feat: allow returning embeddings and metadata from /vector_stores/ methods; disallow changing Provider ID (#4046)

# What does this PR do?

- Updates `/vector_stores/{vector_store_id}/files/{file_id}/content` to
allow returning `embeddings` and `metadata` using the `extra_query`
    -  Updates the UI accordingly to display them.

- Update UI to support CRUD operations in the Vector Stores section and
adds a new modal exposing the functionality.

- Updates Vector Store update to fail if a user tries to update Provider
ID (which doesn't make sense to allow)

```python
In  [1]: client.vector_stores.files.content(
    vector_store_id=vector_store.id, 
    file_id=file.id, 
    extra_query={"include_embeddings": True, "include_metadata": True}
)
Out [1]: FileContentResponse(attributes={}, content=[Content(text='This is a test document to check if embeddings are generated properly.\n', type='text', embedding=[0.33760684728622437, ...,], chunk_metadata={'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'source': None, 'created_timestamp': 1762053437, 'updated_timestamp': 1762053437, 'chunk_window': '0-13', 'chunk_tokenizer': 'DEFAULT_TIKTOKEN_TOKENIZER', 'chunk_embedding_model': 'sentence-transformers/nomic
-ai/nomic-embed-text-v1.5', 'chunk_embedding_dimension': 768, 'content_token_count': 13, 'metadata_token_count': 9}, metadata={'filename': 'test-embedding.txt', 'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'token_count': 13, 'metadata_token_count': 9})], file_id='file-27291dbc679642ac94ffac6d2810c339', filename='test-embedding.txt')
```

Screenshots of UI are displayed below:

### List Vector Store with Added "Create New Vector Store"
<img width="1912" height="491" alt="Screenshot 2025-11-06 at 10 47
25 PM"
src="https://github.com/user-attachments/assets/a3a3ddd9-758d-4005-ac9c-5047f03916f3"
/>

### Create New Vector Store
<img width="1918" height="1048" alt="Screenshot 2025-11-06 at 10 47
49 PM"
src="https://github.com/user-attachments/assets/b4dc0d31-696f-4e68-b109-27915090f158"
/>

### Edit Vector Store
<img width="1916" height="1355" alt="Screenshot 2025-11-06 at 10 48
32 PM"
src="https://github.com/user-attachments/assets/ec879c63-4cf7-489f-bb1e-57ccc7931414"
/>


### Vector Store Files Contents page (with Embeddings)
<img width="1914" height="849" alt="Screenshot 2025-11-06 at 11 54
32 PM"
src="https://github.com/user-attachments/assets/3095520d-0e90-41f7-83bd-652f6c3fbf27"
/>

### Vector Store Files Contents Details page (with Embeddings)
<img width="1916" height="1221" alt="Screenshot 2025-11-06 at 11 55
00 PM"
src="https://github.com/user-attachments/assets/e71dbdc5-5b49-472b-a43a-5785f58d196c"
/>

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan
Tests added for Middleware extension and Provider failures.

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

client-sdks/stainless/openapi.yml

@@ -2691,7 +2691,8 @@ paths:
       responses:
         '200':
           description: >-
-            A VectorStoreFileContentResponse representing the file contents.
+            File contents, optionally with embeddings and metadata based on query
+            parameters.
           content:
             application/json:
               schema:
@@ -2726,6 +2727,20 @@ paths:
           required: true
           schema:
             type: string
+        - name: include_embeddings
+          in: query
+          description: >-
+            Whether to include embedding vectors in the response.
+          required: false
+          schema:
+            $ref: '#/components/schemas/bool'
+        - name: include_metadata
+          in: query
+          description: >-
+            Whether to include chunk metadata in the response.
+          required: false
+          schema:
+            $ref: '#/components/schemas/bool'
       deprecated: false
   /v1/vector_stores/{vector_store_id}/search:
     post:
@@ -10091,6 +10106,8 @@ components:
       title: VectorStoreFileDeleteResponse
       description: >-
         Response from deleting a vector store file.
+    bool:
+      type: boolean
     VectorStoreContent:
       type: object
       properties:
@@ -10102,6 +10119,26 @@ components:
         text:
           type: string
           description: The actual text content
+        embedding:
+          type: array
+          items:
+            type: number
+          description: >-
+            Optional embedding vector for this content chunk
+        chunk_metadata:
+          $ref: '#/components/schemas/ChunkMetadata'
+          description: Optional chunk metadata
+        metadata:
+          type: object
+          additionalProperties:
+            oneOf:
+              - type: 'null'
+              - type: boolean
+              - type: number
+              - type: string
+              - type: array
+              - type: object
+          description: Optional user-defined metadata
       additionalProperties: false
       required:
         - type
@@ -10125,6 +10162,7 @@ components:
           description: Parsed content of the file
         has_more:
           type: boolean
+          default: false
           description: >-
             Indicates if there are more content pages to fetch
         next_page:

docs/static/llama-stack-spec.yaml

@@ -2688,7 +2688,8 @@ paths:
       responses:
         '200':
           description: >-
-            A VectorStoreFileContentResponse representing the file contents.
+            File contents, optionally with embeddings and metadata based on query
+            parameters.
           content:
             application/json:
               schema:
@@ -2723,6 +2724,20 @@ paths:
           required: true
           schema:
             type: string
+        - name: include_embeddings
+          in: query
+          description: >-
+            Whether to include embedding vectors in the response.
+          required: false
+          schema:
+            $ref: '#/components/schemas/bool'
+        - name: include_metadata
+          in: query
+          description: >-
+            Whether to include chunk metadata in the response.
+          required: false
+          schema:
+            $ref: '#/components/schemas/bool'
       deprecated: false
   /v1/vector_stores/{vector_store_id}/search:
     post:
@@ -9375,6 +9390,8 @@ components:
       title: VectorStoreFileDeleteResponse
       description: >-
         Response from deleting a vector store file.
+    bool:
+      type: boolean
     VectorStoreContent:
       type: object
       properties:
@@ -9386,6 +9403,26 @@ components:
         text:
           type: string
           description: The actual text content
+        embedding:
+          type: array
+          items:
+            type: number
+          description: >-
+            Optional embedding vector for this content chunk
+        chunk_metadata:
+          $ref: '#/components/schemas/ChunkMetadata'
+          description: Optional chunk metadata
+        metadata:
+          type: object
+          additionalProperties:
+            oneOf:
+              - type: 'null'
+              - type: boolean
+              - type: number
+              - type: string
+              - type: array
+              - type: object
+          description: Optional user-defined metadata
       additionalProperties: false
       required:
         - type
@@ -9409,6 +9446,7 @@ components:
           description: Parsed content of the file
         has_more:
           type: boolean
+          default: false
           description: >-
             Indicates if there are more content pages to fetch
         next_page:

docs/static/stainless-llama-stack-spec.yaml

@@ -2691,7 +2691,8 @@ paths:
       responses:
         '200':
           description: >-
-            A VectorStoreFileContentResponse representing the file contents.
+            File contents, optionally with embeddings and metadata based on query
+            parameters.
           content:
             application/json:
               schema:
@@ -2726,6 +2727,20 @@ paths:
           required: true
           schema:
             type: string
+        - name: include_embeddings
+          in: query
+          description: >-
+            Whether to include embedding vectors in the response.
+          required: false
+          schema:
+            $ref: '#/components/schemas/bool'
+        - name: include_metadata
+          in: query
+          description: >-
+            Whether to include chunk metadata in the response.
+          required: false
+          schema:
+            $ref: '#/components/schemas/bool'
       deprecated: false
   /v1/vector_stores/{vector_store_id}/search:
     post:
@@ -10091,6 +10106,8 @@ components:
       title: VectorStoreFileDeleteResponse
       description: >-
         Response from deleting a vector store file.
+    bool:
+      type: boolean
     VectorStoreContent:
       type: object
       properties:
@@ -10102,6 +10119,26 @@ components:
         text:
           type: string
           description: The actual text content
+        embedding:
+          type: array
+          items:
+            type: number
+          description: >-
+            Optional embedding vector for this content chunk
+        chunk_metadata:
+          $ref: '#/components/schemas/ChunkMetadata'
+          description: Optional chunk metadata
+        metadata:
+          type: object
+          additionalProperties:
+            oneOf:
+              - type: 'null'
+              - type: boolean
+              - type: number
+              - type: string
+              - type: array
+              - type: object
+          description: Optional user-defined metadata
       additionalProperties: false
       required:
         - type
@@ -10125,6 +10162,7 @@ components:
           description: Parsed content of the file
         has_more:
           type: boolean
+          default: false
           description: >-
             Indicates if there are more content pages to fetch
         next_page:

src/llama_stack/apis/vector_io/vector_io.py

@@ -10,7 +10,7 @@
 # the root directory of this source tree.
 from typing import Annotated, Any, Literal, Protocol, runtime_checkable
 
-from fastapi import Body
+from fastapi import Body, Query
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.common.tracing import telemetry_traceable
@@ -224,10 +224,16 @@ class VectorStoreContent(BaseModel):
 
     :param type: Content type, currently only "text" is supported
     :param text: The actual text content
+    :param embedding: Optional embedding vector for this content chunk
+    :param chunk_metadata: Optional chunk metadata
+    :param metadata: Optional user-defined metadata
     """
 
     type: Literal["text"]
     text: str
+    embedding: list[float] | None = None
+    chunk_metadata: ChunkMetadata | None = None
+    metadata: dict[str, Any] | None = None
 
 
 @json_schema_type
@@ -280,6 +286,22 @@ class VectorStoreDeleteResponse(BaseModel):
     deleted: bool = True
 
 
+@json_schema_type
+class VectorStoreFileContentResponse(BaseModel):
+    """Represents the parsed content of a vector store file.
+
+    :param object: The object type, which is always `vector_store.file_content.page`
+    :param data: Parsed content of the file
+    :param has_more: Indicates if there are more content pages to fetch
+    :param next_page: The token for the next page, if any
+    """
+
+    object: Literal["vector_store.file_content.page"] = "vector_store.file_content.page"
+    data: list[VectorStoreContent]
+    has_more: bool = False
+    next_page: str | None = None
+
+
 @json_schema_type
 class VectorStoreChunkingStrategyAuto(BaseModel):
     """Automatic chunking strategy for vector store files.
@@ -395,22 +417,6 @@ class VectorStoreListFilesResponse(BaseModel):
     has_more: bool = False
 
 
-@json_schema_type
-class VectorStoreFileContentResponse(BaseModel):
-    """Represents the parsed content of a vector store file.
-
-    :param object: The object type, which is always `vector_store.file_content.page`
-    :param data: Parsed content of the file
-    :param has_more: Indicates if there are more content pages to fetch
-    :param next_page: The token for the next page, if any
-    """
-
-    object: Literal["vector_store.file_content.page"] = "vector_store.file_content.page"
-    data: list[VectorStoreContent]
-    has_more: bool
-    next_page: str | None = None
-
-
 @json_schema_type
 class VectorStoreFileDeleteResponse(BaseModel):
     """Response from deleting a vector store file.
@@ -732,12 +738,16 @@ class VectorIO(Protocol):
         self,
         vector_store_id: str,
         file_id: str,
+        include_embeddings: Annotated[bool | None, Query(default=False)] = False,
+        include_metadata: Annotated[bool | None, Query(default=False)] = False,
     ) -> VectorStoreFileContentResponse:
         """Retrieves the contents of a vector store file.
 
         :param vector_store_id: The ID of the vector store containing the file to retrieve.
         :param file_id: The ID of the file to retrieve.
-        :returns: A VectorStoreFileContentResponse representing the file contents.
+        :param include_embeddings: Whether to include embedding vectors in the response.
+        :param include_metadata: Whether to include chunk metadata in the response.
+        :returns: File contents, optionally with embeddings and metadata based on query parameters.
         """
         ...
 

src/llama_stack/core/library_client.py

@@ -389,6 +389,12 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         matched_func, path_params, route_path, webmethod = find_matching_route(options.method, path, self.route_impls)
         body |= path_params
 
+        # Pass through params that aren't already handled as path params
+        if options.params:
+            extra_query_params = {k: v for k, v in options.params.items() if k not in path_params}
+            if extra_query_params:
+                body["extra_query"] = extra_query_params
+
         body, field_names = self._handle_file_uploads(options, body)
 
         body = self._convert_body(matched_func, body, exclude_params=set(field_names))

src/llama_stack/core/routers/vector_io.py

@@ -247,6 +247,13 @@ class VectorIORouter(VectorIO):
         metadata: dict[str, Any] | None = None,
     ) -> VectorStoreObject:
         logger.debug(f"VectorIORouter.openai_update_vector_store: {vector_store_id}")
+
+        # Check if provider_id is being changed (not supported)
+        if metadata and "provider_id" in metadata:
+            current_store = await self.routing_table.get_object_by_identifier("vector_store", vector_store_id)
+            if current_store and current_store.provider_id != metadata["provider_id"]:
+                raise ValueError("provider_id cannot be changed after vector store creation")
+
         provider = await self.routing_table.get_provider_impl(vector_store_id)
         return await provider.openai_update_vector_store(
             vector_store_id=vector_store_id,
@@ -338,12 +345,19 @@ class VectorIORouter(VectorIO):
         self,
         vector_store_id: str,
         file_id: str,
+        include_embeddings: bool | None = False,
+        include_metadata: bool | None = False,
     ) -> VectorStoreFileContentResponse:
-        logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}")
+        logger.debug(
-        provider = await self.routing_table.get_provider_impl(vector_store_id)
+            f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}, "
-        return await provider.openai_retrieve_vector_store_file_contents(
+            f"include_embeddings={include_embeddings}, include_metadata={include_metadata}"
+        )
+
+        return await self.routing_table.openai_retrieve_vector_store_file_contents(
             vector_store_id=vector_store_id,
             file_id=file_id,
+            include_embeddings=include_embeddings,
+            include_metadata=include_metadata,
         )
 
     async def openai_update_vector_store_file(

src/llama_stack/core/routing_tables/vector_stores.py

@@ -195,12 +195,17 @@ class VectorStoresRoutingTable(CommonRoutingTableImpl):
         self,
         vector_store_id: str,
         file_id: str,
+        include_embeddings: bool | None = False,
+        include_metadata: bool | None = False,
     ) -> VectorStoreFileContentResponse:
         await self.assert_action_allowed("read", "vector_store", vector_store_id)
+
         provider = await self.get_provider_impl(vector_store_id)
         return await provider.openai_retrieve_vector_store_file_contents(
             vector_store_id=vector_store_id,
             file_id=file_id,
+            include_embeddings=include_embeddings,
+            include_metadata=include_metadata,
         )
 
     async def openai_update_vector_store_file(

src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -704,34 +704,35 @@ class OpenAIVectorStoreMixin(ABC):
             # Unknown filter type, default to no match
             raise ValueError(f"Unsupported filter type: {filter_type}")
 
-    def _chunk_to_vector_store_content(self, chunk: Chunk) -> list[VectorStoreContent]:
+    def _chunk_to_vector_store_content(
-        # content is InterleavedContent
+        self, chunk: Chunk, include_embeddings: bool = False, include_metadata: bool = False
+    ) -> list[VectorStoreContent]:
+        def extract_fields() -> dict:
+            """Extract embedding and metadata fields from chunk based on include flags."""
+            return {
+                "embedding": chunk.embedding if include_embeddings else None,
+                "chunk_metadata": chunk.chunk_metadata if include_metadata else None,
+                "metadata": chunk.metadata if include_metadata else None,
+            }
+
+        fields = extract_fields()
+
         if isinstance(chunk.content, str):
-            content = [
+            content_item = VectorStoreContent(type="text", text=chunk.content, **fields)
-                VectorStoreContent(
+            content = [content_item]
-                    type="text",
-                    text=chunk.content,
-                )
-            ]
         elif isinstance(chunk.content, list):
             # TODO: Add support for other types of content
-            content = [
+            content = []
-                VectorStoreContent(
+            for item in chunk.content:
-                    type="text",
+                if item.type == "text":
-                    text=item.text,
+                    content_item = VectorStoreContent(type="text", text=item.text, **fields)
-                )
+                    content.append(content_item)
-                for item in chunk.content
-                if item.type == "text"
-            ]
         else:
             if chunk.content.type != "text":
                 raise ValueError(f"Unsupported content type: {chunk.content.type}")
-            content = [
+
-                VectorStoreContent(
+            content_item = VectorStoreContent(type="text", text=chunk.content.text, **fields)
-                    type="text",
+            content = [content_item]
-                    text=chunk.content.text,
-                )
-            ]
         return content
 
     async def openai_attach_file_to_vector_store(
@@ -820,13 +821,12 @@ class OpenAIVectorStoreMixin(ABC):
                 message=str(e),
             )
 
-        # Create OpenAI vector store file metadata
+        # Save vector store file to persistent storage AFTER insert_chunks
+        # so that chunks include the embeddings that were generated
         file_info = vector_store_file_object.model_dump(exclude={"last_error"})
         file_info["filename"] = file_response.filename if file_response else ""
 
-        # Save vector store file to persistent storage (provider-specific)
         dict_chunks = [c.model_dump() for c in chunks]
-        # This should be updated to include chunk_id
         await self._save_openai_vector_store_file(vector_store_id, file_id, file_info, dict_chunks)
 
         # Update file_ids and file_counts in vector store metadata
@@ -921,21 +921,27 @@ class OpenAIVectorStoreMixin(ABC):
         self,
         vector_store_id: str,
         file_id: str,
+        include_embeddings: bool | None = False,
+        include_metadata: bool | None = False,
     ) -> VectorStoreFileContentResponse:
         """Retrieves the contents of a vector store file."""
         if vector_store_id not in self.openai_vector_stores:
             raise VectorStoreNotFoundError(vector_store_id)
 
+        # Parameters are already provided directly
+        # include_embeddings and include_metadata are now function parameters
+
         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]
         content = []
         for chunk in chunks:
-            content.extend(self._chunk_to_vector_store_content(chunk))
+            content.extend(
+                self._chunk_to_vector_store_content(
+                    chunk, include_embeddings=include_embeddings or False, include_metadata=include_metadata or False
+                )
+            )
         return VectorStoreFileContentResponse(
-            object="vector_store.file_content.page",
             data=content,
-            has_more=False,
-            next_page=None,
         )
 
     async def openai_update_vector_store_file(

src/llama_stack_ui/app/logs/vector-stores/page.tsx

@@ -8,6 +8,9 @@ import type {
 import { useRouter } from "next/navigation";
 import { usePagination } from "@/hooks/use-pagination";
 import { Button } from "@/components/ui/button";
+import { Plus, Trash2, Search, Edit, X } from "lucide-react";
+import { useState } from "react";
+import { Input } from "@/components/ui/input";
 import {
   Table,
   TableBody,
@@ -17,9 +20,21 @@ import {
   TableRow,
 } from "@/components/ui/table";
 import { Skeleton } from "@/components/ui/skeleton";
+import { useAuthClient } from "@/hooks/use-auth-client";
+import {
+  VectorStoreEditor,
+  VectorStoreFormData,
+} from "@/components/vector-stores/vector-store-editor";
 
 export default function VectorStoresPage() {
   const router = useRouter();
+  const client = useAuthClient();
+  const [deletingStores, setDeletingStores] = useState<Set<string>>(new Set());
+  const [searchTerm, setSearchTerm] = useState("");
+  const [showVectorStoreModal, setShowVectorStoreModal] = useState(false);
+  const [editingStore, setEditingStore] = useState<VectorStore | null>(null);
+  const [modalError, setModalError] = useState<string | null>(null);
+  const [showSuccessState, setShowSuccessState] = useState(false);
   const {
     data: stores,
     status,
@@ -47,6 +62,142 @@ export default function VectorStoresPage() {
     }
   }, [status, hasMore, loadMore]);
 
+  // Handle ESC key to close modal
+  React.useEffect(() => {
+    const handleEscape = (event: KeyboardEvent) => {
+      if (event.key === "Escape" && showVectorStoreModal) {
+        handleCancel();
+      }
+    };
+
+    document.addEventListener("keydown", handleEscape);
+    return () => document.removeEventListener("keydown", handleEscape);
+  }, [showVectorStoreModal]);
+
+  const handleDeleteVectorStore = async (storeId: string) => {
+    if (
+      !confirm(
+        "Are you sure you want to delete this vector store? This action cannot be undone."
+      )
+    ) {
+      return;
+    }
+
+    setDeletingStores(prev => new Set([...prev, storeId]));
+
+    try {
+      await client.vectorStores.delete(storeId);
+      // Reload the data to reflect the deletion
+      window.location.reload();
+    } catch (err: unknown) {
+      console.error("Failed to delete vector store:", err);
+      const errorMessage = err instanceof Error ? err.message : "Unknown error";
+      alert(`Failed to delete vector store: ${errorMessage}`);
+    } finally {
+      setDeletingStores(prev => {
+        const newSet = new Set(prev);
+        newSet.delete(storeId);
+        return newSet;
+      });
+    }
+  };
+
+  const handleSaveVectorStore = async (formData: VectorStoreFormData) => {
+    try {
+      setModalError(null);
+
+      if (editingStore) {
+        // Update existing vector store
+        const updateParams: {
+          name?: string;
+          extra_body?: Record<string, unknown>;
+        } = {};
+
+        // Only include fields that have changed or are provided
+        if (formData.name && formData.name !== editingStore.name) {
+          updateParams.name = formData.name;
+        }
+
+        // Add all parameters to extra_body (except provider_id which can't be changed)
+        const extraBody: Record<string, unknown> = {};
+        if (formData.embedding_model) {
+          extraBody.embedding_model = formData.embedding_model;
+        }
+        if (formData.embedding_dimension) {
+          extraBody.embedding_dimension = formData.embedding_dimension;
+        }
+
+        if (Object.keys(extraBody).length > 0) {
+          updateParams.extra_body = extraBody;
+        }
+
+        await client.vectorStores.update(editingStore.id, updateParams);
+
+        // Show success state with close button
+        setShowSuccessState(true);
+        setModalError(
+          "✅ Vector store updated successfully! You can close this modal and refresh the page to see changes."
+        );
+        return;
+      }
+
+      const createParams: {
+        name?: string;
+        provider_id?: string;
+        extra_body?: Record<string, unknown>;
+      } = {
+        name: formData.name || undefined,
+      };
+
+      // Extract provider_id to top-level (like Python client does)
+      if (formData.provider_id) {
+        createParams.provider_id = formData.provider_id;
+      }
+
+      // Add remaining parameters to extra_body
+      const extraBody: Record<string, unknown> = {};
+      if (formData.provider_id) {
+        extraBody.provider_id = formData.provider_id;
+      }
+      if (formData.embedding_model) {
+        extraBody.embedding_model = formData.embedding_model;
+      }
+      if (formData.embedding_dimension) {
+        extraBody.embedding_dimension = formData.embedding_dimension;
+      }
+
+      if (Object.keys(extraBody).length > 0) {
+        createParams.extra_body = extraBody;
+      }
+
+      await client.vectorStores.create(createParams);
+
+      // Show success state with close button
+      setShowSuccessState(true);
+      setModalError(
+        "✅ Vector store created successfully! You can close this modal and refresh the page to see changes."
+      );
+    } catch (err: unknown) {
+      console.error("Failed to create vector store:", err);
+      const errorMessage =
+        err instanceof Error ? err.message : "Failed to create vector store";
+      setModalError(errorMessage);
+    }
+  };
+
+  const handleEditVectorStore = (store: VectorStore) => {
+    setEditingStore(store);
+    setShowVectorStoreModal(true);
+    setModalError(null);
+  };
+
+  const handleCancel = () => {
+    setShowVectorStoreModal(false);
+    setEditingStore(null);
+    setModalError(null);
+    setShowSuccessState(false);
+  };
+
   const renderContent = () => {
     if (status === "loading") {
       return (
@@ -66,7 +217,38 @@ export default function VectorStoresPage() {
       return <p>No vector stores found.</p>;
     }
 
+    // Filter stores based on search term
+    const filteredStores = stores.filter(store => {
+      if (!searchTerm) return true;
+
+      const searchLower = searchTerm.toLowerCase();
       return (
+        store.id.toLowerCase().includes(searchLower) ||
+        (store.name && store.name.toLowerCase().includes(searchLower)) ||
+        (store.metadata?.provider_id &&
+          String(store.metadata.provider_id)
+            .toLowerCase()
+            .includes(searchLower)) ||
+        (store.metadata?.provider_vector_db_id &&
+          String(store.metadata.provider_vector_db_id)
+            .toLowerCase()
+            .includes(searchLower))
+      );
+    });
+
+    return (
+      <div className="space-y-4">
+        {/* Search Bar */}
+        <div className="relative flex-1 max-w-md">
+          <Search className="absolute left-3 top-1/2 transform -translate-y-1/2 text-muted-foreground h-4 w-4" />
+          <Input
+            placeholder="Search vector stores..."
+            value={searchTerm}
+            onChange={e => setSearchTerm(e.target.value)}
+            className="pl-10"
+          />
+        </div>
+
         <div className="overflow-auto flex-1 min-h-0">
           <Table>
             <TableHeader>
@@ -82,10 +264,11 @@ export default function VectorStoresPage() {
                 <TableHead>Usage Bytes</TableHead>
                 <TableHead>Provider ID</TableHead>
                 <TableHead>Provider Vector DB ID</TableHead>
+                <TableHead>Actions</TableHead>
               </TableRow>
             </TableHeader>
             <TableBody>
-            {stores.map(store => {
+              {filteredStores.map(store => {
                 const fileCounts = store.file_counts;
                 const metadata = store.metadata || {};
                 const providerId = metadata.provider_id ?? "";
@@ -94,7 +277,9 @@ export default function VectorStoresPage() {
                 return (
                   <TableRow
                     key={store.id}
-                  onClick={() => router.push(`/logs/vector-stores/${store.id}`)}
+                    onClick={() =>
+                      router.push(`/logs/vector-stores/${store.id}`)
+                    }
                     className="cursor-pointer hover:bg-muted/50"
                   >
                     <TableCell>
@@ -120,19 +305,102 @@ export default function VectorStoresPage() {
                     <TableCell>{store.usage_bytes}</TableCell>
                     <TableCell>{providerId}</TableCell>
                     <TableCell>{providerDbId}</TableCell>
+                    <TableCell>
+                      <div className="flex gap-2">
+                        <Button
+                          variant="outline"
+                          size="sm"
+                          onClick={e => {
+                            e.stopPropagation();
+                            handleEditVectorStore(store);
+                          }}
+                        >
+                          <Edit className="h-4 w-4" />
+                        </Button>
+                        <Button
+                          variant="outline"
+                          size="sm"
+                          onClick={e => {
+                            e.stopPropagation();
+                            handleDeleteVectorStore(store.id);
+                          }}
+                          disabled={deletingStores.has(store.id)}
+                        >
+                          {deletingStores.has(store.id) ? (
+                            "Deleting..."
+                          ) : (
+                            <>
+                              <Trash2 className="h-4 w-4" />
+                            </>
+                          )}
+                        </Button>
+                      </div>
+                    </TableCell>
                   </TableRow>
                 );
               })}
             </TableBody>
           </Table>
         </div>
+      </div>
     );
   };
 
   return (
     <div className="space-y-4">
+      <div className="flex items-center justify-between">
         <h1 className="text-2xl font-semibold">Vector Stores</h1>
+        <Button
+          onClick={() => setShowVectorStoreModal(true)}
+          disabled={status === "loading"}
+        >
+          <Plus className="h-4 w-4 mr-2" />
+          New Vector Store
+        </Button>
+      </div>
       {renderContent()}
+
+      {/* Create Vector Store Modal */}
+      {showVectorStoreModal && (
+        <div className="fixed inset-0 bg-black/50 flex items-center justify-center z-50">
+          <div className="bg-background border rounded-lg shadow-lg max-w-2xl w-full mx-4 max-h-[90vh] overflow-hidden">
+            <div className="p-6 border-b flex items-center justify-between">
+              <h2 className="text-2xl font-bold">
+                {editingStore ? "Edit Vector Store" : "Create New Vector Store"}
+              </h2>
+              <Button
+                variant="ghost"
+                size="sm"
+                onClick={handleCancel}
+                className="p-1 h-auto"
+              >
+                <X className="h-4 w-4" />
+              </Button>
+            </div>
+            <div className="p-6 overflow-y-auto max-h-[calc(90vh-120px)]">
+              <VectorStoreEditor
+                onSave={handleSaveVectorStore}
+                onCancel={handleCancel}
+                error={modalError}
+                showSuccessState={showSuccessState}
+                isEditing={!!editingStore}
+                initialData={
+                  editingStore
+                    ? {
+                        name: editingStore.name || "",
+                        embedding_model:
+                          editingStore.metadata?.embedding_model || "",
+                        embedding_dimension:
+                          editingStore.metadata?.embedding_dimension || 768,
+                        provider_id: editingStore.metadata?.provider_id || "",
+                      }
+                    : undefined
+                }
+              />
+            </div>
+          </div>
+        </div>
+      )}
     </div>
   );
 }

src/llama_stack_ui/components/prompts/prompt-editor.test.tsx

@@ -2,7 +2,7 @@ import React from "react";
 import { render, screen, fireEvent } from "@testing-library/react";
 import "@testing-library/jest-dom";
 import { PromptEditor } from "./prompt-editor";
-import type { Prompt, PromptFormData } from "./types";
+import type { Prompt } from "./types";
 
 describe("PromptEditor", () => {
   const mockOnSave = jest.fn();

src/llama_stack_ui/components/vector-stores/vector-store-detail.test.tsx

@@ -12,6 +12,20 @@ jest.mock("next/navigation", () => ({
   }),
 }));
 
+// Mock NextAuth
+jest.mock("next-auth/react", () => ({
+  useSession: () => ({
+    data: {
+      accessToken: "mock-access-token",
+      user: {
+        id: "mock-user-id",
+        email: "test@example.com",
+      },
+    },
+    status: "authenticated",
+  }),
+}));
+
 describe("VectorStoreDetailView", () => {
   const defaultProps = {
     store: null,

src/llama_stack_ui/components/vector-stores/vector-store-detail.tsx

@@ -1,16 +1,18 @@
 "use client";
 
 import { useRouter } from "next/navigation";
+import { useState, useEffect } from "react";
 import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
 import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files";
 import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
 import { Skeleton } from "@/components/ui/skeleton";
 import { Button } from "@/components/ui/button";
+import { useAuthClient } from "@/hooks/use-auth-client";
+import { Edit2, Trash2, X } from "lucide-react";
 import {
   DetailLoadingView,
   DetailErrorView,
   DetailNotFoundView,
-  DetailLayout,
   PropertiesCard,
   PropertyItem,
 } from "@/components/layout/detail-layout";
@@ -23,6 +25,7 @@ import {
   TableHeader,
   TableRow,
 } from "@/components/ui/table";
+import { VectorStoreEditor, VectorStoreFormData } from "./vector-store-editor";
 
 interface VectorStoreDetailViewProps {
   store: VectorStore | null;
@@ -43,21 +46,122 @@ export function VectorStoreDetailView({
   errorFiles,
   id,
 }: VectorStoreDetailViewProps) {
-  const title = "Vector Store Details";
   const router = useRouter();
+  const client = useAuthClient();
+  const [isDeleting, setIsDeleting] = useState(false);
+  const [showEditModal, setShowEditModal] = useState(false);
+  const [modalError, setModalError] = useState<string | null>(null);
+  const [showSuccessState, setShowSuccessState] = useState(false);
+
+  // Handle ESC key to close modal
+  useEffect(() => {
+    const handleEscape = (event: KeyboardEvent) => {
+      if (event.key === "Escape" && showEditModal) {
+        handleCancel();
+      }
+    };
+
+    document.addEventListener("keydown", handleEscape);
+    return () => document.removeEventListener("keydown", handleEscape);
+  }, [showEditModal]);
 
   const handleFileClick = (fileId: string) => {
     router.push(`/logs/vector-stores/${id}/files/${fileId}`);
   };
 
+  const handleEditVectorStore = () => {
+    setShowEditModal(true);
+    setModalError(null);
+    setShowSuccessState(false);
+  };
+
+  const handleCancel = () => {
+    setShowEditModal(false);
+    setModalError(null);
+    setShowSuccessState(false);
+  };
+
+  const handleSaveVectorStore = async (formData: VectorStoreFormData) => {
+    try {
+      setModalError(null);
+
+      // Update existing vector store (same logic as list page)
+      const updateParams: {
+        name?: string;
+        extra_body?: Record<string, unknown>;
+      } = {};
+
+      // Only include fields that have changed or are provided
+      if (formData.name && formData.name !== store?.name) {
+        updateParams.name = formData.name;
+      }
+
+      // Add all parameters to extra_body (except provider_id which can't be changed)
+      const extraBody: Record<string, unknown> = {};
+      if (formData.embedding_model) {
+        extraBody.embedding_model = formData.embedding_model;
+      }
+      if (formData.embedding_dimension) {
+        extraBody.embedding_dimension = formData.embedding_dimension;
+      }
+
+      if (Object.keys(extraBody).length > 0) {
+        updateParams.extra_body = extraBody;
+      }
+
+      await client.vectorStores.update(id, updateParams);
+
+      // Show success state
+      setShowSuccessState(true);
+      setModalError(
+        "✅ Vector store updated successfully! You can close this modal and refresh the page to see changes."
+      );
+    } catch (err: unknown) {
+      console.error("Failed to update vector store:", err);
+      const errorMessage =
+        err instanceof Error ? err.message : "Failed to update vector store";
+      setModalError(errorMessage);
+    }
+  };
+
+  const handleDeleteVectorStore = async () => {
+    if (
+      !confirm(
+        "Are you sure you want to delete this vector store? This action cannot be undone."
+      )
+    ) {
+      return;
+    }
+
+    setIsDeleting(true);
+
+    try {
+      await client.vectorStores.delete(id);
+      // Redirect to the vector stores list after successful deletion
+      router.push("/logs/vector-stores");
+    } catch (err: unknown) {
+      console.error("Failed to delete vector store:", err);
+      const errorMessage = err instanceof Error ? err.message : "Unknown error";
+      alert(`Failed to delete vector store: ${errorMessage}`);
+    } finally {
+      setIsDeleting(false);
+    }
+  };
+
   if (errorStore) {
-    return <DetailErrorView title={title} id={id} error={errorStore} />;
+    return (
+      <DetailErrorView
+        title="Vector Store Details"
+        id={id}
+        error={errorStore}
+      />
+    );
   }
   if (isLoadingStore) {
-    return <DetailLoadingView title={title} />;
+    return <DetailLoadingView />;
   }
   if (!store) {
-    return <DetailNotFoundView title={title} id={id} />;
+    return <DetailNotFoundView title="Vector Store Details" id={id} />;
   }
 
   const mainContent = (
@@ -138,6 +242,73 @@ export function VectorStoreDetailView({
   );
 
   return (
-    <DetailLayout title={title} mainContent={mainContent} sidebar={sidebar} />
+    <>
+      <div className="flex items-center justify-between mb-6">
+        <h1 className="text-2xl font-bold">Vector Store Details</h1>
+        <div className="flex gap-2">
+          <Button
+            variant="outline"
+            onClick={handleEditVectorStore}
+            disabled={isDeleting}
+          >
+            <Edit2 className="h-4 w-4 mr-2" />
+            Edit
+          </Button>
+          <Button
+            variant="destructive"
+            onClick={handleDeleteVectorStore}
+            disabled={isDeleting}
+          >
+            {isDeleting ? (
+              "Deleting..."
+            ) : (
+              <>
+                <Trash2 className="h-4 w-4 mr-2" />
+                Delete
+              </>
+            )}
+          </Button>
+        </div>
+      </div>
+      <div className="flex flex-col md:flex-row gap-6">
+        <div className="flex-grow md:w-2/3 space-y-6">{mainContent}</div>
+        <div className="md:w-1/3">{sidebar}</div>
+      </div>
+
+      {/* Edit Vector Store Modal */}
+      {showEditModal && (
+        <div className="fixed inset-0 bg-black/50 flex items-center justify-center z-50">
+          <div className="bg-background border rounded-lg shadow-lg max-w-2xl w-full mx-4 max-h-[90vh] overflow-hidden">
+            <div className="p-6 border-b flex items-center justify-between">
+              <h2 className="text-2xl font-bold">Edit Vector Store</h2>
+              <Button
+                variant="ghost"
+                size="sm"
+                onClick={handleCancel}
+                className="p-1 h-auto"
+              >
+                <X className="h-4 w-4" />
+              </Button>
+            </div>
+            <div className="p-6 overflow-y-auto max-h-[calc(90vh-120px)]">
+              <VectorStoreEditor
+                onSave={handleSaveVectorStore}
+                onCancel={handleCancel}
+                error={modalError}
+                showSuccessState={showSuccessState}
+                isEditing={true}
+                initialData={{
+                  name: store?.name || "",
+                  embedding_model: store?.metadata?.embedding_model || "",
+                  embedding_dimension:
+                    store?.metadata?.embedding_dimension || 768,
+                  provider_id: store?.metadata?.provider_id || "",
+                }}
+              />
+            </div>
+          </div>
+        </div>
+      )}
+    </>
   );
 }

src/llama_stack_ui/components/vector-stores/vector-store-editor.tsx

@@ -0,0 +1,235 @@
+"use client";
+
+import { useState, useEffect } from "react";
+import { Button } from "@/components/ui/button";
+import { Input } from "@/components/ui/input";
+import { Label } from "@/components/ui/label";
+import { Card, CardContent } from "@/components/ui/card";
+import {
+  Select,
+  SelectContent,
+  SelectItem,
+  SelectTrigger,
+  SelectValue,
+} from "@/components/ui/select";
+import { useAuthClient } from "@/hooks/use-auth-client";
+import type { Model } from "llama-stack-client/resources/models";
+
+export interface VectorStoreFormData {
+  name: string;
+  embedding_model?: string;
+  embedding_dimension?: number;
+  provider_id?: string;
+}
+
+interface VectorStoreEditorProps {
+  onSave: (formData: VectorStoreFormData) => Promise<void>;
+  onCancel: () => void;
+  error?: string | null;
+  initialData?: VectorStoreFormData;
+  showSuccessState?: boolean;
+  isEditing?: boolean;
+}
+
+export function VectorStoreEditor({
+  onSave,
+  onCancel,
+  error,
+  initialData,
+  showSuccessState,
+  isEditing = false,
+}: VectorStoreEditorProps) {
+  const client = useAuthClient();
+  const [formData, setFormData] = useState<VectorStoreFormData>(
+    initialData || {
+      name: "",
+      embedding_model: "",
+      embedding_dimension: 768,
+      provider_id: "",
+    }
+  );
+  const [loading, setLoading] = useState(false);
+  const [models, setModels] = useState<Model[]>([]);
+  const [modelsLoading, setModelsLoading] = useState(true);
+  const [modelsError, setModelsError] = useState<string | null>(null);
+
+  const embeddingModels = models.filter(
+    model => model.custom_metadata?.model_type === "embedding"
+  );
+
+  useEffect(() => {
+    const fetchModels = async () => {
+      try {
+        setModelsLoading(true);
+        setModelsError(null);
+        const modelList = await client.models.list();
+        setModels(modelList);
+
+        // Set default embedding model if available
+        const embeddingModelsList = modelList.filter(model => {
+          return model.custom_metadata?.model_type === "embedding";
+        });
+        if (embeddingModelsList.length > 0 && !formData.embedding_model) {
+          setFormData(prev => ({
+            ...prev,
+            embedding_model: embeddingModelsList[0].id,
+          }));
+        }
+      } catch (err) {
+        console.error("Failed to load models:", err);
+        setModelsError(
+          err instanceof Error ? err.message : "Failed to load models"
+        );
+      } finally {
+        setModelsLoading(false);
+      }
+    };
+
+    fetchModels();
+  }, [client]);
+
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    setLoading(true);
+
+    try {
+      await onSave(formData);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    <Card>
+      <CardContent className="pt-6">
+        <form onSubmit={handleSubmit} className="space-y-4">
+          <div className="space-y-2">
+            <Label htmlFor="name">Name</Label>
+            <Input
+              id="name"
+              value={formData.name}
+              onChange={e => setFormData({ ...formData, name: e.target.value })}
+              placeholder="Enter vector store name"
+              required
+            />
+          </div>
+
+          <div className="space-y-2">
+            <Label htmlFor="embedding_model">Embedding Model (Optional)</Label>
+            {modelsLoading ? (
+              <div className="text-sm text-muted-foreground">
+                Loading models... ({models.length} loaded)
+              </div>
+            ) : modelsError ? (
+              <div className="text-sm text-destructive">
+                Error: {modelsError}
+              </div>
+            ) : embeddingModels.length === 0 ? (
+              <div className="text-sm text-muted-foreground">
+                No embedding models available ({models.length} total models)
+              </div>
+            ) : (
+              <Select
+                value={formData.embedding_model}
+                onValueChange={value =>
+                  setFormData({ ...formData, embedding_model: value })
+                }
+              >
+                <SelectTrigger>
+                  <SelectValue placeholder="Select Embedding Model" />
+                </SelectTrigger>
+                <SelectContent>
+                  {embeddingModels.map((model, index) => (
+                    <SelectItem key={model.id} value={model.id}>
+                      {model.id}
+                    </SelectItem>
+                  ))}
+                </SelectContent>
+              </Select>
+            )}
+            {formData.embedding_model && (
+              <p className="text-xs text-muted-foreground mt-1">
+                Dimension:{" "}
+                {embeddingModels.find(m => m.id === formData.embedding_model)
+                  ?.custom_metadata?.embedding_dimension || "Unknown"}
+              </p>
+            )}
+          </div>
+
+          <div className="space-y-2">
+            <Label htmlFor="embedding_dimension">
+              Embedding Dimension (Optional)
+            </Label>
+            <Input
+              id="embedding_dimension"
+              type="number"
+              value={formData.embedding_dimension}
+              onChange={e =>
+                setFormData({
+                  ...formData,
+                  embedding_dimension: parseInt(e.target.value) || 768,
+                })
+              }
+              placeholder="768"
+            />
+          </div>
+
+          <div className="space-y-2">
+            <Label htmlFor="provider_id">
+              Provider ID {isEditing ? "(Read-only)" : "(Optional)"}
+            </Label>
+            <Input
+              id="provider_id"
+              value={formData.provider_id}
+              onChange={e =>
+                setFormData({ ...formData, provider_id: e.target.value })
+              }
+              placeholder="e.g., faiss, chroma, sqlite"
+              disabled={isEditing}
+            />
+            {isEditing && (
+              <p className="text-xs text-muted-foreground">
+                Provider ID cannot be changed after vector store creation
+              </p>
+            )}
+          </div>
+
+          {error && (
+            <div
+              className={`text-sm p-3 rounded ${
+                error.startsWith("✅")
+                  ? "text-green-700 bg-green-50 border border-green-200"
+                  : "text-destructive bg-destructive/10"
+              }`}
+            >
+              {error}
+            </div>
+          )}
+
+          <div className="flex gap-2 pt-4">
+            {showSuccessState ? (
+              <Button type="button" onClick={onCancel}>
+                Close
+              </Button>
+            ) : (
+              <>
+                <Button type="submit" disabled={loading}>
+                  {loading
+                    ? initialData
+                      ? "Updating..."
+                      : "Creating..."
+                    : initialData
+                      ? "Update Vector Store"
+                      : "Create Vector Store"}
+                </Button>
+                <Button type="button" variant="outline" onClick={onCancel}>
+                  Cancel
+                </Button>
+              </>
+            )}
+          </div>
+        </form>
+      </CardContent>
+    </Card>
+  );
+}

src/llama_stack_ui/lib/contents-api.ts

@@ -34,9 +34,35 @@ export class ContentsAPI {
 
   async getFileContents(
     vectorStoreId: string,
-    fileId: string
+    fileId: string,
+    includeEmbeddings: boolean = true,
+    includeMetadata: boolean = true
   ): Promise<VectorStoreContentsResponse> {
-    return this.client.vectorStores.files.content(vectorStoreId, fileId);
+    try {
+      // Use query parameters to pass embeddings and metadata flags (OpenAI-compatible pattern)
+      const extraQuery: Record<string, boolean> = {};
+      if (includeEmbeddings) {
+        extraQuery.include_embeddings = true;
+      }
+      if (includeMetadata) {
+        extraQuery.include_metadata = true;
+      }
+
+      const result = await this.client.vectorStores.files.content(
+        vectorStoreId,
+        fileId,
+        {
+          query: {
+            include_embeddings: includeEmbeddings,
+            include_metadata: includeMetadata,
+          },
+        }
+      );
+      return result;
+    } catch (error) {
+      console.error("ContentsAPI.getFileContents error:", error);
+      throw error;
+    }
   }
 
   async getContent(
@@ -70,11 +96,15 @@ export class ContentsAPI {
       order?: string;
       after?: string;
       before?: string;
+      includeEmbeddings?: boolean;
+      includeMetadata?: boolean;
     }
   ): Promise<VectorStoreListContentsResponse> {
-    const fileContents = await this.client.vectorStores.files.content(
+    const fileContents = await this.getFileContents(
       vectorStoreId,
-      fileId
+      fileId,
+      options?.includeEmbeddings ?? true,
+      options?.includeMetadata ?? true
     );
     const contentItems: VectorStoreContentItem[] = [];
 
@@ -82,7 +112,7 @@ export class ContentsAPI {
       const rawContent = content as Record<string, unknown>;
 
       // Extract actual fields from the API response
-      const embedding = rawContent.embedding || undefined;
+      const embedding = rawContent.embedding as number[] | undefined;
       const created_timestamp =
         rawContent.created_timestamp ||
         rawContent.created_at ||

tests/integration/vector_io/test_openai_vector_stores.py

@@ -11,6 +11,7 @@ import pytest
 from llama_stack_client import BadRequestError
 from openai import BadRequestError as OpenAIBadRequestError
 
+from llama_stack.apis.files import ExpiresAfter
 from llama_stack.apis.vector_io import Chunk
 from llama_stack.core.library_client import LlamaStackAsLibraryClient
 from llama_stack.log import get_logger
@@ -1604,3 +1605,97 @@ def test_openai_vector_store_embedding_config_from_metadata(
 
     assert "metadata_config_store" in store_names
     assert "consistent_config_store" in store_names
+
+
+@vector_provider_wrapper
+def test_openai_vector_store_file_contents_with_extra_query(
+    compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
+):
+    """Test that vector store file contents endpoint supports extra_query parameter."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(
+        name="test_extra_query_store",
+        extra_body={
+            "embedding_model": embedding_model_id,
+            "provider_id": vector_io_provider_id,
+        },
+    )
+
+    # Create and attach a file
+    test_content = b"This is test content for extra_query validation."
+    with BytesIO(test_content) as file_buffer:
+        file_buffer.name = "test_extra_query.txt"
+        file = compat_client.files.create(
+            file=file_buffer,
+            purpose="assistants",
+            expires_after=ExpiresAfter(anchor="created_at", seconds=86400),
+        )
+
+    file_attach_response = compat_client.vector_stores.files.create(
+        vector_store_id=vector_store.id,
+        file_id=file.id,
+        extra_body={"embedding_model": embedding_model_id},
+    )
+    assert file_attach_response.status == "completed"
+
+    # Wait for processing
+    time.sleep(2)
+
+    # Test that extra_query parameter is accepted and processed
+    content_with_extra_query = compat_client.vector_stores.files.content(
+        vector_store_id=vector_store.id,
+        file_id=file.id,
+        extra_query={"include_embeddings": True, "include_metadata": True},
+    )
+
+    # Test without extra_query for comparison
+    content_without_extra_query = compat_client.vector_stores.files.content(
+        vector_store_id=vector_store.id,
+        file_id=file.id,
+    )
+
+    # Validate that both calls succeed
+    assert content_with_extra_query is not None
+    assert content_without_extra_query is not None
+    assert len(content_with_extra_query.data) > 0
+    assert len(content_without_extra_query.data) > 0
+
+    # Validate that extra_query parameter is processed correctly
+    # Both should have the embedding/metadata fields available (may be None based on flags)
+    first_chunk_with_flags = content_with_extra_query.data[0]
+    first_chunk_without_flags = content_without_extra_query.data[0]
+
+    # The key validation: extra_query fields are present in the response
+    # Handle both dict and object responses (different clients may return different formats)
+    def has_field(obj, field):
+        if isinstance(obj, dict):
+            return field in obj
+        else:
+            return hasattr(obj, field)
+
+    # Validate that all expected fields are present in both responses
+    expected_fields = ["embedding", "chunk_metadata", "metadata", "text"]
+    for field in expected_fields:
+        assert has_field(first_chunk_with_flags, field), f"Field '{field}' missing from response with extra_query"
+        assert has_field(first_chunk_without_flags, field), f"Field '{field}' missing from response without extra_query"
+
+    # Validate content is the same
+    def get_field(obj, field):
+        if isinstance(obj, dict):
+            return obj[field]
+        else:
+            return getattr(obj, field)
+
+    assert get_field(first_chunk_with_flags, "text") == test_content.decode("utf-8")
+    assert get_field(first_chunk_without_flags, "text") == test_content.decode("utf-8")
+
+    with_flags_embedding = get_field(first_chunk_with_flags, "embedding")
+    without_flags_embedding = get_field(first_chunk_without_flags, "embedding")
+
+    # Validate that embeddings are included when requested and excluded when not requested
+    assert with_flags_embedding is not None, "Embeddings should be included when include_embeddings=True"
+    assert len(with_flags_embedding) > 0, "Embedding should be a non-empty list"
+    assert without_flags_embedding is None, "Embeddings should not be included when include_embeddings=False"

tests/unit/core/routers/test_vector_io.py

@@ -55,3 +55,65 @@ async def test_create_vector_stores_multiple_providers_missing_provider_id_error
 
     with pytest.raises(ValueError, match="Multiple vector_io providers available"):
         await router.openai_create_vector_store(request)
+
+
+async def test_update_vector_store_provider_id_change_fails():
+    """Test that updating a vector store with a different provider_id fails with clear error."""
+    mock_routing_table = Mock()
+
+    # Mock an existing vector store with provider_id "faiss"
+    mock_existing_store = Mock()
+    mock_existing_store.provider_id = "inline::faiss"
+    mock_existing_store.identifier = "vs_123"
+
+    mock_routing_table.get_object_by_identifier = AsyncMock(return_value=mock_existing_store)
+    mock_routing_table.get_provider_impl = AsyncMock(
+        return_value=Mock(openai_update_vector_store=AsyncMock(return_value=Mock(id="vs_123")))
+    )
+
+    router = VectorIORouter(mock_routing_table)
+
+    # Try to update with different provider_id in metadata - this should fail
+    with pytest.raises(ValueError, match="provider_id cannot be changed after vector store creation"):
+        await router.openai_update_vector_store(
+            vector_store_id="vs_123",
+            name="updated_name",
+            metadata={"provider_id": "inline::sqlite"},  # Different provider_id
+        )
+
+    # Verify the existing store was looked up to check provider_id
+    mock_routing_table.get_object_by_identifier.assert_called_once_with("vector_store", "vs_123")
+
+    # Provider should not be called since validation failed
+    mock_routing_table.get_provider_impl.assert_not_called()
+
+
+async def test_update_vector_store_same_provider_id_succeeds():
+    """Test that updating a vector store with the same provider_id succeeds."""
+    mock_routing_table = Mock()
+
+    # Mock an existing vector store with provider_id "faiss"
+    mock_existing_store = Mock()
+    mock_existing_store.provider_id = "inline::faiss"
+    mock_existing_store.identifier = "vs_123"
+
+    mock_routing_table.get_object_by_identifier = AsyncMock(return_value=mock_existing_store)
+    mock_routing_table.get_provider_impl = AsyncMock(
+        return_value=Mock(openai_update_vector_store=AsyncMock(return_value=Mock(id="vs_123")))
+    )
+
+    router = VectorIORouter(mock_routing_table)
+
+    # Update with same provider_id should succeed
+    await router.openai_update_vector_store(
+        vector_store_id="vs_123",
+        name="updated_name",
+        metadata={"provider_id": "inline::faiss"},  # Same provider_id
+    )
+
+    # Verify the provider update method was called
+    mock_routing_table.get_provider_impl.assert_called_once_with("vs_123")
+    provider = await mock_routing_table.get_provider_impl("vs_123")
+    provider.openai_update_vector_store.assert_called_once_with(
+        vector_store_id="vs_123", name="updated_name", expires_after=None, metadata={"provider_id": "inline::faiss"}
+    )

tests/unit/server/test_sse.py

@@ -104,12 +104,18 @@ async def test_paginated_response_url_setting():
 
     route_handler = create_dynamic_typed_route(mock_api_method, "get", "/test/route")
 
-    # Mock minimal request
+    # Mock minimal request with proper state object
     request = MagicMock()
     request.scope = {"user_attributes": {}, "principal": ""}
     request.headers = {}
     request.body = AsyncMock(return_value=b"")
 
+    # Create a simple state object without auto-generating attributes
+    class MockState:
+        pass
+
+    request.state = MockState()
+
     result = await route_handler(request)
 
     assert isinstance(result, PaginatedResponse)