refactor to only configuration of model at build time

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

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_EXPANSION_PROMPT
 from llama_stack_api import (
     Api,
     Benchmark,
@@ -381,9 +382,17 @@ class VectorStoresConfig(BaseModel):
         description="Default LLM model for query expansion/rewriting in vector search.",
     )
     query_expansion_prompt: str = Field(
-        default="Expand this query with relevant synonyms and related terms. Return only the improved query, no explanations:\n\n{query}\n\nImproved query:",
+        default=DEFAULT_QUERY_EXPANSION_PROMPT,
         description="Prompt template for query expansion. Use {query} as placeholder for the original query.",
     )
+    query_expansion_max_tokens: int = Field(
+        default=100,
+        description="Maximum number of tokens for query expansion responses.",
+    )
+    query_expansion_temperature: float = Field(
+        default=0.3,
+        description="Temperature for query expansion model (0.0 = deterministic, 1.0 = creative).",
+    )
 
 
 class SafetyConfig(BaseModel):

src/llama_stack/core/resolver.py

@@ -374,13 +374,6 @@ async def instantiate_provider(
         method = "get_adapter_impl"
         args = [config, deps]
 
-        # Add vector_stores_config for vector_io providers
-        if (
-            "vector_stores_config" in inspect.signature(getattr(module, method)).parameters
-            and provider_spec.api == Api.vector_io
-        ):
-            args.append(run_config.vector_stores)
-
     elif isinstance(provider_spec, AutoRoutedProviderSpec):
         method = "get_auto_router_impl"
 
@@ -401,11 +394,6 @@ async def instantiate_provider(
             args.append(policy)
         if "telemetry_enabled" in inspect.signature(getattr(module, method)).parameters and run_config.telemetry:
             args.append(run_config.telemetry.enabled)
-        if (
-            "vector_stores_config" in inspect.signature(getattr(module, method)).parameters
-            and provider_spec.api == Api.vector_io
-        ):
-            args.append(run_config.vector_stores)
 
     fn = getattr(module, method)
     impl = await fn(*args)

src/llama_stack/core/routers/vector_io.py

@@ -99,19 +99,6 @@ class VectorIORouter(VectorIO):
     ) -> QueryChunksResponse:
         logger.debug(f"VectorIORouter.query_chunks: {vector_store_id}")
         provider = await self.routing_table.get_provider_impl(vector_store_id)
-
-        # Ensure params dict exists and add vector_stores_config for query rewriting
-        if params is None:
-            params = {}
-
-        logger.debug(f"Router vector_stores_config: {self.vector_stores_config}")
-        if self.vector_stores_config and hasattr(self.vector_stores_config, "default_query_expansion_model"):
-            logger.debug(
-                f"Router default_query_expansion_model: {self.vector_stores_config.default_query_expansion_model}"
-            )
-
-        params["vector_stores_config"] = self.vector_stores_config
-
         return await provider.query_chunks(vector_store_id, query, params)
 
     # OpenAI Vector Stores API endpoints

src/llama_stack/core/stack.py

@@ -144,35 +144,62 @@ async def validate_vector_stores_config(vector_stores_config: VectorStoresConfig
     if vector_stores_config is None:
         return
 
+    # Validate default embedding model
     default_embedding_model = vector_stores_config.default_embedding_model
-    if default_embedding_model is None:
+    if default_embedding_model is not None:
-        return
+        provider_id = default_embedding_model.provider_id
+        model_id = default_embedding_model.model_id
+        default_model_id = f"{provider_id}/{model_id}"
 
-    provider_id = default_embedding_model.provider_id
+        if Api.models not in impls:
-    model_id = default_embedding_model.model_id
+            raise ValueError(
-    default_model_id = f"{provider_id}/{model_id}"
+                f"Models API is not available but vector_stores config requires model '{default_model_id}'"
+            )
 
-    if Api.models not in impls:
+        models_impl = impls[Api.models]
-        raise ValueError(f"Models API is not available but vector_stores config requires model '{default_model_id}'")
+        response = await models_impl.list_models()
+        models_list = {m.identifier: m for m in response.data if m.model_type == "embedding"}
 
-    models_impl = impls[Api.models]
+        default_model = models_list.get(default_model_id)
-    response = await models_impl.list_models()
+        if default_model is None:
-    models_list = {m.identifier: m for m in response.data if m.model_type == "embedding"}
+            raise ValueError(
+                f"Embedding model '{default_model_id}' not found. Available embedding models: {models_list}"
+            )
 
-    default_model = models_list.get(default_model_id)
+        embedding_dimension = default_model.metadata.get("embedding_dimension")
-    if default_model is None:
+        if embedding_dimension is None:
-        raise ValueError(f"Embedding model '{default_model_id}' not found. Available embedding models: {models_list}")
+            raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
 
-    embedding_dimension = default_model.metadata.get("embedding_dimension")
+        try:
-    if embedding_dimension is None:
+            int(embedding_dimension)
-        raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
+        except ValueError as err:
+            raise ValueError(f"Embedding dimension '{embedding_dimension}' cannot be converted to an integer") from err
 
-    try:
+        logger.debug(f"Validated default embedding model: {default_model_id} (dimension: {embedding_dimension})")
-        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})")
+    # Validate default query expansion model
+    default_query_expansion_model = vector_stores_config.default_query_expansion_model
+    if default_query_expansion_model is not None:
+        provider_id = default_query_expansion_model.provider_id
+        model_id = default_query_expansion_model.model_id
+        query_model_id = f"{provider_id}/{model_id}"
+
+        if Api.models not in impls:
+            raise ValueError(
+                f"Models API is not available but vector_stores config requires query expansion model '{query_model_id}'"
+            )
+
+        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"}
+
+        query_expansion_model = llm_models_list.get(query_model_id)
+        if query_expansion_model is None:
+            raise ValueError(
+                f"Query expansion model '{query_model_id}' not found. Available LLM models: {list(llm_models_list.keys())}"
+            )
+
+        logger.debug(f"Validated default query expansion model: {query_model_id}")
 
 
 async def validate_safety_config(safety_config: SafetyConfig | None, impls: dict[Api, Any]):
@@ -437,6 +464,12 @@ class Stack:
         await refresh_registry_once(impls)
         await validate_vector_stores_config(self.run_config.vector_stores, impls)
         await validate_safety_config(self.run_config.safety, impls)
+
+        # Set global query expansion configuration from stack config
+        from llama_stack.providers.utils.memory.query_expansion_config import set_default_query_expansion_config
+
+        set_default_query_expansion_config(self.run_config.vector_stores)
+
         self.impls = impls
 
     def create_registry_refresh_task(self):

src/llama_stack/distributions/ci-tests/run-with-postgres-store.yaml

@@ -296,5 +296,7 @@ vector_stores:
 
 
     Improved query:'
+  query_expansion_max_tokens: 100
+  query_expansion_temperature: 0.3
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/ci-tests/run.yaml

@@ -287,5 +287,7 @@ vector_stores:
 
 
     Improved query:'
+  query_expansion_max_tokens: 100
+  query_expansion_temperature: 0.3
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/starter-gpu/run-with-postgres-store.yaml

@@ -299,5 +299,7 @@ vector_stores:
 
 
     Improved query:'
+  query_expansion_max_tokens: 100
+  query_expansion_temperature: 0.3
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/starter-gpu/run.yaml

@@ -290,5 +290,7 @@ vector_stores:
 
 
     Improved query:'
+  query_expansion_max_tokens: 100
+  query_expansion_temperature: 0.3
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/starter/run-with-postgres-store.yaml

@@ -296,5 +296,7 @@ vector_stores:
 
 
     Improved query:'
+  query_expansion_max_tokens: 100
+  query_expansion_temperature: 0.3
 safety:
   default_shield_id: llama-guard

src/llama_stack/distributions/starter/run.yaml

@@ -287,5 +287,7 @@ vector_stores:
 
 
     Improved query:'
+  query_expansion_max_tokens: 100
+  query_expansion_temperature: 0.3
 safety:
   default_shield_id: llama-guard

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

@@ -6,19 +6,16 @@
 
 from typing import Any
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack_api import Api
 
 from .config import FaissVectorIOConfig
 
 
-async def get_provider_impl(
+async def get_provider_impl(config: FaissVectorIOConfig, deps: dict[Api, Any]):
-    config: FaissVectorIOConfig, deps: dict[Api, Any], vector_stores_config: VectorStoresConfig | None = None
-):
     from .faiss import FaissVectorIOAdapter
 
     assert isinstance(config, FaissVectorIOConfig), f"Unexpected config type: {type(config)}"
 
-    impl = FaissVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
+    impl = FaissVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -14,7 +14,6 @@ import faiss  # type: ignore[import-untyped]
 import numpy as np
 from numpy.typing import NDArray
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.core.storage.kvstore import kvstore_impl
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
@@ -190,12 +189,10 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
         config: FaissVectorIOConfig,
         inference_api: Inference,
         files_api: Files | None,
-        vector_stores_config: VectorStoresConfig | None = None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.vector_stores_config = vector_stores_config
         self.cache: dict[str, VectorStoreWithIndex] = {}
 
     async def initialize(self) -> None:
@@ -211,7 +208,6 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
                 vector_store,
                 await FaissIndex.create(vector_store.embedding_dimension, self.kvstore, vector_store.identifier),
                 self.inference_api,
-                self.vector_stores_config,
             )
             self.cache[vector_store.identifier] = index
 
@@ -250,7 +246,6 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
             vector_store=vector_store,
             index=await FaissIndex.create(vector_store.embedding_dimension, self.kvstore, vector_store.identifier),
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
 
     async def list_vector_stores(self) -> list[VectorStore]:
@@ -284,7 +279,6 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
             vector_store=vector_store,
             index=await FaissIndex.create(vector_store.embedding_dimension, self.kvstore, vector_store.identifier),
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
         self.cache[vector_store_id] = index
         return index

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

@@ -6,18 +6,15 @@
 
 from typing import Any
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack_api import Api
 
 from .config import SQLiteVectorIOConfig
 
 
-async def get_provider_impl(
+async def get_provider_impl(config: SQLiteVectorIOConfig, deps: dict[Api, Any]):
-    config: SQLiteVectorIOConfig, deps: dict[Api, Any], vector_stores_config: VectorStoresConfig | None = None
-):
     from .sqlite_vec import SQLiteVecVectorIOAdapter
 
     assert isinstance(config, SQLiteVectorIOConfig), f"Unexpected config type: {type(config)}"
-    impl = SQLiteVecVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
+    impl = SQLiteVecVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -14,7 +14,6 @@ import numpy as np
 import sqlite_vec  # type: ignore[import-untyped]
 from numpy.typing import NDArray
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.core.storage.kvstore import kvstore_impl
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
@@ -391,12 +390,10 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
         config,
         inference_api: Inference,
         files_api: Files | None,
-        vector_stores_config: VectorStoresConfig | None = None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.vector_stores_config = vector_stores_config
         self.cache: dict[str, VectorStoreWithIndex] = {}
         self.vector_store_table = None
 
@@ -411,9 +408,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
             index = await SQLiteVecIndex.create(
                 vector_store.embedding_dimension, self.config.db_path, vector_store.identifier
             )
-            self.cache[vector_store.identifier] = VectorStoreWithIndex(
+            self.cache[vector_store.identifier] = VectorStoreWithIndex(vector_store, index, self.inference_api)
-                vector_store, index, self.inference_api, self.vector_stores_config
-            )
 
         # Load existing OpenAI vector stores into the in-memory cache
         await self.initialize_openai_vector_stores()
@@ -437,9 +432,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
         index = await SQLiteVecIndex.create(
             vector_store.embedding_dimension, self.config.db_path, vector_store.identifier
         )
-        self.cache[vector_store.identifier] = VectorStoreWithIndex(
+        self.cache[vector_store.identifier] = VectorStoreWithIndex(vector_store, index, self.inference_api)
-            vector_store, index, self.inference_api, self.vector_stores_config
-        )
 
     async def _get_and_cache_vector_store_index(self, vector_store_id: str) -> VectorStoreWithIndex | None:
         if vector_store_id in self.cache:
@@ -464,7 +457,6 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
                 kvstore=self.kvstore,
             ),
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
         self.cache[vector_store_id] = index
         return index

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

@@ -4,17 +4,14 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack_api import Api, ProviderSpec
 
 from .config import ChromaVectorIOConfig
 
 
-async def get_adapter_impl(
+async def get_adapter_impl(config: ChromaVectorIOConfig, deps: dict[Api, ProviderSpec]):
-    config: ChromaVectorIOConfig, deps: dict[Api, ProviderSpec], vector_stores_config: VectorStoresConfig | None = None
-):
     from .chroma import ChromaVectorIOAdapter
 
-    impl = ChromaVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
+    impl = ChromaVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -11,7 +11,6 @@ from urllib.parse import urlparse
 import chromadb
 from numpy.typing import NDArray
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.core.storage.kvstore import kvstore_impl
 from llama_stack.log import get_logger
 from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
@@ -126,13 +125,11 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
         config: RemoteChromaVectorIOConfig | InlineChromaVectorIOConfig,
         inference_api: Inference,
         files_api: Files | None,
-        vector_stores_config: VectorStoresConfig | None = None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         log.info(f"Initializing ChromaVectorIOAdapter with url: {config}")
         self.config = config
         self.inference_api = inference_api
-        self.vector_stores_config = vector_stores_config
         self.client = None
         self.cache = {}
         self.vector_store_table = None
@@ -165,7 +162,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
             )
         )
         self.cache[vector_store.identifier] = VectorStoreWithIndex(
-            vector_store, ChromaIndex(self.client, collection), self.inference_api, self.vector_stores_config
+            vector_store, ChromaIndex(self.client, collection), self.inference_api
         )
 
     async def unregister_vector_store(self, vector_store_id: str) -> None:
@@ -210,9 +207,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
         collection = await maybe_await(self.client.get_collection(vector_store_id))
         if not collection:
             raise ValueError(f"Vector DB {vector_store_id} not found in Chroma")
-        index = VectorStoreWithIndex(
+        index = VectorStoreWithIndex(vector_store, ChromaIndex(self.client, collection), self.inference_api)
-            vector_store, ChromaIndex(self.client, collection), self.inference_api, self.vector_stores_config
-        )
         self.cache[vector_store_id] = index
         return index
 

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

@@ -4,18 +4,15 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack_api import Api, ProviderSpec
 
 from .config import MilvusVectorIOConfig
 
 
-async def get_adapter_impl(
+async def get_adapter_impl(config: MilvusVectorIOConfig, deps: dict[Api, ProviderSpec]):
-    config: MilvusVectorIOConfig, deps: dict[Api, ProviderSpec], vector_stores_config: VectorStoresConfig | None = None
-):
     from .milvus import MilvusVectorIOAdapter
 
     assert isinstance(config, MilvusVectorIOConfig), f"Unexpected config type: {type(config)}"
-    impl = MilvusVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
+    impl = MilvusVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -11,7 +11,6 @@ from typing import Any
 from numpy.typing import NDArray
 from pymilvus import AnnSearchRequest, DataType, Function, FunctionType, MilvusClient, RRFRanker, WeightedRanker
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.core.storage.kvstore import kvstore_impl
 from llama_stack.log import get_logger
 from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
@@ -273,14 +272,12 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
         config: RemoteMilvusVectorIOConfig | InlineMilvusVectorIOConfig,
         inference_api: Inference,
         files_api: Files | None,
-        vector_stores_config: VectorStoresConfig | None = None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.cache = {}
         self.client = None
         self.inference_api = inference_api
-        self.vector_stores_config = vector_stores_config
         self.vector_store_table = None
         self.metadata_collection_name = "openai_vector_stores_metadata"
 
@@ -301,7 +298,6 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
                     kvstore=self.kvstore,
                 ),
                 inference_api=self.inference_api,
-                vector_stores_config=self.vector_stores_config,
             )
             self.cache[vector_store.identifier] = index
         if isinstance(self.config, RemoteMilvusVectorIOConfig):
@@ -329,7 +325,6 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
             vector_store=vector_store,
             index=MilvusIndex(self.client, vector_store.identifier, consistency_level=consistency_level),
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
 
         self.cache[vector_store.identifier] = index
@@ -352,7 +347,6 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
             vector_store=vector_store,
             index=MilvusIndex(client=self.client, collection_name=vector_store.identifier, kvstore=self.kvstore),
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
         self.cache[vector_store_id] = index
         return index

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

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack_api import Api, ProviderSpec
 
 from .config import PGVectorVectorIOConfig
@@ -13,10 +12,9 @@ from .config import PGVectorVectorIOConfig
 async def get_adapter_impl(
     config: PGVectorVectorIOConfig,
     deps: dict[Api, ProviderSpec],
-    vector_stores_config: VectorStoresConfig | None = None,
 ):
     from .pgvector import PGVectorVectorIOAdapter
 
-    impl = PGVectorVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
+    impl = PGVectorVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -13,7 +13,6 @@ from psycopg2 import sql
 from psycopg2.extras import Json, execute_values
 from pydantic import BaseModel, TypeAdapter
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.core.storage.kvstore import kvstore_impl
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
@@ -335,12 +334,10 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
         config: PGVectorVectorIOConfig,
         inference_api: Inference,
         files_api: Files | None = None,
-        vector_stores_config: VectorStoresConfig | None = None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.vector_stores_config = vector_stores_config
         self.conn = None
         self.cache = {}
         self.vector_store_table = None
@@ -396,7 +393,6 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
                 vector_store,
                 index=pgvector_index,
                 inference_api=self.inference_api,
-                vector_stores_config=self.vector_stores_config,
             )
             self.cache[vector_store.identifier] = index
 
@@ -428,7 +424,6 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
             vector_store,
             index=pgvector_index,
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
         self.cache[vector_store.identifier] = index
 
@@ -469,9 +464,7 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
         vector_store = VectorStore.model_validate_json(vector_store_data)
         index = PGVectorIndex(vector_store, vector_store.embedding_dimension, self.conn)
         await index.initialize()
-        self.cache[vector_store_id] = VectorStoreWithIndex(
+        self.cache[vector_store_id] = VectorStoreWithIndex(vector_store, index, self.inference_api)
-            vector_store, index, self.inference_api, self.vector_stores_config
-        )
         return self.cache[vector_store_id]
 
     async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:

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

@@ -4,17 +4,14 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack_api import Api, ProviderSpec
 
 from .config import QdrantVectorIOConfig
 
 
-async def get_adapter_impl(
+async def get_adapter_impl(config: QdrantVectorIOConfig, deps: dict[Api, ProviderSpec]):
-    config: QdrantVectorIOConfig, deps: dict[Api, ProviderSpec], vector_stores_config: VectorStoresConfig | None = None
-):
     from .qdrant import QdrantVectorIOAdapter
 
-    impl = QdrantVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
+    impl = QdrantVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -13,7 +13,6 @@ from numpy.typing import NDArray
 from qdrant_client import AsyncQdrantClient, models
 from qdrant_client.models import PointStruct
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.core.storage.kvstore import kvstore_impl
 from llama_stack.log import get_logger
 from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
@@ -153,14 +152,12 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
         config: RemoteQdrantVectorIOConfig | InlineQdrantVectorIOConfig,
         inference_api: Inference,
         files_api: Files | None = None,
-        vector_stores_config: VectorStoresConfig | None = None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.client: AsyncQdrantClient = None
         self.cache = {}
         self.inference_api = inference_api
-        self.vector_stores_config = vector_stores_config
         self.vector_store_table = None
         self._qdrant_lock = asyncio.Lock()
 
@@ -179,7 +176,6 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
                 vector_store,
                 QdrantIndex(self.client, vector_store.identifier),
                 self.inference_api,
-                self.vector_stores_config,
             )
             self.cache[vector_store.identifier] = index
         self.openai_vector_stores = await self._load_openai_vector_stores()
@@ -199,7 +195,6 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
             vector_store=vector_store,
             index=QdrantIndex(self.client, vector_store.identifier),
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
 
         self.cache[vector_store.identifier] = index
@@ -231,7 +226,6 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
             vector_store=vector_store,
             index=QdrantIndex(client=self.client, collection_name=vector_store.identifier),
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
         self.cache[vector_store_id] = index
         return index

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

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack_api import Api, ProviderSpec
 
 from .config import WeaviateVectorIOConfig
@@ -13,10 +12,9 @@ from .config import WeaviateVectorIOConfig
 async def get_adapter_impl(
     config: WeaviateVectorIOConfig,
     deps: dict[Api, ProviderSpec],
-    vector_stores_config: VectorStoresConfig | None = None,
 ):
     from .weaviate import WeaviateVectorIOAdapter
 
-    impl = WeaviateVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
+    impl = WeaviateVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -12,7 +12,6 @@ from numpy.typing import NDArray
 from weaviate.classes.init import Auth
 from weaviate.classes.query import Filter, HybridFusion
 
-from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.core.request_headers import NeedsRequestProviderData
 from llama_stack.core.storage.kvstore import kvstore_impl
 from llama_stack.log import get_logger
@@ -268,12 +267,10 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
         config: WeaviateVectorIOConfig,
         inference_api: Inference,
         files_api: Files | None,
-        vector_stores_config: VectorStoresConfig | None = None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.vector_stores_config = vector_stores_config
         self.client_cache = {}
         self.cache = {}
         self.vector_store_table = None
@@ -319,7 +316,6 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
                     vector_store=vector_store,
                     index=idx,
                     inference_api=self.inference_api,
-                    vector_stores_config=self.vector_stores_config,
                 )
 
             # Load OpenAI vector stores metadata into cache
@@ -348,7 +344,6 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
             vector_store,
             WeaviateIndex(client=client, collection_name=sanitized_collection_name),
             self.inference_api,
-            self.vector_stores_config,
         )
 
     async def unregister_vector_store(self, vector_store_id: str) -> None:
@@ -383,7 +378,6 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
             vector_store=vector_store,
             index=WeaviateIndex(client=client, collection_name=vector_store.identifier),
             inference_api=self.inference_api,
-            vector_stores_config=self.vector_stores_config,
         )
         self.cache[vector_store_id] = index
         return index

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_EXPANSION_PROMPT
+
+__all__ = ["DEFAULT_QUERY_EXPANSION_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 expansion in vector search
+DEFAULT_QUERY_EXPANSION_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

@@ -379,11 +379,6 @@ class OpenAIVectorStoreMixin(ABC):
                 f"Using embedding config from extra_body: model='{embedding_model}', dimension={embedding_dimension}"
             )
 
-        # Extract query expansion model from extra_body if provided
-        query_expansion_model = extra_body.get("query_expansion_model")
-        if query_expansion_model:
-            logger.debug(f"Using per-store query expansion model: {query_expansion_model}")
-
         # use provider_id set by router; fallback to provider's own ID when used directly via --stack-config
         provider_id = extra_body.get("provider_id") or getattr(self, "__provider_id__", None)
         # Derive the canonical vector_store_id (allow override, else generate)
@@ -407,7 +402,6 @@ class OpenAIVectorStoreMixin(ABC):
             provider_id=provider_id,
             provider_resource_id=vector_store_id,
             vector_store_name=params.name,
-            query_expansion_model=query_expansion_model,
         )
         await self.register_vector_store(vector_store)
 
@@ -621,9 +615,6 @@ class OpenAIVectorStoreMixin(ABC):
                 "rewrite_query": rewrite_query,
             }
 
-            # Add vector_stores_config if available (for query rewriting)
-            if hasattr(self, "vector_stores_config"):
-                params["vector_stores_config"] = self.vector_stores_config
             # TODO: Add support for ranking_options.ranker
 
             response = await self.query_chunks(

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

@@ -0,0 +1,37 @@
+# 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.
+
+from llama_stack.core.datatypes import QualifiedModel, VectorStoresConfig
+from llama_stack.providers.utils.memory.constants import DEFAULT_QUERY_EXPANSION_PROMPT
+
+# Global configuration for query expansion - set during stack startup
+_DEFAULT_QUERY_EXPANSION_MODEL: QualifiedModel | None = None
+_DEFAULT_QUERY_EXPANSION_MAX_TOKENS: int = 100
+_DEFAULT_QUERY_EXPANSION_TEMPERATURE: float = 0.3
+_QUERY_EXPANSION_PROMPT_OVERRIDE: str | None = None
+
+
+def set_default_query_expansion_config(vector_stores_config: VectorStoresConfig | None):
+    """Set the global default query expansion configuration from stack config."""
+    global \
+        _DEFAULT_QUERY_EXPANSION_MODEL, \
+        _QUERY_EXPANSION_PROMPT_OVERRIDE, \
+        _DEFAULT_QUERY_EXPANSION_MAX_TOKENS, \
+        _DEFAULT_QUERY_EXPANSION_TEMPERATURE
+    if vector_stores_config:
+        _DEFAULT_QUERY_EXPANSION_MODEL = vector_stores_config.default_query_expansion_model
+        # Only set override if user provided a custom prompt different from default
+        if vector_stores_config.query_expansion_prompt != DEFAULT_QUERY_EXPANSION_PROMPT:
+            _QUERY_EXPANSION_PROMPT_OVERRIDE = vector_stores_config.query_expansion_prompt
+        else:
+            _QUERY_EXPANSION_PROMPT_OVERRIDE = None
+        _DEFAULT_QUERY_EXPANSION_MAX_TOKENS = vector_stores_config.query_expansion_max_tokens
+        _DEFAULT_QUERY_EXPANSION_TEMPERATURE = vector_stores_config.query_expansion_temperature
+    else:
+        _DEFAULT_QUERY_EXPANSION_MODEL = None
+        _QUERY_EXPANSION_PROMPT_OVERRIDE = None
+        _DEFAULT_QUERY_EXPANSION_MAX_TOKENS = 100
+        _DEFAULT_QUERY_EXPANSION_TEMPERATURE = 0.3

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

@@ -17,7 +17,6 @@ import numpy as np
 from numpy.typing import NDArray
 from pydantic import BaseModel
 
-from llama_stack.core.datatypes import QualifiedModel, VectorStoresConfig
 from llama_stack.log import get_logger
 from llama_stack.models.llama.llama3.tokenizer import Tokenizer
 from llama_stack.providers.utils.inference.prompt_adapter import (
@@ -30,19 +29,18 @@ from llama_stack_api import (
     Chunk,
     ChunkMetadata,
     InterleavedContent,
+    OpenAIChatCompletionRequestWithExtraBody,
     OpenAIEmbeddingsRequestWithExtraBody,
     QueryChunksResponse,
     RAGDocument,
     VectorStore,
 )
-from llama_stack_api.inference import (
-    OpenAIChatCompletionRequestWithExtraBody,
-    OpenAIUserMessageParam,
-)
-from llama_stack_api.models import ModelType
 
 log = get_logger(name=__name__, category="providers::utils")
 
+from llama_stack.providers.utils.memory import query_expansion_config
+from llama_stack.providers.utils.memory.constants import DEFAULT_QUERY_EXPANSION_PROMPT
+
 
 class ChunkForDeletion(BaseModel):
     """Information needed to delete a chunk from a vector store.
@@ -268,7 +266,6 @@ class VectorStoreWithIndex:
     vector_store: VectorStore
     index: EmbeddingIndex
     inference_api: Api.inference
-    vector_stores_config: VectorStoresConfig | None = None
 
     async def insert_chunks(
         self,
@@ -296,6 +293,39 @@ class VectorStoreWithIndex:
         embeddings = np.array([c.embedding for c in chunks], dtype=np.float32)
         await self.index.add_chunks(chunks, embeddings)
 
+    async def _rewrite_query_for_file_search(self, query: str) -> str:
+        """Rewrite a search query using the globally configured LLM model for better retrieval results."""
+        if not query_expansion_config._DEFAULT_QUERY_EXPANSION_MODEL:
+            log.debug("No default query expansion model configured, using original query")
+            return query
+
+        model_id = f"{query_expansion_config._DEFAULT_QUERY_EXPANSION_MODEL.provider_id}/{query_expansion_config._DEFAULT_QUERY_EXPANSION_MODEL.model_id}"
+
+        # Use custom prompt from config if provided, otherwise use built-in default
+        # Users only need to configure the model - prompt is automatic with optional override
+        if query_expansion_config._QUERY_EXPANSION_PROMPT_OVERRIDE:
+            # Custom prompt from config - format if it contains {query} placeholder
+            prompt = (
+                query_expansion_config._QUERY_EXPANSION_PROMPT_OVERRIDE.format(query=query)
+                if "{query}" in query_expansion_config._QUERY_EXPANSION_PROMPT_OVERRIDE
+                else query_expansion_config._QUERY_EXPANSION_PROMPT_OVERRIDE
+            )
+        else:
+            # Use built-in default prompt and format with query
+            prompt = DEFAULT_QUERY_EXPANSION_PROMPT.format(query=query)
+
+        request = OpenAIChatCompletionRequestWithExtraBody(
+            model=model_id,
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=query_expansion_config._DEFAULT_QUERY_EXPANSION_MAX_TOKENS,
+            temperature=query_expansion_config._DEFAULT_QUERY_EXPANSION_TEMPERATURE,
+        )
+
+        response = await self.inference_api.openai_chat_completion(request)
+        rewritten_query = response.choices[0].message.content.strip()
+        log.debug(f"Query rewritten: '{query}' → '{rewritten_query}'")
+        return rewritten_query
+
     async def query_chunks(
         self,
         query: InterleavedContent,
@@ -304,10 +334,6 @@ class VectorStoreWithIndex:
         if params is None:
             params = {}
 
-        # Extract configuration if provided by router
-        if "vector_stores_config" in params:
-            self.vector_stores_config = params["vector_stores_config"]
-
         k = params.get("max_chunks", 3)
         mode = params.get("mode")
         score_threshold = params.get("score_threshold", 0.0)
@@ -331,18 +357,9 @@ class VectorStoreWithIndex:
 
         query_string = interleaved_content_as_str(query)
 
-        # Apply query rewriting if enabled
+        # Apply query rewriting if enabled and model is configured
         if params.get("rewrite_query", False):
-            if self.vector_stores_config:
+            query_string = await self._rewrite_query_for_file_search(query_string)
-                log.debug(f"VectorStoreWithIndex received config: {self.vector_stores_config}")
-                if hasattr(self.vector_stores_config, "default_query_expansion_model"):
-                    log.debug(
-                        f"Config has default_query_expansion_model: {self.vector_stores_config.default_query_expansion_model}"
-                    )
-            else:
-                log.debug("No vector_stores_config found - cannot perform query rewriting")
-
-            query_string = await self._rewrite_query_for_search(query_string)
 
         if mode == "keyword":
             return await self.index.query_keyword(query_string, k, score_threshold)
@@ -359,88 +376,3 @@ class VectorStoreWithIndex:
             )
         else:
             return await self.index.query_vector(query_vector, k, score_threshold)
-
-    async def _rewrite_query_for_search(self, query: str) -> str:
-        """Rewrite the user query to improve vector search performance.
-
-        :param query: The original user query
-        :returns: The rewritten query optimized for vector search
-        """
-        expansion_model = None
-
-        # Check for per-store query expansion model first
-        if self.vector_store.query_expansion_model:
-            # Parse the model string into provider_id and model_id
-            model_parts = self.vector_store.query_expansion_model.split("/", 1)
-            if len(model_parts) == 2:
-                expansion_model = QualifiedModel(provider_id=model_parts[0], model_id=model_parts[1])
-                log.debug(f"Using per-store query expansion model: {expansion_model}")
-            else:
-                log.warning(
-                    f"Invalid query_expansion_model format: {self.vector_store.query_expansion_model}. Expected 'provider_id/model_id'"
-                )
-
-        # Fall back to global default if no per-store model
-        if not expansion_model:
-            if not self.vector_stores_config:
-                raise ValueError(
-                    f"No vector_stores_config found and no per-store query_expansion_model! self.vector_stores_config is: {self.vector_stores_config}"
-                )
-            if not self.vector_stores_config.default_query_expansion_model:
-                raise ValueError(
-                    f"No default_query_expansion_model configured and no per-store query_expansion_model! vector_stores_config: {self.vector_stores_config}, default_query_expansion_model: {self.vector_stores_config.default_query_expansion_model}"
-                )
-            expansion_model = self.vector_stores_config.default_query_expansion_model
-            log.debug(f"Using global default query expansion model: {expansion_model}")
-
-        chat_model = f"{expansion_model.provider_id}/{expansion_model.model_id}"
-
-        # Validate that the model is available and is an LLM
-        try:
-            models_response = await self.inference_api.routing_table.list_models()
-        except Exception as e:
-            raise RuntimeError(f"Failed to list available models for validation: {e}") from e
-
-        model_found = False
-        for model in models_response.data:
-            if model.identifier == chat_model:
-                if model.model_type != ModelType.llm:
-                    raise ValueError(
-                        f"Configured query expansion model '{chat_model}' is not an LLM model "
-                        f"(found type: {model.model_type}). Query rewriting requires an LLM model."
-                    )
-                model_found = True
-                break
-
-        if not model_found:
-            available_llm_models = [m.identifier for m in models_response.data if m.model_type == ModelType.llm]
-            raise ValueError(
-                f"Configured query expansion model '{chat_model}' is not available. "
-                f"Available LLM models: {available_llm_models}"
-            )
-
-        # Use the configured prompt (has a default value)
-        rewrite_prompt = self.vector_stores_config.query_expansion_prompt.format(query=query)
-
-        chat_request = OpenAIChatCompletionRequestWithExtraBody(
-            model=chat_model,
-            messages=[
-                OpenAIUserMessageParam(
-                    role="user",
-                    content=rewrite_prompt,
-                )
-            ],
-            max_tokens=100,
-        )
-
-        try:
-            response = await self.inference_api.openai_chat_completion(chat_request)
-        except Exception as e:
-            raise RuntimeError(f"Failed to generate rewritten query: {e}") from e
-
-        if response.choices and len(response.choices) > 0:
-            rewritten_query = response.choices[0].message.content.strip()
-            log.info(f"Query rewritten: '{query}' → '{rewritten_query}'")
-            return rewritten_query
-        else:
-            raise RuntimeError("No response received from LLM model for query rewriting")

src/llama_stack_api/vector_stores.py

@@ -25,7 +25,6 @@ class VectorStore(Resource):
     embedding_model: str
     embedding_dimension: int
     vector_store_name: str | None = None
-    query_expansion_model: str | None = None
 
     @property
     def vector_store_id(self) -> str:

tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py

@@ -1233,94 +1233,122 @@ async def test_embedding_config_required_model_missing(vector_io_adapter):
 
 
 async def test_query_expansion_functionality(vector_io_adapter):
-    """Test query expansion with per-store models, global defaults, and error validation."""
+    """Test query expansion with simplified global configuration approach."""
     from unittest.mock import MagicMock
 
-    from llama_stack.core.datatypes import QualifiedModel, VectorStoresConfig
+    from llama_stack.core.datatypes import QualifiedModel
+    from llama_stack.providers.utils.memory.constants import DEFAULT_QUERY_EXPANSION_PROMPT
+    from llama_stack.providers.utils.memory.query_expansion_config import set_default_query_expansion_config
     from llama_stack.providers.utils.memory.vector_store import VectorStoreWithIndex
-    from llama_stack_api.models import Model, ModelType
+    from llama_stack_api import QueryChunksResponse
 
-    vector_io_adapter.register_vector_store = AsyncMock()
+    # Mock a simple vector store and index
-    vector_io_adapter.__provider_id__ = "test_provider"
-
-    # Test 1: Per-store model usage
-    params = OpenAICreateVectorStoreRequestWithExtraBody(
-        name="test_store",
-        metadata={},
-        **{"embedding_model": "test/embedding", "query_expansion_model": "test/llama-model"},
-    )
-    await vector_io_adapter.openai_create_vector_store(params)
-    call_args = vector_io_adapter.register_vector_store.call_args[0][0]
-    assert call_args.query_expansion_model == "test/llama-model"
-
-    # Test 2: Global default fallback
-    vector_io_adapter.register_vector_store.reset_mock()
-    params_no_model = OpenAICreateVectorStoreRequestWithExtraBody(
-        name="test_store2", metadata={}, **{"embedding_model": "test/embedding"}
-    )
-    await vector_io_adapter.openai_create_vector_store(params_no_model)
-    call_args2 = vector_io_adapter.register_vector_store.call_args[0][0]
-    assert call_args2.query_expansion_model is None
-
-    # Test query rewriting scenarios
-    mock_inference_api = MagicMock()
-
-    # Per-store model scenario
     mock_vector_store = MagicMock()
-    mock_vector_store.query_expansion_model = "test/llama-model"
+    mock_vector_store.embedding_model = "test/embedding"
-    mock_inference_api.routing_table.list_models = AsyncMock(
+    mock_inference_api = MagicMock()
-        return_value=MagicMock(
+    mock_index = MagicMock()
-            data=[Model(identifier="test/llama-model", provider_id="test", model_type=ModelType.llm)]
-        )
-    )
-    mock_inference_api.openai_chat_completion = AsyncMock(
-        return_value=MagicMock(choices=[MagicMock(message=MagicMock(content="per-store expanded"))])
-    )
 
+    # Create VectorStoreWithIndex with simplified constructor
     vector_store_with_index = VectorStoreWithIndex(
         vector_store=mock_vector_store,
-        index=MagicMock(),
+        index=mock_index,
         inference_api=mock_inference_api,
-        vector_stores_config=VectorStoresConfig(
-            default_query_expansion_model=QualifiedModel(provider_id="global", model_id="default")
-        ),
     )
 
-    result = await vector_store_with_index._rewrite_query_for_search("test")
+    # Mock the query_vector method to return a simple response
-    assert mock_inference_api.openai_chat_completion.call_args[0][0].model == "test/llama-model"
+    mock_response = QueryChunksResponse(chunks=[], scores=[])
-    assert result == "per-store expanded"
+    mock_index.query_vector = AsyncMock(return_value=mock_response)
 
-    # Global default fallback scenario
+    # Mock embeddings generation
-    mock_inference_api.reset_mock()
+    mock_inference_api.openai_embeddings = AsyncMock(
-    mock_vector_store.query_expansion_model = None
+        return_value=MagicMock(data=[MagicMock(embedding=[0.1, 0.2, 0.3])])
-    mock_inference_api.routing_table.list_models = AsyncMock(
-        return_value=MagicMock(
-            data=[Model(identifier="global/default", provider_id="global", model_type=ModelType.llm)]
-        )
     )
+
+    # Test 1: Query expansion with default prompt (no custom prompt configured)
+    mock_vector_stores_config = MagicMock()
+    mock_vector_stores_config.default_query_expansion_model = QualifiedModel(provider_id="test", model_id="llama")
+    mock_vector_stores_config.query_expansion_prompt = None  # Use built-in default prompt
+    mock_vector_stores_config.query_expansion_max_tokens = 100  # Default value
+    mock_vector_stores_config.query_expansion_temperature = 0.3  # Default value
+
+    # Set global config
+    set_default_query_expansion_config(mock_vector_stores_config)
+
+    # Mock chat completion for query rewriting
     mock_inference_api.openai_chat_completion = AsyncMock(
-        return_value=MagicMock(choices=[MagicMock(message=MagicMock(content="global expanded"))])
+        return_value=MagicMock(choices=[MagicMock(message=MagicMock(content="expanded test query"))])
     )
 
-    result = await vector_store_with_index._rewrite_query_for_search("test")
+    params = {"rewrite_query": True, "max_chunks": 5}
-    assert mock_inference_api.openai_chat_completion.call_args[0][0].model == "global/default"
+    result = await vector_store_with_index.query_chunks("test query", params)
-    assert result == "global expanded"
 
-    # Test 3: Error cases
+    # Verify chat completion was called for query rewriting
-    # Model not found
+    mock_inference_api.openai_chat_completion.assert_called_once()
-    mock_vector_store.query_expansion_model = "missing/model"
+    chat_call_args = mock_inference_api.openai_chat_completion.call_args[0][0]
-    mock_inference_api.routing_table.list_models = AsyncMock(return_value=MagicMock(data=[]))
+    assert chat_call_args.model == "test/llama"
 
-    with pytest.raises(ValueError, match="Configured query expansion model .* is not available"):
+    # Verify default prompt is used (contains our built-in prompt text)
-        await vector_store_with_index._rewrite_query_for_search("test")
+    prompt_text = chat_call_args.messages[0].content
+    expected_prompt = DEFAULT_QUERY_EXPANSION_PROMPT.format(query="test query")
+    assert prompt_text == expected_prompt
 
-    # Non-LLM model
+    # Verify default inference parameters are used
-    mock_vector_store.query_expansion_model = "test/embedding-model"
+    assert chat_call_args.max_tokens == 100  # Default value
-    mock_inference_api.routing_table.list_models = AsyncMock(
+    assert chat_call_args.temperature == 0.3  # Default value
-        return_value=MagicMock(
-            data=[Model(identifier="test/embedding-model", provider_id="test", model_type=ModelType.embedding)]
-        )
-    )
 
-    with pytest.raises(ValueError, match="is not an LLM model.*Query rewriting requires an LLM model"):
+    # Verify the rest of the flow proceeded normally
-        await vector_store_with_index._rewrite_query_for_search("test")
+    mock_inference_api.openai_embeddings.assert_called_once()
+    mock_index.query_vector.assert_called_once()
+    assert result == mock_response
+
+    # Test 1b: Query expansion with custom prompt override and inference parameters
+    mock_inference_api.reset_mock()
+    mock_index.reset_mock()
+
+    mock_vector_stores_config.query_expansion_prompt = "Custom prompt for rewriting: {query}"
+    mock_vector_stores_config.query_expansion_max_tokens = 150
+    mock_vector_stores_config.query_expansion_temperature = 0.7
+    set_default_query_expansion_config(mock_vector_stores_config)
+
+    result = await vector_store_with_index.query_chunks("test query", params)
+
+    # Verify custom prompt and parameters are used
+    mock_inference_api.openai_chat_completion.assert_called_once()
+    chat_call_args = mock_inference_api.openai_chat_completion.call_args[0][0]
+    prompt_text = chat_call_args.messages[0].content
+    assert prompt_text == "Custom prompt for rewriting: test query"
+    assert "Expand this query with relevant synonyms" not in prompt_text  # Default not used
+
+    # Verify custom inference parameters
+    assert chat_call_args.max_tokens == 150
+    assert chat_call_args.temperature == 0.7
+
+    # Test 2: No query expansion when no global model is configured
+    mock_inference_api.reset_mock()
+    mock_index.reset_mock()
+
+    # Clear global config
+    set_default_query_expansion_config(None)
+
+    params = {"rewrite_query": True, "max_chunks": 5}
+    result2 = await vector_store_with_index.query_chunks("test query", params)
+
+    # Verify chat completion was NOT called
+    mock_inference_api.openai_chat_completion.assert_not_called()
+    # But normal flow should still work
+    mock_inference_api.openai_embeddings.assert_called_once()
+    mock_index.query_vector.assert_called_once()
+    assert result2 == mock_response
+
+    # Test 3: Normal behavior without rewrite_query parameter
+    mock_inference_api.reset_mock()
+    mock_index.reset_mock()
+
+    params_no_rewrite = {"max_chunks": 5}
+    result3 = await vector_store_with_index.query_chunks("test query", params_no_rewrite)
+
+    # Neither chat completion nor query rewriting should be called
+    mock_inference_api.openai_chat_completion.assert_not_called()
+    mock_inference_api.openai_embeddings.assert_called_once()
+    mock_index.query_vector.assert_called_once()
+    assert result3 == mock_response