feat: Add OpenAI compat /v1/vector_store APIs (#2423)

Adding OpenAI compat `/v1/vector-store` apis. 
This PR implements the `faiss` provider with followup PRs coming up for
other providers.

Added routes to create, update, delete, list vector stores. 
Also added route to search a vector store

Inserting into vector stores is missing and will be a follow up diff. 

### Test Plan 
- Added new integration test for testing the faiss provider 
```
pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```

llama_stack/apis/vector_io/vector_io.py

@@ -37,6 +37,85 @@ class QueryChunksResponse(BaseModel):
     scores: list[float]
 
 
+@json_schema_type
+class VectorStoreObject(BaseModel):
+    """OpenAI Vector Store object."""
+
+    id: str
+    object: str = "vector_store"
+    created_at: int
+    name: str | None = None
+    usage_bytes: int = 0
+    file_counts: dict[str, int] = Field(default_factory=dict)
+    status: str = "completed"
+    expires_after: dict[str, Any] | None = None
+    expires_at: int | None = None
+    last_active_at: int | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+@json_schema_type
+class VectorStoreCreateRequest(BaseModel):
+    """Request to create a vector store."""
+
+    name: str | None = None
+    file_ids: list[str] = Field(default_factory=list)
+    expires_after: dict[str, Any] | None = None
+    chunking_strategy: dict[str, Any] | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+@json_schema_type
+class VectorStoreModifyRequest(BaseModel):
+    """Request to modify a vector store."""
+
+    name: str | None = None
+    expires_after: dict[str, Any] | None = None
+    metadata: dict[str, Any] | None = None
+
+
+@json_schema_type
+class VectorStoreListResponse(BaseModel):
+    """Response from listing vector stores."""
+
+    object: str = "list"
+    data: list[VectorStoreObject]
+    first_id: str | None = None
+    last_id: str | None = None
+    has_more: bool = False
+
+
+@json_schema_type
+class VectorStoreSearchRequest(BaseModel):
+    """Request to search a vector store."""
+
+    query: str | list[str]
+    filters: dict[str, Any] | None = None
+    max_num_results: int = 10
+    ranking_options: dict[str, Any] | None = None
+    rewrite_query: bool = False
+
+
+@json_schema_type
+class VectorStoreSearchResponse(BaseModel):
+    """Response from searching a vector store."""
+
+    object: str = "vector_store.search_results.page"
+    search_query: str
+    data: list[dict[str, Any]]
+    has_more: bool = False
+    next_page: str | None = None
+
+
+@json_schema_type
+class VectorStoreDeleteResponse(BaseModel):
+    """Response from deleting a vector store."""
+
+    id: str
+    object: str = "vector_store.deleted"
+    deleted: bool = True
+
+
 class VectorDBStore(Protocol):
     def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
 
@@ -81,3 +160,116 @@ class VectorIO(Protocol):
         :returns: A QueryChunksResponse.
         """
         ...
+
+    # OpenAI Vector Stores API endpoints
+    @webmethod(route="/openai/v1/vector_stores", method="POST")
+    async def openai_create_vector_store(
+        self,
+        name: str | None = None,
+        file_ids: list[str] | None = None,
+        expires_after: dict[str, Any] | None = None,
+        chunking_strategy: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+        embedding_model: str | None = None,
+        embedding_dimension: int | None = 384,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorStoreObject:
+        """Creates a vector store.
+
+        :param name: A name for the vector store.
+        :param file_ids: A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files.
+        :param expires_after: The expiration policy for a vector store.
+        :param chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy.
+        :param metadata: Set of 16 key-value pairs that can be attached to an object.
+        :param embedding_model: The embedding model to use for this vector store.
+        :param embedding_dimension: The dimension of the embedding vectors (default: 384).
+        :param provider_id: The ID of the provider to use for this vector store.
+        :param provider_vector_db_id: The provider-specific vector database ID.
+        :returns: A VectorStoreObject representing the created vector store.
+        """
+        ...
+
+    @webmethod(route="/openai/v1/vector_stores", method="GET")
+    async def openai_list_vector_stores(
+        self,
+        limit: int = 20,
+        order: str = "desc",
+        after: str | None = None,
+        before: str | None = None,
+    ) -> VectorStoreListResponse:
+        """Returns a list of vector stores.
+
+        :param limit: A limit on the number of objects to be returned. Limit can range between 1 and 100, and the default is 20.
+        :param order: Sort order by the `created_at` timestamp of the objects. `asc` for ascending order and `desc` for descending order.
+        :param after: A cursor for use in pagination. `after` is an object ID that defines your place in the list.
+        :param before: A cursor for use in pagination. `before` is an object ID that defines your place in the list.
+        :returns: A VectorStoreListResponse containing the list of vector stores.
+        """
+        ...
+
+    @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="GET")
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        """Retrieves a vector store.
+
+        :param vector_store_id: The ID of the vector store to retrieve.
+        :returns: A VectorStoreObject representing the vector store.
+        """
+        ...
+
+    @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="POST")
+    async def openai_update_vector_store(
+        self,
+        vector_store_id: str,
+        name: str | None = None,
+        expires_after: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> VectorStoreObject:
+        """Updates a vector store.
+
+        :param vector_store_id: The ID of the vector store to update.
+        :param name: The name of the vector store.
+        :param expires_after: The expiration policy for a vector store.
+        :param metadata: Set of 16 key-value pairs that can be attached to an object.
+        :returns: A VectorStoreObject representing the updated vector store.
+        """
+        ...
+
+    @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="DELETE")
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        """Delete a vector store.
+
+        :param vector_store_id: The ID of the vector store to delete.
+        :returns: A VectorStoreDeleteResponse indicating the deletion status.
+        """
+        ...
+
+    @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/search", method="POST")
+    async def openai_search_vector_store(
+        self,
+        vector_store_id: str,
+        query: str | list[str],
+        filters: dict[str, Any] | None = None,
+        max_num_results: int = 10,
+        ranking_options: dict[str, Any] | None = None,
+        rewrite_query: bool = False,
+    ) -> VectorStoreSearchResponse:
+        """Search for chunks in a vector store.
+
+        Searches a vector store for relevant chunks based on a query and optional file attribute filters.
+
+        :param vector_store_id: The ID of the vector store to search.
+        :param query: The query string or array for performing the search.
+        :param filters: Filters based on file attributes to narrow the search results.
+        :param max_num_results: Maximum number of results to return (1 to 50 inclusive, default 10).
+        :param ranking_options: Ranking options for fine-tuning the search results.
+        :param rewrite_query: Whether to rewrite the natural language query for vector search (default false)
+        :returns: A VectorStoreSearchResponse containing the search results.
+        """
+        ...

llama_stack/distribution/routers/vector_io.py

@@ -9,7 +9,16 @@ from typing import Any
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
 )
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.models import ModelType
+from llama_stack.apis.vector_io import (
+    Chunk,
+    QueryChunksResponse,
+    VectorIO,
+    VectorStoreDeleteResponse,
+    VectorStoreListResponse,
+    VectorStoreObject,
+    VectorStoreSearchResponse,
+)
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import RoutingTable
 
@@ -34,6 +43,31 @@ class VectorIORouter(VectorIO):
         logger.debug("VectorIORouter.shutdown")
         pass
 
+    async def _get_first_embedding_model(self) -> tuple[str, int] | None:
+        """Get the first available embedding model identifier."""
+        try:
+            # Get all models from the routing table
+            all_models = await self.routing_table.get_all_with_type("model")
+
+            # Filter for embedding models
+            embedding_models = [
+                model
+                for model in all_models
+                if hasattr(model, "model_type") and model.model_type == ModelType.embedding
+            ]
+
+            if embedding_models:
+                dimension = embedding_models[0].metadata.get("embedding_dimension", None)
+                if dimension is None:
+                    raise ValueError(f"Embedding model {embedding_models[0].identifier} has no embedding dimension")
+                return embedding_models[0].identifier, dimension
+            else:
+                logger.warning("No embedding models found in the routing table")
+                return None
+        except Exception as e:
+            logger.error(f"Error getting embedding models: {e}")
+            return None
+
     async def register_vector_db(
         self,
         vector_db_id: str,
@@ -70,3 +104,153 @@ class VectorIORouter(VectorIO):
     ) -> QueryChunksResponse:
         logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}")
         return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)
+
+    # OpenAI Vector Stores API endpoints
+    async def openai_create_vector_store(
+        self,
+        name: str | None = None,
+        file_ids: list[str] | None = None,
+        expires_after: dict[str, Any] | None = None,
+        chunking_strategy: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+        embedding_model: str | None = None,
+        embedding_dimension: int | None = None,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorStoreObject:
+        logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
+
+        # If no embedding model is provided, use the first available one
+        if embedding_model is None:
+            embedding_model_info = await self._get_first_embedding_model()
+            if embedding_model_info is None:
+                raise ValueError("No embedding model provided and no embedding models available in the system")
+            embedding_model, embedding_dimension = embedding_model_info
+            logger.info(f"No embedding model specified, using first available: {embedding_model}")
+
+        vector_db_id = name
+        registered_vector_db = await self.routing_table.register_vector_db(
+            vector_db_id,
+            embedding_model,
+            embedding_dimension,
+            provider_id,
+            provider_vector_db_id,
+        )
+
+        return await self.routing_table.get_provider_impl(registered_vector_db.identifier).openai_create_vector_store(
+            vector_db_id,
+            file_ids=file_ids,
+            expires_after=expires_after,
+            chunking_strategy=chunking_strategy,
+            metadata=metadata,
+            embedding_model=embedding_model,
+            embedding_dimension=embedding_dimension,
+            provider_id=registered_vector_db.provider_id,
+            provider_vector_db_id=registered_vector_db.provider_resource_id,
+        )
+
+    async def openai_list_vector_stores(
+        self,
+        limit: int = 20,
+        order: str = "desc",
+        after: str | None = None,
+        before: str | None = None,
+    ) -> VectorStoreListResponse:
+        logger.debug(f"VectorIORouter.openai_list_vector_stores: limit={limit}")
+        # Route to default provider for now - could aggregate from all providers in the future
+        # call retrieve on each vector dbs to get list of vector stores
+        vector_dbs = await self.routing_table.get_all_with_type("vector_db")
+        all_stores = []
+        for vector_db in vector_dbs:
+            vector_store = await self.routing_table.get_provider_impl(
+                vector_db.identifier
+            ).openai_retrieve_vector_store(vector_db.identifier)
+            all_stores.append(vector_store)
+
+        # Sort by created_at
+        reverse_order = order == "desc"
+        all_stores.sort(key=lambda x: x.created_at, reverse=reverse_order)
+
+        # Apply cursor-based pagination
+        if after:
+            after_index = next((i for i, store in enumerate(all_stores) if store.id == after), -1)
+            if after_index >= 0:
+                all_stores = all_stores[after_index + 1 :]
+
+        if before:
+            before_index = next((i for i, store in enumerate(all_stores) if store.id == before), len(all_stores))
+            all_stores = all_stores[:before_index]
+
+        # Apply limit
+        limited_stores = all_stores[:limit]
+
+        # Determine pagination info
+        has_more = len(all_stores) > limit
+        first_id = limited_stores[0].id if limited_stores else None
+        last_id = limited_stores[-1].id if limited_stores else None
+
+        return VectorStoreListResponse(
+            data=limited_stores,
+            has_more=has_more,
+            first_id=first_id,
+            last_id=last_id,
+        )
+
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        logger.debug(f"VectorIORouter.openai_retrieve_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store(vector_store_id)
+
+    async def openai_update_vector_store(
+        self,
+        vector_store_id: str,
+        name: str | None = None,
+        expires_after: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> VectorStoreObject:
+        logger.debug(f"VectorIORouter.openai_update_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store(
+            vector_store_id=vector_store_id,
+            name=name,
+            expires_after=expires_after,
+            metadata=metadata,
+        )
+
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        logger.debug(f"VectorIORouter.openai_delete_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        result = await provider.openai_delete_vector_store(vector_store_id)
+        # drop from registry
+        await self.routing_table.unregister_vector_db(vector_store_id)
+        return result
+
+    async def openai_search_vector_store(
+        self,
+        vector_store_id: str,
+        query: str | list[str],
+        filters: dict[str, Any] | None = None,
+        max_num_results: int = 10,
+        ranking_options: dict[str, Any] | None = None,
+        rewrite_query: bool = False,
+    ) -> VectorStoreSearchResponse:
+        logger.debug(f"VectorIORouter.openai_search_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.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,
+        )

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

@@ -9,16 +9,26 @@ import base64
 import io
 import json
 import logging
-from typing import Any
+import time
+import uuid
+from typing import Any, Literal
 
 import faiss
 import numpy as np
 from numpy.typing import NDArray
 
-from llama_stack.apis.common.content_types import InterleavedContent
+from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.inference.inference import Inference
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_io import (
+    Chunk,
+    QueryChunksResponse,
+    VectorIO,
+    VectorStoreDeleteResponse,
+    VectorStoreListResponse,
+    VectorStoreObject,
+    VectorStoreSearchResponse,
+)
 from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
@@ -34,6 +44,11 @@ logger = logging.getLogger(__name__)
 VERSION = "v3"
 VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"
 FAISS_INDEX_PREFIX = f"faiss_index:{VERSION}::"
+OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:{VERSION}::"
+
+
+# In faiss, since we do
+CHUNK_MULTIPLIER = 5
 
 
 class FaissIndex(EmbeddingIndex):
@@ -131,6 +146,7 @@ class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         self.inference_api = inference_api
         self.cache: dict[str, VectorDBWithIndex] = {}
         self.kvstore: KVStore | None = None
+        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
 
     async def initialize(self) -> None:
         self.kvstore = await kvstore_impl(self.config.kvstore)
@@ -148,6 +164,15 @@ class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
             )
             self.cache[vector_db.identifier] = index
 
+        # Load existing OpenAI vector stores
+        start_key = OPENAI_VECTOR_STORES_PREFIX
+        end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff"
+        stored_openai_stores = await self.kvstore.values_in_range(start_key, end_key)
+
+        for store_data in stored_openai_stores:
+            store_info = json.loads(store_data)
+            self.openai_vector_stores[store_info["id"]] = store_info
+
     async def shutdown(self) -> None:
         # Cleanup if needed
         pass
@@ -208,3 +233,286 @@ class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
             raise ValueError(f"Vector DB {vector_db_id} not found")
 
         return await index.query_chunks(query, params)
+
+    # OpenAI Vector Stores API endpoints implementation
+    async def openai_create_vector_store(
+        self,
+        name: str | None = None,
+        file_ids: list[str] | None = None,
+        expires_after: dict[str, Any] | None = None,
+        chunking_strategy: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+        embedding_model: str | None = None,
+        embedding_dimension: int | None = 384,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorStoreObject:
+        """Creates a vector store."""
+        assert self.kvstore is not None
+        # store and vector_db have the same id
+        store_id = name or str(uuid.uuid4())
+        created_at = int(time.time())
+
+        if provider_id is None:
+            raise ValueError("Provider ID is required")
+
+        if embedding_model is None:
+            raise ValueError("Embedding model is required")
+
+        # Use provided embedding dimension or default to 384
+        if embedding_dimension is None:
+            raise ValueError("Embedding dimension is required")
+
+        provider_vector_db_id = provider_vector_db_id or store_id
+        vector_db = VectorDB(
+            identifier=store_id,
+            embedding_dimension=embedding_dimension,
+            embedding_model=embedding_model,
+            provider_id=provider_id,
+            provider_resource_id=provider_vector_db_id,
+        )
+
+        # Register the vector DB
+        await self.register_vector_db(vector_db)
+
+        # Create OpenAI vector store metadata
+        store_info = {
+            "id": store_id,
+            "object": "vector_store",
+            "created_at": created_at,
+            "name": store_id,
+            "usage_bytes": 0,
+            "file_counts": {},
+            "status": "completed",
+            "expires_after": expires_after,
+            "expires_at": None,
+            "last_active_at": created_at,
+            "file_ids": file_ids or [],
+            "chunking_strategy": chunking_strategy,
+        }
+
+        # Add provider information to metadata if provided
+        metadata = metadata or {}
+        if provider_id:
+            metadata["provider_id"] = provider_id
+        if provider_vector_db_id:
+            metadata["provider_vector_db_id"] = provider_vector_db_id
+        store_info["metadata"] = metadata
+
+        # Store in kvstore
+        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
+        await self.kvstore.set(key=key, value=json.dumps(store_info))
+
+        # Store in memory cache
+        self.openai_vector_stores[store_id] = store_info
+
+        return VectorStoreObject(
+            id=store_id,
+            created_at=created_at,
+            name=store_id,
+            usage_bytes=0,
+            file_counts={},
+            status="completed",
+            expires_after=expires_after,
+            expires_at=None,
+            last_active_at=created_at,
+            metadata=metadata,
+        )
+
+    async def openai_list_vector_stores(
+        self,
+        limit: int = 20,
+        order: str = "desc",
+        after: str | None = None,
+        before: str | None = None,
+    ) -> VectorStoreListResponse:
+        """Returns a list of vector stores."""
+        # Get all vector stores
+        all_stores = list(self.openai_vector_stores.values())
+
+        # Sort by created_at
+        reverse_order = order == "desc"
+        all_stores.sort(key=lambda x: x["created_at"], reverse=reverse_order)
+
+        # Apply cursor-based pagination
+        if after:
+            after_index = next((i for i, store in enumerate(all_stores) if store["id"] == after), -1)
+            if after_index >= 0:
+                all_stores = all_stores[after_index + 1 :]
+
+        if before:
+            before_index = next((i for i, store in enumerate(all_stores) if store["id"] == before), len(all_stores))
+            all_stores = all_stores[:before_index]
+
+        # Apply limit
+        limited_stores = all_stores[:limit]
+        # Convert to VectorStoreObject instances
+        data = [VectorStoreObject(**store) for store in limited_stores]
+
+        # Determine pagination info
+        has_more = len(all_stores) > limit
+        first_id = data[0].id if data else None
+        last_id = data[-1].id if data else None
+
+        return VectorStoreListResponse(
+            data=data,
+            has_more=has_more,
+            first_id=first_id,
+            last_id=last_id,
+        )
+
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        """Retrieves a vector store."""
+        if vector_store_id not in self.openai_vector_stores:
+            raise ValueError(f"Vector store {vector_store_id} not found")
+
+        store_info = self.openai_vector_stores[vector_store_id]
+        return VectorStoreObject(**store_info)
+
+    async def openai_update_vector_store(
+        self,
+        vector_store_id: str,
+        name: str | None = None,
+        expires_after: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> VectorStoreObject:
+        """Modifies a vector store."""
+        assert self.kvstore is not None
+        if vector_store_id not in self.openai_vector_stores:
+            raise ValueError(f"Vector store {vector_store_id} not found")
+
+        store_info = self.openai_vector_stores[vector_store_id].copy()
+
+        # Update fields if provided
+        if name is not None:
+            store_info["name"] = name
+        if expires_after is not None:
+            store_info["expires_after"] = expires_after
+        if metadata is not None:
+            store_info["metadata"] = metadata
+
+        # Update last_active_at
+        store_info["last_active_at"] = int(time.time())
+
+        # Save to kvstore
+        key = f"{OPENAI_VECTOR_STORES_PREFIX}{vector_store_id}"
+        await self.kvstore.set(key=key, value=json.dumps(store_info))
+
+        # Update in-memory cache
+        self.openai_vector_stores[vector_store_id] = store_info
+
+        return VectorStoreObject(**store_info)
+
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        """Delete a vector store."""
+        assert self.kvstore is not None
+        if vector_store_id not in self.openai_vector_stores:
+            raise ValueError(f"Vector store {vector_store_id} not found")
+
+        # Delete from kvstore
+        key = f"{OPENAI_VECTOR_STORES_PREFIX}{vector_store_id}"
+        await self.kvstore.delete(key)
+
+        # Delete from in-memory cache
+        del self.openai_vector_stores[vector_store_id]
+
+        # Also delete the underlying vector DB
+        try:
+            await self.unregister_vector_db(vector_store_id)
+        except Exception as e:
+            logger.warning(f"Failed to delete underlying vector DB {vector_store_id}: {e}")
+
+        return VectorStoreDeleteResponse(
+            id=vector_store_id,
+            deleted=True,
+        )
+
+    async def openai_search_vector_store(
+        self,
+        vector_store_id: str,
+        query: str | list[str],
+        filters: dict[str, Any] | None = None,
+        max_num_results: int = 10,
+        ranking_options: dict[str, Any] | None = None,
+        rewrite_query: bool = False,
+        search_mode: Literal["keyword", "vector", "hybrid"] = "vector",
+    ) -> VectorStoreSearchResponse:
+        """Search for chunks in a vector store."""
+        if vector_store_id not in self.openai_vector_stores:
+            raise ValueError(f"Vector store {vector_store_id} not found")
+
+        if isinstance(query, list):
+            search_query = " ".join(query)
+        else:
+            search_query = query
+
+        try:
+            score_threshold = ranking_options.get("score_threshold", 0.0) if ranking_options else 0.0
+            params = {
+                "max_chunks": max_num_results * CHUNK_MULTIPLIER,
+                "score_threshold": score_threshold,
+                "mode": search_mode,
+            }
+            # TODO: Add support for ranking_options.ranker
+
+            response = await self.query_chunks(
+                vector_db_id=vector_store_id,
+                query=search_query,
+                params=params,
+            )
+
+            # Convert response to OpenAI format
+            data = []
+            for i, (chunk, score) in enumerate(zip(response.chunks, response.scores, strict=False)):
+                # Apply score based filtering
+                if score < score_threshold:
+                    continue
+
+                # Apply filters if provided
+                if filters:
+                    # Simple metadata filtering
+                    if not self._matches_filters(chunk.metadata, filters):
+                        continue
+
+                chunk_data = {
+                    "id": f"chunk_{i}",
+                    "object": "vector_store.search_result",
+                    "score": score,
+                    "content": chunk.content.content if hasattr(chunk.content, "content") else str(chunk.content),
+                    "metadata": chunk.metadata,
+                }
+                data.append(chunk_data)
+                if len(data) >= max_num_results:
+                    break
+
+            return VectorStoreSearchResponse(
+                search_query=search_query,
+                data=data,
+                has_more=False,  # For simplicity, we don't implement pagination here
+                next_page=None,
+            )
+
+        except Exception as e:
+            logger.error(f"Error searching vector store {vector_store_id}: {e}")
+            # Return empty results on error
+            return VectorStoreSearchResponse(
+                search_query=search_query,
+                data=[],
+                has_more=False,
+                next_page=None,
+            )
+
+    def _matches_filters(self, metadata: dict[str, Any], filters: dict[str, Any]) -> bool:
+        """Check if metadata matches the provided filters."""
+        for key, value in filters.items():
+            if key not in metadata:
+                return False
+            if metadata[key] != value:
+                return False
+        return True

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

@@ -6,11 +6,13 @@
 
 import asyncio
 import hashlib
+import json
 import logging
 import sqlite3
 import struct
+import time
 import uuid
-from typing import Any
+from typing import Any, Literal
 
 import numpy as np
 import sqlite_vec
@@ -18,7 +20,15 @@ from numpy.typing import NDArray
 
 from llama_stack.apis.inference.inference import Inference
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_io import (
+    Chunk,
+    QueryChunksResponse,
+    VectorIO,
+    VectorStoreDeleteResponse,
+    VectorStoreListResponse,
+    VectorStoreObject,
+    VectorStoreSearchResponse,
+)
 from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.utils.memory.vector_store import EmbeddingIndex, VectorDBWithIndex
 
@@ -29,6 +39,11 @@ VECTOR_SEARCH = "vector"
 KEYWORD_SEARCH = "keyword"
 SEARCH_MODES = {VECTOR_SEARCH, KEYWORD_SEARCH}
 
+# Constants for OpenAI vector stores (similar to faiss)
+VERSION = "v3"
+OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:{VERSION}::"
+CHUNK_MULTIPLIER = 5
+
 
 def serialize_vector(vector: list[float]) -> bytes:
     """Serialize a list of floats into a compact binary representation."""
@@ -299,6 +314,7 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         self.config = config
         self.inference_api = inference_api
         self.cache: dict[str, VectorDBWithIndex] = {}
+        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
 
     async def initialize(self) -> None:
         def _setup_connection():
@@ -313,17 +329,29 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
                         metadata TEXT
                     );
                 """)
+                # Create a table to persist OpenAI vector stores.
+                cur.execute("""
+                    CREATE TABLE IF NOT EXISTS openai_vector_stores (
+                        id TEXT PRIMARY KEY,
+                        metadata TEXT
+                    );
+                """)
                 connection.commit()
                 # Load any existing vector DB registrations.
                 cur.execute("SELECT metadata FROM vector_dbs")
-                rows = cur.fetchall()
-                return rows
+                vector_db_rows = cur.fetchall()
+                # Load any existing OpenAI vector stores.
+                cur.execute("SELECT metadata FROM openai_vector_stores")
+                openai_store_rows = cur.fetchall()
+                return vector_db_rows, openai_store_rows
             finally:
                 cur.close()
                 connection.close()
 
-        rows = await asyncio.to_thread(_setup_connection)
-        for row in rows:
+        vector_db_rows, openai_store_rows = await asyncio.to_thread(_setup_connection)
+
+        # Load existing vector DBs
+        for row in vector_db_rows:
             vector_db_data = row[0]
             vector_db = VectorDB.model_validate_json(vector_db_data)
             index = await SQLiteVecIndex.create(
@@ -331,6 +359,12 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
             )
             self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api)
 
+        # Load existing OpenAI vector stores
+        for row in openai_store_rows:
+            store_data = row[0]
+            store_info = json.loads(store_data)
+            self.openai_vector_stores[store_info["id"]] = store_info
+
     async def shutdown(self) -> None:
         # nothing to do since we don't maintain a persistent connection
         pass
@@ -389,6 +423,318 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
             raise ValueError(f"Vector DB {vector_db_id} not found")
         return await self.cache[vector_db_id].query_chunks(query, params)
 
+    async def openai_create_vector_store(
+        self,
+        name: str | None = None,
+        file_ids: list[str] | None = None,
+        expires_after: dict[str, Any] | None = None,
+        chunking_strategy: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+        embedding_model: str | None = None,
+        embedding_dimension: int | None = 384,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorStoreObject:
+        """Creates a vector store."""
+        # store and vector_db have the same id
+        store_id = name or str(uuid.uuid4())
+        created_at = int(time.time())
+
+        if provider_id is None:
+            raise ValueError("Provider ID is required")
+
+        if embedding_model is None:
+            raise ValueError("Embedding model is required")
+
+        # Use provided embedding dimension or default to 384
+        if embedding_dimension is None:
+            raise ValueError("Embedding dimension is required")
+
+        provider_vector_db_id = provider_vector_db_id or store_id
+        vector_db = VectorDB(
+            identifier=store_id,
+            embedding_dimension=embedding_dimension,
+            embedding_model=embedding_model,
+            provider_id=provider_id,
+            provider_resource_id=provider_vector_db_id,
+        )
+
+        # Register the vector DB
+        await self.register_vector_db(vector_db)
+
+        # Create OpenAI vector store metadata
+        store_info = {
+            "id": store_id,
+            "object": "vector_store",
+            "created_at": created_at,
+            "name": store_id,
+            "usage_bytes": 0,
+            "file_counts": {},
+            "status": "completed",
+            "expires_after": expires_after,
+            "expires_at": None,
+            "last_active_at": created_at,
+            "file_ids": file_ids or [],
+            "chunking_strategy": chunking_strategy,
+        }
+
+        # Add provider information to metadata if provided
+        metadata = metadata or {}
+        if provider_id:
+            metadata["provider_id"] = provider_id
+        if provider_vector_db_id:
+            metadata["provider_vector_db_id"] = provider_vector_db_id
+        store_info["metadata"] = metadata
+
+        # Store in SQLite database
+        def _store_openai_vector_store():
+            connection = _create_sqlite_connection(self.config.db_path)
+            cur = connection.cursor()
+            try:
+                cur.execute(
+                    "INSERT OR REPLACE INTO openai_vector_stores (id, metadata) VALUES (?, ?)",
+                    (store_id, json.dumps(store_info)),
+                )
+                connection.commit()
+            finally:
+                cur.close()
+                connection.close()
+
+        await asyncio.to_thread(_store_openai_vector_store)
+
+        # Store in memory cache
+        self.openai_vector_stores[store_id] = store_info
+
+        return VectorStoreObject(
+            id=store_id,
+            created_at=created_at,
+            name=store_id,
+            usage_bytes=0,
+            file_counts={},
+            status="completed",
+            expires_after=expires_after,
+            expires_at=None,
+            last_active_at=created_at,
+            metadata=metadata,
+        )
+
+    async def openai_list_vector_stores(
+        self,
+        limit: int = 20,
+        order: str = "desc",
+        after: str | None = None,
+        before: str | None = None,
+    ) -> VectorStoreListResponse:
+        """Returns a list of vector stores."""
+        # Get all vector stores
+        all_stores = list(self.openai_vector_stores.values())
+
+        # Sort by created_at
+        reverse_order = order == "desc"
+        all_stores.sort(key=lambda x: x["created_at"], reverse=reverse_order)
+
+        # Apply cursor-based pagination
+        if after:
+            after_index = next((i for i, store in enumerate(all_stores) if store["id"] == after), -1)
+            if after_index >= 0:
+                all_stores = all_stores[after_index + 1 :]
+
+        if before:
+            before_index = next((i for i, store in enumerate(all_stores) if store["id"] == before), len(all_stores))
+            all_stores = all_stores[:before_index]
+
+        # Apply limit
+        limited_stores = all_stores[:limit]
+        # Convert to VectorStoreObject instances
+        data = [VectorStoreObject(**store) for store in limited_stores]
+
+        # Determine pagination info
+        has_more = len(all_stores) > limit
+        first_id = data[0].id if data else None
+        last_id = data[-1].id if data else None
+
+        return VectorStoreListResponse(
+            data=data,
+            has_more=has_more,
+            first_id=first_id,
+            last_id=last_id,
+        )
+
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        """Retrieves a vector store."""
+        if vector_store_id not in self.openai_vector_stores:
+            raise ValueError(f"Vector store {vector_store_id} not found")
+
+        store_info = self.openai_vector_stores[vector_store_id]
+        return VectorStoreObject(**store_info)
+
+    async def openai_update_vector_store(
+        self,
+        vector_store_id: str,
+        name: str | None = None,
+        expires_after: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> VectorStoreObject:
+        """Modifies a vector store."""
+        if vector_store_id not in self.openai_vector_stores:
+            raise ValueError(f"Vector store {vector_store_id} not found")
+
+        store_info = self.openai_vector_stores[vector_store_id].copy()
+
+        # Update fields if provided
+        if name is not None:
+            store_info["name"] = name
+        if expires_after is not None:
+            store_info["expires_after"] = expires_after
+        if metadata is not None:
+            store_info["metadata"] = metadata
+
+        # Update last_active_at
+        store_info["last_active_at"] = int(time.time())
+
+        # Save to SQLite database
+        def _update_openai_vector_store():
+            connection = _create_sqlite_connection(self.config.db_path)
+            cur = connection.cursor()
+            try:
+                cur.execute(
+                    "UPDATE openai_vector_stores SET metadata = ? WHERE id = ?",
+                    (json.dumps(store_info), vector_store_id),
+                )
+                connection.commit()
+            finally:
+                cur.close()
+                connection.close()
+
+        await asyncio.to_thread(_update_openai_vector_store)
+
+        # Update in-memory cache
+        self.openai_vector_stores[vector_store_id] = store_info
+
+        return VectorStoreObject(**store_info)
+
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        """Delete a vector store."""
+        if vector_store_id not in self.openai_vector_stores:
+            raise ValueError(f"Vector store {vector_store_id} not found")
+
+        # Delete from SQLite database
+        def _delete_openai_vector_store():
+            connection = _create_sqlite_connection(self.config.db_path)
+            cur = connection.cursor()
+            try:
+                cur.execute("DELETE FROM openai_vector_stores WHERE id = ?", (vector_store_id,))
+                connection.commit()
+            finally:
+                cur.close()
+                connection.close()
+
+        await asyncio.to_thread(_delete_openai_vector_store)
+
+        # Delete from in-memory cache
+        del self.openai_vector_stores[vector_store_id]
+
+        # Also delete the underlying vector DB
+        try:
+            await self.unregister_vector_db(vector_store_id)
+        except Exception as e:
+            logger.warning(f"Failed to delete underlying vector DB {vector_store_id}: {e}")
+
+        return VectorStoreDeleteResponse(
+            id=vector_store_id,
+            deleted=True,
+        )
+
+    async def openai_search_vector_store(
+        self,
+        vector_store_id: str,
+        query: str | list[str],
+        filters: dict[str, Any] | None = None,
+        max_num_results: int = 10,
+        ranking_options: dict[str, Any] | None = None,
+        rewrite_query: bool = False,
+        search_mode: Literal["keyword", "vector", "hybrid"] = "vector",
+    ) -> VectorStoreSearchResponse:
+        """Search for chunks in a vector store."""
+        if vector_store_id not in self.openai_vector_stores:
+            raise ValueError(f"Vector store {vector_store_id} not found")
+
+        if isinstance(query, list):
+            search_query = " ".join(query)
+        else:
+            search_query = query
+
+        try:
+            score_threshold = ranking_options.get("score_threshold", 0.0) if ranking_options else 0.0
+            params = {
+                "max_chunks": max_num_results * CHUNK_MULTIPLIER,
+                "score_threshold": score_threshold,
+                "mode": search_mode,
+            }
+            # TODO: Add support for ranking_options.ranker
+
+            response = await self.query_chunks(
+                vector_db_id=vector_store_id,
+                query=search_query,
+                params=params,
+            )
+
+            # Convert response to OpenAI format
+            data = []
+            for i, (chunk, score) in enumerate(zip(response.chunks, response.scores, strict=False)):
+                # Apply score based filtering
+                if score < score_threshold:
+                    continue
+
+                # Apply filters if provided
+                if filters:
+                    # Simple metadata filtering
+                    if not self._matches_filters(chunk.metadata, filters):
+                        continue
+
+                chunk_data = {
+                    "id": f"chunk_{i}",
+                    "object": "vector_store.search_result",
+                    "score": score,
+                    "content": chunk.content.content if hasattr(chunk.content, "content") else str(chunk.content),
+                    "metadata": chunk.metadata,
+                }
+                data.append(chunk_data)
+                if len(data) >= max_num_results:
+                    break
+
+            return VectorStoreSearchResponse(
+                search_query=search_query,
+                data=data,
+                has_more=False,  # For simplicity, we don't implement pagination here
+                next_page=None,
+            )
+
+        except Exception as e:
+            logger.error(f"Error searching vector store {vector_store_id}: {e}")
+            # Return empty results on error
+            return VectorStoreSearchResponse(
+                search_query=search_query,
+                data=[],
+                has_more=False,
+                next_page=None,
+            )
+
+    def _matches_filters(self, metadata: dict[str, Any], filters: dict[str, Any]) -> bool:
+        """Check if metadata matches the provided filters."""
+        for key, value in filters.items():
+            if key not in metadata:
+                return False
+            if metadata[key] != value:
+                return False
+        return True
+
 
 def generate_chunk_id(document_id: str, chunk_text: str) -> str:
     """Generate a unique chunk ID using a hash of document ID and chunk text."""

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

@@ -6,7 +6,7 @@
 import asyncio
 import json
 import logging
-from typing import Any
+from typing import Any, Literal
 from urllib.parse import urlparse
 
 import chromadb
@@ -14,7 +14,15 @@ from numpy.typing import NDArray
 
 from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_io import (
+    Chunk,
+    QueryChunksResponse,
+    VectorIO,
+    VectorStoreDeleteResponse,
+    VectorStoreListResponse,
+    VectorStoreObject,
+    VectorStoreSearchResponse,
+)
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
@@ -178,3 +186,59 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         index = VectorDBWithIndex(vector_db, ChromaIndex(self.client, collection), self.inference_api)
         self.cache[vector_db_id] = index
         return index
+
+    async def openai_create_vector_store(
+        self,
+        name: str | None = None,
+        file_ids: list[str] | None = None,
+        expires_after: dict[str, Any] | None = None,
+        chunking_strategy: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+        embedding_model: str | None = None,
+        embedding_dimension: int | None = 384,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
+
+    async def openai_list_vector_stores(
+        self,
+        limit: int = 20,
+        order: str = "desc",
+        after: str | None = None,
+        before: str | None = None,
+    ) -> VectorStoreListResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
+
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
+
+    async def openai_update_vector_store(
+        self,
+        vector_store_id: str,
+        name: str | None = None,
+        expires_after: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
+
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
+
+    async def openai_search_vector_store(
+        self,
+        vector_store_id: str,
+        query: str | list[str],
+        filters: dict[str, Any] | None = None,
+        max_num_results: int = 10,
+        ranking_options: dict[str, Any] | None = None,
+        rewrite_query: bool = False,
+        search_mode: Literal["keyword", "vector", "hybrid"] = "vector",
+    ) -> VectorStoreSearchResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")

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

@@ -9,14 +9,22 @@ import hashlib
 import logging
 import os
 import uuid
-from typing import Any
+from typing import Any, Literal
 
 from numpy.typing import NDArray
 from pymilvus import MilvusClient
 
 from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_io import (
+    Chunk,
+    QueryChunksResponse,
+    VectorIO,
+    VectorStoreDeleteResponse,
+    VectorStoreListResponse,
+    VectorStoreObject,
+    VectorStoreSearchResponse,
+)
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
@@ -177,6 +185,62 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
 
         return await index.query_chunks(query, params)
 
+    async def openai_create_vector_store(
+        self,
+        name: str | None = None,
+        file_ids: list[str] | None = None,
+        expires_after: dict[str, Any] | None = None,
+        chunking_strategy: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+        embedding_model: str | None = None,
+        embedding_dimension: int | None = 384,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_list_vector_stores(
+        self,
+        limit: int = 20,
+        order: str = "desc",
+        after: str | None = None,
+        before: str | None = None,
+    ) -> VectorStoreListResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_update_vector_store(
+        self,
+        vector_store_id: str,
+        name: str | None = None,
+        expires_after: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_search_vector_store(
+        self,
+        vector_store_id: str,
+        query: str | list[str],
+        filters: dict[str, Any] | None = None,
+        max_num_results: int = 10,
+        ranking_options: dict[str, Any] | None = None,
+        rewrite_query: bool = False,
+        search_mode: Literal["keyword", "vector", "hybrid"] = "vector",
+    ) -> VectorStoreSearchResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
 
 def generate_chunk_id(document_id: str, chunk_text: str) -> str:
     """Generate a unique chunk ID using a hash of document ID and chunk text."""

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

@@ -6,7 +6,7 @@
 
 import logging
 import uuid
-from typing import Any
+from typing import Any, Literal
 
 from numpy.typing import NDArray
 from qdrant_client import AsyncQdrantClient, models
@@ -14,7 +14,15 @@ from qdrant_client.models import PointStruct
 
 from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_io import (
+    Chunk,
+    QueryChunksResponse,
+    VectorIO,
+    VectorStoreDeleteResponse,
+    VectorStoreListResponse,
+    VectorStoreObject,
+    VectorStoreSearchResponse,
+)
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
@@ -178,3 +186,59 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
             raise ValueError(f"Vector DB {vector_db_id} not found")
 
         return await index.query_chunks(query, params)
+
+    async def openai_create_vector_store(
+        self,
+        name: str | None = None,
+        file_ids: list[str] | None = None,
+        expires_after: dict[str, Any] | None = None,
+        chunking_strategy: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+        embedding_model: str | None = None,
+        embedding_dimension: int | None = 384,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_list_vector_stores(
+        self,
+        limit: int = 20,
+        order: str = "desc",
+        after: str | None = None,
+        before: str | None = None,
+    ) -> VectorStoreListResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_update_vector_store(
+        self,
+        vector_store_id: str,
+        name: str | None = None,
+        expires_after: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> VectorStoreObject:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    async def openai_search_vector_store(
+        self,
+        vector_store_id: str,
+        query: str | list[str],
+        filters: dict[str, Any] | None = None,
+        max_num_results: int = 10,
+        ranking_options: dict[str, Any] | None = None,
+        rewrite_query: bool = False,
+        search_mode: Literal["keyword", "vector", "hybrid"] = "vector",
+    ) -> VectorStoreSearchResponse:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")