feat(api): Add vector store file batches api (#3642)

# What does this PR do?

Add Open AI Compatible vector store file batches api. This functionality
is needed to attach many files to a vector store as a batch.
https://github.com/llamastack/llama-stack/issues/3533

API Stubs have been merged
https://github.com/llamastack/llama-stack/pull/3615
Adds persistence for file batches as discussed in diff
https://github.com/llamastack/llama-stack/pull/3544
(Used claude code for generation and reviewed by me)


## Test Plan
1. Unit tests pass
2. Also verified the cc-vec integration with LLamaStackClient works with
the file batches api. https://github.com/raghotham/cc-vec
2. Integration tests pass

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

@@ -140,14 +140,13 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         inference_api: Api.inference,
         files_api: Files | 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.client = None
         self.cache = {}
-        self.kvstore: KVStore | None = None
         self.vector_db_store = None
-        self.files_api = files_api
 
     async def initialize(self) -> None:
         self.kvstore = await kvstore_impl(self.config.kvstore)

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

@@ -309,14 +309,12 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         inference_api: Inference,
         files_api: Files | None,
     ) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.cache = {}
         self.client = None
         self.inference_api = inference_api
-        self.files_api = files_api
-        self.kvstore: KVStore | None = None
         self.vector_db_store = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.metadata_collection_name = "openai_vector_stores_metadata"
 
     async def initialize(self) -> None:

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

@@ -345,14 +345,12 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
         inference_api: Api.inference,
         files_api: Files | None = None,
     ) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
         self.conn = None
         self.cache = {}
-        self.files_api = files_api
-        self.kvstore: KVStore | None = None
         self.vector_db_store = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.metadata_collection_name = "openai_vector_stores_metadata"
 
     async def initialize(self) -> None:

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

@@ -27,7 +27,7 @@ from llama_stack.apis.vector_io import (
 from llama_stack.log import get_logger
 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.kvstore import KVStore, kvstore_impl
+from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
     ChunkForDeletion,
@@ -162,14 +162,12 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         inference_api: Api.inference,
         files_api: Files | 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.files_api = files_api
         self.vector_db_store = None
-        self.kvstore: KVStore | None = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self._qdrant_lock = asyncio.Lock()
 
     async def initialize(self) -> None:

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

@@ -284,14 +284,12 @@ class WeaviateVectorIOAdapter(
         inference_api: Api.inference,
         files_api: Files | None,
     ) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
         self.client_cache = {}
         self.cache = {}
-        self.files_api = files_api
-        self.kvstore: KVStore | None = None
         self.vector_db_store = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.metadata_collection_name = "openai_vector_stores_metadata"
 
     def _get_client(self) -> weaviate.WeaviateClient: