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>

src/llama_stack/core/datatypes.py

@@ -18,6 +18,7 @@ from llama_stack.core.storage.datatypes import (
     StorageConfig,
 )
 from llama_stack.log import LoggingConfig
+from llama_stack.providers.utils.memory.constants import DEFAULT_QUERY_REWRITE_PROMPT
 from llama_stack_api import (
     Api,
     Benchmark,
@@ -349,6 +350,27 @@ class QualifiedModel(BaseModel):
     model_id: str
 
 
+class RewriteQueryParams(BaseModel):
+    """Parameters for query rewriting/expansion."""
+
+    model: QualifiedModel | None = Field(
+        default=None,
+        description="LLM model for query rewriting/expansion in vector search.",
+    )
+    prompt: str = Field(
+        default=DEFAULT_QUERY_REWRITE_PROMPT,
+        description="Prompt template for query rewriting. Use {query} as placeholder for the original query.",
+    )
+    max_tokens: int = Field(
+        default=100,
+        description="Maximum number of tokens for query expansion responses.",
+    )
+    temperature: float = Field(
+        default=0.3,
+        description="Temperature for query expansion model (0.0 = deterministic, 1.0 = creative).",
+    )
+
+
 class VectorStoresConfig(BaseModel):
     """Configuration for vector stores in the stack."""
 
@@ -360,6 +382,10 @@ class VectorStoresConfig(BaseModel):
         default=None,
         description="Default embedding model configuration for vector stores.",
     )
+    rewrite_query_params: RewriteQueryParams | None = Field(
+        default=None,
+        description="Parameters for query rewriting/expansion. None disables query rewriting.",
+    )
 
 
 class SafetyConfig(BaseModel):

src/llama_stack/core/resolver.py

@@ -199,6 +199,13 @@ def specs_for_autorouted_apis(apis_to_serve: list[str] | set[str]) -> dict[str,
             )
         }
 
+        # Add inference as an optional dependency for vector_io to enable query rewriting
+        optional_deps = []
+        deps_list = [info.routing_table_api.value]
+        if info.router_api == Api.vector_io:
+            optional_deps = [Api.inference]
+            deps_list.append(Api.inference.value)
+
         specs[info.router_api.value] = {
             "__builtin__": ProviderWithSpec(
                 provider_id="__autorouted__",
@@ -209,7 +216,8 @@ def specs_for_autorouted_apis(apis_to_serve: list[str] | set[str]) -> dict[str,
                     module="llama_stack.core.routers",
                     routing_table_api=info.routing_table_api,
                     api_dependencies=[info.routing_table_api],
-                    deps__=([info.routing_table_api.value]),
+                    optional_api_dependencies=optional_deps,
+                    deps__=deps_list,
                 ),
             )
         }
@@ -315,6 +323,13 @@ async def instantiate_providers(
             api = Api(api_str)
             impls[api] = impl
 
+    # Post-instantiation: Inject VectorIORouter into VectorStoresRoutingTable
+    if Api.vector_io in impls and Api.vector_stores in impls:
+        vector_io_router = impls[Api.vector_io]
+        vector_stores_routing_table = impls[Api.vector_stores]
+        if hasattr(vector_stores_routing_table, "vector_io_router"):
+            vector_stores_routing_table.vector_io_router = vector_io_router
+
     return impls
 
 

src/llama_stack/core/routers/__init__.py

@@ -87,6 +87,7 @@ async def get_auto_router_impl(
         api_to_dep_impl["store"] = inference_store
     elif api == Api.vector_io:
         api_to_dep_impl["vector_stores_config"] = run_config.vector_stores
+        api_to_dep_impl["inference_api"] = deps.get(Api.inference)
     elif api == Api.safety:
         api_to_dep_impl["safety_config"] = run_config.safety
 

src/llama_stack/core/routers/vector_io.py

@@ -16,12 +16,15 @@ from llama_stack_api import (
     Chunk,
     HealthResponse,
     HealthStatus,
+    Inference,
     InterleavedContent,
     ModelNotFoundError,
     ModelType,
     ModelTypeError,
+    OpenAIChatCompletionRequestWithExtraBody,
     OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
     OpenAICreateVectorStoreRequestWithExtraBody,
+    OpenAIUserMessageParam,
     QueryChunksResponse,
     RoutingTable,
     SearchRankingOptions,
@@ -51,10 +54,11 @@ class VectorIORouter(VectorIO):
         self,
         routing_table: RoutingTable,
         vector_stores_config: VectorStoresConfig | None = None,
+        inference_api: Inference | None = None,
     ) -> None:
-        logger.debug("Initializing VectorIORouter")
         self.routing_table = routing_table
         self.vector_stores_config = vector_stores_config
+        self.inference_api = inference_api
 
     async def initialize(self) -> None:
         logger.debug("VectorIORouter.initialize")
@@ -64,6 +68,46 @@ class VectorIORouter(VectorIO):
         logger.debug("VectorIORouter.shutdown")
         pass
 
+    async def _rewrite_query_for_search(self, query: str) -> str:
+        """Rewrite a search query using the configured LLM model for better retrieval results."""
+        if (
+            not self.vector_stores_config
+            or not self.vector_stores_config.rewrite_query_params
+            or not self.vector_stores_config.rewrite_query_params.model
+        ):
+            logger.warning(
+                "User is trying to use vector_store query rewriting, but it is not configured. Please configure rewrite_query_params.model in vector_stores config."
+            )
+            raise ValueError("Query rewriting is not available")
+
+        if not self.inference_api:
+            logger.warning("Query rewriting requires inference API but it is not available")
+            raise ValueError("Query rewriting is not available")
+
+        model = self.vector_stores_config.rewrite_query_params.model
+        model_id = f"{model.provider_id}/{model.model_id}"
+
+        prompt = self.vector_stores_config.rewrite_query_params.prompt.format(query=query)
+
+        request = OpenAIChatCompletionRequestWithExtraBody(
+            model=model_id,
+            messages=[OpenAIUserMessageParam(role="user", content=prompt)],
+            max_tokens=self.vector_stores_config.rewrite_query_params.max_tokens or 100,
+            temperature=self.vector_stores_config.rewrite_query_params.temperature or 0.3,
+        )
+
+        try:
+            response = await self.inference_api.openai_chat_completion(request)
+            content = response.choices[0].message.content
+            if content is None:
+                logger.error(f"LLM returned None content for query rewriting. Model: {model_id}")
+                raise RuntimeError("Query rewrite failed due to an internal error")
+            rewritten_query: str = content.strip()
+            return rewritten_query
+        except Exception as e:
+            logger.error(f"Query rewrite failed with LLM call error. Model: {model_id}, Error: {e}")
+            raise RuntimeError("Query rewrite failed due to an internal error") from e
+
     async def _get_embedding_model_dimension(self, embedding_model_id: str) -> int:
         """Get the embedding dimension for a specific embedding model."""
         all_models = await self.routing_table.get_all_with_type("model")
@@ -292,14 +336,24 @@ class VectorIORouter(VectorIO):
         search_mode: str | None = "vector",
     ) -> VectorStoreSearchResponsePage:
         logger.debug(f"VectorIORouter.openai_search_vector_store: {vector_store_id}")
+
+        # Handle query rewriting at the router level
+        search_query = query
+        if rewrite_query:
+            if isinstance(query, list):
+                original_query = " ".join(query)
+            else:
+                original_query = query
+            search_query = await self._rewrite_query_for_search(original_query)
+
         provider = await self.routing_table.get_provider_impl(vector_store_id)
         return await provider.openai_search_vector_store(
             vector_store_id=vector_store_id,
-            query=query,
+            query=search_query,
             filters=filters,
             max_num_results=max_num_results,
             ranking_options=ranking_options,
-            rewrite_query=rewrite_query,
+            rewrite_query=False,  # Already handled at router level
             search_mode=search_mode,
         )
 

src/llama_stack/core/routing_tables/vector_stores.py

@@ -40,6 +40,15 @@ class VectorStoresRoutingTable(CommonRoutingTableImpl):
     Only provides internal routing functionality for VectorIORouter.
     """
 
+    def __init__(
+        self,
+        impls_by_provider_id: dict[str, Any],
+        dist_registry: Any,
+        policy: list[Any],
+    ) -> None:
+        super().__init__(impls_by_provider_id, dist_registry, policy)
+        self.vector_io_router = None  # Will be set post-instantiation
+
     # Internal methods only - no public API exposure
 
     async def register_vector_store(
@@ -133,6 +142,20 @@ class VectorStoresRoutingTable(CommonRoutingTableImpl):
         search_mode: str | None = "vector",
     ) -> VectorStoreSearchResponsePage:
         await self.assert_action_allowed("read", "vector_store", vector_store_id)
+
+        # Delegate to VectorIORouter if available (which handles query rewriting)
+        if self.vector_io_router is not None:
+            return await self.vector_io_router.openai_search_vector_store(
+                vector_store_id=vector_store_id,
+                query=query,
+                filters=filters,
+                max_num_results=max_num_results,
+                ranking_options=ranking_options,
+                rewrite_query=rewrite_query,
+                search_mode=search_mode,
+            )
+
+        # Fallback to direct provider call if VectorIORouter not available
         provider = await self.get_provider_impl(vector_store_id)
         return await provider.openai_search_vector_store(
             vector_store_id=vector_store_id,

src/llama_stack/core/stack.py

@@ -16,7 +16,7 @@ import yaml
 from pydantic import BaseModel
 
 from llama_stack.core.conversations.conversations import ConversationServiceConfig, ConversationServiceImpl
-from llama_stack.core.datatypes import Provider, SafetyConfig, StackConfig, VectorStoresConfig
+from llama_stack.core.datatypes import Provider, QualifiedModel, SafetyConfig, StackConfig, VectorStoresConfig
 from llama_stack.core.distribution import get_provider_registry
 from llama_stack.core.inspect import DistributionInspectConfig, DistributionInspectImpl
 from llama_stack.core.prompts.prompts import PromptServiceConfig, PromptServiceImpl
@@ -221,35 +221,71 @@ async def validate_vector_stores_config(vector_stores_config: VectorStoresConfig
     if vector_stores_config is None:
         return
 
-    default_embedding_model = vector_stores_config.default_embedding_model
-    if default_embedding_model is None:
-        return
+    # Validate default embedding model
+    if vector_stores_config.default_embedding_model is not None:
+        await _validate_embedding_model(vector_stores_config.default_embedding_model, impls)
 
-    provider_id = default_embedding_model.provider_id
-    model_id = default_embedding_model.model_id
-    default_model_id = f"{provider_id}/{model_id}"
+    # Validate rewrite query params
+    if vector_stores_config.rewrite_query_params:
+        if vector_stores_config.rewrite_query_params.model:
+            await _validate_rewrite_query_model(vector_stores_config.rewrite_query_params.model, impls)
+        if "{query}" not in vector_stores_config.rewrite_query_params.prompt:
+            raise ValueError("'{query}' placeholder is required in the prompt template")
+
+
+async def _validate_embedding_model(embedding_model: QualifiedModel, impls: dict[Api, Any]) -> None:
+    """Validate that an embedding model exists and has required metadata."""
+    provider_id = embedding_model.provider_id
+    model_id = embedding_model.model_id
+    model_identifier = f"{provider_id}/{model_id}"
 
     if Api.models not in impls:
-        raise ValueError(f"Models API is not available but vector_stores config requires model '{default_model_id}'")
+        raise ValueError(f"Models API is not available but vector_stores config requires model '{model_identifier}'")
 
     models_impl = impls[Api.models]
     response = await models_impl.list_models()
     models_list = {m.identifier: m for m in response.data if m.model_type == "embedding"}
 
-    default_model = models_list.get(default_model_id)
-    if default_model is None:
-        raise ValueError(f"Embedding model '{default_model_id}' not found. Available embedding models: {models_list}")
+    model = models_list.get(model_identifier)
+    if model is None:
+        raise ValueError(
+            f"Embedding model '{model_identifier}' not found. Available embedding models: {list(models_list.keys())}"
+        )
 
-    embedding_dimension = default_model.metadata.get("embedding_dimension")
+    embedding_dimension = model.metadata.get("embedding_dimension")
     if embedding_dimension is None:
-        raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
+        raise ValueError(f"Embedding model '{model_identifier}' is missing 'embedding_dimension' in metadata")
 
     try:
         int(embedding_dimension)
     except ValueError as err:
         raise ValueError(f"Embedding dimension '{embedding_dimension}' cannot be converted to an integer") from err
 
-    logger.debug(f"Validated default embedding model: {default_model_id} (dimension: {embedding_dimension})")
+    logger.debug(f"Validated embedding model: {model_identifier} (dimension: {embedding_dimension})")
+
+
+async def _validate_rewrite_query_model(rewrite_query_model: QualifiedModel, impls: dict[Api, Any]) -> None:
+    """Validate that a rewrite query model exists and is accessible."""
+    provider_id = rewrite_query_model.provider_id
+    model_id = rewrite_query_model.model_id
+    model_identifier = f"{provider_id}/{model_id}"
+
+    if Api.models not in impls:
+        raise ValueError(
+            f"Models API is not available but vector_stores config requires rewrite query model '{model_identifier}'"
+        )
+
+    models_impl = impls[Api.models]
+    response = await models_impl.list_models()
+    llm_models_list = {m.identifier: m for m in response.data if m.model_type == "llm"}
+
+    model = llm_models_list.get(model_identifier)
+    if model is None:
+        raise ValueError(
+            f"Rewrite query model '{model_identifier}' not found. Available LLM models: {list(llm_models_list.keys())}"
+        )
+
+    logger.debug(f"Validated rewrite query model: {model_identifier}")
 
 
 async def validate_safety_config(safety_config: SafetyConfig | None, impls: dict[Api, Any]):

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

@@ -3,3 +3,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+
+from .constants import DEFAULT_QUERY_REWRITE_PROMPT
+
+__all__ = ["DEFAULT_QUERY_REWRITE_PROMPT"]

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

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Default prompt template for query rewriting in vector search
+DEFAULT_QUERY_REWRITE_PROMPT = "Expand this query with relevant synonyms and related terms. Return only the improved query, no explanations:\n\n{query}\n\nImproved query:"

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

@@ -592,7 +592,11 @@ class OpenAIVectorStoreMixin(ABC):
             str | None
         ) = "vector",  # Using str instead of Literal due to OpenAPI schema generator limitations
     ) -> VectorStoreSearchResponsePage:
-        """Search for chunks in a vector store."""
+        """Search for chunks in a vector store.
+
+        Note: Query rewriting is handled at the router level, not here.
+        The rewrite_query parameter is kept for API compatibility but is ignored.
+        """
         max_num_results = max_num_results or 10
 
         # Validate search_mode

tests/integration/common/recordings/models-d98e7566147f9d534bc0461f2efe61e3f525c18360a07bb3dda397579e25c27b-a882f554.json

@@ -0,0 +1,647 @@
+{
+  "test_id": null,
+  "request": {
+    "method": "POST",
+    "url": "https://generativelanguage.googleapis.com/v1beta/openai/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/embedding-gecko-001",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Embedding Gecko"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.5-pro-preview-03-25",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.5 Pro Preview 03-25"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.5-flash-preview-05-20",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.5 Flash Preview 05-20"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.5-flash",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.5 Flash"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.5-flash-lite-preview-06-17",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.5 Flash-Lite Preview 06-17"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.5-pro-preview-05-06",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.5 Pro Preview 05-06"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.5-pro-preview-06-05",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.5 Pro Preview"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.5-pro",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.5 Pro"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.0-flash-exp",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.0 Flash Experimental"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.0-flash",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.0 Flash"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.0-flash-001",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.0 Flash 001"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.0-flash-exp-image-generation",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.0 Flash (Image Generation) Experimental"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "models/gemini-2.0-flash-lite-001",
+          "created": null,
+          "object": "model",
+          "owned_by": "google",
+          "display_name": "Gemini 2.0 Flash-Lite 001"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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tests/integration/vector_io/recordings/models-52b8e996a4f2c944c6bae30ac5274b714ed42392139e2024601a849af31b9253-a882f554.json

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tests/integration/vector_io/recordings/models-cd7b914c23d754f03d8c0a789ce0df17329547d12cbec8bbcc581dc38df5fdbb-a882f554.json

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tests/integration/vector_io/recordings/models-e3257f8a2366634242cfecf5298c0bc7af95853da455d1f7c9316fb7e9d1419b-a882f554.json

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+          "created": 1746673257,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-2024-08-06",
+          "created": 1722814719,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "object": "model",
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+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
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+          "object": "model",
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+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1734387424,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-image-1-mini",
+          "created": 1758845821,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5-mini",
+          "created": 1754425928,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-image-1",
+          "created": 1745517030,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5-mini-2025-08-07",
+          "created": 1754425867,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1732734466,
+          "object": "model",
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+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5",
+          "created": 1754425777,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "o3-pro",
+          "created": 1748475349,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1671217299,
+          "object": "model",
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+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1754426303,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
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+        "__data__": {
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+          "created": 1734034239,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5-nano",
+          "created": 1754426384,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1762800353,
+          "object": "model",
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+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1699053533,
+          "object": "model",
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+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1699046015,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1706048358,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-audio-mini",
+          "created": 1759512027,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-audio-mini-2025-10-06",
+          "created": 1759512137,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5.1",
+          "created": 1762800673,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-mini-audio-preview-2024-12-17",
+          "created": 1734115920,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1738010200,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "o3-mini",
+          "created": 1737146383,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-realtime-preview-2024-12-17",
+          "created": 1733945430,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "o3-pro-2025-06-10",
+          "created": 1749166761,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1698957206,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "babbage-002",
+          "created": 1692634615,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5-chat-latest",
+          "created": 1754073306,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-3.5-turbo",
+          "created": 1677610602,
+          "object": "model",
+          "owned_by": "openai"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5-2025-08-07",
+          "created": 1754075360,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "chatgpt-4o-latest",
+          "created": 1723515131,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-transcribe",
+          "created": 1742068463,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "sora-2",
+          "created": 1759708615,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "sora-2-pro",
+          "created": 1759708663,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1759469707,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o",
+          "created": 1715367049,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-realtime-preview",
+          "created": 1727659998,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-realtime-mini",
+          "created": 1759517133,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-realtime-mini-2025-10-06",
+          "created": 1759517175,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "o4-mini",
+          "created": 1744225351,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "o4-mini-2025-04-16",
+          "created": 1744133506,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-realtime-preview-2025-06-03",
+          "created": 1748907838,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-audio-preview",
+          "created": 1727460443,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-audio-preview-2025-06-03",
+          "created": 1748908498,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5-pro",
+          "created": 1759469822,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "text-embedding-3-large",
+          "created": 1705953180,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "o3-deep-research",
+          "created": 1749840121,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1750865219,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-mini-transcribe",
+          "created": 1742068596,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-mini-realtime-preview",
+          "created": 1734387380,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1734112601,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
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+          "created": 1744225308,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "o3-2025-04-16",
+          "created": 1744133301,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-transcribe-diarize",
+          "created": 1750798887,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5.1-codex",
+          "created": 1762988221,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-audio-preview-2024-10-01",
+          "created": 1727389042,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-4o-realtime-preview-2024-10-01",
+          "created": 1727131766,
+          "object": "model",
+          "owned_by": "system"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-5-codex",
+          "created": 1757527818,
+          "object": "model",
+          "owned_by": "system"
+        }
+      }
+    ],
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/vector_io/test_openai_vector_stores.py

@@ -1704,3 +1704,50 @@ def test_openai_vector_store_file_contents_with_extra_query(
     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"
+
+
+@vector_provider_wrapper
+def test_openai_vector_store_search_with_rewrite_query(
+    compat_client_with_empty_stores,
+    client_with_models,
+    sample_chunks,
+    embedding_model_id,
+    embedding_dimension,
+    vector_io_provider_id,
+):
+    """Test that rewrite_query parameter is properly passed through and handled."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+    llama_client = client_with_models
+
+    # Create vector store and insert chunks
+    vector_store = compat_client.vector_stores.create(
+        name="rewrite_test",
+        extra_body={"embedding_model": embedding_model_id, "provider_id": vector_io_provider_id},
+    )
+    llama_client.vector_io.insert(
+        vector_store_id=vector_store.id,
+        chunks=sample_chunks,
+    )
+
+    response_no_rewrite = compat_client.vector_stores.search(
+        vector_store_id=vector_store.id,
+        query="programming",
+        max_num_results=2,
+        rewrite_query=False,
+    )
+    assert response_no_rewrite is not None
+
+    # Test rewrite_query=True should fail with proper error when no LLM models are configured
+    with pytest.raises((BadRequestError, OpenAIBadRequestError, ValueError)) as exc_info:
+        compat_client.vector_stores.search(
+            vector_store_id=vector_store.id,
+            query="programming",
+            max_num_results=2,
+            rewrite_query=True,
+        )
+
+    # Verify the error message indicates missing query rewriting configuration
+    error_message = str(exc_info.value)
+    assert "Query rewriting is not available" in error_message

tests/unit/core/routers/test_vector_io.py

@@ -153,3 +153,156 @@ async def test_create_vector_store_with_wrong_model_type_raises_error():
 
     with pytest.raises(ModelTypeError, match="Model 'text-model' is of type"):
         await router.openai_create_vector_store(request)
+
+
+async def test_query_rewrite_functionality():
+    """Test query rewriting at the router level."""
+    from unittest.mock import MagicMock
+
+    from llama_stack.core.datatypes import QualifiedModel, RewriteQueryParams, VectorStoresConfig
+    from llama_stack.providers.utils.memory.constants import DEFAULT_QUERY_REWRITE_PROMPT
+    from llama_stack_api import VectorStoreSearchResponsePage
+
+    mock_routing_table = Mock()
+
+    # Mock provider that returns search results
+    mock_provider = Mock()
+    mock_search_response = VectorStoreSearchResponsePage(search_query=["rewritten test query"], data=[], has_more=False)
+    mock_provider.openai_search_vector_store = AsyncMock(return_value=mock_search_response)
+    mock_routing_table.get_provider_impl = AsyncMock(return_value=mock_provider)
+
+    # Mock inference API for query rewriting
+    mock_inference_api = Mock()
+    mock_inference_api.openai_chat_completion = AsyncMock(
+        return_value=MagicMock(choices=[MagicMock(message=MagicMock(content="rewritten test query"))])
+    )
+
+    # Create config with rewrite params
+    vector_stores_config = VectorStoresConfig(
+        rewrite_query_params=RewriteQueryParams(
+            model=QualifiedModel(provider_id="test", model_id="llama"),
+            max_tokens=100,
+            temperature=0.3,
+        )
+    )
+
+    router = VectorIORouter(mock_routing_table, vector_stores_config, mock_inference_api)
+
+    # Test query rewrite with rewrite_query=True
+    result = await router.openai_search_vector_store(
+        vector_store_id="vs_123",
+        query="test query",
+        rewrite_query=True,
+        max_num_results=5,
+    )
+
+    # Verify chat completion was called for query rewriting
+    assert mock_inference_api.openai_chat_completion.called
+    chat_call_args = mock_inference_api.openai_chat_completion.call_args[0][0]
+    assert chat_call_args.model == "test/llama"
+
+    # Verify default prompt is used
+    prompt_text = chat_call_args.messages[0].content
+    expected_prompt = DEFAULT_QUERY_REWRITE_PROMPT.format(query="test query")
+    assert prompt_text == expected_prompt
+
+    # Verify provider was called with rewritten query and rewrite_query=False
+    mock_provider.openai_search_vector_store.assert_called_once()
+    call_kwargs = mock_provider.openai_search_vector_store.call_args.kwargs
+    assert call_kwargs["query"] == "rewritten test query"
+    assert call_kwargs["rewrite_query"] is False  # Should be False since router handled it
+
+    assert result is not None
+
+
+async def test_query_rewrite_error_when_not_configured():
+    """Test that query rewriting fails with proper error when not configured."""
+    mock_routing_table = Mock()
+    mock_provider = Mock()
+    mock_routing_table.get_provider_impl = AsyncMock(return_value=mock_provider)
+
+    # No config or inference API
+    router = VectorIORouter(mock_routing_table)
+
+    with pytest.raises(ValueError, match="Query rewriting is not available"):
+        await router.openai_search_vector_store(
+            vector_store_id="vs_123",
+            query="test query",
+            rewrite_query=True,
+            max_num_results=5,
+        )
+
+
+async def test_query_rewrite_with_custom_prompt():
+    """Test query rewriting with custom prompt."""
+    from unittest.mock import MagicMock
+
+    from llama_stack.core.datatypes import QualifiedModel, RewriteQueryParams, VectorStoresConfig
+    from llama_stack_api import VectorStoreSearchResponsePage
+
+    mock_routing_table = Mock()
+
+    mock_provider = Mock()
+    mock_search_response = VectorStoreSearchResponsePage(search_query=["custom rewrite"], data=[], has_more=False)
+    mock_provider.openai_search_vector_store = AsyncMock(return_value=mock_search_response)
+    mock_routing_table.get_provider_impl = AsyncMock(return_value=mock_provider)
+
+    mock_inference_api = Mock()
+    mock_inference_api.openai_chat_completion = AsyncMock(
+        return_value=MagicMock(choices=[MagicMock(message=MagicMock(content="custom rewrite"))])
+    )
+
+    vector_stores_config = VectorStoresConfig(
+        rewrite_query_params=RewriteQueryParams(
+            model=QualifiedModel(provider_id="test", model_id="llama"),
+            prompt="Custom prompt: {query}",
+            max_tokens=150,
+            temperature=0.7,
+        )
+    )
+
+    router = VectorIORouter(mock_routing_table, vector_stores_config, mock_inference_api)
+
+    await router.openai_search_vector_store(
+        vector_store_id="vs_123",
+        query="test query",
+        rewrite_query=True,
+        max_num_results=5,
+    )
+
+    # Verify custom prompt was used
+    chat_call_args = mock_inference_api.openai_chat_completion.call_args[0][0]
+    assert chat_call_args.messages[0].content == "Custom prompt: test query"
+    assert chat_call_args.max_tokens == 150
+    assert chat_call_args.temperature == 0.7
+
+
+async def test_search_without_rewrite():
+    """Test that search without rewrite_query doesn't call inference API."""
+    from llama_stack_api import VectorStoreSearchResponsePage
+
+    mock_routing_table = Mock()
+
+    mock_provider = Mock()
+    mock_search_response = VectorStoreSearchResponsePage(search_query=["test query"], data=[], has_more=False)
+    mock_provider.openai_search_vector_store = AsyncMock(return_value=mock_search_response)
+    mock_routing_table.get_provider_impl = AsyncMock(return_value=mock_provider)
+
+    mock_inference_api = Mock()
+    mock_inference_api.openai_chat_completion = AsyncMock()
+
+    router = VectorIORouter(mock_routing_table, inference_api=mock_inference_api)
+
+    await router.openai_search_vector_store(
+        vector_store_id="vs_123",
+        query="test query",
+        rewrite_query=False,
+        max_num_results=5,
+    )
+
+    # Verify inference API was NOT called
+    assert not mock_inference_api.openai_chat_completion.called
+
+    # Verify provider was called with original query
+    call_kwargs = mock_provider.openai_search_vector_store.call_args.kwargs
+    assert call_kwargs["query"] == "test query"

tests/unit/core/test_stack_validation.py

@@ -10,7 +10,13 @@ from unittest.mock import AsyncMock
 
 import pytest
 
-from llama_stack.core.datatypes import QualifiedModel, SafetyConfig, StackConfig, VectorStoresConfig
+from llama_stack.core.datatypes import (
+    QualifiedModel,
+    RewriteQueryParams,
+    SafetyConfig,
+    StackConfig,
+    VectorStoresConfig,
+)
 from llama_stack.core.stack import validate_safety_config, validate_vector_stores_config
 from llama_stack.core.storage.datatypes import ServerStoresConfig, StorageConfig
 from llama_stack_api import Api, ListModelsResponse, ListShieldsResponse, Model, ModelType, Shield
@@ -82,6 +88,17 @@ class TestVectorStoresValidation:
 
         await validate_vector_stores_config(run_config.vector_stores, {Api.models: mock_models})
 
+    async def test_validate_rewrite_query_prompt_missing_placeholder(self):
+        """Test validation fails when prompt template is missing {query} placeholder."""
+        config = VectorStoresConfig(
+            rewrite_query_params=RewriteQueryParams(
+                prompt="This prompt has no placeholder",
+            ),
+        )
+
+        with pytest.raises(ValueError, match="'\\{query\\}' placeholder is required"):
+            await validate_vector_stores_config(config, {})
+
 
 class TestSafetyConfigValidation:
     async def test_validate_success(self):

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