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/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")