Merge branch 'main' into agent_session_unit_test

.github/actions/setup-runner/action.yml

@@ -28,7 +28,7 @@ runs:
         # Install llama-stack-client-python based on the client-version input
         if [ "${{ inputs.client-version }}" = "latest" ]; then
           echo "Installing latest llama-stack-client-python from main branch"
-          uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main
+          uv pip install git+https://github.com/llamastack/llama-stack-client-python.git@main
         elif [ "${{ inputs.client-version }}" = "published" ]; then
           echo "Installing published llama-stack-client-python from PyPI"
           uv pip install llama-stack-client

.github/workflows/integration-tests.yml

@@ -52,7 +52,8 @@ jobs:
         run: |
           # Get test directories dynamically, excluding non-test directories
           # NOTE: we are excluding post_training since the tests take too long
-          TEST_TYPES=$(find tests/integration -maxdepth 1 -mindepth 1 -type d -printf "%f\n" |
+          TEST_TYPES=$(find tests/integration -maxdepth 1 -mindepth 1 -type d |
+            sed 's|tests/integration/||' |
             grep -Ev "^(__pycache__|fixtures|test_cases|recordings|non_ci|post_training)$" |
             sort | jq -R -s -c 'split("\n")[:-1]')
           echo "test-types=$TEST_TYPES" >> $GITHUB_OUTPUT

.github/workflows/integration-vector-io-tests.yml

@@ -164,9 +164,9 @@ jobs:
           ENABLE_WEAVIATE: ${{ matrix.vector-io-provider == 'remote::weaviate' && 'true' || '' }}
           WEAVIATE_CLUSTER_URL: ${{ matrix.vector-io-provider == 'remote::weaviate' && 'localhost:8080' || '' }}
         run: |
-          uv run pytest -sv --stack-config="inference=inline::sentence-transformers,vector_io=${{ matrix.vector-io-provider }}" \
+          uv run pytest -sv --stack-config="files=inline::localfs,inference=inline::sentence-transformers,vector_io=${{ matrix.vector-io-provider }}" \
             tests/integration/vector_io \
-            --embedding-model sentence-transformers/all-MiniLM-L6-v2
+            --embedding-model inline::sentence-transformers/all-MiniLM-L6-v2
 
       - name: Check Storage and Memory Available After Tests
         if: ${{ always() }}

CONTRIBUTING.md

@@ -1,13 +1,82 @@
-# Contributing to Llama-Stack
+# Contributing to Llama Stack
 We want to make contributing to this project as easy and transparent as
 possible.
 
+## Set up your development environment
+
+We use [uv](https://github.com/astral-sh/uv) to manage python dependencies and virtual environments.
+You can install `uv` by following this [guide](https://docs.astral.sh/uv/getting-started/installation/).
+
+You can install the dependencies by running:
+
+```bash
+cd llama-stack
+uv sync --group dev
+uv pip install -e .
+source .venv/bin/activate
+```
+
+```{note}
+You can use a specific version of Python with `uv` by adding the `--python <version>` flag (e.g. `--python 3.12`).
+Otherwise, `uv` will automatically select a Python version according to the `requires-python` section of the `pyproject.toml`.
+For more info, see the [uv docs around Python versions](https://docs.astral.sh/uv/concepts/python-versions/).
+```
+
+Note that you can create a dotenv file `.env` that includes necessary environment variables:
+```
+LLAMA_STACK_BASE_URL=http://localhost:8321
+LLAMA_STACK_CLIENT_LOG=debug
+LLAMA_STACK_PORT=8321
+LLAMA_STACK_CONFIG=<provider-name>
+TAVILY_SEARCH_API_KEY=
+BRAVE_SEARCH_API_KEY=
+```
+
+And then use this dotenv file when running client SDK tests via the following:
+```bash
+uv run --env-file .env -- pytest -v tests/integration/inference/test_text_inference.py --text-model=meta-llama/Llama-3.1-8B-Instruct
+```
+
+### Pre-commit Hooks
+
+We use [pre-commit](https://pre-commit.com/) to run linting and formatting checks on your code. You can install the pre-commit hooks by running:
+
+```bash
+uv run pre-commit install
+```
+
+After that, pre-commit hooks will run automatically before each commit.
+
+Alternatively, if you don't want to install the pre-commit hooks, you can run the checks manually by running:
+
+```bash
+uv run pre-commit run --all-files
+```
+
+```{caution}
+Before pushing your changes, make sure that the pre-commit hooks have passed successfully.
+```
+
 ## Discussions -> Issues -> Pull Requests
 
 We actively welcome your pull requests. However, please read the following. This is heavily inspired by [Ghostty](https://github.com/ghostty-org/ghostty/blob/main/CONTRIBUTING.md).
 
 If in doubt, please open a [discussion](https://github.com/meta-llama/llama-stack/discussions); we can always convert that to an issue later.
 
+### Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](http://facebook.com/whitehat/info) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+### Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
 **I'd like to contribute!**
 
 If you are new to the project, start by looking at the issues tagged with "good first issue". If you're interested
@@ -51,93 +120,15 @@ Please avoid picking up too many issues at once. This helps you stay focused and
 
 Please keep pull requests (PRs) small and focused. If you have a large set of changes, consider splitting them into logically grouped, smaller PRs to facilitate review and testing.
 
-> [!TIP]
-> As a general guideline:
-> - Experienced contributors should try to keep no more than 5 open PRs at a time.
-> - New contributors are encouraged to have only one open PR at a time until they’re familiar with the codebase and process.
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Meta's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Meta has a [bounty program](http://facebook.com/whitehat/info) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-
-## Set up your development environment
-
-We use [uv](https://github.com/astral-sh/uv) to manage python dependencies and virtual environments.
-You can install `uv` by following this [guide](https://docs.astral.sh/uv/getting-started/installation/).
-
-You can install the dependencies by running:
-
-```bash
-cd llama-stack
-uv sync --group dev
-uv pip install -e .
-source .venv/bin/activate
+```{tip}
+As a general guideline:
+- Experienced contributors should try to keep no more than 5 open PRs at a time.
+- New contributors are encouraged to have only one open PR at a time until they’re familiar with the codebase and process.
 ```
 
-> [!NOTE]
-> You can use a specific version of Python with `uv` by adding the `--python <version>` flag (e.g. `--python 3.12`)
-> Otherwise, `uv` will automatically select a Python version according to the `requires-python` section of the `pyproject.toml`.
-> For more info, see the [uv docs around Python versions](https://docs.astral.sh/uv/concepts/python-versions/).
+## Repository guidelines
 
-Note that you can create a dotenv file `.env` that includes necessary environment variables:
-```
-LLAMA_STACK_BASE_URL=http://localhost:8321
-LLAMA_STACK_CLIENT_LOG=debug
-LLAMA_STACK_PORT=8321
-LLAMA_STACK_CONFIG=<provider-name>
-TAVILY_SEARCH_API_KEY=
-BRAVE_SEARCH_API_KEY=
-```
-
-And then use this dotenv file when running client SDK tests via the following:
-```bash
-uv run --env-file .env -- pytest -v tests/integration/inference/test_text_inference.py --text-model=meta-llama/Llama-3.1-8B-Instruct
-```
-
-## Pre-commit Hooks
-
-We use [pre-commit](https://pre-commit.com/) to run linting and formatting checks on your code. You can install the pre-commit hooks by running:
-
-```bash
-uv run pre-commit install
-```
-
-After that, pre-commit hooks will run automatically before each commit.
-
-Alternatively, if you don't want to install the pre-commit hooks, you can run the checks manually by running:
-
-```bash
-uv run pre-commit run --all-files
-```
-
-> [!CAUTION]
-> Before pushing your changes, make sure that the pre-commit hooks have passed successfully.
-
-## Running tests
-
-You can find the Llama Stack testing documentation [here](https://github.com/meta-llama/llama-stack/blob/main/tests/README.md).
-
-## Adding a new dependency to the project
-
-To add a new dependency to the project, you can use the `uv` command. For example, to add `foo` to the project, you can run:
-
-```bash
-uv add foo
-uv sync
-```
-
-## Coding Style
+### Coding Style
 
 * Comments should provide meaningful insights into the code. Avoid filler comments that simply
   describe the next step, as they create unnecessary clutter, same goes for docstrings.
@@ -159,6 +150,10 @@ uv sync
 * When possible, use keyword arguments only when calling functions.
 * Llama Stack utilizes [custom Exception classes](llama_stack/apis/common/errors.py) for certain Resources that should be used where applicable.
 
+### License
+By contributing to Llama, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.
+
 ## Common Tasks
 
 Some tips about common tasks you work on while contributing to Llama Stack:
@@ -210,8 +205,4 @@ If you modify or add new API endpoints, update the API documentation accordingly
 uv run ./docs/openapi_generator/run_openapi_generator.sh
 ```
 
-The generated API documentation will be available in `docs/_static/`. Make sure to review the changes before committing.
-
-## License
-By contributing to Llama, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
+The generated API documentation will be available in `docs/_static/`. Make sure to review the changes before committing.

docs/source/contributing/index.md

@@ -2,17 +2,6 @@
 ```{include} ../../../CONTRIBUTING.md
 ```
 
-## Testing
-
-See the [Test Page](testing.md) which describes how to test your changes.
-```{toctree}
-:maxdepth: 1
-:hidden:
-:caption: Testing
-
-testing
-```
-
 ## Adding a New Provider
 
 See the [Adding a New API Provider Page](new_api_provider.md) which describes how to add new API providers to the Stack.
@@ -27,3 +16,14 @@ See the [External Provider Page](../providers/external/index.md) which describes
 new_api_provider
 new_vector_database
 ```
+
+## Testing
+
+See the [Test Page](testing.md) which describes how to test your changes.
+```{toctree}
+:maxdepth: 1
+:hidden:
+:caption: Testing
+
+testing
+```

llama_stack/core/build.py

@@ -91,7 +91,7 @@ def get_provider_dependencies(
 
 
 def print_pip_install_help(config: BuildConfig):
-    normal_deps, special_deps = get_provider_dependencies(config)
+    normal_deps, special_deps, _ = get_provider_dependencies(config)
 
     cprint(
         f"Please install needed dependencies using the following commands:\n\nuv pip install {' '.join(normal_deps)}",

llama_stack/core/routers/inference.py

@@ -65,7 +65,7 @@ from llama_stack.providers.datatypes import HealthResponse, HealthStatus, Routin
 from llama_stack.providers.utils.inference.inference_store import InferenceStore
 from llama_stack.providers.utils.telemetry.tracing import get_current_span
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="inference")
 
 
 class InferenceRouter(Inference):
@@ -854,4 +854,5 @@ class InferenceRouter(Inference):
                     model=model.identifier,
                     object="chat.completion",
                 )
+                logger.debug(f"InferenceRouter.completion_response: {final_response}")
                 await self.store.store_chat_completion(final_response, messages)

llama_stack/core/routing_tables/models.py

@@ -63,6 +63,8 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
 
     async def get_provider_impl(self, model_id: str) -> Any:
         model = await lookup_model(self, model_id)
+        if model.provider_id not in self.impls_by_provider_id:
+            raise ValueError(f"Provider {model.provider_id} not found in the routing table")
         return self.impls_by_provider_id[model.provider_id]
 
     async def register_model(

llama_stack/log.py

@@ -32,6 +32,7 @@ CATEGORIES = [
     "tools",
     "client",
     "telemetry",
+    "openai_responses",
 ]
 
 # Initialize category levels with default level

llama_stack/models/llama/llama3/chat_format.py

@@ -236,6 +236,7 @@ class ChatFormat:
                     arguments_json=json.dumps(tool_arguments),
                 )
             )
+            content = ""
 
         return RawMessage(
             role="assistant",

llama_stack/providers/inline/agents/meta_reference/openai_responses.py

@@ -488,8 +488,12 @@ class OpenAIResponsesImpl:
             # Convert collected chunks to complete response
             if chat_response_tool_calls:
                 tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())]
+
+                # when there are tool calls, we need to clear the content
+                chat_response_content = []
             else:
                 tool_calls = None
+
             assistant_message = OpenAIAssistantMessageParam(
                 content="".join(chat_response_content),
                 tool_calls=tool_calls,

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -33,6 +33,7 @@ from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
+    ChunkForDeletion,
     EmbeddingIndex,
     VectorDBWithIndex,
 )
@@ -128,11 +129,12 @@ class FaissIndex(EmbeddingIndex):
         # Save updated index
         await self._save_index()
 
-    async def delete_chunk(self, chunk_id: str) -> None:
-        if chunk_id not in self.chunk_ids:
+    async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
+        chunk_ids = [c.chunk_id for c in chunks_for_deletion]
+        if not set(chunk_ids).issubset(self.chunk_ids):
             return
 
-        async with self.chunk_id_lock:
+        def remove_chunk(chunk_id: str):
             index = self.chunk_ids.index(chunk_id)
             self.index.remove_ids(np.array([index]))
 
@@ -146,6 +148,10 @@ class FaissIndex(EmbeddingIndex):
             self.chunk_by_index = new_chunk_by_index
             self.chunk_ids.pop(index)
 
+        async with self.chunk_id_lock:
+            for chunk_id in chunk_ids:
+                remove_chunk(chunk_id)
+
         await self._save_index()
 
     async def query_vector(
@@ -297,8 +303,7 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
 
         return await index.query_chunks(query, params)
 
-    async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
-        """Delete a chunk from a faiss index"""
+    async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
+        """Delete chunks from a faiss index"""
         faiss_index = self.cache[store_id].index
-        for chunk_id in chunk_ids:
-            await faiss_index.delete_chunk(chunk_id)
+        await faiss_index.delete_chunks(chunks_for_deletion)

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -31,6 +31,7 @@ from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIV
 from llama_stack.providers.utils.memory.vector_store import (
     RERANKER_TYPE_RRF,
     RERANKER_TYPE_WEIGHTED,
+    ChunkForDeletion,
     EmbeddingIndex,
     VectorDBWithIndex,
 )
@@ -426,34 +427,36 @@ class SQLiteVecIndex(EmbeddingIndex):
 
         return QueryChunksResponse(chunks=chunks, scores=scores)
 
-    async def delete_chunk(self, chunk_id: str) -> None:
+    async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Remove a chunk from the SQLite vector store."""
+        chunk_ids = [c.chunk_id for c in chunks_for_deletion]
 
-        def _delete_chunk():
+        def _delete_chunks():
             connection = _create_sqlite_connection(self.db_path)
             cur = connection.cursor()
             try:
                 cur.execute("BEGIN TRANSACTION")
 
                 # Delete from metadata table
-                cur.execute(f"DELETE FROM {self.metadata_table} WHERE id = ?", (chunk_id,))
+                placeholders = ",".join("?" * len(chunk_ids))
+                cur.execute(f"DELETE FROM {self.metadata_table} WHERE id IN ({placeholders})", chunk_ids)
 
                 # Delete from vector table
-                cur.execute(f"DELETE FROM {self.vector_table} WHERE id = ?", (chunk_id,))
+                cur.execute(f"DELETE FROM {self.vector_table} WHERE id IN ({placeholders})", chunk_ids)
 
                 # Delete from FTS table
-                cur.execute(f"DELETE FROM {self.fts_table} WHERE id = ?", (chunk_id,))
+                cur.execute(f"DELETE FROM {self.fts_table} WHERE id IN ({placeholders})", chunk_ids)
 
                 connection.commit()
             except Exception as e:
                 connection.rollback()
-                logger.error(f"Error deleting chunk {chunk_id}: {e}")
+                logger.error(f"Error deleting chunks: {e}")
                 raise
             finally:
                 cur.close()
                 connection.close()
 
-        await asyncio.to_thread(_delete_chunk)
+        await asyncio.to_thread(_delete_chunks)
 
 
 class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
@@ -551,12 +554,10 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
             raise VectorStoreNotFoundError(vector_db_id)
         return await index.query_chunks(query, params)
 
-    async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
-        """Delete a chunk from a sqlite_vec index."""
+    async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
+        """Delete chunks from a sqlite_vec index."""
         index = await self._get_and_cache_vector_db_index(store_id)
         if not index:
             raise VectorStoreNotFoundError(store_id)
 
-        for chunk_id in chunk_ids:
-            # Use the index's delete_chunk method
-            await index.index.delete_chunk(chunk_id)
+        await index.index.delete_chunks(chunks_for_deletion)

llama_stack/providers/registry/vector_io.py

@@ -342,6 +342,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 """,
             ),
             api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
         ),
         InlineProviderSpec(
             api=Api.vector_io,
@@ -350,6 +351,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
             module="llama_stack.providers.inline.vector_io.chroma",
             config_class="llama_stack.providers.inline.vector_io.chroma.ChromaVectorIOConfig",
             api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
             description="""
 [Chroma](https://www.trychroma.com/) is an inline and remote vector
 database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
@@ -464,6 +466,7 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
 """,
             ),
             api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
         ),
         InlineProviderSpec(
             api=Api.vector_io,
@@ -731,6 +734,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
 """,
             ),
             api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
         ),
         InlineProviderSpec(
             api=Api.vector_io,

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -235,6 +235,7 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
 
         llama_model = self.get_llama_model(request.model)
         if isinstance(request, ChatCompletionRequest):
+            # TODO: tools are never added to the request, so we need to add them here
             if media_present or not llama_model:
                 input_dict["messages"] = [
                     await convert_message_to_openai_dict(m, download=True) for m in request.messages
@@ -378,6 +379,7 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
         # Fireworks chat completions OpenAI-compatible API does not support
         # tool calls properly.
         llama_model = self.get_llama_model(model_obj.provider_resource_id)
+
         if llama_model:
             return await OpenAIChatCompletionToLlamaStackMixin.openai_chat_completion(
                 self,
@@ -431,4 +433,5 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
             user=user,
         )
 
+        logger.debug(f"fireworks params: {params}")
         return await self._get_openai_client().chat.completions.create(model=model_obj.provider_resource_id, **params)

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -26,6 +26,7 @@ from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
+    ChunkForDeletion,
     EmbeddingIndex,
     VectorDBWithIndex,
 )
@@ -115,8 +116,10 @@ class ChromaIndex(EmbeddingIndex):
     ) -> QueryChunksResponse:
         raise NotImplementedError("Keyword search is not supported in Chroma")
 
-    async def delete_chunk(self, chunk_id: str) -> None:
-        raise NotImplementedError("delete_chunk is not supported in Chroma")
+    async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
+        """Delete a single chunk from the Chroma collection by its ID."""
+        ids = [f"{chunk.document_id}:{chunk.chunk_id}" for chunk in chunks_for_deletion]
+        await maybe_await(self.collection.delete(ids=ids))
 
     async def query_hybrid(
         self,
@@ -144,6 +147,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self.cache = {}
         self.kvstore: KVStore | None = None
         self.vector_db_store = None
+        self.files_api = files_api
 
     async def initialize(self) -> None:
         self.kvstore = await kvstore_impl(self.config.kvstore)
@@ -227,5 +231,10 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self.cache[vector_db_id] = index
         return index
 
-    async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
+    async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
+        """Delete chunks from a Chroma vector store."""
+        index = await self._get_and_cache_vector_db_index(store_id)
+        if not index:
+            raise ValueError(f"Vector DB {store_id} not found")
+
+        await index.index.delete_chunks(chunks_for_deletion)

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -28,6 +28,7 @@ from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
     RERANKER_TYPE_WEIGHTED,
+    ChunkForDeletion,
     EmbeddingIndex,
     VectorDBWithIndex,
 )
@@ -287,14 +288,17 @@ class MilvusIndex(EmbeddingIndex):
 
         return QueryChunksResponse(chunks=filtered_chunks, scores=filtered_scores)
 
-    async def delete_chunk(self, chunk_id: str) -> None:
+    async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Remove a chunk from the Milvus collection."""
+        chunk_ids = [c.chunk_id for c in chunks_for_deletion]
         try:
+            # Use IN clause with square brackets and single quotes for VARCHAR field
+            chunk_ids_str = ", ".join(f"'{chunk_id}'" for chunk_id in chunk_ids)
             await asyncio.to_thread(
-                self.client.delete, collection_name=self.collection_name, filter=f'chunk_id == "{chunk_id}"'
+                self.client.delete, collection_name=self.collection_name, filter=f"chunk_id in [{chunk_ids_str}]"
             )
         except Exception as e:
-            logger.error(f"Error deleting chunk {chunk_id} from Milvus collection {self.collection_name}: {e}")
+            logger.error(f"Error deleting chunks from Milvus collection {self.collection_name}: {e}")
             raise
 
 
@@ -420,12 +424,10 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
 
         return await index.query_chunks(query, params)
 
-    async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
+    async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Delete a chunk from a milvus vector store."""
         index = await self._get_and_cache_vector_db_index(store_id)
         if not index:
             raise VectorStoreNotFoundError(store_id)
 
-        for chunk_id in chunk_ids:
-            # Use the index's delete_chunk method
-            await index.index.delete_chunk(chunk_id)
+        await index.index.delete_chunks(chunks_for_deletion)

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -27,6 +27,7 @@ from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
+    ChunkForDeletion,
     EmbeddingIndex,
     VectorDBWithIndex,
 )
@@ -163,10 +164,11 @@ class PGVectorIndex(EmbeddingIndex):
         with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
             cur.execute(f"DROP TABLE IF EXISTS {self.table_name}")
 
-    async def delete_chunk(self, chunk_id: str) -> None:
+    async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Remove a chunk from the PostgreSQL table."""
+        chunk_ids = [c.chunk_id for c in chunks_for_deletion]
         with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
-            cur.execute(f"DELETE FROM {self.table_name} WHERE id = %s", (chunk_id,))
+            cur.execute(f"DELETE FROM {self.table_name} WHERE id = ANY(%s)", (chunk_ids,))
 
 
 class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
@@ -275,12 +277,10 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
         self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api)
         return self.cache[vector_db_id]
 
-    async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
+    async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Delete a chunk from a PostgreSQL vector store."""
         index = await self._get_and_cache_vector_db_index(store_id)
         if not index:
             raise VectorStoreNotFoundError(store_id)
 
-        for chunk_id in chunk_ids:
-            # Use the index's delete_chunk method
-            await index.index.delete_chunk(chunk_id)
+        await index.index.delete_chunks(chunks_for_deletion)

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

@@ -29,6 +29,7 @@ from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig a
 from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
+    ChunkForDeletion,
     EmbeddingIndex,
     VectorDBWithIndex,
 )
@@ -88,15 +89,16 @@ class QdrantIndex(EmbeddingIndex):
 
         await self.client.upsert(collection_name=self.collection_name, points=points)
 
-    async def delete_chunk(self, chunk_id: str) -> None:
+    async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Remove a chunk from the Qdrant collection."""
+        chunk_ids = [convert_id(c.chunk_id) for c in chunks_for_deletion]
         try:
             await self.client.delete(
                 collection_name=self.collection_name,
-                points_selector=models.PointIdsList(points=[convert_id(chunk_id)]),
+                points_selector=models.PointIdsList(points=chunk_ids),
             )
         except Exception as e:
-            log.error(f"Error deleting chunk {chunk_id} from Qdrant collection {self.collection_name}: {e}")
+            log.error(f"Error deleting chunks from Qdrant collection {self.collection_name}: {e}")
             raise
 
     async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
@@ -264,12 +266,14 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
     ) -> VectorStoreFileObject:
         # Qdrant doesn't allow multiple clients to access the same storage path simultaneously.
         async with self._qdrant_lock:
-            await super().openai_attach_file_to_vector_store(vector_store_id, file_id, attributes, chunking_strategy)
+            return await super().openai_attach_file_to_vector_store(
+                vector_store_id, file_id, attributes, chunking_strategy
+            )
 
-    async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
+    async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Delete chunks from a Qdrant vector store."""
         index = await self._get_and_cache_vector_db_index(store_id)
         if not index:
             raise ValueError(f"Vector DB {store_id} not found")
-        for chunk_id in chunk_ids:
-            await index.index.delete_chunk(chunk_id)
+
+        await index.index.delete_chunks(chunks_for_deletion)

llama_stack/providers/remote/vector_io/weaviate/weaviate.py

@@ -26,6 +26,7 @@ from llama_stack.providers.utils.memory.openai_vector_store_mixin import (
     OpenAIVectorStoreMixin,
 )
 from llama_stack.providers.utils.memory.vector_store import (
+    ChunkForDeletion,
     EmbeddingIndex,
     VectorDBWithIndex,
 )
@@ -67,6 +68,7 @@ class WeaviateIndex(EmbeddingIndex):
             data_objects.append(
                 wvc.data.DataObject(
                     properties={
+                        "chunk_id": chunk.chunk_id,
                         "chunk_content": chunk.model_dump_json(),
                     },
                     vector=embeddings[i].tolist(),
@@ -79,10 +81,11 @@ class WeaviateIndex(EmbeddingIndex):
         # TODO: make this async friendly
         collection.data.insert_many(data_objects)
 
-    async def delete_chunk(self, chunk_id: str) -> None:
+    async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
         collection = self.client.collections.get(sanitized_collection_name)
-        collection.data.delete_many(where=Filter.by_property("id").contains_any([chunk_id]))
+        chunk_ids = [chunk.chunk_id for chunk in chunks_for_deletion]
+        collection.data.delete_many(where=Filter.by_property("chunk_id").contains_any(chunk_ids))
 
     async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
         sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
@@ -307,10 +310,10 @@ class WeaviateVectorIOAdapter(
 
         return await index.query_chunks(query, params)
 
-    async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
+    async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         sanitized_collection_name = sanitize_collection_name(store_id, weaviate_format=True)
         index = await self._get_and_cache_vector_db_index(sanitized_collection_name)
         if not index:
             raise ValueError(f"Vector DB {sanitized_collection_name} not found")
 
-        await index.delete(chunk_ids)
+        await index.index.delete_chunks(chunks_for_deletion)

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -6,7 +6,6 @@
 
 import asyncio
 import json
-import logging
 import mimetypes
 import time
 import uuid
@@ -37,10 +36,15 @@ from llama_stack.apis.vector_io import (
     VectorStoreSearchResponse,
     VectorStoreSearchResponsePage,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore.api import KVStore
-from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, make_overlapped_chunks
+from llama_stack.providers.utils.memory.vector_store import (
+    ChunkForDeletion,
+    content_from_data_and_mime_type,
+    make_overlapped_chunks,
+)
 
-logger = logging.getLogger(__name__)
+logger = get_logger(__name__, category="vector_io")
 
 # Constants for OpenAI vector stores
 CHUNK_MULTIPLIER = 5
@@ -154,8 +158,8 @@ class OpenAIVectorStoreMixin(ABC):
         self.openai_vector_stores = await self._load_openai_vector_stores()
 
     @abstractmethod
-    async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
-        """Delete a chunk from a vector store."""
+    async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
+        """Delete chunks from a vector store."""
         pass
 
     @abstractmethod
@@ -614,7 +618,7 @@ class OpenAIVectorStoreMixin(ABC):
                 )
                 vector_store_file_object.status = "completed"
         except Exception as e:
-            logger.error(f"Error attaching file to vector store: {e}")
+            logger.exception("Error attaching file to vector store")
             vector_store_file_object.status = "failed"
             vector_store_file_object.last_error = VectorStoreFileLastError(
                 code="server_error",
@@ -767,7 +771,21 @@ class OpenAIVectorStoreMixin(ABC):
 
         dict_chunks = await self._load_openai_vector_store_file_contents(vector_store_id, file_id)
         chunks = [Chunk.model_validate(c) for c in dict_chunks]
-        await self.delete_chunks(vector_store_id, [str(c.chunk_id) for c in chunks if c.chunk_id])
+
+        # Create ChunkForDeletion objects with both chunk_id and document_id
+        chunks_for_deletion = []
+        for c in chunks:
+            if c.chunk_id:
+                document_id = c.metadata.get("document_id") or (
+                    c.chunk_metadata.document_id if c.chunk_metadata else None
+                )
+                if document_id:
+                    chunks_for_deletion.append(ChunkForDeletion(chunk_id=str(c.chunk_id), document_id=document_id))
+                else:
+                    logger.warning(f"Chunk {c.chunk_id} has no document_id, skipping deletion")
+
+        if chunks_for_deletion:
+            await self.delete_chunks(vector_store_id, chunks_for_deletion)
 
         store_info = self.openai_vector_stores[vector_store_id].copy()
 

llama_stack/providers/utils/memory/vector_store.py

@@ -16,6 +16,7 @@ from urllib.parse import unquote
 import httpx
 import numpy as np
 from numpy.typing import NDArray
+from pydantic import BaseModel
 
 from llama_stack.apis.common.content_types import (
     URL,
@@ -34,6 +35,18 @@ from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
 
 log = logging.getLogger(__name__)
 
+
+class ChunkForDeletion(BaseModel):
+    """Information needed to delete a chunk from a vector store.
+
+    :param chunk_id: The ID of the chunk to delete
+    :param document_id: The ID of the document this chunk belongs to
+    """
+
+    chunk_id: str
+    document_id: str
+
+
 # Constants for reranker types
 RERANKER_TYPE_RRF = "rrf"
 RERANKER_TYPE_WEIGHTED = "weighted"
@@ -232,7 +245,7 @@ class EmbeddingIndex(ABC):
         raise NotImplementedError()
 
     @abstractmethod
-    async def delete_chunk(self, chunk_id: str):
+    async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]):
         raise NotImplementedError()
 
     @abstractmethod

tests/common/mcp.py

@@ -16,13 +16,10 @@ MCP_TOOLGROUP_ID = "mcp::localmcp"
 
 def default_tools():
     """Default tools for backward compatibility."""
-    from mcp import types
     from mcp.server.fastmcp import Context
 
-    async def greet_everyone(
-        url: str, ctx: Context
-    ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
-        return [types.TextContent(type="text", text="Hello, world!")]
+    async def greet_everyone(url: str, ctx: Context) -> str:
+        return "Hello, world!"
 
     async def get_boiling_point(liquid_name: str, celsius: bool = True) -> int:
         """
@@ -45,7 +42,6 @@ def default_tools():
 
 def dependency_tools():
     """Tools with natural dependencies for multi-turn testing."""
-    from mcp import types
     from mcp.server.fastmcp import Context
 
     async def get_user_id(username: str, ctx: Context) -> str:
@@ -106,7 +102,7 @@ def dependency_tools():
         else:
             access = "no"
 
-        return [types.TextContent(type="text", text=access)]
+        return access
 
     async def get_experiment_id(experiment_name: str, ctx: Context) -> str:
         """
@@ -245,7 +241,6 @@ def make_mcp_server(required_auth_token: str | None = None, tools: dict[str, Cal
     try:
         yield {"server_url": server_url}
     finally:
-        print("Telling SSE server to exit")
         server_instance.should_exit = True
         time.sleep(0.5)
 
@@ -269,4 +264,3 @@ def make_mcp_server(required_auth_token: str | None = None, tools: dict[str, Cal
 
         AppStatus.should_exit = False
         AppStatus.should_exit_event = None
-        print("SSE server exited")

tests/integration/fixtures/common.py

@@ -270,7 +270,7 @@ def openai_client(client_with_models):
 
 @pytest.fixture(params=["openai_client", "client_with_models"])
 def compat_client(request, client_with_models):
-    if isinstance(client_with_models, LlamaStackAsLibraryClient):
+    if request.param == "openai_client" and isinstance(client_with_models, LlamaStackAsLibraryClient):
         # OpenAI client expects a server, so unless we also rewrite OpenAI client's requests
         # to go via the Stack library client (which itself rewrites requests to be served inline),
         # we cannot do this.

tests/integration/non_ci/responses/fixtures/test_cases/responses.yaml

@@ -137,7 +137,7 @@ test_response_multi_turn_tool_execution:
         server_url: "<FILLED_BY_TEST_RUNNER>"
       output: "yes"
     - case_id: "experiment_results_lookup"
-      input: "I need to get the results for the 'boiling_point' experiment. First, get the experiment ID for 'boiling_point', then use that ID to get the experiment results. Tell me what you found."
+      input: "I need to get the results for the 'boiling_point' experiment. First, get the experiment ID for 'boiling_point', then use that ID to get the experiment results. Tell me the boiling point in Celsius."
       tools:
       - type: mcp
         server_label: "localmcp"
@@ -149,7 +149,7 @@ test_response_multi_turn_tool_execution_streaming:
   test_params:
     case:
     - case_id: "user_permissions_workflow"
-      input: "Help me with this security check: First, get the user ID for 'charlie', then get the permissions for that user ID, and finally check if that user can access 'secret_file.txt'. Stream your progress as you work through each step."
+      input: "Help me with this security check: First, get the user ID for 'charlie', then get the permissions for that user ID, and finally check if that user can access 'secret_file.txt'. Stream your progress as you work through each step. Return only one tool call per step. Summarize the final result with a single 'yes' or 'no' response."
       tools:
       - type: mcp
         server_label: "localmcp"
@@ -157,7 +157,7 @@ test_response_multi_turn_tool_execution_streaming:
       stream: true
       output: "no"
     - case_id: "experiment_analysis_streaming"
-      input: "I need a complete analysis: First, get the experiment ID for 'chemical_reaction', then get the results for that experiment, and tell me if the yield was above 80%. Please stream your analysis process."
+      input: "I need a complete analysis: First, get the experiment ID for 'chemical_reaction', then get the results for that experiment, and tell me if the yield was above 80%. Return only one tool call per step.  Please stream your analysis process."
       tools:
       - type: mcp
         server_label: "localmcp"

tests/integration/non_ci/responses/test_responses.py

@@ -363,6 +363,9 @@ def test_response_non_streaming_file_search_empty_vector_store(request, compat_c
     ids=case_id_generator,
 )
 def test_response_non_streaming_mcp_tool(request, compat_client, text_model_id, case):
+    if not isinstance(compat_client, LlamaStackAsLibraryClient):
+        pytest.skip("in-process MCP server is only supported in library client")
+
     with make_mcp_server() as mcp_server_info:
         tools = case["tools"]
         for tool in tools:
@@ -485,8 +488,11 @@ def test_response_non_streaming_multi_turn_image(request, compat_client, text_mo
     responses_test_cases["test_response_multi_turn_tool_execution"]["test_params"]["case"],
     ids=case_id_generator,
 )
-def test_response_non_streaming_multi_turn_tool_execution(request, compat_client, text_model_id, case):
+def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
     """Test multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
+    if not isinstance(compat_client, LlamaStackAsLibraryClient):
+        pytest.skip("in-process MCP server is only supported in library client")
+
     with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
         tools = case["tools"]
         # Replace the placeholder URL with the actual server URL
@@ -541,8 +547,11 @@ def test_response_non_streaming_multi_turn_tool_execution(request, compat_client
     responses_test_cases["test_response_multi_turn_tool_execution_streaming"]["test_params"]["case"],
     ids=case_id_generator,
 )
-async def test_response_streaming_multi_turn_tool_execution(request, compat_client, text_model_id, case):
+def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
     """Test streaming multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
+    if not isinstance(compat_client, LlamaStackAsLibraryClient):
+        pytest.skip("in-process MCP server is only supported in library client")
+
     with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
         tools = case["tools"]
         # Replace the placeholder URL with the actual server URL
@@ -634,7 +643,7 @@ async def test_response_streaming_multi_turn_tool_execution(request, compat_clie
         },
     ],
 )
-def test_response_text_format(request, compat_client, text_model_id, text_format):
+def test_response_text_format(compat_client, text_model_id, text_format):
     if isinstance(compat_client, LlamaStackAsLibraryClient):
         pytest.skip("Responses API text format is not yet supported in library client.")
 
@@ -653,7 +662,7 @@ def test_response_text_format(request, compat_client, text_model_id, text_format
 
 
 @pytest.fixture
-def vector_store_with_filtered_files(request, compat_client, text_model_id, tmp_path_factory):
+def vector_store_with_filtered_files(compat_client, text_model_id, tmp_path_factory):
     """Create a vector store with multiple files that have different attributes for filtering tests."""
     if isinstance(compat_client, LlamaStackAsLibraryClient):
         pytest.skip("Responses API file search is not yet supported in library client.")

tests/integration/vector_io/test_openai_vector_stores.py

@@ -9,10 +9,11 @@ import time
 from io import BytesIO
 
 import pytest
-from llama_stack_client import BadRequestError, LlamaStackClient
+from llama_stack_client import BadRequestError
 from openai import BadRequestError as OpenAIBadRequestError
 
 from llama_stack.apis.vector_io import Chunk
+from llama_stack.core.library_client import LlamaStackAsLibraryClient
 
 logger = logging.getLogger(__name__)
 
@@ -475,9 +476,6 @@ def test_openai_vector_store_attach_file(compat_client_with_empty_stores, client
     """Test OpenAI vector store attach file."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
 
     # Create a vector store
@@ -526,9 +524,6 @@ def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_s
     """Test OpenAI vector store attach files on creation."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
 
     # Create some files and attach them to the vector store
@@ -582,9 +577,6 @@ def test_openai_vector_store_list_files(compat_client_with_empty_stores, client_
     """Test OpenAI vector store list files."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files list is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
 
     # Create a vector store
@@ -597,16 +589,20 @@ def test_openai_vector_store_list_files(compat_client_with_empty_stores, client_
             file_buffer.name = f"openai_test_{i}.txt"
             file = compat_client.files.create(file=file_buffer, purpose="assistants")
 
-        compat_client.vector_stores.files.create(
+        response = compat_client.vector_stores.files.create(
             vector_store_id=vector_store.id,
             file_id=file.id,
         )
+        assert response is not None
+        assert response.status == "completed", (
+            f"Failed to attach file {file.id} to vector store {vector_store.id}: {response=}"
+        )
         file_ids.append(file.id)
 
     files_list = compat_client.vector_stores.files.list(vector_store_id=vector_store.id)
     assert files_list
     assert files_list.object == "list"
-    assert files_list.data
+    assert files_list.data is not None
     assert not files_list.has_more
     assert len(files_list.data) == 3
     assert set(file_ids) == {file.id for file in files_list.data}
@@ -642,12 +638,13 @@ def test_openai_vector_store_list_files_invalid_vector_store(compat_client_with_
     """Test OpenAI vector store list files with invalid vector store ID."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files list is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
+    if isinstance(compat_client, LlamaStackAsLibraryClient):
+        errors = ValueError
+    else:
+        errors = (BadRequestError, OpenAIBadRequestError)
 
-    with pytest.raises((BadRequestError, OpenAIBadRequestError)):
+    with pytest.raises(errors):
         compat_client.vector_stores.files.list(vector_store_id="abc123")
 
 
@@ -655,9 +652,6 @@ def test_openai_vector_store_retrieve_file_contents(compat_client_with_empty_sto
     """Test OpenAI vector store retrieve file contents."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files retrieve contents is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
 
     # Create a vector store
@@ -685,9 +679,15 @@ def test_openai_vector_store_retrieve_file_contents(compat_client_with_empty_sto
         file_id=file.id,
     )
 
-    assert file_contents
-    assert file_contents.content[0]["type"] == "text"
-    assert file_contents.content[0]["text"] == test_content.decode("utf-8")
+    assert file_contents is not None
+    assert len(file_contents.content) == 1
+    content = file_contents.content[0]
+
+    # llama-stack-client returns a model, openai-python is a badboy and returns a dict
+    if not isinstance(content, dict):
+        content = content.model_dump()
+    assert content["type"] == "text"
+    assert content["text"] == test_content.decode("utf-8")
     assert file_contents.filename == file_name
     assert file_contents.attributes == attributes
 
@@ -696,9 +696,6 @@ def test_openai_vector_store_delete_file(compat_client_with_empty_stores, client
     """Test OpenAI vector store delete file."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files list is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
 
     # Create a vector store
@@ -751,9 +748,6 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(compat_client
     """Test OpenAI vector store delete file removes from vector store."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
 
     # Create a vector store
@@ -792,9 +786,6 @@ def test_openai_vector_store_update_file(compat_client_with_empty_stores, client
     """Test OpenAI vector store update file."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files update is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
 
     # Create a vector store
@@ -840,9 +831,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(compat_client_wit
     """
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
-    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
-        pytest.skip("Vector Store Files create is not yet supported with LlamaStackClient")
-
     compat_client = compat_client_with_empty_stores
 
     # Create a vector store with files