feat: Add support for query rewrite in vector_store.search (#4171)

# What does this PR do?

Actualize query rewrite in search API, add
`default_query_expansion_model` and `query_expansion_prompt` in
`VectorStoresConfig`.

Makes `rewrite_query` parameter functional in vector store search.
  - `rewrite_query=false` (default): Use original query
- `rewrite_query=true`: Expand query via LLM, or fail gracefully if no
LLM available

Adds 4 parameters to`VectorStoresConfig`:
- `default_query_expansion_model`: LLM model for query expansion
(optional)
- `query_expansion_prompt`: Custom prompt template (optional, uses
built-in default)
- `query_expansion_max_tokens`: Configurable token limit (default: 100)
- `query_expansion_temperature`: Configurable temperature (default: 0.3)

Enabled `run.yaml`:
```yaml
  vector_stores:
    rewrite_query_params:
      model:
        provider_id: "ollama"
        model_id: "llama3.2:3b-instruct-fp16"
      # prompt defaults to built-in
      # max_tokens defaults to 100
      # temperature defaults to 0.3
```

  Fully customized `run.yaml`:
```yaml
  vector_stores:
    default_provider_id: faiss
    default_embedding_model:
      provider_id: sentence-transformers
      model_id: nomic-ai/nomic-embed-text-v1.5
    rewrite_query_params:
      model:
        provider_id: ollama
        model_id: llama3.2:3b-instruct-fp16
      prompt: "Rewrite this search query to improve retrieval results by expanding it with relevant synonyms and related terms: {query}"
      max_tokens: 100
      temperature: 0.3
```

## Test Plan
Added test and recording

Example script as well:

```python
import asyncio
from llama_stack_client import LlamaStackClient
from io import BytesIO

def gen_file(client, text: str=""):
    file_buffer = BytesIO(text.encode('utf-8'))
    file_buffer.name = "my_file.txt"

    uploaded_file = client.files.create(
        file=file_buffer,
        purpose="assistants"
    )
    return uploaded_file

async def test_query_rewriting():
    client = LlamaStackClient(base_url="http://0.0.0.0:8321/")
    uploaded_file = gen_file(client, "banana banana apple")
    uploaded_file2 = gen_file(client, "orange orange kiwi")

    vs = client.vector_stores.create()
    xf_vs = client.vector_stores.files.create(vector_store_id=vs.id, file_id=uploaded_file.id)
    xf_vs1 = client.vector_stores.files.create(vector_store_id=vs.id, file_id=uploaded_file2.id)
    response1 = client.vector_stores.search(
                vector_store_id=vs.id,
                query="apple",
                max_num_results=3,
                rewrite_query=False
            )
    response2 = client.vector_stores.search(
                vector_store_id=vs.id,
                query="kiwi",
                max_num_results=3,
                rewrite_query=True,
            )

    print(f"\n🔵 Response 1 (rewrite_query=False):\n\033[94m{response1}\033[0m")
    print(f"\n🟢 Response 2 (rewrite_query=True):\n\033[92m{response2}\033[0m")

    for f in [uploaded_file.id, uploaded_file2.id]:
        client.files.delete(file_id=f)
    client.vector_stores.delete(vector_store_id=vs.id)

if __name__ == "__main__":
    asyncio.run(test_query_rewriting())
```

And see the screen shot of the server logs showing it worked. 
<img width="1111" height="826" alt="Screenshot 2025-11-19 at 1 16 03 PM"
src="https://github.com/user-attachments/assets/2d188b44-1fef-4df5-b465-2d6728ca49ce"
/>

Notice the log:
```bash
 Query rewritten:
         'kiwi' → 'kiwi, a small brown or green fruit native to New Zealand, or a person having a fuzzy brown outer skin similar in appearance.'
```
So `kiwi` was expanded.

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>

tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py

@@ -1230,3 +1230,40 @@ async def test_embedding_config_required_model_missing(vector_io_adapter):
 
     with pytest.raises(ValueError, match="embedding_model is required"):
         await vector_io_adapter.openai_create_vector_store(params)
+
+
+async def test_search_vector_store_ignores_rewrite_query(vector_io_adapter):
+    """Test that the mixin ignores rewrite_query parameter since rewriting is done at router level."""
+    from llama_stack_api import QueryChunksResponse
+
+    # Create an OpenAI vector store for testing directly in the adapter's cache
+    vector_store_id = "test_store_rewrite"
+    openai_vector_store = {
+        "id": vector_store_id,
+        "name": "Test Store",
+        "description": "A test OpenAI vector store",
+        "vector_store_id": "test_db",
+        "embedding_model": "test/embedding",
+    }
+    vector_io_adapter.openai_vector_stores[vector_store_id] = openai_vector_store
+
+    # Mock query_chunks response from adapter
+    mock_response = QueryChunksResponse(chunks=[], scores=[])
+
+    async def mock_query_chunks(*args, **kwargs):
+        return mock_response
+
+    vector_io_adapter.query_chunks = mock_query_chunks
+
+    # Test that rewrite_query=True doesn't cause an error (it's ignored at mixin level)
+    # The mixin should process the search request without attempting to rewrite the query
+    result = await vector_io_adapter.openai_search_vector_store(
+        vector_store_id=vector_store_id,
+        query="test query",
+        rewrite_query=True,  # This should be ignored at mixin level
+        max_num_results=5,
+    )
+
+    # Search should succeed - the mixin ignores rewrite_query and just does the search
+    assert result is not None
+    assert result.search_query == ["test query"]  # Original query preserved