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>

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]:
-        # content is InterleavedContent
+    def _chunk_to_vector_store_content(
+        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 = [
-                VectorStoreContent(
-                    type="text",
-                    text=chunk.content,
-                )
-            ]
+            content_item = VectorStoreContent(type="text", text=chunk.content, **fields)
+            content = [content_item]
         elif isinstance(chunk.content, list):
             # TODO: Add support for other types of content
-            content = [
-                VectorStoreContent(
-                    type="text",
-                    text=item.text,
-                )
-                for item in chunk.content
-                if item.type == "text"
-            ]
+            content = []
+            for item in chunk.content:
+                if item.type == "text":
+                    content_item = VectorStoreContent(type="text", text=item.text, **fields)
+                    content.append(content_item)
         else:
             if chunk.content.type != "text":
                 raise ValueError(f"Unsupported content type: {chunk.content.type}")
-            content = [
-                VectorStoreContent(
-                    type="text",
-                    text=chunk.content.text,
-                )
-            ]
+
+            content_item = VectorStoreContent(type="text", text=chunk.content.text, **fields)
+            content = [content_item]
         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(