Merge branch 'main' into eval_api_final

2025-03-18 14:49:57 -07:00 · 2025-03-18 14:49:57 -07:00 · 46f2ba5910
commit 46f2ba5910
parent ade3391170 5ece262976
46 changed files with 1030 additions and 423 deletions
--- a/.cursor/rules/general.mdc
+++ b/.cursor/rules/general.mdc
@ -1,9 +0,0 @@
---
-description: General rules always applicable across the project
-globs:
-alwaysApply: true
---
-# Style
-
- Comments must add value to code. Don't write filler comments explaining what you are doing next; they just add noise.
- Add a comment to clarify surprising behavior which would not be obvious. Good variable naming and clear code organization is more important.
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@ -5,4 +5,19 @@ updates:
  - package-ecosystem: "github-actions"
    directory: "/" # Will use the default workflow location of `.github/workflows`
    schedule:
-      interval: "daily"
+      interval: "weekly"
+      day: "saturday"
+    commit-message:
+      prefix: chore(github-deps)
+  - package-ecosystem: "uv"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+      day: "saturday"
+    # ignore all non-security updates: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#open-pull-requests-limit
+    open-pull-requests-limit: 0
+    labels:
+      - type/dependencies
+      - python
+    commit-message:
+      prefix: chore(python-deps)
--- a/.github/workflows/integration-tests.yml
+++ b/.github/workflows/integration-tests.yml
@ -1,4 +1,4 @@
-name: Integration tests
+name: Integration Tests

 on:
  push:
@ -15,8 +15,14 @@ on:
      - '.github/workflows/integration-tests.yml' # This workflow

 jobs:
-  ollama:
+  test-matrix:
    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        # Listing tests manually since some of them currently fail
+        # TODO: generate matrix list from tests/integration when fixed
+        test-type: [inference, datasets, inspect, scoring, post_training, providers]
+      fail-fast: false # we want to run all tests regardless of failure

    steps:
      - name: Checkout repository
@ -43,6 +49,8 @@ jobs:
        run: |
          uv sync --extra dev --extra test
          uv pip install ollama faiss-cpu
+          # always test against the latest version of the client
+          uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main
          uv pip install -e .

      - name: Wait for Ollama to start
@ -72,17 +80,17 @@ jobs:
          echo "Waiting for Llama Stack server..."
          for i in {1..30}; do
            if curl -s http://localhost:8321/v1/health | grep -q "OK"; then
-              echo " Llama Stack server is up!"
+              echo "Llama Stack server is up!"
              exit 0
            fi
            sleep 1
          done
-          echo " Llama Stack server failed to start"
+          echo "Llama Stack server failed to start"
          cat server.log
          exit 1

-      - name: Run Inference Integration Tests
+      - name: Run Integration Tests
        env:
          INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
        run: |
-          uv run pytest -v tests/integration/inference --stack-config=ollama --text-model="meta-llama/Llama-3.2-3B-Instruct" --embedding-model=all-MiniLM-L6-v2
+          uv run pytest -v tests/integration/${{ matrix.test-type }} --stack-config=ollama --text-model="meta-llama/Llama-3.2-3B-Instruct" --embedding-model=all-MiniLM-L6-v2
--- a/CONTRIBUTING.md
+++ b/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
--- a/README.md
+++ b/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)

--- a/docs/_static/llama-stack-spec.html
+++ b/docs/_static/llama-stack-spec.html
@ -1126,6 +1126,7 @@
                            "application/json": {
                                "schema": {
                                    "$ref": "#/components/schemas/Benchmark"
+                                    "$ref": "#/components/schemas/Benchmark"
                                }
                            }
                        }
@ -1168,14 +1169,7 @@
                        "content": {
                            "application/json": {
                                "schema": {
-                                    "oneOf": [
-                                        {
-                                            "$ref": "#/components/schemas/Dataset"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
+                                    "$ref": "#/components/schemas/Dataset"
                                }
                            }
                        }
@ -1250,14 +1244,7 @@
                        "content": {
                            "application/json": {
                                "schema": {
-                                    "oneOf": [
-                                        {
-                                            "$ref": "#/components/schemas/Model"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
+                                    "$ref": "#/components/schemas/Model"
                                }
                            }
                        }
@ -1332,14 +1319,7 @@
                        "content": {
                            "application/json": {
                                "schema": {
-                                    "oneOf": [
-                                        {
-                                            "$ref": "#/components/schemas/Shield"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
+                                    "$ref": "#/components/schemas/Shield"
                                }
                            }
                        }
@ -1642,14 +1622,7 @@
                        "content": {
                            "application/json": {
                                "schema": {
-                                    "oneOf": [
-                                        {
-                                            "$ref": "#/components/schemas/PostTrainingJobArtifactsResponse"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
+                                    "$ref": "#/components/schemas/PostTrainingJobArtifactsResponse"
                                }
                            }
                        }
@ -1691,14 +1664,7 @@
                        "content": {
                            "application/json": {
                                "schema": {
-                                    "oneOf": [
-                                        {
-                                            "$ref": "#/components/schemas/PostTrainingJobStatusResponse"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
+                                    "$ref": "#/components/schemas/PostTrainingJobStatusResponse"
                                }
                            }
                        }
@ -1773,14 +1739,7 @@
                        "content": {
                            "application/json": {
                                "schema": {
-                                    "oneOf": [
-                                        {
-                                            "$ref": "#/components/schemas/FileUploadResponse"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
+                                    "$ref": "#/components/schemas/FileUploadResponse"
                                }
                            }
                        }
@ -1882,14 +1841,7 @@
                        "content": {
                            "application/json": {
                                "schema": {
-                                    "oneOf": [
-                                        {
-                                            "$ref": "#/components/schemas/VectorDB"
-                                        },
-                                        {
-                                            "type": "null"
-                                        }
-                                    ]
+                                    "$ref": "#/components/schemas/VectorDB"
                                }
                            }
                        }
--- a/docs/_static/llama-stack-spec.yaml
+++ b/docs/_static/llama-stack-spec.yaml
@ -774,6 +774,7 @@ paths:
            application/json:
              schema:
                $ref: '#/components/schemas/Benchmark'
+                $ref: '#/components/schemas/Benchmark'
        '400':
          $ref: '#/components/responses/BadRequest400'
        '429':
@ -802,9 +803,7 @@ paths:
          content:
            application/json:
              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/Dataset'
-                  - type: 'null'
+                $ref: '#/components/schemas/Dataset'
        '400':
          $ref: '#/components/responses/BadRequest400'
        '429':
@ -855,9 +854,7 @@ paths:
          content:
            application/json:
              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/Model'
-                  - type: 'null'
+                $ref: '#/components/schemas/Model'
        '400':
          $ref: '#/components/responses/BadRequest400'
        '429':
@ -908,9 +905,7 @@ paths:
          content:
            application/json:
              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/Shield'
-                  - type: 'null'
+                $ref: '#/components/schemas/Shield'
        '400':
          $ref: '#/components/responses/BadRequest400'
        '429':
@ -1112,9 +1107,7 @@ paths:
          content:
            application/json:
              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/PostTrainingJobArtifactsResponse'
-                  - type: 'null'
+                $ref: '#/components/schemas/PostTrainingJobArtifactsResponse'
        '400':
          $ref: '#/components/responses/BadRequest400'
        '429':
@ -1142,9 +1135,7 @@ paths:
          content:
            application/json:
              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/PostTrainingJobStatusResponse'
-                  - type: 'null'
+                $ref: '#/components/schemas/PostTrainingJobStatusResponse'
        '400':
          $ref: '#/components/responses/BadRequest400'
        '429':
@ -1195,9 +1186,7 @@ paths:
          content:
            application/json:
              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/FileUploadResponse'
-                  - type: 'null'
+                $ref: '#/components/schemas/FileUploadResponse'
        '400':
          $ref: '#/components/responses/BadRequest400'
        '429':
@ -1266,9 +1255,7 @@ paths:
          content:
            application/json:
              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/VectorDB'
-                  - type: 'null'
+                $ref: '#/components/schemas/VectorDB'
        '400':
          $ref: '#/components/responses/BadRequest400'
        '429':
--- a/docs/openapi_generator/generate.py
+++ b/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
--- a/docs/openapi_generator/pyopenapi/utility.py
+++ b/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
--- a/docs/source/distributions/building_distro.md
+++ b/docs/source/distributions/building_distro.md
@ -185,8 +185,12 @@ llama stack build --config llama_stack/templates/ollama/build.yaml
 :::

 :::{tab-item} Building Container
-> [!TIP]
-> Podman is supported as an alternative to Docker. Set `CONTAINER_BINARY` to `podman` in your environment to use Podman.
+
+```{admonition} Podman Alternative
+:class: tip
+
+Podman is supported as an alternative to Docker. Set `CONTAINER_BINARY` to `podman` in your environment to use Podman.
+```

 To build a container image, you may start off from a template and use the `--image-type container` flag to specify `container` as the build image type.

--- a/docs/source/playground/index.md
+++ b/docs/source/playground/index.md
@ -92,6 +92,8 @@ Interactive pages for users to play with and explore Llama Stack API capabilitie

 ## Starting the Llama Stack Playground

+### Llama CLI
+
 To start the Llama Stack Playground, run the following commands:

 1. Start up the Llama Stack API server
@ -107,3 +109,28 @@ cd llama_stack/distribution/ui
 pip install -r requirements.txt
 streamlit run app.py
 ```
+
+### Docker
+
+Playground can also be started in a docker image:
+
+```sh
+export LLAMA_STACK_URL=http://localhost:11434
+
+docker run \
+  -p 8501:8501 \
+  -e LLAMA_STACK_ENDPOINT=$LLAMA_STACK_URL \
+  quay.io/jland/llama-stack-playground
+```
+
+## Configurable Environment Variables
+
+## Environment Variables
+
+| Environment Variable       | Description                        | Default Value             |
+|----------------------------|------------------------------------|---------------------------|
+| LLAMA_STACK_ENDPOINT       | The endpoint for the Llama Stack   | http://localhost:8321     |
+| FIREWORKS_API_KEY          | API key for Fireworks provider     | (empty string)            |
+| TOGETHER_API_KEY           | API key for Together provider      | (empty string)            |
+| SAMBANOVA_API_KEY          | API key for SambaNova provider     | (empty string)            |
+| OPENAI_API_KEY             | API key for OpenAI provider        | (empty string)            |
--- a/docs/source/providers/vector_io/qdrant.md
+++ b/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
--- a/llama_stack/apis/datasets/datasets.py
+++ b/llama_stack/apis/datasets/datasets.py
@ -201,7 +201,7 @@ 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: ...
--- a/llama_stack/apis/eval/eval.py
+++ b/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.
--- a/llama_stack/apis/files/files.py
+++ b/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

--- a/llama_stack/apis/models/models.py
+++ b/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(
--- a/llama_stack/apis/post_training/post_training.py
+++ b/llama_stack/apis/post_training/post_training.py
@ -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: ...
--- a/llama_stack/apis/scoring_functions/scoring_functions.py
+++ b/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(
--- a/llama_stack/apis/shields/shields.py
+++ b/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(
--- a/llama_stack/apis/vector_dbs/vector_dbs.py
+++ b/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(
--- a/llama_stack/distribution/routers/routing_tables.py
+++ b/llama_stack/distribution/routers/routing_tables.py
@ -219,8 +219,11 @@ 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]:
-        return await self.get_object_by_identifier("model", model_id)
+    async def get_model(self, model_id: str) -> Model:
+        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 +270,11 @@ 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]:
-        return await self.get_object_by_identifier("shield", identifier)
+    async def get_shield(self, identifier: str) -> Shield:
+        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 +309,11 @@ 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]:
-        return await self.get_object_by_identifier("vector_db", vector_db_id)
+    async def get_vector_db(self, vector_db_id: str) -> VectorDB:
+        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 +364,11 @@ 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]:
-        return await self.get_object_by_identifier("dataset", dataset_id)
+    async def get_dataset(self, dataset_id: str) -> Dataset:
+        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,
@ -408,8 +420,11 @@ 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]:
-        return await self.get_object_by_identifier("scoring_function", scoring_fn_id)
+    async def get_scoring_function(self, scoring_fn_id: str) -> ScoringFn:
+        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 +460,11 @@ 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]:
-        return await self.get_object_by_identifier("benchmark", benchmark_id)
+    async def get_benchmark(self, benchmark_id: str) -> Benchmark:
+        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 +508,10 @@ 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)
--- a/llama_stack/distribution/ui/Containerfile
+++ b/llama_stack/distribution/ui/Containerfile
@ -0,0 +1,11 @@
+# More info on playground configuration can be found here:
+# https://llama-stack.readthedocs.io/en/latest/playground
+
+FROM python:3.9-slim
+WORKDIR /app
+COPY . /app/
+RUN /usr/local/bin/python -m pip install --upgrade pip && \
+    /usr/local/bin/pip3 install -r requirements.txt
+EXPOSE 8501
+
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
--- a/llama_stack/distribution/ui/README.md
+++ b/llama_stack/distribution/ui/README.md
@ -40,3 +40,13 @@ cd llama_stack/distribution/ui
 pip install -r requirements.txt
 streamlit run app.py
 ```
+
+## Environment Variables
+
+| Environment Variable       | Description                        | Default Value             |
+|----------------------------|------------------------------------|---------------------------|
+| LLAMA_STACK_ENDPOINT       | The endpoint for the Llama Stack   | http://localhost:8321     |
+| FIREWORKS_API_KEY          | API key for Fireworks provider     | (empty string)            |
+| TOGETHER_API_KEY           | API key for Together provider      | (empty string)            |
+| SAMBANOVA_API_KEY          | API key for SambaNova provider     | (empty string)            |
+| OPENAI_API_KEY             | API key for OpenAI provider        | (empty string)            |
--- a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
+++ b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
@ -614,118 +614,133 @@ class ChatAgent(ShieldRunnerMixin):
                    logger.debug(f"completion message with EOM (iter: {n_iter}): {str(message)}")
                    input_messages = input_messages + [message]
            else:
-                logger.debug(f"completion message (iter: {n_iter}) from the model: {str(message)}")
-                # 1. Start the tool execution step and progress
-                step_id = str(uuid.uuid4())
-                yield AgentTurnResponseStreamChunk(
-                    event=AgentTurnResponseEvent(
-                        payload=AgentTurnResponseStepStartPayload(
-                            step_type=StepType.tool_execution.value,
-                            step_id=step_id,
-                        )
-                    )
-                )
-                tool_call = message.tool_calls[0]
-                yield AgentTurnResponseStreamChunk(
-                    event=AgentTurnResponseEvent(
-                        payload=AgentTurnResponseStepProgressPayload(
-                            step_type=StepType.tool_execution.value,
-                            step_id=step_id,
-                            tool_call=tool_call,
-                            delta=ToolCallDelta(
-                                parse_status=ToolCallParseStatus.in_progress,
-                                tool_call=tool_call,
-                            ),
-                        )
-                    )
-                )
+                input_messages = input_messages + [message]

-                # If tool is a client tool, yield CompletionMessage and return
-                if tool_call.tool_name in client_tools:
-                    # NOTE: mark end_of_message to indicate to client that it may
-                    # call the tool and continue the conversation with the tool's response.
-                    message.stop_reason = StopReason.end_of_message
+                # Process tool calls in the message
+                client_tool_calls = []
+                non_client_tool_calls = []
+
+                # Separate client and non-client tool calls
+                for tool_call in message.tool_calls:
+                    if tool_call.tool_name in client_tools:
+                        client_tool_calls.append(tool_call)
+                    else:
+                        non_client_tool_calls.append(tool_call)
+
+                # Process non-client tool calls first
+                for tool_call in non_client_tool_calls:
+                    step_id = str(uuid.uuid4())
+                    yield AgentTurnResponseStreamChunk(
+                        event=AgentTurnResponseEvent(
+                            payload=AgentTurnResponseStepStartPayload(
+                                step_type=StepType.tool_execution.value,
+                                step_id=step_id,
+                            )
+                        )
+                    )
+
+                    yield AgentTurnResponseStreamChunk(
+                        event=AgentTurnResponseEvent(
+                            payload=AgentTurnResponseStepProgressPayload(
+                                step_type=StepType.tool_execution.value,
+                                step_id=step_id,
+                                delta=ToolCallDelta(
+                                    parse_status=ToolCallParseStatus.in_progress,
+                                    tool_call=tool_call,
+                                ),
+                            )
+                        )
+                    )
+
+                    # Execute the tool call
+                    async with tracing.span(
+                        "tool_execution",
+                        {
+                            "tool_name": tool_call.tool_name,
+                            "input": message.model_dump_json(),
+                        },
+                    ) as span:
+                        tool_execution_start_time = datetime.now(timezone.utc).isoformat()
+                        tool_result = await self.execute_tool_call_maybe(
+                            session_id,
+                            tool_call,
+                        )
+                        if tool_result.content is None:
+                            raise ValueError(
+                                f"Tool call result (id: {tool_call.call_id}, name: {tool_call.tool_name}) does not have any content"
+                            )
+                        result_message = ToolResponseMessage(
+                            call_id=tool_call.call_id,
+                            content=tool_result.content,
+                        )
+                        span.set_attribute("output", result_message.model_dump_json())
+
+                        # Store tool execution step
+                        tool_execution_step = ToolExecutionStep(
+                            step_id=step_id,
+                            turn_id=turn_id,
+                            tool_calls=[tool_call],
+                            tool_responses=[
+                                ToolResponse(
+                                    call_id=tool_call.call_id,
+                                    tool_name=tool_call.tool_name,
+                                    content=tool_result.content,
+                                    metadata=tool_result.metadata,
+                                )
+                            ],
+                            started_at=tool_execution_start_time,
+                            completed_at=datetime.now(timezone.utc).isoformat(),
+                        )
+
+                        # Yield the step completion event
+                        yield AgentTurnResponseStreamChunk(
+                            event=AgentTurnResponseEvent(
+                                payload=AgentTurnResponseStepCompletePayload(
+                                    step_type=StepType.tool_execution.value,
+                                    step_id=step_id,
+                                    step_details=tool_execution_step,
+                                )
+                            )
+                        )
+
+                        # Add the result message to input_messages for the next iteration
+                        input_messages.append(result_message)
+
+                        # TODO: add tool-input touchpoint and a "start" event for this step also
+                        # but that needs a lot more refactoring of Tool code potentially
+                        if (type(result_message.content) is str) and (
+                            out_attachment := _interpret_content_as_attachment(result_message.content)
+                        ):
+                            # NOTE: when we push this message back to the model, the model may ignore the
+                            # attached file path etc. since the model is trained to only provide a user message
+                            # with the summary. We keep all generated attachments and then attach them to final message
+                            output_attachments.append(out_attachment)
+
+                # If there are client tool calls, yield a message with only those tool calls
+                if client_tool_calls:
                    await self.storage.set_in_progress_tool_call_step(
                        session_id,
                        turn_id,
                        ToolExecutionStep(
                            step_id=step_id,
                            turn_id=turn_id,
-                            tool_calls=[tool_call],
+                            tool_calls=client_tool_calls,
                            tool_responses=[],
                            started_at=datetime.now(timezone.utc).isoformat(),
                        ),
                    )
-                    yield message
+
+                    # Create a copy of the message with only client tool calls
+                    client_message = message.model_copy(deep=True)
+                    client_message.tool_calls = client_tool_calls
+                    # NOTE: mark end_of_message to indicate to client that it may
+                    # call the tool and continue the conversation with the tool's response.
+                    client_message.stop_reason = StopReason.end_of_message
+
+                    # Yield the message with client tool calls
+                    yield client_message
                    return

-                # If tool is a builtin server tool, execute it
-                tool_name = tool_call.tool_name
-                if isinstance(tool_name, BuiltinTool):
-                    tool_name = tool_name.value
-                async with tracing.span(
-                    "tool_execution",
-                    {
-                        "tool_name": tool_name,
-                        "input": message.model_dump_json(),
-                    },
-                ) as span:
-                    tool_execution_start_time = datetime.now(timezone.utc).isoformat()
-                    tool_call = message.tool_calls[0]
-                    tool_result = await self.execute_tool_call_maybe(
-                        session_id,
-                        tool_call,
-                    )
-                    if tool_result.content is None:
-                        raise ValueError(
-                            f"Tool call result (id: {tool_call.call_id}, name: {tool_call.tool_name}) does not have any content"
-                        )
-                    result_messages = [
-                        ToolResponseMessage(
-                            call_id=tool_call.call_id,
-                            content=tool_result.content,
-                        )
-                    ]
-                    assert len(result_messages) == 1, "Currently not supporting multiple messages"
-                    result_message = result_messages[0]
-                    span.set_attribute("output", result_message.model_dump_json())
-
-                yield AgentTurnResponseStreamChunk(
-                    event=AgentTurnResponseEvent(
-                        payload=AgentTurnResponseStepCompletePayload(
-                            step_type=StepType.tool_execution.value,
-                            step_id=step_id,
-                            step_details=ToolExecutionStep(
-                                step_id=step_id,
-                                turn_id=turn_id,
-                                tool_calls=[tool_call],
-                                tool_responses=[
-                                    ToolResponse(
-                                        call_id=result_message.call_id,
-                                        tool_name=tool_call.tool_name,
-                                        content=result_message.content,
-                                        metadata=tool_result.metadata,
-                                    )
-                                ],
-                                started_at=tool_execution_start_time,
-                                completed_at=datetime.now(timezone.utc).isoformat(),
-                            ),
-                        )
-                    )
-                )
-
-                # TODO: add tool-input touchpoint and a "start" event for this step also
-                # but that needs a lot more refactoring of Tool code potentially
-                if (type(result_message.content) is str) and (
-                    out_attachment := _interpret_content_as_attachment(result_message.content)
-                ):
-                    # NOTE: when we push this message back to the model, the model may ignore the
-                    # attached file path etc. since the model is trained to only provide a user message
-                    # with the summary. We keep all generated attachments and then attach them to final message
-                    output_attachments.append(out_attachment)
-
-                input_messages = input_messages + [message, result_message]
-
    async def _initialize_tools(
        self,
        toolgroups_for_turn: Optional[List[AgentToolGroup]] = None,
@ -891,16 +906,14 @@ class ChatAgent(ShieldRunnerMixin):
        if memory_tool and code_interpreter_tool:
            # if both memory and code_interpreter are available, we download the URLs
            # and attach the data to the last message.
-            msg = await attachment_message(self.tempdir, url_items)
-            input_messages.append(msg)
+            await attachment_message(self.tempdir, url_items, input_messages[-1])
            # Since memory is present, add all the data to the memory bank
            await self.add_to_session_vector_db(session_id, documents)
        elif code_interpreter_tool:
            # if only code_interpreter is available, we download the URLs to a tempdir
            # and attach the path to them as a message to inference with the
            # assumption that the model invokes the code_interpreter tool with the path
-            msg = await attachment_message(self.tempdir, url_items)
-            input_messages.append(msg)
+            await attachment_message(self.tempdir, url_items, input_messages[-1])
        elif memory_tool:
            # if only memory is available, we load the data from the URLs and content items to the memory bank
            await self.add_to_session_vector_db(session_id, documents)
@ -967,8 +980,8 @@ async def load_data_from_urls(urls: List[URL]) -> List[str]:
    return data


-async def attachment_message(tempdir: str, urls: List[URL]) -> ToolResponseMessage:
-    content = []
+async def attachment_message(tempdir: str, urls: List[URL], message: UserMessage) -> None:
+    contents = []

    for url in urls:
        uri = url.uri
@ -988,16 +1001,19 @@ async def attachment_message(tempdir: str, urls: List[URL]) -> ToolResponseMessa
        else:
            raise ValueError(f"Unsupported URL {url}")

-        content.append(
+        contents.append(
            TextContentItem(
                text=f'# User provided a file accessible to you at "{filepath}"\nYou can use code_interpreter to load and inspect it.'
            )
        )

-    return ToolResponseMessage(
-        call_id="",
-        content=content,
-    )
+    if isinstance(message.content, list):
+        message.content.extend(contents)
+    else:
+        if isinstance(message.content, str):
+            message.content = [TextContentItem(text=message.content)] + contents
+        else:
+            message.content = [message.content] + contents


 def _interpret_content_as_attachment(
--- a/llama_stack/providers/inline/safety/llama_guard/llama_guard.py
+++ b/llama_stack/providers/inline/safety/llama_guard/llama_guard.py
@ -227,13 +227,6 @@ class LlamaGuardShield:
        if len(messages) >= 2 and (messages[0].role == Role.user.value and messages[1].role == Role.user.value):
            messages = messages[1:]

-        for i in range(1, len(messages)):
-            if messages[i].role == messages[i - 1].role:
-                for i, m in enumerate(messages):
-                    print(f"{i}: {m.role}: {m.content}")
-                raise ValueError(
-                    f"Messages must alternate between user and assistant. Message {i} has the same role as message {i - 1}"
-                )
        return messages

    async def run(self, messages: List[Message]) -> RunShieldResponse:
--- a/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py
+++ b/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]
--- a/llama_stack/providers/inline/vector_io/qdrant/init.py
+++ b/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
--- a/llama_stack/providers/inline/vector_io/qdrant/config.py
+++ b/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",
+        }
--- a/llama_stack/providers/registry/vector_io.py
+++ b/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(
--- a/llama_stack/providers/remote/inference/nvidia/nvidia.py
+++ b/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(
-            base_url=f"{self._config.url}/v1",
-            api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
-            timeout=self._config.timeout,
-        )
+
+    @lru_cache  # noqa: B019
+    def _get_client(self, provider_model_id: str) -> AsyncOpenAI:
+        """
+        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

--- a/llama_stack/providers/remote/inference/vllm/config.py
+++ b/llama_stack/providers/remote/inference/vllm/config.py
@ -25,6 +25,10 @@ class VLLMInferenceAdapterConfig(BaseModel):
        default="fake",
        description="The API token",
    )
+    tls_verify: bool = Field(
+        default=True,
+        description="Whether to verify TLS certificates",
+    )

    @classmethod
    def sample_run_config(
@ -36,4 +40,5 @@ class VLLMInferenceAdapterConfig(BaseModel):
            "url": url,
            "max_tokens": "${env.VLLM_MAX_TOKENS:4096}",
            "api_token": "${env.VLLM_API_TOKEN:fake}",
+            "tls_verify": "${env.VLLM_TLS_VERIFY:true}",
        }
--- a/llama_stack/providers/remote/inference/vllm/vllm.py
+++ b/llama_stack/providers/remote/inference/vllm/vllm.py
@ -7,6 +7,7 @@ import json
 import logging
 from typing import AsyncGenerator, List, Optional, Union

+import httpx
 from openai import AsyncOpenAI
 from openai.types.chat.chat_completion_chunk import (
    ChatCompletionChunk as OpenAIChatCompletionChunk,
@ -229,7 +230,11 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):

    async def initialize(self) -> None:
        log.info(f"Initializing VLLM client with base_url={self.config.url}")
-        self.client = AsyncOpenAI(base_url=self.config.url, api_key=self.config.api_token)
+        self.client = AsyncOpenAI(
+            base_url=self.config.url,
+            api_key=self.config.api_token,
+            http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False),
+        )

    async def shutdown(self) -> None:
        pass
--- a/llama_stack/providers/remote/vector_io/qdrant/config.py
+++ b/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]:
--- a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py
+++ b/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]
--- a/llama_stack/providers/utils/inference/litellm_openai_mixin.py
+++ b/llama_stack/providers/utils/inference/litellm_openai_mixin.py
@ -192,7 +192,11 @@ class LiteLLMOpenAIMixin(
        if request.tools:
            input_dict["tools"] = [convert_tooldef_to_openai_tool(tool) for tool in request.tools]
            if request.tool_config.tool_choice:
-                input_dict["tool_choice"] = request.tool_config.tool_choice.value
+                input_dict["tool_choice"] = (
+                    request.tool_config.tool_choice.value
+                    if isinstance(request.tool_config.tool_choice, ToolChoice)
+                    else request.tool_config.tool_choice
+                )

        provider_data = self.get_request_provider_data()
        key_field = self.provider_data_api_key_field
--- a/llama_stack/providers/utils/inference/openai_compat.py
+++ b/llama_stack/providers/utils/inference/openai_compat.py
@ -527,26 +527,30 @@ async def convert_message_to_openai_dict_new(
    async def _convert_message_content(
        content: InterleavedContent,
    ) -> Union[str, Iterable[OpenAIChatCompletionContentPartParam]]:
-        async def impl():
+        async def impl(
+            content_: InterleavedContent,
+        ) -> Union[str, OpenAIChatCompletionContentPartParam, List[OpenAIChatCompletionContentPartParam]]:
            # Llama Stack and OpenAI spec match for str and text input
-            if isinstance(content, str):
-                return content
-            elif isinstance(content, TextContentItem):
+            if isinstance(content_, str):
+                return content_
+            elif isinstance(content_, TextContentItem):
                return OpenAIChatCompletionContentPartTextParam(
                    type="text",
-                    text=content.text,
+                    text=content_.text,
                )
-            elif isinstance(content, ImageContentItem):
+            elif isinstance(content_, ImageContentItem):
                return OpenAIChatCompletionContentPartImageParam(
                    type="image_url",
-                    image_url=OpenAIImageURL(url=await convert_image_content_to_url(content)),
+                    image_url=OpenAIImageURL(url=await convert_image_content_to_url(content_)),
                )
-            elif isinstance(content, list):
-                return [await _convert_message_content(item) for item in content]
+            elif isinstance(content_, list):
+                return [await impl(item) for item in content_]
            else:
-                raise ValueError(f"Unsupported content type: {type(content)}")
+                raise ValueError(f"Unsupported content type: {type(content_)}")

-        ret = await impl()
+        ret = await impl(content)
+
+        # OpenAI*Message expects a str or list
        if isinstance(ret, str) or isinstance(ret, list):
            return ret
        else:
@ -566,13 +570,14 @@ async def convert_message_to_openai_dict_new(
                OpenAIChatCompletionMessageToolCall(
                    id=tool.call_id,
                    function=OpenAIFunction(
-                        name=tool.tool_name,
+                        name=tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value,
                        arguments=json.dumps(tool.arguments),
                    ),
                    type="function",
                )
                for tool in message.tool_calls
-            ],
+            ]
+            or None,
        )
    elif isinstance(message, ToolResponseMessage):
        out = OpenAIChatCompletionToolMessage(
@ -858,7 +863,8 @@ async def convert_openai_chat_completion_stream(
    event_type = ChatCompletionResponseEventType.progress

    stop_reason = None
-    toolcall_buffer = {}
+    tool_call_idx_to_buffer = {}
+
    async for chunk in stream:
        choice = chunk.choices[0]  # assuming only one choice per chunk

@ -868,7 +874,6 @@ async def convert_openai_chat_completion_stream(

        # if there's a tool call, emit an event for each tool in the list
        # if tool call and content, emit both separately
-
        if choice.delta.tool_calls:
            # the call may have content and a tool call. ChatCompletionResponseEvent
            # does not support both, so we emit the content first
@ -889,44 +894,53 @@ async def convert_openai_chat_completion_stream(
                )

            if not enable_incremental_tool_calls:
-                yield ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=next(event_type),
-                        delta=ToolCallDelta(
-                            tool_call=_convert_openai_tool_calls(choice.delta.tool_calls)[0],
-                            parse_status=ToolCallParseStatus.succeeded,
-                        ),
-                        logprobs=_convert_openai_logprobs(logprobs),
+                for tool_call in choice.delta.tool_calls:
+                    yield ChatCompletionResponseStreamChunk(
+                        event=ChatCompletionResponseEvent(
+                            event_type=event_type,
+                            delta=ToolCallDelta(
+                                tool_call=_convert_openai_tool_calls([tool_call])[0],
+                                parse_status=ToolCallParseStatus.succeeded,
+                            ),
+                            logprobs=_convert_openai_logprobs(logprobs),
+                        )
                    )
-                )
            else:
-                tool_call = choice.delta.tool_calls[0]
-                if "name" not in toolcall_buffer:
-                    toolcall_buffer["call_id"] = tool_call.id
-                    toolcall_buffer["name"] = None
-                    toolcall_buffer["content"] = ""
-                if "arguments" not in toolcall_buffer:
-                    toolcall_buffer["arguments"] = ""
+                for tool_call in choice.delta.tool_calls:
+                    idx = tool_call.index if hasattr(tool_call, "index") else 0

-                if tool_call.function.name:
-                    toolcall_buffer["name"] = tool_call.function.name
-                    delta = f"{toolcall_buffer['name']}("
-                if tool_call.function.arguments:
-                    toolcall_buffer["arguments"] += tool_call.function.arguments
-                    delta = toolcall_buffer["arguments"]
+                    if idx not in tool_call_idx_to_buffer:
+                        tool_call_idx_to_buffer[idx] = {
+                            "call_id": tool_call.id,
+                            "name": None,
+                            "arguments": "",
+                            "content": "",
+                        }

-                toolcall_buffer["content"] += delta
-                yield ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=event_type,
-                        delta=ToolCallDelta(
-                            tool_call=delta,
-                            parse_status=ToolCallParseStatus.in_progress,
-                        ),
-                        logprobs=_convert_openai_logprobs(logprobs),
-                    )
-                )
-        else:
+                    buffer = tool_call_idx_to_buffer[idx]
+
+                    if tool_call.function:
+                        if tool_call.function.name:
+                            buffer["name"] = tool_call.function.name
+                            delta = f"{buffer['name']}("
+                            buffer["content"] += delta
+
+                        if tool_call.function.arguments:
+                            delta = tool_call.function.arguments
+                            buffer["arguments"] += delta
+                            buffer["content"] += delta
+
+                        yield ChatCompletionResponseStreamChunk(
+                            event=ChatCompletionResponseEvent(
+                                event_type=event_type,
+                                delta=ToolCallDelta(
+                                    tool_call=delta,
+                                    parse_status=ToolCallParseStatus.in_progress,
+                                ),
+                                logprobs=_convert_openai_logprobs(logprobs),
+                            )
+                        )
+        elif choice.delta.content:
            yield ChatCompletionResponseStreamChunk(
                event=ChatCompletionResponseEvent(
                    event_type=event_type,
@ -935,47 +949,51 @@ async def convert_openai_chat_completion_stream(
                )
            )

-    if toolcall_buffer:
-        delta = ")"
-        toolcall_buffer["content"] += delta
-        yield ChatCompletionResponseStreamChunk(
-            event=ChatCompletionResponseEvent(
-                event_type=event_type,
-                delta=ToolCallDelta(
-                    tool_call=delta,
-                    parse_status=ToolCallParseStatus.in_progress,
-                ),
-                logprobs=_convert_openai_logprobs(logprobs),
-            )
-        )
-        try:
-            arguments = json.loads(toolcall_buffer["arguments"])
-            tool_call = ToolCall(
-                call_id=toolcall_buffer["call_id"],
-                tool_name=toolcall_buffer["name"],
-                arguments=arguments,
-            )
+    for idx, buffer in tool_call_idx_to_buffer.items():
+        logger.debug(f"toolcall_buffer[{idx}]: {buffer}")
+        if buffer["name"]:
+            delta = ")"
+            buffer["content"] += delta
            yield ChatCompletionResponseStreamChunk(
                event=ChatCompletionResponseEvent(
-                    event_type=ChatCompletionResponseEventType.progress,
+                    event_type=event_type,
                    delta=ToolCallDelta(
-                        tool_call=tool_call,
-                        parse_status=ToolCallParseStatus.succeeded,
+                        tool_call=delta,
+                        parse_status=ToolCallParseStatus.in_progress,
                    ),
-                    stop_reason=stop_reason,
+                    logprobs=None,
                )
            )
-        except json.JSONDecodeError:
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=ChatCompletionResponseEventType.complete,
-                    delta=ToolCallDelta(
-                        tool_call=toolcall_buffer["content"],
-                        parse_status=ToolCallParseStatus.failed,
-                    ),
-                    stop_reason=stop_reason,
+
+            try:
+                arguments = json.loads(buffer["arguments"])
+                tool_call = ToolCall(
+                    call_id=buffer["call_id"],
+                    tool_name=buffer["name"],
+                    arguments=arguments,
+                )
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=ChatCompletionResponseEventType.progress,
+                        delta=ToolCallDelta(
+                            tool_call=tool_call,
+                            parse_status=ToolCallParseStatus.succeeded,
+                        ),
+                        stop_reason=stop_reason,
+                    )
+                )
+            except json.JSONDecodeError as e:
+                print(f"Failed to parse arguments: {e}")
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=ChatCompletionResponseEventType.progress,
+                        delta=ToolCallDelta(
+                            tool_call=buffer["content"],
+                            parse_status=ToolCallParseStatus.failed,
+                        ),
+                        stop_reason=stop_reason,
+                    )
                )
-            )

    yield ChatCompletionResponseStreamChunk(
        event=ChatCompletionResponseEvent(
--- a/llama_stack/templates/remote-vllm/run-with-safety.yaml
+++ b/llama_stack/templates/remote-vllm/run-with-safety.yaml
@ -18,12 +18,14 @@ providers:
      url: ${env.VLLM_URL}
      max_tokens: ${env.VLLM_MAX_TOKENS:4096}
      api_token: ${env.VLLM_API_TOKEN:fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:true}
  - provider_id: vllm-safety
    provider_type: remote::vllm
    config:
      url: ${env.SAFETY_VLLM_URL}
      max_tokens: ${env.VLLM_MAX_TOKENS:4096}
      api_token: ${env.VLLM_API_TOKEN:fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:true}
  - provider_id: sentence-transformers
    provider_type: inline::sentence-transformers
    config: {}
--- a/llama_stack/templates/remote-vllm/run.yaml
+++ b/llama_stack/templates/remote-vllm/run.yaml
@ -18,6 +18,7 @@ providers:
      url: ${env.VLLM_URL}
      max_tokens: ${env.VLLM_MAX_TOKENS:4096}
      api_token: ${env.VLLM_API_TOKEN:fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:true}
  - provider_id: sentence-transformers
    provider_type: inline::sentence-transformers
    config: {}
--- a/pyproject.toml
+++ b/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",
@ -248,6 +247,7 @@ 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/",
--- a/scripts/gen-changelog.py
+++ b/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(
-            f"Error fetching releases: {response.status_code}, {response.text}"
-        )
+        raise Exception(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")
--- a/tests/integration/agents/test_agents.py
+++ b/tests/integration/agents/test_agents.py
@ -271,7 +271,7 @@ def test_custom_tool(llama_stack_client_with_mocked_inference, agent_config):
    client_tool = get_boiling_point
    agent_config = {
        **agent_config,
-        "tools": ["builtin::websearch", client_tool],
+        "tools": [client_tool],
    }

    agent = Agent(llama_stack_client_with_mocked_inference, **agent_config)
@ -320,42 +320,55 @@ def test_custom_tool_infinite_loop(llama_stack_client_with_mocked_inference, age
    assert num_tool_calls <= 5


-def test_tool_choice(llama_stack_client_with_mocked_inference, agent_config):
-    def run_agent(tool_choice):
-        client_tool = get_boiling_point
-
-        test_agent_config = {
-            **agent_config,
-            "tool_config": {"tool_choice": tool_choice},
-            "tools": [client_tool],
-        }
-
-        agent = Agent(llama_stack_client_with_mocked_inference, **test_agent_config)
-        session_id = agent.create_session(f"test-session-{uuid4()}")
-
-        response = agent.create_turn(
-            messages=[
-                {
-                    "role": "user",
-                    "content": "What is the boiling point of polyjuice?",
-                },
-            ],
-            session_id=session_id,
-            stream=False,
-        )
-
-        return [step for step in response.steps if step.step_type == "tool_execution"]
-
-    tool_execution_steps = run_agent("required")
+def test_tool_choice_required(llama_stack_client_with_mocked_inference, agent_config):
+    tool_execution_steps = run_agent_with_tool_choice(
+        llama_stack_client_with_mocked_inference, agent_config, "required"
+    )
    assert len(tool_execution_steps) > 0

-    tool_execution_steps = run_agent("none")
+
+def test_tool_choice_none(llama_stack_client_with_mocked_inference, agent_config):
+    tool_execution_steps = run_agent_with_tool_choice(llama_stack_client_with_mocked_inference, agent_config, "none")
    assert len(tool_execution_steps) == 0

-    tool_execution_steps = run_agent("get_boiling_point")
+
+def test_tool_choice_get_boiling_point(llama_stack_client_with_mocked_inference, agent_config):
+    if "llama" not in agent_config["model"].lower():
+        pytest.xfail("NotImplemented for non-llama models")
+
+    tool_execution_steps = run_agent_with_tool_choice(
+        llama_stack_client_with_mocked_inference, agent_config, "get_boiling_point"
+    )
    assert len(tool_execution_steps) >= 1 and tool_execution_steps[0].tool_calls[0].tool_name == "get_boiling_point"


+def run_agent_with_tool_choice(client, agent_config, tool_choice):
+    client_tool = get_boiling_point
+
+    test_agent_config = {
+        **agent_config,
+        "tool_config": {"tool_choice": tool_choice},
+        "tools": [client_tool],
+        "max_infer_iters": 2,
+    }
+
+    agent = Agent(client, **test_agent_config)
+    session_id = agent.create_session(f"test-session-{uuid4()}")
+
+    response = agent.create_turn(
+        messages=[
+            {
+                "role": "user",
+                "content": "What is the boiling point of polyjuice?",
+            },
+        ],
+        session_id=session_id,
+        stream=False,
+    )
+
+    return [step for step in response.steps if step.step_type == "tool_execution"]
+
+
@pytest.mark.parametrize("rag_tool_name", ["builtin::rag/knowledge_search", "builtin::rag"])
 def test_rag_agent(llama_stack_client_with_mocked_inference, agent_config, rag_tool_name):
    urls = ["chat.rst", "llama3.rst", "memory_optimizations.rst", "lora_finetune.rst"]
@ -571,7 +584,7 @@ def test_rag_and_code_agent(llama_stack_client_with_mocked_inference, agent_conf
    [(get_boiling_point, False), (get_boiling_point_with_metadata, True)],
 )
 def test_create_turn_response(llama_stack_client_with_mocked_inference, agent_config, client_tools):
-    client_tool, expectes_metadata = client_tools
+    client_tool, expects_metadata = client_tools
    agent_config = {
        **agent_config,
        "input_shields": [],
@ -597,7 +610,7 @@ def test_create_turn_response(llama_stack_client_with_mocked_inference, agent_co
    assert steps[0].step_type == "inference"
    assert steps[1].step_type == "tool_execution"
    assert steps[1].tool_calls[0].tool_name.startswith("get_boiling_point")
-    if expectes_metadata:
+    if expects_metadata:
        assert steps[1].tool_responses[0].metadata["source"] == "https://www.google.com"
    assert steps[2].step_type == "inference"

@ -609,3 +622,44 @@ def test_create_turn_response(llama_stack_client_with_mocked_inference, agent_co
            assert last_step_completed_at < step.started_at
            assert step.started_at < step.completed_at
            last_step_completed_at = step.completed_at
+
+
+def test_multi_tool_calls(llama_stack_client_with_mocked_inference, agent_config):
+    if "gpt" not in agent_config["model"]:
+        pytest.xfail("Only tested on GPT models")
+
+    agent_config = {
+        **agent_config,
+        "tools": [get_boiling_point],
+    }
+
+    agent = Agent(llama_stack_client_with_mocked_inference, **agent_config)
+    session_id = agent.create_session(f"test-session-{uuid4()}")
+
+    response = agent.create_turn(
+        messages=[
+            {
+                "role": "user",
+                "content": "Call get_boiling_point twice to answer: What is the boiling point of polyjuice in both celsius and fahrenheit?",
+            },
+        ],
+        session_id=session_id,
+        stream=False,
+    )
+    steps = response.steps
+    assert len(steps) == 7
+    assert steps[0].step_type == "shield_call"
+    assert steps[1].step_type == "inference"
+    assert steps[2].step_type == "shield_call"
+    assert steps[3].step_type == "tool_execution"
+    assert steps[4].step_type == "shield_call"
+    assert steps[5].step_type == "inference"
+    assert steps[6].step_type == "shield_call"
+
+    tool_execution_step = steps[3]
+    assert len(tool_execution_step.tool_calls) == 2
+    assert tool_execution_step.tool_calls[0].tool_name.startswith("get_boiling_point")
+    assert tool_execution_step.tool_calls[1].tool_name.startswith("get_boiling_point")
+
+    output = response.output_message.content.lower()
+    assert "-100" in output and "-212" in output
--- a/tests/unit/providers/inference/test_remote_vllm.py
+++ b/tests/unit/providers/inference/test_remote_vllm.py
@ -187,8 +187,8 @@ def test_chat_completion_doesnt_block_event_loop(caplog):
    loop.set_debug(True)
    caplog.set_level(logging.WARNING)

-    # Log when event loop is blocked for more than 100ms
-    loop.slow_callback_duration = 0.1
+    # Log when event loop is blocked for more than 200ms
+    loop.slow_callback_duration = 0.2
    # Sleep for 500ms in our delayed http response
    sleep_time = 0.5

--- a/tests/unit/providers/vector_io/conftest.py
+++ b/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])
--- a/tests/unit/providers/vector_io/test_qdrant.py
+++ b/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
--- a/tests/unit/providers/vector_io/test_sqlite_vec.py
+++ b/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):
-    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])
+async def sqlite_vec_index(sqlite_connection, embedding_dimension):
+    return await SQLiteVecIndex.create(dimension=embedding_dimension, connection=sqlite_connection, bank_id="test_bank")


@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)

--- a/uv.lock
+++ b/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 >= '3.12' and platform_machine == 'aarch64' 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') 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')",
+    "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'",
 ]

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+[[package]]
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+
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 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 = [
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+[[package]]
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+dependencies = [
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+dependencies = [
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+    { name = "grpcio-tools" },
+    { name = "httpx", extra = ["http2"] },
+    { name = "numpy" },
+    { name = "portalocker" },
+    { name = "pydantic" },
+    { name = "urllib3" },
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 version = "3.12.2"
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    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
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-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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+    "(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'",
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