-   Then, run the following commands:
-   ```bash
-    git clone git@github.com:meta-llama/llama-stack.git
-    cd llama-stack
-
-    uv sync
-    uv pip install -e .
-   ```
 
 ### Documentation
 

docs/_static/llama-stack-spec.html

@@ -1101,14 +1101,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/Benchmark"
-                                        {
-                                            "$ref": "#/components/schemas/Benchmark"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -1150,14 +1143,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/Dataset"
-                                        {
-                                            "$ref": "#/components/schemas/Dataset"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -1232,14 +1218,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/Model"
-                                        {
-                                            "$ref": "#/components/schemas/Model"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -1314,14 +1293,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/ScoringFn"
-                                        {
-                                            "$ref": "#/components/schemas/ScoringFn"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -1363,14 +1335,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/Shield"
-                                        {
-                                            "$ref": "#/components/schemas/Shield"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -1673,14 +1638,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/PostTrainingJobArtifactsResponse"
-                                        {
-                                            "$ref": "#/components/schemas/PostTrainingJobArtifactsResponse"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -1722,14 +1680,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/PostTrainingJobStatusResponse"
-                                        {
-                                            "$ref": "#/components/schemas/PostTrainingJobStatusResponse"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -1804,14 +1755,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/FileUploadResponse"
-                                        {
-                                            "$ref": "#/components/schemas/FileUploadResponse"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -1913,14 +1857,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/VectorDB"
-                                        {
-                                            "$ref": "#/components/schemas/VectorDB"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -2246,14 +2183,7 @@
                         "content": {
                             "application/json": {
                                 "schema": {
-                                    "oneOf": [
+                                    "$ref": "#/components/schemas/JobStatus"
-                                        {
-                                            "$ref": "#/components/schemas/JobStatus"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
                                 }
                             }
                         }
@@ -4229,70 +4159,80 @@
                         ]
                     },
                     "arguments": {
-                        "type": "object",
+                        "oneOf": [
-                        "additionalProperties": {
+                            {
-                            "oneOf": [
+                                "type": "string"
-                                {
+                            },
-                                    "type": "string"
+                            {
-                                },
+                                "type": "object",
-                                {
+                                "additionalProperties": {
-                                    "type": "integer"
+                                    "oneOf": [
-                                },
+                                        {
-                                {
+                                            "type": "string"
-                                    "type": "number"
+                                        },
-                                },
+                                        {
-                                {
+                                            "type": "integer"
-                                    "type": "boolean"
+                                        },
-                                },
+                                        {
-                                {
+                                            "type": "number"
-                                    "type": "null"
+                                        },
-                                },
+                                        {
-                                {
+                                            "type": "boolean"
-                                    "type": "array",
+                                        },
-                                    "items": {
+                                        {
-                                        "oneOf": [
+                                            "type": "null"
-                                            {
+                                        },
-                                                "type": "string"
+                                        {
-                                            },
+                                            "type": "array",
-                                            {
+                                            "items": {
-                                                "type": "integer"
+                                                "oneOf": [
-                                            },
+                                                    {
-                                            {
+                                                        "type": "string"
-                                                "type": "number"
+                                                    },
-                                            },
+                                                    {
-                                            {
+                                                        "type": "integer"
-                                                "type": "boolean"
+                                                    },
-                                            },
+                                                    {
-                                            {
+                                                        "type": "number"
-                                                "type": "null"
+                                                    },
+                                                    {
+                                                        "type": "boolean"
+                                                    },
+                                                    {
+                                                        "type": "null"
+                                                    }
+                                                ]
                                             }
-                                        ]
+                                        },
-                                    }
+                                        {
-                                },
+                                            "type": "object",
-                                {
+                                            "additionalProperties": {
-                                    "type": "object",
+                                                "oneOf": [
-                                    "additionalProperties": {
+                                                    {
-                                        "oneOf": [
+                                                        "type": "string"
-                                            {
+                                                    },
-                                                "type": "string"
+                                                    {
-                                            },
+                                                        "type": "integer"
-                                            {
+                                                    },
-                                                "type": "integer"
+                                                    {
-                                            },
+                                                        "type": "number"
-                                            {
+                                                    },
-                                                "type": "number"
+                                                    {
-                                            },
+                                                        "type": "boolean"
-                                            {
+                                                    },
-                                                "type": "boolean"
+                                                    {
-                                            },
+                                                        "type": "null"
-                                            {
+                                                    }
-                                                "type": "null"
+                                                ]
                                             }
-                                        ]
+                                        }
-                                    }
+                                    ]
                                 }
-                            ]
+                            }
-                        }
+                        ]
+                    },
+                    "arguments_json": {
+                        "type": "string"
                     }
                 },
                 "additionalProperties": false,
@@ -7857,7 +7797,8 @@
                 "type": "object",
                 "properties": {
                     "document_id": {
-                        "type": "string"
+                        "type": "string",
+                        "description": "The unique identifier for the document."
                     },
                     "content": {
                         "oneOf": [
@@ -7876,10 +7817,12 @@
                             {
                                 "$ref": "#/components/schemas/URL"
                             }
-                        ]
+                        ],
+                        "description": "The content of the document."
                     },
                     "mime_type": {
-                        "type": "string"
+                        "type": "string",
+                        "description": "The MIME type of the document."
                     },
                     "metadata": {
                         "type": "object",
@@ -7904,7 +7847,8 @@
                                     "type": "object"
                                 }
                             ]
-                        }
+                        },
+                        "description": "Additional metadata for the document."
                     }
                 },
                 "additionalProperties": false,
@@ -7913,7 +7857,8 @@
                     "content",
                     "metadata"
                 ],
-                "title": "RAGDocument"
+                "title": "RAGDocument",
+                "description": "A document to be used for document ingestion in the RAG Tool."
             },
             "InsertRequest": {
                 "type": "object",
@@ -9917,23 +9862,6 @@
                 ],
                 "title": "ScoreBatchResponse"
             },
-            "AlgorithmConfig": {
-                "oneOf": [
-                    {
-                        "$ref": "#/components/schemas/LoraFinetuningConfig"
-                    },
-                    {
-                        "$ref": "#/components/schemas/QATFinetuningConfig"
-                    }
-                ],
-                "discriminator": {
-                    "propertyName": "type",
-                    "mapping": {
-                        "LoRA": "#/components/schemas/LoraFinetuningConfig",
-                        "QAT": "#/components/schemas/QATFinetuningConfig"
-                    }
-                }
-            },
             "LoraFinetuningConfig": {
                 "type": "object",
                 "properties": {
@@ -10069,7 +9997,14 @@
                         "type": "string"
                     },
                     "algorithm_config": {
-                        "$ref": "#/components/schemas/AlgorithmConfig"
+                        "oneOf": [
+                            {
+                                "$ref": "#/components/schemas/LoraFinetuningConfig"
+                            },
+                            {
+                                "$ref": "#/components/schemas/QATFinetuningConfig"
+                            }
+                        ]
                     }
                 },
                 "additionalProperties": false,

docs/_static/llama-stack-spec.yaml

@@ -757,9 +757,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/Benchmark'
-                  - $ref: '#/components/schemas/Benchmark'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -787,9 +785,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/Dataset'
-                  - $ref: '#/components/schemas/Dataset'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -840,9 +836,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/Model'
-                  - $ref: '#/components/schemas/Model'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -893,9 +887,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/ScoringFn'
-                  - $ref: '#/components/schemas/ScoringFn'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -923,9 +915,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/Shield'
-                  - $ref: '#/components/schemas/Shield'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -1127,9 +1117,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/PostTrainingJobArtifactsResponse'
-                  - $ref: '#/components/schemas/PostTrainingJobArtifactsResponse'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -1157,9 +1145,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/PostTrainingJobStatusResponse'
-                  - $ref: '#/components/schemas/PostTrainingJobStatusResponse'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -1210,9 +1196,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/FileUploadResponse'
-                  - $ref: '#/components/schemas/FileUploadResponse'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -1281,9 +1265,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/VectorDB'
-                  - $ref: '#/components/schemas/VectorDB'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -1509,9 +1491,7 @@ paths:
           content:
             application/json:
               schema:
-                oneOf:
+                $ref: '#/components/schemas/JobStatus'
-                  - $ref: '#/components/schemas/JobStatus'
-                  - type: 'null'
         '400':
           $ref: '#/components/responses/BadRequest400'
         '429':
@@ -2884,30 +2864,34 @@ components:
               title: BuiltinTool
             - type: string
         arguments:
-          type: object
+          oneOf:
-          additionalProperties:
+            - type: string
-            oneOf:
+            - type: object
-              - type: string
+              additionalProperties:
-              - type: integer
+                oneOf:
-              - type: number
+                  - type: string
-              - type: boolean
+                  - type: integer
-              - type: 'null'
+                  - type: number
-              - type: array
+                  - type: boolean
-                items:
+                  - type: 'null'
-                  oneOf:
+                  - type: array
-                    - type: string
+                    items:
-                    - type: integer
+                      oneOf:
-                    - type: number
+                        - type: string
-                    - type: boolean
+                        - type: integer
-                    - type: 'null'
+                        - type: number
-              - type: object
+                        - type: boolean
-                additionalProperties:
+                        - type: 'null'
-                  oneOf:
+                  - type: object
-                    - type: string
+                    additionalProperties:
-                    - type: integer
+                      oneOf:
-                    - type: number
+                        - type: string
-                    - type: boolean
+                        - type: integer
-                    - type: 'null'
+                        - type: number
+                        - type: boolean
+                        - type: 'null'
+        arguments_json:
+          type: string
       additionalProperties: false
       required:
         - call_id
@@ -5395,6 +5379,7 @@ components:
       properties:
         document_id:
           type: string
+          description: The unique identifier for the document.
         content:
           oneOf:
             - type: string
@@ -5403,8 +5388,10 @@ components:
               items:
                 $ref: '#/components/schemas/InterleavedContentItem'
             - $ref: '#/components/schemas/URL'
+          description: The content of the document.
         mime_type:
           type: string
+          description: The MIME type of the document.
         metadata:
           type: object
           additionalProperties:
@@ -5415,12 +5402,15 @@ components:
               - type: string
               - type: array
               - type: object
+          description: Additional metadata for the document.
       additionalProperties: false
       required:
         - document_id
         - content
         - metadata
       title: RAGDocument
+      description: >-
+        A document to be used for document ingestion in the RAG Tool.
     InsertRequest:
       type: object
       properties:
@@ -6698,15 +6688,6 @@ components:
       required:
         - results
       title: ScoreBatchResponse
-    AlgorithmConfig:
-      oneOf:
-        - $ref: '#/components/schemas/LoraFinetuningConfig'
-        - $ref: '#/components/schemas/QATFinetuningConfig'
-      discriminator:
-        propertyName: type
-        mapping:
-          LoRA: '#/components/schemas/LoraFinetuningConfig'
-          QAT: '#/components/schemas/QATFinetuningConfig'
     LoraFinetuningConfig:
       type: object
       properties:
@@ -6790,7 +6771,9 @@ components:
         checkpoint_dir:
           type: string
         algorithm_config:
-          $ref: '#/components/schemas/AlgorithmConfig'
+          oneOf:
+            - $ref: '#/components/schemas/LoraFinetuningConfig'
+            - $ref: '#/components/schemas/QATFinetuningConfig'
       additionalProperties: false
       required:
         - job_uuid

docs/openapi_generator/generate.py

@@ -12,7 +12,7 @@
 
 from datetime import datetime
 from pathlib import Path
-
+import sys
 import fire
 import ruamel.yaml as yaml
 
@@ -21,7 +21,7 @@ from llama_stack.distribution.stack import LlamaStack  # noqa: E402
 
 from .pyopenapi.options import Options  # noqa: E402
 from .pyopenapi.specification import Info, Server  # noqa: E402
-from .pyopenapi.utility import Specification  # noqa: E402
+from .pyopenapi.utility import Specification, validate_api_method_return_types  # noqa: E402
 
 
 def str_presenter(dumper, data):
@@ -39,6 +39,14 @@ def main(output_dir: str):
     if not output_dir.exists():
         raise ValueError(f"Directory {output_dir} does not exist")
 
+    # Validate API protocols before generating spec
+    print("Validating API method return types...")
+    return_type_errors = validate_api_method_return_types()
+    if return_type_errors:
+        print("\nAPI Method Return Type Validation Errors:\n")
+        for error in return_type_errors:
+            print(error)
+        sys.exit(1)
     now = str(datetime.now())
     print(
         "Converting the spec to YAML (openapi.yaml) and HTML (openapi.html) at " + now

docs/openapi_generator/pyopenapi/utility.py

@@ -6,16 +6,19 @@
 
 import json
 import typing
+import inspect
+import os
 from pathlib import Path
 from typing import TextIO
+from typing import Any, Dict, List, Optional, Protocol, Type, Union, get_type_hints, get_origin, get_args
 
 from llama_stack.strong_typing.schema import object_to_json, StrictJsonType
+from llama_stack.distribution.resolver import api_protocol_map
 
 from .generator import Generator
 from .options import Options
 from .specification import Document
 
-
 THIS_DIR = Path(__file__).parent
 
 
@@ -114,3 +117,37 @@ class Specification:
         )
 
         f.write(html)
+
+def is_optional_type(type_: Any) -> bool:
+    """Check if a type is Optional."""
+    origin = get_origin(type_)
+    args = get_args(type_)
+    return origin is Optional or (origin is Union and type(None) in args)
+
+
+def validate_api_method_return_types() -> List[str]:
+    """Validate that all API methods have proper return types."""
+    errors = []
+    protocols = api_protocol_map()
+
+    for protocol_name, protocol in protocols.items():
+        methods = inspect.getmembers(protocol, predicate=inspect.isfunction)
+
+        for method_name, method in methods:
+            if not hasattr(method, '__webmethod__'):
+                continue
+
+            # Only check GET methods
+            if method.__webmethod__.method != "GET":
+                continue
+
+            hints = get_type_hints(method)
+
+            if 'return' not in hints:
+                errors.append(f"Method {protocol_name}.{method_name} has no return type annotation")
+            else:
+                return_type = hints['return']
+                if is_optional_type(return_type):
+                    errors.append(f"Method {protocol_name}.{method_name} returns Optional type")
+
+    return errors

docs/source/index.md

@@ -15,8 +15,6 @@ Llama Stack defines and standardizes the core building blocks needed to bring ge
 - **Multiple developer interfaces** like CLI and SDKs for Python, Node, iOS, and Android
 - **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack
 
-We focus on making it easy to build production applications with the Llama model family - from the latest Llama 3.3 to specialized models like Llama Guard for safety.
-
 ```{image} ../_static/llama-stack.png
 :alt: Llama Stack
 :width: 400px

docs/source/introduction/index.md

@@ -48,7 +48,7 @@ Llama Stack addresses these challenges through a service-oriented, API-first app
 
 **Robust Ecosystem**
 - Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies).
-- Ecosystem offers tailored infrastructure, software, and services for deploying Llama models.
+- Ecosystem offers tailored infrastructure, software, and services for deploying a variety of models.
 
 
 ### Our Philosophy
@@ -57,7 +57,6 @@ Llama Stack addresses these challenges through a service-oriented, API-first app
 - **Composability**: Every component is independent but works together seamlessly
 - **Production Ready**: Built for real-world applications, not just demos
 - **Turnkey Solutions**: Easy to deploy built in solutions for popular deployment scenarios
-- **Llama First**: Explicit focus on Meta's Llama models and partnering ecosystem
 
 
 With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations.

docs/source/providers/vector_io/qdrant.md

@@ -3,21 +3,36 @@ orphan: true
 ---
 # Qdrant
 
-[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It
+[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
 That means you'll get fast and efficient vector retrieval.
 
+> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
+> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
+>
+> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
+
+
+
 ## Features
 
-- Easy to use
+- Lightweight and easy to use
 - Fully integrated with Llama Stack
+- Apache 2.0 license terms
+- Store embeddings and their metadata
+- Supports search by
+  [Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
+  and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
+- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
+- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
+- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
 
 ## Usage
 
 To use Qdrant in your Llama Stack project, follow these steps:
 
 1. Install the necessary dependencies.
-2. Configure your Llama Stack project to use Faiss.
+2. Configure your Llama Stack project to use Qdrant.
 3. Start storing and querying vectors.
 
 ## Installation

llama_stack/apis/benchmarks/benchmarks.py

@@ -52,7 +52,7 @@ class Benchmarks(Protocol):
     async def get_benchmark(
         self,
         benchmark_id: str,
-    ) -> Optional[Benchmark]: ...
+    ) -> Benchmark: ...
 
     @webmethod(route="/eval/benchmarks", method="POST")
     async def register_benchmark(

llama_stack/apis/datasets/datasets.py

@@ -121,8 +121,6 @@ class Dataset(CommonDatasetFields, Resource):
 
 class DatasetInput(CommonDatasetFields, BaseModel):
     dataset_id: str
-    provider_id: Optional[str] = None
-    provider_dataset_id: Optional[str] = None
 
 
 class ListDatasetsResponse(BaseModel):
@@ -201,7 +199,7 @@ class Datasets(Protocol):
     async def get_dataset(
         self,
         dataset_id: str,
-    ) -> Optional[Dataset]: ...
+    ) -> Dataset: ...
 
     @webmethod(route="/datasets", method="GET")
     async def list_datasets(self) -> ListDatasetsResponse: ...

llama_stack/apis/eval/eval.py

@@ -117,7 +117,7 @@ class Eval(Protocol):
         """
 
     @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="GET")
-    async def job_status(self, benchmark_id: str, job_id: str) -> Optional[JobStatus]:
+    async def job_status(self, benchmark_id: str, job_id: str) -> JobStatus:
         """Get the status of a job.
 
         :param benchmark_id: The ID of the benchmark to run the evaluation on.

llama_stack/apis/files/files.py

@@ -115,7 +115,7 @@ class Files(Protocol):
     async def get_upload_session_info(
         self,
         upload_id: str,
-    ) -> Optional[FileUploadResponse]:
+    ) -> FileUploadResponse:
         """
         Returns information about an existsing upload session
 

llama_stack/apis/models/models.py

@@ -66,7 +66,7 @@ class Models(Protocol):
     async def get_model(
         self,
         model_id: str,
-    ) -> Optional[Model]: ...
+    ) -> Model: ...
 
     @webmethod(route="/models", method="POST")
     async def register_model(

llama_stack/apis/post_training/post_training.py

@@ -6,7 +6,7 @@
 
 from datetime import datetime
 from enum import Enum
-from typing import Any, Dict, List, Literal, Optional, Protocol, Union
+from typing import Any, Dict, List, Literal, Optional, Protocol
 
 from pydantic import BaseModel, Field
 from typing_extensions import Annotated
@@ -89,7 +89,7 @@ class QATFinetuningConfig(BaseModel):
 
 
 AlgorithmConfig = register_schema(
-    Annotated[Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")],
+    Annotated[LoraFinetuningConfig | QATFinetuningConfig, Field(discriminator="type")],
     name="AlgorithmConfig",
 )
 
@@ -184,7 +184,7 @@ class PostTraining(Protocol):
             description="Model descriptor from `llama model list`",
         ),
         checkpoint_dir: Optional[str] = None,
-        algorithm_config: Optional[AlgorithmConfig] = None,
+        algorithm_config: Optional[LoraFinetuningConfig | QATFinetuningConfig] = None,
     ) -> PostTrainingJob: ...
 
     @webmethod(route="/post-training/preference-optimize", method="POST")
@@ -202,10 +202,10 @@ class PostTraining(Protocol):
     async def get_training_jobs(self) -> ListPostTrainingJobsResponse: ...
 
     @webmethod(route="/post-training/job/status", method="GET")
-    async def get_training_job_status(self, job_uuid: str) -> Optional[PostTrainingJobStatusResponse]: ...
+    async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse: ...
 
     @webmethod(route="/post-training/job/cancel", method="POST")
     async def cancel_training_job(self, job_uuid: str) -> None: ...
 
     @webmethod(route="/post-training/job/artifacts", method="GET")
-    async def get_training_job_artifacts(self, job_uuid: str) -> Optional[PostTrainingJobArtifactsResponse]: ...
+    async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse: ...

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -135,7 +135,7 @@ class ScoringFunctions(Protocol):
     async def list_scoring_functions(self) -> ListScoringFunctionsResponse: ...
 
     @webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="GET")
-    async def get_scoring_function(self, scoring_fn_id: str, /) -> Optional[ScoringFn]: ...
+    async def get_scoring_function(self, scoring_fn_id: str, /) -> ScoringFn: ...
 
     @webmethod(route="/scoring-functions", method="POST")
     async def register_scoring_function(

llama_stack/apis/shields/shields.py

@@ -49,7 +49,7 @@ class Shields(Protocol):
     async def list_shields(self) -> ListShieldsResponse: ...
 
     @webmethod(route="/shields/{identifier:path}", method="GET")
-    async def get_shield(self, identifier: str) -> Optional[Shield]: ...
+    async def get_shield(self, identifier: str) -> Shield: ...
 
     @webmethod(route="/shields", method="POST")
     async def register_shield(

llama_stack/apis/tools/rag_tool.py

@@ -17,6 +17,15 @@ from llama_stack.schema_utils import json_schema_type, register_schema, webmetho
 
 @json_schema_type
 class RAGDocument(BaseModel):
+    """
+    A document to be used for document ingestion in the RAG Tool.
+
+    :param document_id: The unique identifier for the document.
+    :param content: The content of the document.
+    :param mime_type: The MIME type of the document.
+    :param metadata: Additional metadata for the document.
+    """
+
     document_id: str
     content: InterleavedContent | URL
     mime_type: str | None = None

llama_stack/apis/vector_dbs/vector_dbs.py

@@ -50,7 +50,7 @@ class VectorDBs(Protocol):
     async def get_vector_db(
         self,
         vector_db_id: str,
-    ) -> Optional[VectorDB]: ...
+    ) -> VectorDB: ...
 
     @webmethod(route="/vector-dbs", method="POST")
     async def register_vector_db(

llama_stack/distribution/datatypes.py

@@ -125,6 +125,13 @@ class LoggingConfig(BaseModel):
     )
 
 
+class AuthenticationConfig(BaseModel):
+    endpoint: str = Field(
+        ...,
+        description="Endpoint URL to validate authentication tokens",
+    )
+
+
 class ServerConfig(BaseModel):
     port: int = Field(
         default=8321,
@@ -140,6 +147,10 @@ class ServerConfig(BaseModel):
         default=None,
         description="Path to TLS key file for HTTPS",
     )
+    auth: Optional[AuthenticationConfig] = Field(
+        default=None,
+        description="Authentication configuration for the server",
+    )
 
 
 class StackRunConfig(BaseModel):

llama_stack/distribution/routers/routing_tables.py

@@ -20,6 +20,8 @@ from llama_stack.apis.datasets import (
     DatasetType,
     DataSource,
     ListDatasetsResponse,
+    RowsDataSource,
+    URIDataSource,
 )
 from llama_stack.apis.models import ListModelsResponse, Model, Models, ModelType
 from llama_stack.apis.resource import ResourceType
@@ -219,8 +221,11 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
     async def list_models(self) -> ListModelsResponse:
         return ListModelsResponse(data=await self.get_all_with_type("model"))
 
-    async def get_model(self, model_id: str) -> Optional[Model]:
+    async def get_model(self, model_id: str) -> Model:
-        return await self.get_object_by_identifier("model", model_id)
+        model = await self.get_object_by_identifier("model", model_id)
+        if model is None:
+            raise ValueError(f"Model '{model_id}' not found")
+        return model
 
     async def register_model(
         self,
@@ -267,8 +272,11 @@ class ShieldsRoutingTable(CommonRoutingTableImpl, Shields):
     async def list_shields(self) -> ListShieldsResponse:
         return ListShieldsResponse(data=await self.get_all_with_type(ResourceType.shield.value))
 
-    async def get_shield(self, identifier: str) -> Optional[Shield]:
+    async def get_shield(self, identifier: str) -> Shield:
-        return await self.get_object_by_identifier("shield", identifier)
+        shield = await self.get_object_by_identifier("shield", identifier)
+        if shield is None:
+            raise ValueError(f"Shield '{identifier}' not found")
+        return shield
 
     async def register_shield(
         self,
@@ -303,8 +311,11 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
     async def list_vector_dbs(self) -> ListVectorDBsResponse:
         return ListVectorDBsResponse(data=await self.get_all_with_type("vector_db"))
 
-    async def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]:
+    async def get_vector_db(self, vector_db_id: str) -> VectorDB:
-        return await self.get_object_by_identifier("vector_db", vector_db_id)
+        vector_db = await self.get_object_by_identifier("vector_db", vector_db_id)
+        if vector_db is None:
+            raise ValueError(f"Vector DB '{vector_db_id}' not found")
+        return vector_db
 
     async def register_vector_db(
         self,
@@ -355,8 +366,11 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
     async def list_datasets(self) -> ListDatasetsResponse:
         return ListDatasetsResponse(data=await self.get_all_with_type(ResourceType.dataset.value))
 
-    async def get_dataset(self, dataset_id: str) -> Optional[Dataset]:
+    async def get_dataset(self, dataset_id: str) -> Dataset:
-        return await self.get_object_by_identifier("dataset", dataset_id)
+        dataset = await self.get_object_by_identifier("dataset", dataset_id)
+        if dataset is None:
+            raise ValueError(f"Dataset '{dataset_id}' not found")
+        return dataset
 
     async def register_dataset(
         self,
@@ -365,6 +379,12 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
         metadata: Optional[Dict[str, Any]] = None,
         dataset_id: Optional[str] = None,
     ) -> Dataset:
+        if isinstance(source, dict):
+            if source["type"] == "uri":
+                source = URIDataSource.parse_obj(source)
+            elif source["type"] == "rows":
+                source = RowsDataSource.parse_obj(source)
+
         if not dataset_id:
             dataset_id = f"dataset-{str(uuid.uuid4())}"
 
@@ -408,8 +428,11 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):
     async def list_scoring_functions(self) -> ListScoringFunctionsResponse:
         return ListScoringFunctionsResponse(data=await self.get_all_with_type(ResourceType.scoring_function.value))
 
-    async def get_scoring_function(self, scoring_fn_id: str) -> Optional[ScoringFn]:
+    async def get_scoring_function(self, scoring_fn_id: str) -> ScoringFn:
-        return await self.get_object_by_identifier("scoring_function", scoring_fn_id)
+        scoring_fn = await self.get_object_by_identifier("scoring_function", scoring_fn_id)
+        if scoring_fn is None:
+            raise ValueError(f"Scoring function '{scoring_fn_id}' not found")
+        return scoring_fn
 
     async def register_scoring_function(
         self,
@@ -445,8 +468,11 @@ class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks):
     async def list_benchmarks(self) -> ListBenchmarksResponse:
         return ListBenchmarksResponse(data=await self.get_all_with_type("benchmark"))
 
-    async def get_benchmark(self, benchmark_id: str) -> Optional[Benchmark]:
+    async def get_benchmark(self, benchmark_id: str) -> Benchmark:
-        return await self.get_object_by_identifier("benchmark", benchmark_id)
+        benchmark = await self.get_object_by_identifier("benchmark", benchmark_id)
+        if benchmark is None:
+            raise ValueError(f"Benchmark '{benchmark_id}' not found")
+        return benchmark
 
     async def register_benchmark(
         self,
@@ -490,7 +516,10 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
         return ListToolGroupsResponse(data=await self.get_all_with_type("tool_group"))
 
     async def get_tool_group(self, toolgroup_id: str) -> ToolGroup:
-        return await self.get_object_by_identifier("tool_group", toolgroup_id)
+        tool_group = await self.get_object_by_identifier("tool_group", toolgroup_id)
+        if tool_group is None:
+            raise ValueError(f"Tool group '{toolgroup_id}' not found")
+        return tool_group
 
     async def get_tool(self, tool_name: str) -> Tool:
         return await self.get_object_by_identifier("tool", tool_name)

llama_stack/distribution/server/auth.py

@@ -0,0 +1,69 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import json
+from urllib.parse import parse_qs
+
+import httpx
+
+from llama_stack.log import get_logger
+
+logger = get_logger(name=__name__, category="auth")
+
+
+class AuthenticationMiddleware:
+    def __init__(self, app, auth_endpoint):
+        self.app = app
+        self.auth_endpoint = auth_endpoint
+
+    async def __call__(self, scope, receive, send):
+        if scope["type"] == "http":
+            headers = dict(scope.get("headers", []))
+            auth_header = headers.get(b"authorization", b"").decode()
+
+            if not auth_header or not auth_header.startswith("Bearer "):
+                return await self._send_auth_error(send, "Missing or invalid Authorization header")
+
+            api_key = auth_header.split("Bearer ", 1)[1]
+
+            path = scope.get("path", "")
+            request_headers = {k.decode(): v.decode() for k, v in headers.items()}
+
+            query_string = scope.get("query_string", b"").decode()
+            params = parse_qs(query_string)
+
+            auth_data = {
+                "api_key": api_key,
+                "request": {
+                    "path": path,
+                    "headers": request_headers,
+                    "params": params,
+                },
+            }
+
+            # Validate with authentication endpoint
+            try:
+                async with httpx.AsyncClient() as client:
+                    response = await client.post(self.auth_endpoint, json=auth_data)
+                    if response.status_code != 200:
+                        logger.warning(f"Authentication failed: {response.status_code}")
+                        return await self._send_auth_error(send, "Authentication failed")
+            except Exception:
+                logger.exception("Error during authentication")
+                return await self._send_auth_error(send, "Authentication service error")
+
+        return await self.app(scope, receive, send)
+
+    async def _send_auth_error(self, send, message):
+        await send(
+            {
+                "type": "http.response.start",
+                "status": 401,
+                "headers": [[b"content-type", b"application/json"]],
+            }
+        )
+        error_msg = json.dumps({"error": {"message": message}}).encode()
+        await send({"type": "http.response.body", "body": error_msg})

llama_stack/distribution/server/server.py

@@ -52,6 +52,7 @@ from llama_stack.providers.utils.telemetry.tracing import (
     start_trace,
 )
 
+from .auth import AuthenticationMiddleware
 from .endpoints import get_all_api_endpoints
 
 REPO_ROOT = Path(__file__).parent.parent.parent.parent
@@ -351,6 +352,11 @@ def main():
     if not os.environ.get("LLAMA_STACK_DISABLE_VERSION_CHECK"):
         app.add_middleware(ClientVersionMiddleware)
 
+    # Add authentication middleware if configured
+    if config.server.auth and config.server.auth.endpoint:
+        logger.info(f"Enabling authentication with endpoint: {config.server.auth.endpoint}")
+        app.add_middleware(AuthenticationMiddleware, auth_endpoint=config.server.auth.endpoint)
+
     try:
         impls = asyncio.run(construct_stack(config))
     except InvalidProviderError as e:

llama_stack/distribution/ui/page/distribution/datasets.py

@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def datasets():

llama_stack/distribution/ui/page/distribution/eval_tasks.py

@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def benchmarks():

llama_stack/distribution/ui/page/distribution/models.py

@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def models():

llama_stack/distribution/ui/page/distribution/providers.py

@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def providers():

llama_stack/distribution/ui/page/distribution/resources.py

@@ -4,14 +4,15 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from page.distribution.benchmarks import benchmarks
-from page.distribution.datasets import datasets
-from page.distribution.models import models
-from page.distribution.scoring_functions import scoring_functions
-from page.distribution.shields import shields
-from page.distribution.vector_dbs import vector_dbs
 from streamlit_option_menu import option_menu
 
+from llama_stack.distribution.ui.page.distribution.datasets import datasets
+from llama_stack.distribution.ui.page.distribution.eval_tasks import benchmarks
+from llama_stack.distribution.ui.page.distribution.models import models
+from llama_stack.distribution.ui.page.distribution.scoring_functions import scoring_functions
+from llama_stack.distribution.ui.page.distribution.shields import shields
+from llama_stack.distribution.ui.page.distribution.vector_dbs import vector_dbs
+
 
 def resources_page():
     options = [

llama_stack/distribution/ui/page/distribution/scoring_functions.py

@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def scoring_functions():

llama_stack/distribution/ui/page/distribution/shields.py

@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def shields():

llama_stack/distribution/ui/page/distribution/vector_dbs.py

@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def vector_dbs():

llama_stack/distribution/ui/page/evaluations/app_eval.py

@@ -8,8 +8,9 @@ import json
 
 import pandas as pd
 import streamlit as st
-from modules.api import llama_stack_api
+
-from modules.utils import process_dataset
+from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.utils import process_dataset
 
 
 def application_evaluation_page():

llama_stack/distribution/ui/page/evaluations/native_eval.py

@@ -8,7 +8,8 @@ import json
 
 import pandas as pd
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def select_benchmark_1():

llama_stack/distribution/ui/page/playground/chat.py

@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import streamlit as st
-from modules.api import llama_stack_api
+
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 # Sidebar configurations
 with st.sidebar:

llama_stack/distribution/ui/page/playground/rag.py

@@ -7,9 +7,10 @@
 import streamlit as st
 from llama_stack_client.lib.agents.agent import Agent
 from llama_stack_client.lib.agents.event_logger import EventLogger
-from llama_stack_client.types.memory_insert_params import Document
+from llama_stack_client.types.shared.document import Document
-from modules.api import llama_stack_api
+
-from modules.utils import data_url_from_file
+from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.utils import data_url_from_file
 
 
 def rag_chat_page():

llama_stack/models/llama/datatypes.py

@@ -47,7 +47,14 @@ RecursiveType = Union[Primitive, List[Primitive], Dict[str, Primitive]]
 class ToolCall(BaseModel):
     call_id: str
     tool_name: Union[BuiltinTool, str]
-    arguments: Dict[str, RecursiveType]
+    # Plan is to deprecate the Dict in favor of a JSON string
+    # that is parsed on the client side instead of trying to manage
+    # the recursive type here.
+    # Making this a union so that client side can start prepping for this change.
+    # Eventually, we will remove both the Dict and arguments_json field,
+    # and arguments will just be a str
+    arguments: Union[str, Dict[str, RecursiveType]]
+    arguments_json: Optional[str] = None
 
     @field_validator("tool_name", mode="before")
     @classmethod

llama_stack/models/llama/llama3/chat_format.py

@@ -12,6 +12,7 @@
 # the top-level of this source tree.
 
 import io
+import json
 import uuid
 from dataclasses import dataclass
 from typing import Dict, List, Optional, Tuple
@@ -203,9 +204,10 @@ class ChatFormat:
             # This code tries to handle that case
             if tool_name in BuiltinTool.__members__:
                 tool_name = BuiltinTool[tool_name]
-                tool_arguments = {
+                if isinstance(tool_arguments, dict):
-                    "query": list(tool_arguments.values())[0],
+                    tool_arguments = {
-                }
+                        "query": list(tool_arguments.values())[0],
+                    }
         else:
             builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content)
             if builtin_tool_info is not None:
@@ -229,6 +231,7 @@ class ChatFormat:
                     call_id=call_id,
                     tool_name=tool_name,
                     arguments=tool_arguments,
+                    arguments_json=json.dumps(tool_arguments),
                 )
             )
             content = ""

llama_stack/models/llama/llama3/template_data.py

@@ -11,11 +11,8 @@
 # top-level folder for each specific model found within the models/ directory at
 # the top-level of this source tree.
 
-from llama_stack.models.llama.datatypes import (
+
-    BuiltinTool,
+from llama_stack.models.llama.datatypes import BuiltinTool, StopReason, ToolCall
-    StopReason,
-    ToolCall,
-)
 
 from .prompt_templates import (
     BuiltinToolGenerator,

llama_stack/providers/inline/datasetio/localfs/datasetio.py

@@ -35,12 +35,12 @@ class PandasDataframeDataset:
         else:
             return self.df.iloc[idx].to_dict()
 
-    def load(self) -> None:
+    async def load(self) -> None:
         if self.df is not None:
             return
 
         if self.dataset_def.source.type == "uri":
-            self.df = get_dataframe_from_uri(self.dataset_def.source.uri)
+            self.df = await get_dataframe_from_uri(self.dataset_def.source.uri)
         elif self.dataset_def.source.type == "rows":
             self.df = pandas.DataFrame(self.dataset_def.source.rows)
         else:
@@ -95,7 +95,7 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
     ) -> IterrowsResponse:
         dataset_def = self.dataset_infos[dataset_id]
         dataset_impl = PandasDataframeDataset(dataset_def)
-        dataset_impl.load()
+        await dataset_impl.load()
 
         start_index = start_index or 0
 
@@ -114,7 +114,7 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
     async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None:
         dataset_def = self.dataset_infos[dataset_id]
         dataset_impl = PandasDataframeDataset(dataset_def)
-        dataset_impl.load()
+        await dataset_impl.load()
 
         new_rows_df = pandas.DataFrame(rows)
         dataset_impl.df = pandas.concat([dataset_impl.df, new_rows_df], ignore_index=True)

llama_stack/providers/inline/inference/meta_reference/parallel_utils.py

@@ -10,6 +10,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import copy
 import json
 import logging
 import multiprocessing
@@ -213,7 +214,7 @@ def maybe_parse_message(maybe_json: Optional[str]) -> Optional[ProcessingMessage
 
 def parse_message(json_str: str) -> ProcessingMessage:
     data = json.loads(json_str)
-    return ProcessingMessageWrapper(**data).payload
+    return copy.deepcopy(ProcessingMessageWrapper(**data).payload)
 
 
 def worker_process_entrypoint(

llama_stack/providers/inline/inference/vllm/vllm.py

@@ -582,6 +582,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
                     tool_name=t.function.name,
                     # vLLM function args come back as a string. Llama Stack expects JSON.
                     arguments=json.loads(t.function.arguments),
+                    arguments_json=t.function.arguments,
                 )
                 for t in vllm_message.tool_calls
             ],

llama_stack/providers/inline/post_training/common/validator.py

@@ -9,6 +9,9 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+
+from typing import Any
+
 from llama_stack.apis.common.type_system import (
     ChatCompletionInputType,
     DialogType,
@@ -20,7 +23,7 @@ from llama_stack.providers.utils.common.data_schema_validator import (
     validate_dataset_schema,
 )
 
-EXPECTED_DATASET_SCHEMA = {
+EXPECTED_DATASET_SCHEMA: dict[str, list[dict[str, Any]]] = {
     "instruct": [
         {
             ColumnName.chat_completion_input.value: ChatCompletionInputType(),
@@ -41,6 +44,9 @@ async def validate_input_dataset_schema(
     dataset_type: str,
 ) -> None:
     dataset_def = await datasets_api.get_dataset(dataset_id=dataset_id)
+    if not dataset_def:
+        raise ValueError(f"Dataset {dataset_id} does not exist.")
+
     if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
         raise ValueError(f"Dataset {dataset_id} does not have a schema defined.")
 

llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py

@@ -37,7 +37,7 @@ class TorchtuneCheckpointer:
         checkpoint_files: List[str],
         output_dir: str,
         model_type: str,
-    ) -> None:
+    ):
         # Fail fast if ``checkpoint_files`` is invalid
         # TODO: support loading more than one file
         if len(checkpoint_files) != 1:
@@ -58,7 +58,7 @@ class TorchtuneCheckpointer:
         """
         Load Meta checkpoint from file. Currently only loading from a single file is supported.
         """
-        state_dict: Dict[str:Any] = {}
+        state_dict: Dict[str, Any] = {}
         model_state_dict = safe_torch_load(self._checkpoint_path)
         if self._model_type == ModelType.LLAMA3_VISION:
             from torchtune.models.llama3_2_vision._convert_weights import (
@@ -85,10 +85,10 @@ class TorchtuneCheckpointer:
         state_dict: Dict[str, Any],
         epoch: int,
         adapter_only: bool = False,
-        checkpoint_format: str = "meta",
+        checkpoint_format: str | None = None,
     ) -> str:
         model_file_path = Path(self._output_dir) / f"{self._model_id}-{self._training_algorithm}-{epoch}"
-        if checkpoint_format == "meta":
+        if checkpoint_format == "meta" or checkpoint_format is None:
             self._save_meta_format_checkpoint(model_file_path, state_dict, adapter_only)
         elif checkpoint_format == "huggingface":
             # Note: for saving hugging face format checkpoints, we only suppport saving adapter weights now

llama_stack/providers/inline/post_training/torchtune/common/utils.py

@@ -10,7 +10,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Callable, Dict
+from typing import Callable, Dict
 
 import torch
 from pydantic import BaseModel
@@ -25,10 +25,13 @@ from llama_stack.apis.post_training import DatasetFormat
 from llama_stack.models.llama.datatypes import Model
 from llama_stack.models.llama.sku_list import resolve_model
 
+BuildLoraModelCallable = Callable[..., torch.nn.Module]
+BuildTokenizerCallable = Callable[..., Llama3Tokenizer]
+
 
 class ModelConfig(BaseModel):
-    model_definition: Any
+    model_definition: BuildLoraModelCallable
-    tokenizer_type: Any
+    tokenizer_type: BuildTokenizerCallable
     checkpoint_type: str
 
 
@@ -51,10 +54,6 @@ DATA_FORMATS: Dict[str, Transform] = {
 }
 
 
-BuildLoraModelCallable = Callable[..., torch.nn.Module]
-BuildTokenizerCallable = Callable[..., Llama3Tokenizer]
-
-
 def _validate_model_id(model_id: str) -> Model:
     model = resolve_model(model_id)
     if model is None or model.core_model_id.value not in MODEL_CONFIGS:

llama_stack/providers/inline/post_training/torchtune/datasets/sft.py

@@ -55,7 +55,7 @@ class SFTDataset(Dataset):
         if "messages" in transformed_sample:
             validate_messages(transformed_sample["messages"])
 
-        tokenized_dict = self._model_transform(transformed_sample)
+        tokenized_dict: dict[str, Any] = self._model_transform(transformed_sample)
 
         if not ("tokens" in tokenized_dict and "mask" in tokenized_dict):
             keys_str = ", ".join(tokenized_dict.keys())

llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py

@@ -37,10 +37,10 @@ from llama_stack.apis.common.training_types import PostTrainingMetric
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
 from llama_stack.apis.post_training import (
-    AlgorithmConfig,
     Checkpoint,
     LoraFinetuningConfig,
     OptimizerConfig,
+    QATFinetuningConfig,
     TrainingConfig,
 )
 from llama_stack.distribution.utils.config_dirs import DEFAULT_CHECKPOINT_DIR
@@ -73,6 +73,9 @@ class LoraFinetuningSingleDevice:
 
     # Currently logging only logs limited training metrics to local disk
     # will figure out more loggings and how it works with telemetry in future PRs
+
+    _checkpointer: TorchtuneCheckpointer
+
     def __init__(
         self,
         config: TorchtunePostTrainingConfig,
@@ -82,7 +85,7 @@ class LoraFinetuningSingleDevice:
         logger_config: Dict[str, Any],
         model: str,
         checkpoint_dir: Optional[str],
-        algorithm_config: Optional[AlgorithmConfig],
+        algorithm_config: LoraFinetuningConfig | QATFinetuningConfig | None,
         datasetio_api: DatasetIO,
         datasets_api: Datasets,
     ) -> None:
@@ -109,12 +112,12 @@ class LoraFinetuningSingleDevice:
             return str(checkpoint_dir)
 
         if checkpoint_dir and checkpoint_dir != "null":
-            self.checkpoint_dir = config.checkpoint_dir
+            self.checkpoint_dir = checkpoint_dir
         else:
-            model = resolve_model(self.model_id)
+            model_obj = resolve_model(self.model_id)
-            if model is None:
+            if model_obj is None:
                 raise ValueError(f"{self.model_id} not found. Your model id should be in the llama models SKU list")
-            self.checkpoint_dir = model_checkpoint_dir(model)
+            self.checkpoint_dir = model_checkpoint_dir(model_obj)
 
         self._output_dir = str(DEFAULT_CHECKPOINT_DIR)
         self._checkpoint_format = config.checkpoint_format
@@ -135,16 +138,16 @@ class LoraFinetuningSingleDevice:
         self.max_validation_steps = training_config.max_validation_steps
 
         self._clip_grad_norm = 1.0
-        self._enable_activation_checkpointing = (
+
-            (training_config.efficiency_config.enable_activation_checkpointing)
+        self._enable_activation_checkpointing = False
-            if training_config.efficiency_config
+        self._enable_activation_offloading = False
-            else False
+        if training_config.efficiency_config:
-        )
+            if training_config.efficiency_config.enable_activation_checkpointing:
-        self._enable_activation_offloading = (
+                self._enable_activation_checkpointing = (
-            (training_config.efficiency_config.enable_activation_offloading)
+                    training_config.efficiency_config.enable_activation_checkpointing
-            if training_config.efficiency_config
+                )
-            else False
+            if training_config.efficiency_config.enable_activation_offloading:
-        )
+                self._enable_activation_offloading = training_config.efficiency_config.enable_activation_offloading
 
         self.datasetio_api = datasetio_api
         self.datasets_api = datasets_api
@@ -451,12 +454,12 @@ class LoraFinetuningSingleDevice:
         """
         # Initialize tokens count and running loss (for grad accumulation)
         t0 = time.perf_counter()
-        running_loss = 0
+        running_loss: float = 0.0
         num_tokens = 0
 
         # training artifacts
         checkpoints = []
-        memory_stats = {}
+        memory_stats: Dict[str, Any] = {}
 
         # self.epochs_run should be non-zero when we're resuming from a checkpoint
         for curr_epoch in range(self.epochs_run, self.total_epochs):
@@ -484,7 +487,7 @@ class LoraFinetuningSingleDevice:
                 # Loss is normalized by default so we multiply by the number of tokens
                 # This way we can normalize by the total number of tokens if we're accumulating gradients
                 current_loss = await self._loss_step(batch) * current_num_tokens
-                running_loss += current_loss
+                running_loss += current_loss.detach().item()
                 current_loss.backward()
 
                 # Step with optimizer
@@ -500,7 +503,7 @@ class LoraFinetuningSingleDevice:
                     # Update the number of steps when the weights are updated
                     self.global_step += 1
 
-                    loss_to_log = running_loss.item() / num_tokens
+                    loss_to_log = running_loss / num_tokens
 
                     pbar.update(1)
                     pbar.set_description(f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}")
@@ -523,7 +526,7 @@ class LoraFinetuningSingleDevice:
                     )
 
                     # Reset running stats for the next step
-                    running_loss = 0
+                    running_loss = 0.0
                     num_tokens = 0
                     t0 = time.perf_counter()
 

llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 
+import asyncio
 import logging
 import os
 import tempfile
@@ -37,7 +38,7 @@ class CodeInterpreterToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime):
     async def initialize(self):
         pass
 
-    async def register_tool(self, tool: Tool):
+    async def register_tool(self, tool: Tool) -> None:
         pass
 
     async def unregister_tool(self, tool_id: str) -> None:
@@ -65,7 +66,7 @@ class CodeInterpreterToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime):
         # Use environment variable to control bwrap usage
         force_disable_bwrap = os.environ.get("DISABLE_CODE_SANDBOX", "").lower() in ("1", "true", "yes")
         req = CodeExecutionRequest(scripts=[script], use_bwrap=not force_disable_bwrap)
-        res = self.code_executor.execute(req)
+        res = await asyncio.to_thread(self.code_executor.execute, req)
         pieces = [res["process_status"]]
         for out_type in ["stdout", "stderr"]:
             res_out = res[out_type]

llama_stack/providers/inline/vector_io/qdrant/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Dict
+
+from llama_stack.providers.datatypes import Api, ProviderSpec
+
+from .config import QdrantVectorIOConfig
+
+
+async def get_adapter_impl(config: QdrantVectorIOConfig, deps: Dict[Api, ProviderSpec]):
+    from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
+
+    impl = QdrantVectorIOAdapter(config, deps[Api.inference])
+    await impl.initialize()
+    return impl

llama_stack/providers/inline/vector_io/qdrant/config.py

@@ -0,0 +1,23 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+from typing import Any, Dict
+
+from pydantic import BaseModel
+
+from llama_stack.schema_utils import json_schema_type
+
+
+@json_schema_type
+class QdrantVectorIOConfig(BaseModel):
+    path: str
+
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:
+        return {
+            "path": "${env.QDRANT_PATH:~/.llama/" + __distro_dir__ + "}/" + "qdrant.db",
+        }

llama_stack/providers/registry/vector_io.py

@@ -92,6 +92,14 @@ def available_providers() -> List[ProviderSpec]:
             ),
             api_dependencies=[Api.inference],
         ),
+        InlineProviderSpec(
+            api=Api.vector_io,
+            provider_type="inline::qdrant",
+            pip_packages=["qdrant-client"],
+            module="llama_stack.providers.inline.vector_io.qdrant",
+            config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
+            api_dependencies=[Api.inference],
+        ),
         remote_provider_spec(
             Api.vector_io,
             AdapterSpec(

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -6,6 +6,7 @@
 
 import logging
 import warnings
+from functools import lru_cache
 from typing import AsyncIterator, List, Optional, Union
 
 from openai import APIConnectionError, AsyncOpenAI, BadRequestError
@@ -82,12 +83,42 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         #     )
 
         self._config = config
-        # make sure the client lives longer than any async calls
+
-        self._client = AsyncOpenAI(
+    @lru_cache  # noqa: B019
-            base_url=f"{self._config.url}/v1",
+    def _get_client(self, provider_model_id: str) -> AsyncOpenAI:
-            api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
+        """
-            timeout=self._config.timeout,
+        For hosted models, https://integrate.api.nvidia.com/v1 is the primary base_url. However,
-        )
+        some models are hosted on different URLs. This function returns the appropriate client
+        for the given provider_model_id.
+
+        This relies on lru_cache and self._default_client to avoid creating a new client for each request
+        or for each model that is hosted on https://integrate.api.nvidia.com/v1.
+
+        :param provider_model_id: The provider model ID
+        :return: An OpenAI client
+        """
+
+        @lru_cache  # noqa: B019
+        def _get_client_for_base_url(base_url: str) -> AsyncOpenAI:
+            """
+            Maintain a single OpenAI client per base_url.
+            """
+            return AsyncOpenAI(
+                base_url=base_url,
+                api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
+                timeout=self._config.timeout,
+            )
+
+        special_model_urls = {
+            "meta/llama-3.2-11b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-11b-vision-instruct",
+            "meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct",
+        }
+
+        base_url = f"{self._config.url}/v1"
+        if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls:
+            base_url = special_model_urls[provider_model_id]
+
+        return _get_client_for_base_url(base_url)
 
     async def completion(
         self,
@@ -105,9 +136,10 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
 
         await check_health(self._config)  # this raises errors
 
+        provider_model_id = self.get_provider_model_id(model_id)
         request = convert_completion_request(
             request=CompletionRequest(
-                model=self.get_provider_model_id(model_id),
+                model=provider_model_id,
                 content=content,
                 sampling_params=sampling_params,
                 response_format=response_format,
@@ -118,7 +150,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         )
 
         try:
-            response = await self._client.completions.create(**request)
+            response = await self._get_client(provider_model_id).completions.create(**request)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
 
@@ -206,6 +238,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
 
         await check_health(self._config)  # this raises errors
 
+        provider_model_id = self.get_provider_model_id(model_id)
         request = await convert_chat_completion_request(
             request=ChatCompletionRequest(
                 model=self.get_provider_model_id(model_id),
@@ -221,7 +254,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         )
 
         try:
-            response = await self._client.chat.completions.create(**request)
+            response = await self._get_client(provider_model_id).chat.completions.create(**request)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
 

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -90,15 +90,12 @@ def _convert_to_vllm_tool_calls_in_response(
     if not tool_calls:
         return []
 
-    call_function_arguments = None
-    for call in tool_calls:
-        call_function_arguments = json.loads(call.function.arguments)
-
     return [
         ToolCall(
             call_id=call.id,
             tool_name=call.function.name,
-            arguments=call_function_arguments,
+            arguments=json.loads(call.function.arguments),
+            arguments_json=call.function.arguments,
         )
         for call in tool_calls
     ]
@@ -183,6 +180,7 @@ async def _process_vllm_chat_completion_stream_response(
                                 call_id=tool_call_buf.call_id,
                                 tool_name=tool_call_buf.tool_name,
                                 arguments=args,
+                                arguments_json=args_str,
                             ),
                             parse_status=ToolCallParseStatus.succeeded,
                         ),

llama_stack/providers/remote/vector_io/qdrant/config.py

@@ -23,7 +23,6 @@ class QdrantVectorIOConfig(BaseModel):
     prefix: Optional[str] = None
     timeout: Optional[int] = None
     host: Optional[str] = None
-    path: Optional[str] = None
 
     @classmethod
     def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]:

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -6,7 +6,7 @@
 
 import logging
 import uuid
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 from numpy.typing import NDArray
 from qdrant_client import AsyncQdrantClient, models
@@ -16,12 +16,13 @@ from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
 )
 
-from .config import QdrantVectorIOConfig
+from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
 
 log = logging.getLogger(__name__)
 CHUNK_ID_KEY = "_chunk_id"
@@ -99,17 +100,19 @@ class QdrantIndex(EmbeddingIndex):
 
 
 class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
-    def __init__(self, config: QdrantVectorIOConfig, inference_api: Api.inference) -> None:
+    def __init__(
+        self, config: Union[RemoteQdrantVectorIOConfig, InlineQdrantVectorIOConfig], inference_api: Api.inference
+    ) -> None:
         self.config = config
-        self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
+        self.client: AsyncQdrantClient = None
         self.cache = {}
         self.inference_api = inference_api
 
     async def initialize(self) -> None:
-        pass
+        self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
 
     async def shutdown(self) -> None:
-        self.client.close()
+        await self.client.close()
 
     async def register_vector_db(
         self,
@@ -123,6 +126,11 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
 
         self.cache[vector_db.identifier] = index
 
+    async def unregister_vector_db(self, vector_db_id: str) -> None:
+        if vector_db_id in self.cache:
+            await self.cache[vector_db_id].index.delete()
+            del self.cache[vector_db_id]
+
     async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]:
         if vector_db_id in self.cache:
             return self.cache[vector_db_id]

llama_stack/providers/utils/datasetio/url_utils.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import asyncio
 import base64
 import io
 from urllib.parse import unquote
@@ -13,12 +14,15 @@ import pandas
 from llama_stack.providers.utils.memory.vector_store import parse_data_url
 
 
-def get_dataframe_from_uri(uri: str):
+async def get_dataframe_from_uri(uri: str):
     df = None
     if uri.endswith(".csv"):
-        df = pandas.read_csv(uri)
+        # Moving to its own thread to avoid io from blocking the eventloop
+        # This isn't ideal as it moves more then just the IO to a new thread
+        # but it is as close as we can easly get
+        df = await asyncio.to_thread(pandas.read_csv, uri)
     elif uri.endswith(".xlsx"):
-        df = pandas.read_excel(uri)
+        df = await asyncio.to_thread(pandas.read_excel, uri)
     elif uri.startswith("data:"):
         parts = parse_data_url(uri)
         data = parts["data"]

llama_stack/providers/utils/inference/openai_compat.py

@@ -529,7 +529,11 @@ async def convert_message_to_openai_dict_new(
     ) -> Union[str, Iterable[OpenAIChatCompletionContentPartParam]]:
         async def impl(
             content_: InterleavedContent,
-        ) -> Union[str, OpenAIChatCompletionContentPartParam, List[OpenAIChatCompletionContentPartParam]]:
+        ) -> Union[
+            str,
+            OpenAIChatCompletionContentPartParam,
+            List[OpenAIChatCompletionContentPartParam],
+        ]:
             # Llama Stack and OpenAI spec match for str and text input
             if isinstance(content_, str):
                 return content_
@@ -570,7 +574,7 @@ async def convert_message_to_openai_dict_new(
                 OpenAIChatCompletionMessageToolCall(
                     id=tool.call_id,
                     function=OpenAIFunction(
-                        name=tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value,
+                        name=(tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value),
                         arguments=json.dumps(tool.arguments),
                     ),
                     type="function",
@@ -609,6 +613,7 @@ def convert_tool_call(
             call_id=tool_call.id,
             tool_name=tool_call.function.name,
             arguments=json.loads(tool_call.function.arguments),
+            arguments_json=tool_call.function.arguments,
         )
     except Exception:
         return UnparseableToolCall(
@@ -759,6 +764,7 @@ def _convert_openai_tool_calls(
             call_id=call.id,
             tool_name=call.function.name,
             arguments=json.loads(call.function.arguments),
+            arguments_json=call.function.arguments,
         )
         for call in tool_calls
     ]
@@ -890,7 +896,8 @@ async def convert_openai_chat_completion_stream(
             # ChatCompletionResponseEvent only supports one per stream
             if len(choice.delta.tool_calls) > 1:
                 warnings.warn(
-                    "multiple tool calls found in a single delta, using the first, ignoring the rest", stacklevel=2
+                    "multiple tool calls found in a single delta, using the first, ignoring the rest",
+                    stacklevel=2,
                 )
 
             if not enable_incremental_tool_calls:
@@ -971,6 +978,7 @@ async def convert_openai_chat_completion_stream(
                     call_id=buffer["call_id"],
                     tool_name=buffer["name"],
                     arguments=arguments,
+                    arguments_json=buffer["arguments"],
                 )
                 yield ChatCompletionResponseStreamChunk(
                     event=ChatCompletionResponseEvent(

llama_stack/templates/open-benchmark/open_benchmark.py

@@ -170,7 +170,6 @@ def get_distribution_template() -> DistributionTemplate:
     default_datasets = [
         DatasetInput(
             dataset_id="simpleqa",
-            provider_id="huggingface",
             purpose=DatasetPurpose.eval_messages_answer,
             source=URIDataSource(
                 uri="huggingface://datasets/llamastack/simpleqa?split=train",
@@ -178,7 +177,6 @@ def get_distribution_template() -> DistributionTemplate:
         ),
         DatasetInput(
             dataset_id="mmlu_cot",
-            provider_id="huggingface",
             purpose=DatasetPurpose.eval_messages_answer,
             source=URIDataSource(
                 uri="huggingface://datasets/llamastack/mmlu_cot?split=test&name=all",
@@ -186,7 +184,6 @@ def get_distribution_template() -> DistributionTemplate:
         ),
         DatasetInput(
             dataset_id="gpqa_cot",
-            provider_id="huggingface",
             purpose=DatasetPurpose.eval_messages_answer,
             source=URIDataSource(
                 uri="huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main",
@@ -194,7 +191,6 @@ def get_distribution_template() -> DistributionTemplate:
         ),
         DatasetInput(
             dataset_id="math_500",
-            provider_id="huggingface",
             purpose=DatasetPurpose.eval_messages_answer,
             source=URIDataSource(
                 uri="huggingface://datasets/llamastack/math_500?split=test",
@@ -202,7 +198,6 @@ def get_distribution_template() -> DistributionTemplate:
         ),
         DatasetInput(
             dataset_id="bfcl",
-            provider_id="huggingface",
             purpose=DatasetPurpose.eval_messages_answer,
             source=URIDataSource(
                 uri="huggingface://datasets/llamastack/bfcl_v3?split=train",

llama_stack/templates/open-benchmark/run.yaml

@@ -164,35 +164,30 @@ datasets:
     uri: huggingface://datasets/llamastack/simpleqa?split=train
   metadata: {}
   dataset_id: simpleqa
-  provider_id: huggingface
 - purpose: eval/messages-answer
   source:
     type: uri
     uri: huggingface://datasets/llamastack/mmlu_cot?split=test&name=all
   metadata: {}
   dataset_id: mmlu_cot
-  provider_id: huggingface
 - purpose: eval/messages-answer
   source:
     type: uri
     uri: huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main
   metadata: {}
   dataset_id: gpqa_cot
-  provider_id: huggingface
 - purpose: eval/messages-answer
   source:
     type: uri
     uri: huggingface://datasets/llamastack/math_500?split=test
   metadata: {}
   dataset_id: math_500
-  provider_id: huggingface
 - purpose: eval/messages-answer
   source:
     type: uri
     uri: huggingface://datasets/llamastack/bfcl_v3?split=train
   metadata: {}
   dataset_id: bfcl
-  provider_id: huggingface
 scoring_fns: []
 benchmarks:
 - dataset_id: simpleqa

pyproject.toml

@@ -56,7 +56,7 @@ dev = [
     "ruamel.yaml",      # needed for openapi generator
 ]
 # These are the dependencies required for running unit tests.
-unit = ["sqlite-vec", "openai", "aiosqlite", "pypdf", "chardet"]
+unit = ["sqlite-vec", "openai", "aiosqlite", "pypdf", "chardet", "qdrant-client"]
 # These are the core dependencies required for running integration tests. They are shared across all
 # providers. If a provider requires additional dependencies, please add them to your environment
 # separately. If you are using "uv" to execute your tests, you can use the "--with" flag to specify extra
@@ -114,7 +114,6 @@ exclude = [
     "./.git",
     "./docs/*",
     "./build",
-    "./scripts",
     "./venv",
     "*.pyi",
     ".pre-commit-config.yaml",
@@ -229,10 +228,6 @@ exclude = [
     "^llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers\\.py$",
     "^llama_stack/providers/inline/inference/vllm/",
     "^llama_stack/providers/inline/post_training/common/validator\\.py$",
-    "^llama_stack/providers/inline/post_training/torchtune/common/checkpointer\\.py$",
-    "^llama_stack/providers/inline/post_training/torchtune/common/utils\\.py$",
-    "^llama_stack/providers/inline/post_training/torchtune/datasets/sft\\.py$",
-    "^llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device\\.py$",
     "^llama_stack/providers/inline/post_training/torchtune/post_training\\.py$",
     "^llama_stack/providers/inline/safety/code_scanner/",
     "^llama_stack/providers/inline/safety/llama_guard/",
@@ -248,6 +243,7 @@ exclude = [
     "^llama_stack/providers/inline/vector_io/chroma/",
     "^llama_stack/providers/inline/vector_io/faiss/",
     "^llama_stack/providers/inline/vector_io/milvus/",
+    "^llama_stack/providers/inline/vector_io/qdrant/",
     "^llama_stack/providers/inline/vector_io/sqlite_vec/",
     "^llama_stack/providers/remote/agents/sample/",
     "^llama_stack/providers/remote/datasetio/huggingface/",

scripts/gen-changelog.py

@@ -11,7 +11,7 @@ import requests
 
 
 def get_all_releases(token):
-    url = f"https://api.github.com/repos/meta-llama/llama-stack/releases"
+    url = "https://api.github.com/repos/meta-llama/llama-stack/releases"
     headers = {"Accept": "application/vnd.github.v3+json"}
 
     if token:
@@ -22,9 +22,7 @@ def get_all_releases(token):
     if response.status_code == 200:
         return response.json()
     else:
-        raise Exception(
+        raise Exception(f"Error fetching releases: {response.status_code}, {response.text}")
-            f"Error fetching releases: {response.status_code}, {response.text}"
-        )
 
 
 def clean_release_body(body):
@@ -55,7 +53,7 @@ def merge_release_notes(output_file, token=None):
     releases = get_all_releases(token)
 
     with open(output_file, "w", encoding="utf-8") as md_file:
-        md_file.write(f"# Changelog\n\n")
+        md_file.write("# Changelog\n\n")
 
         for release in releases:
             md_file.write(f"# {release['tag_name']}\n")

tests/unit/models/test_prompt_adapter.py

@@ -165,7 +165,10 @@ class PrepareMessagesTests(unittest.IsolatedAsyncioTestCase):
         request.model = MODEL
         request.tool_config.tool_prompt_format = ToolPromptFormat.json
         prompt = await chat_completion_request_to_prompt(request, request.model)
-        self.assertIn('{"type": "function", "name": "custom1", "parameters": {"param1": "value1"}}', prompt)
+        self.assertIn(
+            '{"type": "function", "name": "custom1", "parameters": {"param1": "value1"}}',
+            prompt,
+        )
 
     async def test_user_provided_system_message(self):
         content = "Hello !"

tests/unit/providers/vector_io/conftest.py

@@ -0,0 +1,42 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import random
+
+import numpy as np
+import pytest
+
+from llama_stack.apis.vector_io import Chunk
+
+EMBEDDING_DIMENSION = 384
+
+
+@pytest.fixture
+def vector_db_id() -> str:
+    return f"test-vector-db-{random.randint(1, 100)}"
+
+
+@pytest.fixture(scope="session")
+def embedding_dimension() -> int:
+    return EMBEDDING_DIMENSION
+
+
+@pytest.fixture(scope="session")
+def sample_chunks():
+    """Generates chunks that force multiple batches for a single document to expose ID conflicts."""
+    n, k = 10, 3
+    sample = [
+        Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
+        for j in range(k)
+        for i in range(n)
+    ]
+    return sample
+
+
+@pytest.fixture(scope="session")
+def sample_embeddings(sample_chunks):
+    np.random.seed(42)
+    return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])

tests/unit/providers/vector_io/test_qdrant.py

@@ -0,0 +1,135 @@
+# 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
+import os
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+import pytest_asyncio
+
+from llama_stack.apis.inference import EmbeddingsResponse, Inference
+from llama_stack.apis.vector_io import (
+    QueryChunksResponse,
+    VectorDB,
+    VectorDBStore,
+)
+from llama_stack.providers.inline.vector_io.qdrant.config import (
+    QdrantVectorIOConfig as InlineQdrantVectorIOConfig,
+)
+from llama_stack.providers.remote.vector_io.qdrant.qdrant import (
+    QdrantVectorIOAdapter,
+)
+
+# This test is a unit test for the QdrantVectorIOAdapter class. This should only contain
+# tests which are specific to this class. More general (API-level) tests should be placed in
+# tests/integration/vector_io/
+#
+# How to run this test:
+#
+# pytest tests/unit/providers/vector_io/test_qdrant.py \
+# -v -s --tb=short --disable-warnings --asyncio-mode=auto
+
+
+@pytest.fixture
+def qdrant_config(tmp_path) -> InlineQdrantVectorIOConfig:
+    return InlineQdrantVectorIOConfig(path=os.path.join(tmp_path, "qdrant.db"))
+
+
+@pytest.fixture(scope="session")
+def loop():
+    return asyncio.new_event_loop()
+
+
+@pytest.fixture
+def mock_vector_db(vector_db_id) -> MagicMock:
+    mock_vector_db = MagicMock(spec=VectorDB)
+    mock_vector_db.embedding_model = "embedding_model"
+    mock_vector_db.identifier = vector_db_id
+    return mock_vector_db
+
+
+@pytest.fixture
+def mock_vector_db_store(mock_vector_db) -> MagicMock:
+    mock_store = MagicMock(spec=VectorDBStore)
+    mock_store.get_vector_db = AsyncMock(return_value=mock_vector_db)
+    return mock_store
+
+
+@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))
+    return mock_api_service
+
+
+@pytest_asyncio.fixture
+async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, loop) -> QdrantVectorIOAdapter:
+    adapter = QdrantVectorIOAdapter(config=qdrant_config, inference_api=mock_api_service)
+    adapter.vector_db_store = mock_vector_db_store
+    await adapter.initialize()
+    yield adapter
+    await adapter.shutdown()
+
+
+__QUERY = "Sample query"
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("max_query_chunks, expected_chunks", [(2, 2), (100, 30)])
+async def test_qdrant_adapter_returns_expected_chunks(
+    qdrant_adapter: QdrantVectorIOAdapter,
+    vector_db_id,
+    sample_chunks,
+    sample_embeddings,
+    max_query_chunks,
+    expected_chunks,
+) -> None:
+    assert qdrant_adapter is not None
+    await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
+
+    index = await qdrant_adapter._get_and_cache_vector_db_index(vector_db_id=vector_db_id)
+    assert index is not None
+
+    response = await qdrant_adapter.query_chunks(
+        query=__QUERY,
+        vector_db_id=vector_db_id,
+        params={"max_chunks": max_query_chunks},
+    )
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) == expected_chunks
+
+
+# To by-pass attempt to convert a Mock to JSON
+def _prepare_for_json(value: Any) -> str:
+    return str(value)
+
+
+@patch("llama_stack.providers.utils.telemetry.trace_protocol._prepare_for_json", new=_prepare_for_json)
+@pytest.mark.asyncio
+async def test_qdrant_register_and_unregister_vector_db(
+    qdrant_adapter: QdrantVectorIOAdapter,
+    mock_vector_db,
+    sample_chunks,
+) -> None:
+    # Initially, no collections
+    vector_db_id = mock_vector_db.identifier
+    assert len((await qdrant_adapter.client.get_collections()).collections) == 0
+
+    # Register does not create a collection
+    assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
+    await qdrant_adapter.register_vector_db(mock_vector_db)
+    assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
+
+    # First insert creates the collection
+    await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
+    assert await qdrant_adapter.client.collection_exists(vector_db_id)
+
+    # Unregister deletes the collection
+    await qdrant_adapter.unregister_vector_db(vector_db_id)
+    assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
+    assert len((await qdrant_adapter.client.get_collections()).collections) == 0

tests/unit/providers/vector_io/test_sqlite_vec.py

@@ -29,8 +29,6 @@ from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import (
 # -v -s --tb=short --disable-warnings --asyncio-mode=auto
 
 SQLITE_VEC_PROVIDER = "sqlite_vec"
-EMBEDDING_DIMENSION = 384
-EMBEDDING_MODEL = "all-MiniLM-L6-v2"
 
 
 @pytest.fixture(scope="session")
@@ -50,26 +48,8 @@ def sqlite_connection(loop):
 
 
 @pytest_asyncio.fixture(scope="session", autouse=True)
-async def sqlite_vec_index(sqlite_connection):
+async def sqlite_vec_index(sqlite_connection, embedding_dimension):
-    return await SQLiteVecIndex.create(dimension=EMBEDDING_DIMENSION, connection=sqlite_connection, bank_id="test_bank")
+    return await SQLiteVecIndex.create(dimension=embedding_dimension, connection=sqlite_connection, bank_id="test_bank")
-
-
-@pytest.fixture(scope="session")
-def sample_chunks():
-    """Generates chunks that force multiple batches for a single document to expose ID conflicts."""
-    n, k = 10, 3
-    sample = [
-        Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
-        for j in range(k)
-        for i in range(n)
-    ]
-    return sample
-
-
-@pytest.fixture(scope="session")
-def sample_embeddings(sample_chunks):
-    np.random.seed(42)
-    return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])
 
 
 @pytest.mark.asyncio
@@ -82,21 +62,21 @@ async def test_add_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
 
 
 @pytest.mark.asyncio
-async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
+async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings, embedding_dimension):
     await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings)
-    query_embedding = np.random.rand(EMBEDDING_DIMENSION).astype(np.float32)
+    query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
     response = await sqlite_vec_index.query(query_embedding, k=2, score_threshold=0.0)
     assert isinstance(response, QueryChunksResponse)
     assert len(response.chunks) == 2
 
 
 @pytest.mark.asyncio
-async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks):
+async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dimension):
     """Test that chunk IDs do not conflict across batches when inserting chunks."""
     # Reduce batch size to force multiple batches for same document
     # since there are 10 chunks per document and batch size is 2
     batch_size = 2
-    sample_embeddings = np.random.rand(len(sample_chunks), EMBEDDING_DIMENSION).astype(np.float32)
+    sample_embeddings = np.random.rand(len(sample_chunks), embedding_dimension).astype(np.float32)
 
     await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=batch_size)
 

tests/unit/server/test_auth.py

@@ -0,0 +1,124 @@
+# 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 AsyncMock, patch
+
+import pytest
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+from llama_stack.distribution.server.auth import AuthenticationMiddleware
+
+
+@pytest.fixture
+def mock_auth_endpoint():
+    return "http://mock-auth-service/validate"
+
+
+@pytest.fixture
+def valid_api_key():
+    return "valid_api_key_12345"
+
+
+@pytest.fixture
+def invalid_api_key():
+    return "invalid_api_key_67890"
+
+
+@pytest.fixture
+def app(mock_auth_endpoint):
+    app = FastAPI()
+    app.add_middleware(AuthenticationMiddleware, auth_endpoint=mock_auth_endpoint)
+
+    @app.get("/test")
+    def test_endpoint():
+        return {"message": "Authentication successful"}
+
+    return app
+
+
+@pytest.fixture
+def client(app):
+    return TestClient(app)
+
+
+async def mock_post_success(*args, **kwargs):
+    mock_response = AsyncMock()
+    mock_response.status_code = 200
+    return mock_response
+
+
+async def mock_post_failure(*args, **kwargs):
+    mock_response = AsyncMock()
+    mock_response.status_code = 401
+    return mock_response
+
+
+async def mock_post_exception(*args, **kwargs):
+    raise Exception("Connection error")
+
+
+def test_missing_auth_header(client):
+    response = client.get("/test")
+    assert response.status_code == 401
+    assert "Missing or invalid Authorization header" in response.json()["error"]["message"]
+
+
+def test_invalid_auth_header_format(client):
+    response = client.get("/test", headers={"Authorization": "InvalidFormat token123"})
+    assert response.status_code == 401
+    assert "Missing or invalid Authorization header" in response.json()["error"]["message"]
+
+
+@patch("httpx.AsyncClient.post", new=mock_post_success)
+def test_valid_authentication(client, valid_api_key):
+    response = client.get("/test", headers={"Authorization": f"Bearer {valid_api_key}"})
+    assert response.status_code == 200
+    assert response.json() == {"message": "Authentication successful"}
+
+
+@patch("httpx.AsyncClient.post", new=mock_post_failure)
+def test_invalid_authentication(client, invalid_api_key):
+    response = client.get("/test", headers={"Authorization": f"Bearer {invalid_api_key}"})
+    assert response.status_code == 401
+    assert "Authentication failed" in response.json()["error"]["message"]
+
+
+@patch("httpx.AsyncClient.post", new=mock_post_exception)
+def test_auth_service_error(client, valid_api_key):
+    response = client.get("/test", headers={"Authorization": f"Bearer {valid_api_key}"})
+    assert response.status_code == 401
+    assert "Authentication service error" in response.json()["error"]["message"]
+
+
+def test_auth_request_payload(client, valid_api_key, mock_auth_endpoint):
+    with patch("httpx.AsyncClient.post") as mock_post:
+        mock_response = AsyncMock()
+        mock_response.status_code = 200
+        mock_post.return_value = mock_response
+
+        client.get(
+            "/test?param1=value1&param2=value2",
+            headers={
+                "Authorization": f"Bearer {valid_api_key}",
+                "User-Agent": "TestClient",
+                "Content-Type": "application/json",
+            },
+        )
+
+        # Check that the auth endpoint was called with the correct payload
+        call_args = mock_post.call_args
+        assert call_args is not None
+
+        url, kwargs = call_args[0][0], call_args[1]
+        assert url == mock_auth_endpoint
+
+        payload = kwargs["json"]
+        assert payload["api_key"] == valid_api_key
+        assert payload["request"]["path"] == "/test"
+        assert "authorization" in payload["request"]["headers"]
+        assert "param1" in payload["request"]["params"]
+        assert "param2" in payload["request"]["params"]

uv.lock

@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.10"
 resolution-markers = [
     "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
@@ -8,9 +7,12 @@ resolution-markers = [
     "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
 ]
 
 [[package]]
@@ -793,6 +795,107 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/89/30/2bd0eb03a7dee7727cd2ec643d1e992979e62d5e7443507381cce0455132/googleapis_common_protos-1.67.0-py2.py3-none-any.whl", hash = "sha256:579de760800d13616f51cf8be00c876f00a9f146d3e6510e19d1f4111758b741", size = 164985 },
 ]
 
+[[package]]
+name = "grpcio"
+version = "1.71.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/95/aa11fc09a85d91fbc7dd405dcb2a1e0256989d67bf89fa65ae24b3ba105a/grpcio-1.71.0.tar.gz", hash = "sha256:2b85f7820475ad3edec209d3d89a7909ada16caab05d3f2e08a7e8ae3200a55c", size = 12549828 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7c/c5/ef610b3f988cc0cc67b765f72b8e2db06a1db14e65acb5ae7810a6b7042e/grpcio-1.71.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:c200cb6f2393468142eb50ab19613229dcc7829b5ccee8b658a36005f6669fdd", size = 5210643 },
+    { url = "https://files.pythonhosted.org/packages/bf/de/c84293c961622df302c0d5d07ec6e2d4cd3874ea42f602be2df09c4ad44f/grpcio-1.71.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:b2266862c5ad664a380fbbcdbdb8289d71464c42a8c29053820ee78ba0119e5d", size = 11308962 },
+    { url = "https://files.pythonhosted.org/packages/7c/38/04c9e0dc8c904570c80faa1f1349b190b63e45d6b2782ec8567b050efa9d/grpcio-1.71.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:0ab8b2864396663a5b0b0d6d79495657ae85fa37dcb6498a2669d067c65c11ea", size = 5699236 },
+    { url = "https://files.pythonhosted.org/packages/95/96/e7be331d1298fa605ea7c9ceafc931490edd3d5b33c4f695f1a0667f3491/grpcio-1.71.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c30f393f9d5ff00a71bb56de4aa75b8fe91b161aeb61d39528db6b768d7eac69", size = 6339767 },
+    { url = "https://files.pythonhosted.org/packages/5d/b7/7e7b7bb6bb18baf156fd4f2f5b254150dcdd6cbf0def1ee427a2fb2bfc4d/grpcio-1.71.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f250ff44843d9a0615e350c77f890082102a0318d66a99540f54769c8766ab73", size = 5943028 },
+    { url = "https://files.pythonhosted.org/packages/13/aa/5fb756175995aeb47238d706530772d9a7ac8e73bcca1b47dc145d02c95f/grpcio-1.71.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e6d8de076528f7c43a2f576bc311799f89d795aa6c9b637377cc2b1616473804", size = 6031841 },
+    { url = "https://files.pythonhosted.org/packages/54/93/172783e01eed61f7f180617b7fa4470f504e383e32af2587f664576a7101/grpcio-1.71.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:9b91879d6da1605811ebc60d21ab6a7e4bae6c35f6b63a061d61eb818c8168f6", size = 6651039 },
+    { url = "https://files.pythonhosted.org/packages/6f/99/62654b220a27ed46d3313252214f4bc66261143dc9b58004085cd0646753/grpcio-1.71.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:f71574afdf944e6652203cd1badcda195b2a27d9c83e6d88dc1ce3cfb73b31a5", size = 6198465 },
+    { url = "https://files.pythonhosted.org/packages/68/35/96116de833b330abe4412cc94edc68f99ed2fa3e39d8713ff307b3799e81/grpcio-1.71.0-cp310-cp310-win32.whl", hash = "sha256:8997d6785e93308f277884ee6899ba63baafa0dfb4729748200fcc537858a509", size = 3620382 },
+    { url = "https://files.pythonhosted.org/packages/b7/09/f32ef637e386f3f2c02effac49699229fa560ce9007682d24e9e212d2eb4/grpcio-1.71.0-cp310-cp310-win_amd64.whl", hash = "sha256:7d6ac9481d9d0d129224f6d5934d5832c4b1cddb96b59e7eba8416868909786a", size = 4280302 },
+    { url = "https://files.pythonhosted.org/packages/63/04/a085f3ad4133426f6da8c1becf0749872a49feb625a407a2e864ded3fb12/grpcio-1.71.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:d6aa986318c36508dc1d5001a3ff169a15b99b9f96ef5e98e13522c506b37eef", size = 5210453 },
+    { url = "https://files.pythonhosted.org/packages/b4/d5/0bc53ed33ba458de95020970e2c22aa8027b26cc84f98bea7fcad5d695d1/grpcio-1.71.0-cp311-cp311-macosx_10_14_universal2.whl", hash = "sha256:d2c170247315f2d7e5798a22358e982ad6eeb68fa20cf7a820bb74c11f0736e7", size = 11347567 },
+    { url = "https://files.pythonhosted.org/packages/e3/6d/ce334f7e7a58572335ccd61154d808fe681a4c5e951f8a1ff68f5a6e47ce/grpcio-1.71.0-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:e6f83a583ed0a5b08c5bc7a3fe860bb3c2eac1f03f1f63e0bc2091325605d2b7", size = 5696067 },
+    { url = "https://files.pythonhosted.org/packages/05/4a/80befd0b8b1dc2b9ac5337e57473354d81be938f87132e147c4a24a581bd/grpcio-1.71.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4be74ddeeb92cc87190e0e376dbc8fc7736dbb6d3d454f2fa1f5be1dee26b9d7", size = 6348377 },
+    { url = "https://files.pythonhosted.org/packages/c7/67/cbd63c485051eb78663355d9efd1b896cfb50d4a220581ec2cb9a15cd750/grpcio-1.71.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4dd0dfbe4d5eb1fcfec9490ca13f82b089a309dc3678e2edabc144051270a66e", size = 5940407 },
+    { url = "https://files.pythonhosted.org/packages/98/4b/7a11aa4326d7faa499f764eaf8a9b5a0eb054ce0988ee7ca34897c2b02ae/grpcio-1.71.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:a2242d6950dc892afdf9e951ed7ff89473aaf744b7d5727ad56bdaace363722b", size = 6030915 },
+    { url = "https://files.pythonhosted.org/packages/eb/a2/cdae2d0e458b475213a011078b0090f7a1d87f9a68c678b76f6af7c6ac8c/grpcio-1.71.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:0fa05ee31a20456b13ae49ad2e5d585265f71dd19fbd9ef983c28f926d45d0a7", size = 6648324 },
+    { url = "https://files.pythonhosted.org/packages/27/df/f345c8daaa8d8574ce9869f9b36ca220c8845923eb3087e8f317eabfc2a8/grpcio-1.71.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:3d081e859fb1ebe176de33fc3adb26c7d46b8812f906042705346b314bde32c3", size = 6197839 },
+    { url = "https://files.pythonhosted.org/packages/f2/2c/cd488dc52a1d0ae1bad88b0d203bc302efbb88b82691039a6d85241c5781/grpcio-1.71.0-cp311-cp311-win32.whl", hash = "sha256:d6de81c9c00c8a23047136b11794b3584cdc1460ed7cbc10eada50614baa1444", size = 3619978 },
+    { url = "https://files.pythonhosted.org/packages/ee/3f/cf92e7e62ccb8dbdf977499547dfc27133124d6467d3a7d23775bcecb0f9/grpcio-1.71.0-cp311-cp311-win_amd64.whl", hash = "sha256:24e867651fc67717b6f896d5f0cac0ec863a8b5fb7d6441c2ab428f52c651c6b", size = 4282279 },
+    { url = "https://files.pythonhosted.org/packages/4c/83/bd4b6a9ba07825bd19c711d8b25874cd5de72c2a3fbf635c3c344ae65bd2/grpcio-1.71.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:0ff35c8d807c1c7531d3002be03221ff9ae15712b53ab46e2a0b4bb271f38537", size = 5184101 },
+    { url = "https://files.pythonhosted.org/packages/31/ea/2e0d90c0853568bf714693447f5c73272ea95ee8dad107807fde740e595d/grpcio-1.71.0-cp312-cp312-macosx_10_14_universal2.whl", hash = "sha256:b78a99cd1ece4be92ab7c07765a0b038194ded2e0a26fd654591ee136088d8d7", size = 11310927 },
+    { url = "https://files.pythonhosted.org/packages/ac/bc/07a3fd8af80467390af491d7dc66882db43884128cdb3cc8524915e0023c/grpcio-1.71.0-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:dc1a1231ed23caac1de9f943d031f1bc38d0f69d2a3b243ea0d664fc1fbd7fec", size = 5654280 },
+    { url = "https://files.pythonhosted.org/packages/16/af/21f22ea3eed3d0538b6ef7889fce1878a8ba4164497f9e07385733391e2b/grpcio-1.71.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e6beeea5566092c5e3c4896c6d1d307fb46b1d4bdf3e70c8340b190a69198594", size = 6312051 },
+    { url = "https://files.pythonhosted.org/packages/49/9d/e12ddc726dc8bd1aa6cba67c85ce42a12ba5b9dd75d5042214a59ccf28ce/grpcio-1.71.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d5170929109450a2c031cfe87d6716f2fae39695ad5335d9106ae88cc32dc84c", size = 5910666 },
+    { url = "https://files.pythonhosted.org/packages/d9/e9/38713d6d67aedef738b815763c25f092e0454dc58e77b1d2a51c9d5b3325/grpcio-1.71.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:5b08d03ace7aca7b2fadd4baf291139b4a5f058805a8327bfe9aece7253b6d67", size = 6012019 },
+    { url = "https://files.pythonhosted.org/packages/80/da/4813cd7adbae6467724fa46c952d7aeac5e82e550b1c62ed2aeb78d444ae/grpcio-1.71.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:f903017db76bf9cc2b2d8bdd37bf04b505bbccad6be8a81e1542206875d0e9db", size = 6637043 },
+    { url = "https://files.pythonhosted.org/packages/52/ca/c0d767082e39dccb7985c73ab4cf1d23ce8613387149e9978c70c3bf3b07/grpcio-1.71.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:469f42a0b410883185eab4689060a20488a1a0a00f8bbb3cbc1061197b4c5a79", size = 6186143 },
+    { url = "https://files.pythonhosted.org/packages/00/61/7b2c8ec13303f8fe36832c13d91ad4d4ba57204b1c723ada709c346b2271/grpcio-1.71.0-cp312-cp312-win32.whl", hash = "sha256:ad9f30838550695b5eb302add33f21f7301b882937460dd24f24b3cc5a95067a", size = 3604083 },
+    { url = "https://files.pythonhosted.org/packages/fd/7c/1e429c5fb26122055d10ff9a1d754790fb067d83c633ff69eddcf8e3614b/grpcio-1.71.0-cp312-cp312-win_amd64.whl", hash = "sha256:652350609332de6dac4ece254e5d7e1ff834e203d6afb769601f286886f6f3a8", size = 4272191 },
+    { url = "https://files.pythonhosted.org/packages/04/dd/b00cbb45400d06b26126dcfdbdb34bb6c4f28c3ebbd7aea8228679103ef6/grpcio-1.71.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:cebc1b34ba40a312ab480ccdb396ff3c529377a2fce72c45a741f7215bfe8379", size = 5184138 },
+    { url = "https://files.pythonhosted.org/packages/ed/0a/4651215983d590ef53aac40ba0e29dda941a02b097892c44fa3357e706e5/grpcio-1.71.0-cp313-cp313-macosx_10_14_universal2.whl", hash = "sha256:85da336e3649a3d2171e82f696b5cad2c6231fdd5bad52616476235681bee5b3", size = 11310747 },
+    { url = "https://files.pythonhosted.org/packages/57/a3/149615b247f321e13f60aa512d3509d4215173bdb982c9098d78484de216/grpcio-1.71.0-cp313-cp313-manylinux_2_17_aarch64.whl", hash = "sha256:f9a412f55bb6e8f3bb000e020dbc1e709627dcb3a56f6431fa7076b4c1aab0db", size = 5653991 },
+    { url = "https://files.pythonhosted.org/packages/ca/56/29432a3e8d951b5e4e520a40cd93bebaa824a14033ea8e65b0ece1da6167/grpcio-1.71.0-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:47be9584729534660416f6d2a3108aaeac1122f6b5bdbf9fd823e11fe6fbaa29", size = 6312781 },
+    { url = "https://files.pythonhosted.org/packages/a3/f8/286e81a62964ceb6ac10b10925261d4871a762d2a763fbf354115f9afc98/grpcio-1.71.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7c9c80ac6091c916db81131d50926a93ab162a7e97e4428ffc186b6e80d6dda4", size = 5910479 },
+    { url = "https://files.pythonhosted.org/packages/35/67/d1febb49ec0f599b9e6d4d0d44c2d4afdbed9c3e80deb7587ec788fcf252/grpcio-1.71.0-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:789d5e2a3a15419374b7b45cd680b1e83bbc1e52b9086e49308e2c0b5bbae6e3", size = 6013262 },
+    { url = "https://files.pythonhosted.org/packages/a1/04/f9ceda11755f0104a075ad7163fc0d96e2e3a9fe25ef38adfc74c5790daf/grpcio-1.71.0-cp313-cp313-musllinux_1_1_i686.whl", hash = "sha256:1be857615e26a86d7363e8a163fade914595c81fec962b3d514a4b1e8760467b", size = 6643356 },
+    { url = "https://files.pythonhosted.org/packages/fb/ce/236dbc3dc77cf9a9242adcf1f62538734ad64727fabf39e1346ad4bd5c75/grpcio-1.71.0-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:a76d39b5fafd79ed604c4be0a869ec3581a172a707e2a8d7a4858cb05a5a7637", size = 6186564 },
+    { url = "https://files.pythonhosted.org/packages/10/fd/b3348fce9dd4280e221f513dd54024e765b21c348bc475516672da4218e9/grpcio-1.71.0-cp313-cp313-win32.whl", hash = "sha256:74258dce215cb1995083daa17b379a1a5a87d275387b7ffe137f1d5131e2cfbb", size = 3601890 },
+    { url = "https://files.pythonhosted.org/packages/be/f8/db5d5f3fc7e296166286c2a397836b8b042f7ad1e11028d82b061701f0f7/grpcio-1.71.0-cp313-cp313-win_amd64.whl", hash = "sha256:22c3bc8d488c039a199f7a003a38cb7635db6656fa96437a8accde8322ce2366", size = 4273308 },
+]
+
+[[package]]
+name = "grpcio-tools"
+version = "1.71.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "grpcio" },
+    { name = "protobuf" },
+    { name = "setuptools" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/d2/c0866a48c355a6a4daa1f7e27e210c7fa561b1f3b7c0bce2671e89cfa31e/grpcio_tools-1.71.0.tar.gz", hash = "sha256:38dba8e0d5e0fb23a034e09644fdc6ed862be2371887eee54901999e8f6792a8", size = 5326008 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f9/60/aa7f261eda558d018457e5c8bd8a8079136e5107a0942fd3167477ab50e2/grpcio_tools-1.71.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:f4ad7f0d756546902597053d70b3af2606fbd70d7972876cd75c1e241d22ae00", size = 2385558 },
+    { url = "https://files.pythonhosted.org/packages/0d/e3/e47b96e93e51398ba3462e027d93a10c0c23fffc31733de9bd4f44a2b867/grpcio_tools-1.71.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:64bdb291df61cf570b5256777ad5fe2b1db6d67bc46e55dc56a0a862722ae329", size = 5930039 },
+    { url = "https://files.pythonhosted.org/packages/a6/69/5d8920002483b2a65ae3b03329dfe3b668c3592f001d5358e1538f540012/grpcio_tools-1.71.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:8dd9795e982d77a4b496f7278b943c2563d9afde2069cdee78c111a40cc4d675", size = 2351932 },
+    { url = "https://files.pythonhosted.org/packages/c4/50/8116e307662a2337cdc3f0e1a8b23af197129448b7ff7e0cf1a76c9b0178/grpcio_tools-1.71.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c1b5860c41a36b26fec4f52998f1a451d0525a5c9a4fb06b6ea3e9211abdb925", size = 2744962 },
+    { url = "https://files.pythonhosted.org/packages/e3/4b/d95be4aaf78d7b02dff3bd332c75c228288178e92af0e5228759ac5002a0/grpcio_tools-1.71.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3059c14035e5dc03d462f261e5900b9a077fd1a36976c3865b8507474520bad4", size = 2476716 },
+    { url = "https://files.pythonhosted.org/packages/37/c2/c784a3705b1a1fd277751a8fc881d5a29325a460b9211e3c6164f594b178/grpcio_tools-1.71.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:f360981b215b1d5aff9235b37e7e1826246e35bbac32a53e41d4e990a37b8f4c", size = 2854132 },
+    { url = "https://files.pythonhosted.org/packages/93/8f/173adbf72ed3996e1962182b55abf30151edc8b53daac0bf15cc3dc4b09e/grpcio_tools-1.71.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:bfe3888c3bbe16a5aa39409bc38744a31c0c3d2daa2b0095978c56e106c85b42", size = 3305069 },
+    { url = "https://files.pythonhosted.org/packages/e4/a8/b1e7df63e7f83336275922f92ded1cd6918964c511280b31c872c54538f4/grpcio_tools-1.71.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:145985c0bf12131f0a1503e65763e0f060473f7f3928ed1ff3fb0e8aad5bc8ac", size = 2916636 },
+    { url = "https://files.pythonhosted.org/packages/be/a3/53f1e74c6e1c92ad94d7a0127a60fe913276a3e8c864737a053a1574b05c/grpcio_tools-1.71.0-cp310-cp310-win32.whl", hash = "sha256:82c430edd939bb863550ee0fecf067d78feff828908a1b529bbe33cc57f2419c", size = 949576 },
+    { url = "https://files.pythonhosted.org/packages/97/43/4a3ae830c1405bcb1ba47f2225779dbe9fc009ba341d4a90012919304855/grpcio_tools-1.71.0-cp310-cp310-win_amd64.whl", hash = "sha256:83e90724e3f02415c628e4ead1d6ffe063820aaaa078d9a39176793df958cd5a", size = 1121087 },
+    { url = "https://files.pythonhosted.org/packages/5d/ec/73b9797ffec80e1faf039ce3e2f0513e26e1a68eedc525ed294ae2a44d03/grpcio_tools-1.71.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:1f19b16b49afa5d21473f49c0966dd430c88d089cd52ac02404d8cef67134efb", size = 2385557 },
+    { url = "https://files.pythonhosted.org/packages/bf/87/42c6e192b7b09c9610a53e771797f7826aee4f6e769683985ae406a2d862/grpcio_tools-1.71.0-cp311-cp311-macosx_10_14_universal2.whl", hash = "sha256:459c8f5e00e390aecd5b89de67deb3ec7188a274bc6cb50e43cef35ab3a3f45d", size = 5954404 },
+    { url = "https://files.pythonhosted.org/packages/25/30/3fd385a56d32dce34cde09a64dbaf7cf85d395f2bcd86dd41e4b4ee5938f/grpcio_tools-1.71.0-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:edab7e6518de01196be37f96cb1e138c3819986bf5e2a6c9e1519b4d716b2f5a", size = 2352061 },
+    { url = "https://files.pythonhosted.org/packages/87/eb/e9971c7693a2d85e7f55760f7906211a95ff74af4d41b05d187849d7fb58/grpcio_tools-1.71.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8b93b9f6adc7491d4c10144c0643409db298e5e63c997106a804f6f0248dbaf4", size = 2745033 },
+    { url = "https://files.pythonhosted.org/packages/15/72/4e69beae87a1b334f80da9e93c8e2f5c8fe4860c956a781246a092dc4c97/grpcio_tools-1.71.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6ae5f2efa9e644c10bf1021600bfc099dfbd8e02b184d2d25dc31fcd6c2bc59e", size = 2476743 },
+    { url = "https://files.pythonhosted.org/packages/b5/f3/336d2c83f1bfc00a5376bf20dd2273d7aa891b03dd91b11c71ca47392351/grpcio_tools-1.71.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:65aa082f4435571d65d5ce07fc444f23c3eff4f3e34abef599ef8c9e1f6f360f", size = 2853693 },
+    { url = "https://files.pythonhosted.org/packages/62/ba/cc7ace518c11501a4b8620df5edb8188e81470e5b82dc6829212f3e9b2ff/grpcio_tools-1.71.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:1331e726e08b7bdcbf2075fcf4b47dff07842b04845e6e220a08a4663e232d7f", size = 3304474 },
+    { url = "https://files.pythonhosted.org/packages/00/0d/4b843654af3d5aa2f1a5775df1d583e6e3471e6d569106fd3213ad185a98/grpcio_tools-1.71.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:6693a7d3ba138b0e693b3d1f687cdd9db9e68976c3fa2b951c17a072fea8b583", size = 2916147 },
+    { url = "https://files.pythonhosted.org/packages/e4/14/047e1c817422bc3d434247b9c640c51fd51ca4e047583ff31d927c3dea73/grpcio_tools-1.71.0-cp311-cp311-win32.whl", hash = "sha256:6d11ed3ff7b6023b5c72a8654975324bb98c1092426ba5b481af406ff559df00", size = 949374 },
+    { url = "https://files.pythonhosted.org/packages/86/cb/739a1b6d517672693796022c0f9061f63eaa243ec70cbbfa59bf881ed9fb/grpcio_tools-1.71.0-cp311-cp311-win_amd64.whl", hash = "sha256:072b2a5805ac97e4623b3aa8f7818275f3fb087f4aa131b0fce00471065f6eaa", size = 1120786 },
+    { url = "https://files.pythonhosted.org/packages/de/e4/156956b92ad0298290c3d68e6670bc5a6fbefcccfe1ec3997480605e7135/grpcio_tools-1.71.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:61c0409d5bdac57a7bd0ce0ab01c1c916728fe4c8a03d77a25135ad481eb505c", size = 2385480 },
+    { url = "https://files.pythonhosted.org/packages/c1/08/9930eb4bb38c5214041c9f24f8b35e9864a7938282db986836546c782d52/grpcio_tools-1.71.0-cp312-cp312-macosx_10_14_universal2.whl", hash = "sha256:28784f39921d061d2164a9dcda5164a69d07bf29f91f0ea50b505958292312c9", size = 5951891 },
+    { url = "https://files.pythonhosted.org/packages/73/65/931f29ec9c33719d48e1e30446ecce6f5d2cd4e4934fa73fbe07de41c43b/grpcio_tools-1.71.0-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:192808cf553cedca73f0479cc61d5684ad61f24db7a5f3c4dfe1500342425866", size = 2351967 },
+    { url = "https://files.pythonhosted.org/packages/b8/26/2ec8748534406214f20a4809c36efcfa88d1a26246e8312102e3ef8c295d/grpcio_tools-1.71.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:989ee9da61098230d3d4c8f8f8e27c2de796f1ff21b1c90110e636d9acd9432b", size = 2745003 },
+    { url = "https://files.pythonhosted.org/packages/f1/33/87b4610c86a4e10ee446b543a4d536f94ab04f828bab841f0bc1a083de72/grpcio_tools-1.71.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:541a756276c8a55dec991f6c0106ae20c8c8f5ce8d0bdbfcb01e2338d1a8192b", size = 2476455 },
+    { url = "https://files.pythonhosted.org/packages/00/7c/f7f0cc36a43be9d45b3ce2a55245f3c7d063a24b7930dd719929e58871a4/grpcio_tools-1.71.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:870c0097700d13c403e5517cb7750ab5b4a791ce3e71791c411a38c5468b64bd", size = 2854333 },
+    { url = "https://files.pythonhosted.org/packages/07/c4/34b9ea62b173c13fa7accba5f219355b320c05c80c79c3ba70fe52f47b2f/grpcio_tools-1.71.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:abd57f615e88bf93c3c6fd31f923106e3beb12f8cd2df95b0d256fa07a7a0a57", size = 3304297 },
+    { url = "https://files.pythonhosted.org/packages/5c/ef/9d3449db8a07688dc3de7dcbd2a07048a128610b1a491c5c0cb3e90a00c5/grpcio_tools-1.71.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:753270e2d06d37e6d7af8967d1d059ec635ad215882041a36294f4e2fd502b2e", size = 2916212 },
+    { url = "https://files.pythonhosted.org/packages/2e/c6/990e8194c934dfe7cf89ef307c319fa4f2bc0b78aeca707addbfa1e502f1/grpcio_tools-1.71.0-cp312-cp312-win32.whl", hash = "sha256:0e647794bd7138b8c215e86277a9711a95cf6a03ff6f9e555d54fdf7378b9f9d", size = 948849 },
+    { url = "https://files.pythonhosted.org/packages/42/95/3c36d3205e6bd19853cc2420e44b6ef302eb4cfcf56498973c7e85f6c03b/grpcio_tools-1.71.0-cp312-cp312-win_amd64.whl", hash = "sha256:48debc879570972d28bfe98e4970eff25bb26da3f383e0e49829b2d2cd35ad87", size = 1120294 },
+    { url = "https://files.pythonhosted.org/packages/84/a7/70dc7e9957bcbaccd4dcb6cc11215e0b918f546d55599221522fe0d073e0/grpcio_tools-1.71.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:9a78d07d6c301a25ef5ede962920a522556a1dfee1ccc05795994ceb867f766c", size = 2384758 },
+    { url = "https://files.pythonhosted.org/packages/65/79/57320b28d0a0c5ec94095fd571a65292f8ed7e1c47e59ae4021e8a48d49b/grpcio_tools-1.71.0-cp313-cp313-macosx_10_14_universal2.whl", hash = "sha256:580ac88141c9815557e63c9c04f5b1cdb19b4db8d0cb792b573354bde1ee8b12", size = 5951661 },
+    { url = "https://files.pythonhosted.org/packages/80/3d/343df5ed7c5dd66fc7a19e4ef3e97ccc4f5d802122b04cd6492f0dcd79f5/grpcio_tools-1.71.0-cp313-cp313-manylinux_2_17_aarch64.whl", hash = "sha256:f7c678e68ece0ae908ecae1c4314a0c2c7f83e26e281738b9609860cc2c82d96", size = 2351571 },
+    { url = "https://files.pythonhosted.org/packages/56/2f/b9736e8c84e880c4237f5b880c6c799b4977c5cde190999bc7ab4b2ec445/grpcio_tools-1.71.0-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:56ecd6cc89b5e5eed1de5eb9cafce86c9c9043ee3840888cc464d16200290b53", size = 2744580 },
+    { url = "https://files.pythonhosted.org/packages/76/9b/bdb384967353da7bf64bac4232f4cf8ae43f19d0f2f640978d4d4197e667/grpcio_tools-1.71.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e52a041afc20ab2431d756b6295d727bd7adee813b21b06a3483f4a7a15ea15f", size = 2475978 },
+    { url = "https://files.pythonhosted.org/packages/26/71/1411487fd7862d347b98fda5e3beef611a71b2ac2faac62a965d9e2536b3/grpcio_tools-1.71.0-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:2a1712f12102b60c8d92779b89d0504e0d6f3a59f2b933e5622b8583f5c02992", size = 2853314 },
+    { url = "https://files.pythonhosted.org/packages/03/06/59d0523eb1ba2f64edc72cb150152fa1b2e77061cae3ef3ecd3ef2a87f51/grpcio_tools-1.71.0-cp313-cp313-musllinux_1_1_i686.whl", hash = "sha256:41878cb7a75477e62fdd45e7e9155b3af1b7a5332844021e2511deaf99ac9e6c", size = 3303981 },
+    { url = "https://files.pythonhosted.org/packages/c2/71/fb9fb49f2b738ec1dfbbc8cdce0b26e5f9c5fc0edef72e453580620d6a36/grpcio_tools-1.71.0-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:682e958b476049ccc14c71bedf3f979bced01f6e0c04852efc5887841a32ad6b", size = 2915876 },
+    { url = "https://files.pythonhosted.org/packages/bd/0f/0d49f6fe6fa2d09e9820dd9eeb30437e86002303076be2b6ada0fb52b8f2/grpcio_tools-1.71.0-cp313-cp313-win32.whl", hash = "sha256:0ccfb837152b7b858b9f26bb110b3ae8c46675d56130f6c2f03605c4f129be13", size = 948245 },
+    { url = "https://files.pythonhosted.org/packages/bb/14/ab131a39187bfea950280b2277a82d2033469fe8c86f73b10b19f53cc5ca/grpcio_tools-1.71.0-cp313-cp313-win_amd64.whl", hash = "sha256:ffff9bc5eacb34dd26b487194f7d44a3e64e752fc2cf049d798021bf25053b87", size = 1119649 },
+]
+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -802,6 +905,28 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/95/04/ff642e65ad6b90db43e668d70ffb6736436c7ce41fcc549f4e9472234127/h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761", size = 58259 },
 ]
 
+[[package]]
+name = "h2"
+version = "4.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "hpack" },
+    { name = "hyperframe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1b/38/d7f80fd13e6582fb8e0df8c9a653dcc02b03ca34f4d72f34869298c5baf8/h2-4.2.0.tar.gz", hash = "sha256:c8a52129695e88b1a0578d8d2cc6842bbd79128ac685463b887ee278126ad01f", size = 2150682 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d0/9e/984486f2d0a0bd2b024bf4bc1c62688fcafa9e61991f041fb0e2def4a982/h2-4.2.0-py3-none-any.whl", hash = "sha256:479a53ad425bb29af087f3458a61d30780bc818e4ebcf01f0b536ba916462ed0", size = 60957 },
+]
+
+[[package]]
+name = "hpack"
+version = "4.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2c/48/71de9ed269fdae9c8057e5a4c0aa7402e8bb16f2c6e90b3aa53327b113f8/hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca", size = 51276 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/07/c6/80c95b1b2b94682a72cbdbfb85b81ae2daffa4291fbfa1b1464502ede10d/hpack-4.1.0-py3-none-any.whl", hash = "sha256:157ac792668d995c657d93111f46b4535ed114f0c9c8d672271bbec7eae1b496", size = 34357 },
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.7"
@@ -830,6 +955,11 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517 },
 ]
 
+[package.optional-dependencies]
+http2 = [
+    { name = "h2" },
+]
+
 [[package]]
 name = "httpx-sse"
 version = "0.4.0"
@@ -857,6 +987,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2a/4d/8092df2cb0cafa9fcaf691db851b2fccfe9cad4048e081436bbbdf56e4e1/huggingface_hub-0.29.0-py3-none-any.whl", hash = "sha256:c02daa0b6bafbdacb1320fdfd1dc7151d0940825c88c4ef89837fdb1f6ea0afe", size = 468012 },
 ]
 
+[[package]]
+name = "hyperframe"
+version = "6.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/02/e7/94f8232d4a74cc99514c13a9f995811485a6903d48e5d952771ef6322e30/hyperframe-6.1.0.tar.gz", hash = "sha256:f630908a00854a7adeabd6382b43923a4c4cd4b821fcb527e6ab9e15382a3b08", size = 26566 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/48/30/47d0bf6072f7252e6521f3447ccfa40b421b6824517f82854703d0f5a98b/hyperframe-6.1.0-py3-none-any.whl", hash = "sha256:b03380493a519fce58ea5af42e4a42317bf9bd425596f7a0835ffce80f1a42e5", size = 13007 },
+]
+
 [[package]]
 name = "identify"
 version = "2.6.7"
@@ -1250,6 +1389,7 @@ unit = [
     { name = "chardet" },
     { name = "openai" },
     { name = "pypdf" },
+    { name = "qdrant-client" },
     { name = "sqlite-vec" },
 ]
 
@@ -1290,6 +1430,7 @@ requires-dist = [
     { name = "pytest-cov", marker = "extra == 'dev'" },
     { name = "pytest-html", marker = "extra == 'dev'" },
     { name = "python-dotenv" },
+    { name = "qdrant-client", marker = "extra == 'unit'" },
     { name = "requests" },
     { name = "rich" },
     { name = "rich", marker = "extra == 'codegen'" },
@@ -1314,7 +1455,6 @@ requires-dist = [
     { name = "types-setuptools", marker = "extra == 'dev'" },
     { name = "uvicorn", marker = "extra == 'dev'" },
 ]
-provides-extras = ["dev", "unit", "test", "docs", "codegen"]
 
 [[package]]
 name = "llama-stack-client"
@@ -2062,6 +2202,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669", size = 20556 },
 ]
 
+[[package]]
+name = "portalocker"
+version = "2.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pywin32", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ed/d3/c6c64067759e87af98cc668c1cc75171347d0f1577fab7ca3749134e3cd4/portalocker-2.10.1.tar.gz", hash = "sha256:ef1bf844e878ab08aee7e40184156e1151f228f103aa5c6bd0724cc330960f8f", size = 40891 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9b/fb/a70a4214956182e0d7a9099ab17d50bfcba1056188e9b14f35b9e2b62a0d/portalocker-2.10.1-py3-none-any.whl", hash = "sha256:53a5984ebc86a025552264b459b46a2086e269b21823cb572f8f28ee759e45bf", size = 18423 },
+]
+
 [[package]]
 name = "pre-commit"
 version = "4.1.0"
@@ -2668,6 +2820,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e3/fe/72e7e166bda3885810bee7b23049133e142f7c80c295bae02c562caeea16/pyzmq-26.2.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:bd8fdee945b877aa3bffc6a5a8816deb048dab0544f9df3731ecd0e54d8c84c9", size = 556563 },
 ]
 
+[[package]]
+name = "qdrant-client"
+version = "1.13.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "grpcio" },
+    { name = "grpcio-tools" },
+    { name = "httpx", extra = ["http2"] },
+    { name = "numpy" },
+    { name = "portalocker" },
+    { name = "pydantic" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/eb/58/1e4acd7ff7637ed56a66e5044699e7af6067232703d0b34f05068fc6234b/qdrant_client-1.13.3.tar.gz", hash = "sha256:61ca09e07c6d7ac0dfbdeb13dca4fe5f3e08fa430cb0d74d66ef5d023a70adfc", size = 266278 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dd/b4/bd676f91f5234ab59282e4a110f324029684482cbe08e7a1c77b6338013b/qdrant_client-1.13.3-py3-none-any.whl", hash = "sha256:f52cacbb936e547d3fceb1aaed3e3c56be0ebfd48e8ea495ea3dbc89c671d1d2", size = 306674 },
+]
+
 [[package]]
 name = "rapidfuzz"
 version = "3.12.2"
@@ -3417,7 +3587,8 @@ source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
     "python_full_version < '3.11' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
 ]
 dependencies = [
     { name = "filelock", marker = "sys_platform == 'darwin'" },
@@ -3444,8 +3615,10 @@ resolution-markers = [
     "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
 ]
 dependencies = [
     { name = "filelock", marker = "sys_platform != 'darwin'" },
@@ -3482,8 +3655,10 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
 ]
 dependencies = [
     { name = "numpy", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
@@ -3509,7 +3684,8 @@ source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
     "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 dependencies = [
     { name = "numpy", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },