adding config to providers so that it can properly be used

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

src/llama_stack/core/resolver.py

@@ -374,6 +374,13 @@ 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"
 
@@ -394,6 +401,11 @@ 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

@@ -103,6 +103,13 @@ class VectorIORouter(VectorIO):
         # 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)

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

@@ -6,16 +6,19 @@
 
 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(config: FaissVectorIOConfig, deps: dict[Api, Any]):
+async def get_provider_impl(
+    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))
+    impl = FaissVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
     await impl.initialize()
     return impl

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

@@ -14,6 +14,7 @@ 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
@@ -184,10 +185,17 @@ class FaissIndex(EmbeddingIndex):
 
 
 class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtocolPrivate):
-    def __init__(self, config: FaissVectorIOConfig, inference_api: Inference, files_api: Files | None) -> None:
+    def __init__(
+        self,
+        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:
@@ -203,6 +211,7 @@ 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
 
@@ -241,6 +250,7 @@ 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]:
@@ -274,6 +284,7 @@ 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

@@ -6,15 +6,18 @@
 
 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(config: SQLiteVectorIOConfig, deps: dict[Api, Any]):
+async def get_provider_impl(
+    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))
+    impl = SQLiteVecVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
     await impl.initialize()
     return impl

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

@@ -14,6 +14,7 @@ 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
@@ -385,10 +386,17 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
     and creates a cache of VectorStoreWithIndex instances (each wrapping a SQLiteVecIndex).
     """
 
-    def __init__(self, config, inference_api: Inference, files_api: Files | None) -> None:
+    def __init__(
+        self,
+        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
 
@@ -403,7 +411,9 @@ 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(vector_store, index, self.inference_api)
+            self.cache[vector_store.identifier] = VectorStoreWithIndex(
+                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()
@@ -427,7 +437,9 @@ 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(vector_store, index, self.inference_api)
+        self.cache[vector_store.identifier] = VectorStoreWithIndex(
+            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:
@@ -452,6 +464,7 @@ 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

@@ -4,14 +4,17 @@
 # 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(config: ChromaVectorIOConfig, deps: dict[Api, ProviderSpec]):
+async def get_adapter_impl(
+    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))
+    impl = ChromaVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
     await impl.initialize()
     return impl

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

@@ -11,6 +11,7 @@ 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
@@ -125,11 +126,13 @@ 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
@@ -162,7 +165,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
             )
         )
         self.cache[vector_store.identifier] = VectorStoreWithIndex(
-            vector_store, ChromaIndex(self.client, collection), self.inference_api
+            vector_store, ChromaIndex(self.client, collection), self.inference_api, self.vector_stores_config
         )
 
     async def unregister_vector_store(self, vector_store_id: str) -> None:
@@ -207,7 +210,9 @@ 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(vector_store, ChromaIndex(self.client, collection), self.inference_api)
+        index = VectorStoreWithIndex(
+            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

@@ -4,15 +4,18 @@
 # 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(config: MilvusVectorIOConfig, deps: dict[Api, ProviderSpec]):
+async def get_adapter_impl(
+    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))
+    impl = MilvusVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
     await impl.initialize()
     return impl

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

@@ -11,6 +11,7 @@ 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
@@ -272,12 +273,14 @@ 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"
 
@@ -298,6 +301,7 @@ 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):
@@ -325,6 +329,7 @@ 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
@@ -347,6 +352,7 @@ 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

@@ -4,14 +4,19 @@
 # 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
 
 
-async def get_adapter_impl(config: PGVectorVectorIOConfig, deps: dict[Api, ProviderSpec]):
+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))
+    impl = PGVectorVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
     await impl.initialize()
     return impl

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

@@ -13,6 +13,7 @@ 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
@@ -330,11 +331,16 @@ class PGVectorIndex(EmbeddingIndex):
 
 class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtocolPrivate):
     def __init__(
-        self, config: PGVectorVectorIOConfig, inference_api: Inference, files_api: Files | None = None
+        self,
+        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
@@ -386,7 +392,12 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
                 kvstore=self.kvstore,
             )
             await pgvector_index.initialize()
-            index = VectorStoreWithIndex(vector_store, index=pgvector_index, inference_api=self.inference_api)
+            index = VectorStoreWithIndex(
+                vector_store,
+                index=pgvector_index,
+                inference_api=self.inference_api,
+                vector_stores_config=self.vector_stores_config,
+            )
             self.cache[vector_store.identifier] = index
 
     async def shutdown(self) -> None:
@@ -413,7 +424,12 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
             vector_store=vector_store, dimension=vector_store.embedding_dimension, conn=self.conn, kvstore=self.kvstore
         )
         await pgvector_index.initialize()
-        index = VectorStoreWithIndex(vector_store, index=pgvector_index, inference_api=self.inference_api)
+        index = VectorStoreWithIndex(
+            vector_store,
+            index=pgvector_index,
+            inference_api=self.inference_api,
+            vector_stores_config=self.vector_stores_config,
+        )
         self.cache[vector_store.identifier] = index
 
     async def unregister_vector_store(self, vector_store_id: str) -> None:
@@ -453,7 +469,9 @@ 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(vector_store, index, self.inference_api)
+        self.cache[vector_store_id] = VectorStoreWithIndex(
+            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

@@ -4,14 +4,17 @@
 # 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(config: QdrantVectorIOConfig, deps: dict[Api, ProviderSpec]):
+async def get_adapter_impl(
+    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))
+    impl = QdrantVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
     await impl.initialize()
     return impl

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

@@ -13,6 +13,7 @@ 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
@@ -152,12 +153,14 @@ 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()
 
@@ -173,7 +176,10 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
         for vector_store_data in stored_vector_stores:
             vector_store = VectorStore.model_validate_json(vector_store_data)
             index = VectorStoreWithIndex(
-                vector_store, QdrantIndex(self.client, vector_store.identifier), self.inference_api
+                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()
@@ -193,6 +199,7 @@ 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
@@ -224,6 +231,7 @@ 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

@@ -4,14 +4,19 @@
 # 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
 
 
-async def get_adapter_impl(config: WeaviateVectorIOConfig, deps: dict[Api, ProviderSpec]):
+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))
+    impl = WeaviateVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files), vector_stores_config)
     await impl.initialize()
     return impl

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

@@ -12,6 +12,7 @@ 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
@@ -262,10 +263,17 @@ class WeaviateIndex(EmbeddingIndex):
 
 
 class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProviderData, VectorStoresProtocolPrivate):
-    def __init__(self, config: WeaviateVectorIOConfig, inference_api: Inference, files_api: Files | None) -> None:
+    def __init__(
+        self,
+        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
@@ -308,7 +316,10 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
                 client = self._get_client()
                 idx = WeaviateIndex(client=client, collection_name=vector_store.identifier, kvstore=self.kvstore)
                 self.cache[vector_store.identifier] = VectorStoreWithIndex(
-                    vector_store=vector_store, index=idx, inference_api=self.inference_api
+                    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
@@ -334,7 +345,10 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
             )
 
         self.cache[vector_store.identifier] = VectorStoreWithIndex(
-            vector_store, WeaviateIndex(client=client, collection_name=sanitized_collection_name), self.inference_api
+            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:
@@ -369,6 +383,7 @@ 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/openai_vector_store_mixin.py

@@ -613,6 +613,9 @@ class OpenAIVectorStoreMixin(ABC):
                 "mode": search_mode,
                 "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/vector_store.py

@@ -333,6 +333,15 @@ class VectorStoreWithIndex:
 
         # Apply query rewriting if enabled
         if params.get("rewrite_query", False):
+            if self.vector_stores_config:
+                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":
@@ -358,8 +367,14 @@ class VectorStoreWithIndex:
         :returns: The rewritten query optimized for vector search
         """
         # Check if query expansion model is configured
-        if not self.vector_stores_config or not self.vector_stores_config.default_query_expansion_model:
+        if not self.vector_stores_config:
-            raise ValueError("No default_query_expansion_model configured for query rewriting")
+            raise ValueError(
+                f"No vector_stores_config found! 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! vector_stores_config: {self.vector_stores_config}, default_query_expansion_model: {self.vector_stores_config.default_query_expansion_model}"
+            )
 
         # Use the configured model
         expansion_model = self.vector_stores_config.default_query_expansion_model