Merge c9be8c15c2 into 188a56af5c

llama_stack/core/routing_tables/vector_dbs.py

@@ -245,3 +245,65 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
             vector_store_id=vector_store_id,
             file_id=file_id,
         )
+
+    async def openai_create_vector_store_file_batch(
+        self,
+        vector_store_id: str,
+        file_ids: list[str],
+        attributes: dict[str, Any] | None = None,
+        chunking_strategy: Any | None = None,
+    ):
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_create_vector_store_file_batch(
+            vector_store_id=vector_store_id,
+            file_ids=file_ids,
+            attributes=attributes,
+            chunking_strategy=chunking_strategy,
+        )
+
+    async def openai_retrieve_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+    ):
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file_batch(
+            batch_id=batch_id,
+            vector_store_id=vector_store_id,
+        )
+
+    async def openai_list_files_in_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+        after: str | None = None,
+        before: str | None = None,
+        filter: str | None = None,
+        limit: int | None = 20,
+        order: str | None = "desc",
+    ):
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_list_files_in_vector_store_file_batch(
+            batch_id=batch_id,
+            vector_store_id=vector_store_id,
+            after=after,
+            before=before,
+            filter=filter,
+            limit=limit,
+            order=order,
+        )
+
+    async def openai_cancel_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+    ):
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_cancel_vector_store_file_batch(
+            batch_id=batch_id,
+            vector_store_id=vector_store_id,
+        )

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

@@ -200,12 +200,10 @@ class FaissIndex(EmbeddingIndex):
 
 class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
     def __init__(self, config: FaissVectorIOConfig, inference_api: Inference, files_api: Files | None) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.files_api = files_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)

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

@@ -410,12 +410,10 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
     """
 
     def __init__(self, config, inference_api: Inference, files_api: Files | None) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.files_api = files_api
         self.cache: dict[str, VectorDBWithIndex] = {}
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
-        self.kvstore: KVStore | None = None
 
     async def initialize(self) -> None:
         self.kvstore = await kvstore_impl(self.config.kvstore)

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:

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -12,6 +12,8 @@ import uuid
 from abc import ABC, abstractmethod
 from typing import Any
 
+from pydantic import TypeAdapter
+
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files, OpenAIFileObject
 from llama_stack.apis.vector_dbs import VectorDB
@@ -50,12 +52,16 @@ logger = get_logger(name=__name__, category="providers::utils")
 
 # Constants for OpenAI vector stores
 CHUNK_MULTIPLIER = 5
+FILE_BATCH_CLEANUP_INTERVAL_SECONDS = 24 * 60 * 60  # 1 day in seconds
+MAX_CONCURRENT_FILES_PER_BATCH = 5  # Maximum concurrent file processing within a batch
+FILE_BATCH_CHUNK_SIZE = 10  # Process files in chunks of this size (2x concurrency)
 
 VERSION = "v3"
 VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"
 OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:{VERSION}::"
 OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:{VERSION}::"
 OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:{VERSION}::"
+OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX = f"openai_vector_stores_file_batches:{VERSION}::"
 
 
 class OpenAIVectorStoreMixin(ABC):
@@ -65,11 +71,15 @@ class OpenAIVectorStoreMixin(ABC):
     an openai_vector_stores in-memory cache.
     """
 
-    # These should be provided by the implementing class
+    # Implementing classes should call super().__init__() in their __init__ method
-    openai_vector_stores: dict[str, dict[str, Any]]
+    # to properly initialize the mixin attributes.
-    files_api: Files | None
+    def __init__(self, files_api: Files | None = None, kvstore: KVStore | None = None):
-    # KV store for persisting OpenAI vector store metadata
+        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
-    kvstore: KVStore | None
+        self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self.files_api = files_api
+        self.kvstore = kvstore
+        self._last_file_batch_cleanup_time = 0
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
 
     async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
         """Save vector store metadata to persistent storage."""
@@ -159,9 +169,74 @@ class OpenAIVectorStoreMixin(ABC):
         for idx in range(len(raw_items)):
             await self.kvstore.delete(f"{contents_prefix}{idx}")
 
+    async def _save_openai_vector_store_file_batch(self, batch_id: str, batch_info: dict[str, Any]) -> None:
+        """Save file batch metadata to persistent storage."""
+        assert self.kvstore
+        key = f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}{batch_id}"
+        await self.kvstore.set(key=key, value=json.dumps(batch_info))
+        # update in-memory cache
+        self.openai_file_batches[batch_id] = batch_info
+
+    async def _load_openai_vector_store_file_batches(self) -> dict[str, dict[str, Any]]:
+        """Load all file batch metadata from persistent storage."""
+        assert self.kvstore
+        start_key = OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX
+        end_key = f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}\xff"
+        stored_data = await self.kvstore.values_in_range(start_key, end_key)
+
+        batches: dict[str, dict[str, Any]] = {}
+        for item in stored_data:
+            info = json.loads(item)
+            batches[info["id"]] = info
+        return batches
+
+    async def _delete_openai_vector_store_file_batch(self, batch_id: str) -> None:
+        """Delete file batch metadata from persistent storage and in-memory cache."""
+        assert self.kvstore
+        key = f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}{batch_id}"
+        await self.kvstore.delete(key)
+        # remove from in-memory cache
+        self.openai_file_batches.pop(batch_id, None)
+
+    async def _cleanup_expired_file_batches(self) -> None:
+        """Clean up expired file batches from persistent storage."""
+        assert self.kvstore
+        start_key = OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX
+        end_key = f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}\xff"
+        stored_data = await self.kvstore.values_in_range(start_key, end_key)
+
+        current_time = int(time.time())
+        expired_count = 0
+
+        for item in stored_data:
+            info = json.loads(item)
+            expires_at = info.get("expires_at")
+            if expires_at and current_time > expires_at:
+                logger.info(f"Cleaning up expired file batch: {info['id']}")
+                await self.kvstore.delete(f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}{info['id']}")
+                # Remove from in-memory cache if present
+                self.openai_file_batches.pop(info["id"], None)
+                expired_count += 1
+
+        if expired_count > 0:
+            logger.info(f"Cleaned up {expired_count} expired file batches")
+
+    async def _resume_incomplete_batches(self) -> None:
+        """Resume processing of incomplete file batches after server restart."""
+        for batch_id, batch_info in self.openai_file_batches.items():
+            if batch_info["status"] == "in_progress":
+                logger.info(f"Resuming incomplete file batch: {batch_id}")
+                # Restart the background processing task
+                task = asyncio.create_task(self._process_file_batch_async(batch_id, batch_info))
+                self._file_batch_tasks[batch_id] = task
+
     async def initialize_openai_vector_stores(self) -> None:
-        """Load existing OpenAI vector stores into the in-memory cache."""
+        """Load existing OpenAI vector stores and file batches into the in-memory cache."""
         self.openai_vector_stores = await self._load_openai_vector_stores()
+        self.openai_file_batches = await self._load_openai_vector_store_file_batches()
+        self._file_batch_tasks = {}
+        await self._resume_incomplete_batches()
+        self._last_file_batch_cleanup_time = 0
 
     @abstractmethod
     async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
@@ -615,7 +690,6 @@ class OpenAIVectorStoreMixin(ABC):
                 chunk_overlap_tokens,
                 attributes,
             )
-
             if not chunks:
                 vector_store_file_object.status = "failed"
                 vector_store_file_object.last_error = VectorStoreFileLastError(
@@ -828,7 +902,227 @@ class OpenAIVectorStoreMixin(ABC):
         chunking_strategy: VectorStoreChunkingStrategy | None = None,
     ) -> VectorStoreFileBatchObject:
         """Create a vector store file batch."""
-        raise NotImplementedError("openai_create_vector_store_file_batch is not implemented yet")
+        if vector_store_id not in self.openai_vector_stores:
+            raise VectorStoreNotFoundError(vector_store_id)
+
+        chunking_strategy = chunking_strategy or VectorStoreChunkingStrategyAuto()
+
+        created_at = int(time.time())
+        batch_id = f"batch_{uuid.uuid4()}"
+        # File batches expire after 7 days
+        expires_at = created_at + (7 * 24 * 60 * 60)
+
+        # Initialize batch file counts - all files start as in_progress
+        file_counts = VectorStoreFileCounts(
+            completed=0,
+            cancelled=0,
+            failed=0,
+            in_progress=len(file_ids),
+            total=len(file_ids),
+        )
+
+        # Create batch object immediately with in_progress status
+        batch_object = VectorStoreFileBatchObject(
+            id=batch_id,
+            created_at=created_at,
+            vector_store_id=vector_store_id,
+            status="in_progress",
+            file_counts=file_counts,
+        )
+
+        batch_info = {
+            **batch_object.model_dump(),
+            "file_ids": file_ids,
+            "attributes": attributes,
+            "chunking_strategy": chunking_strategy.model_dump(),
+            "expires_at": expires_at,
+        }
+        await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+        # Start background processing of files
+        task = asyncio.create_task(self._process_file_batch_async(batch_id, batch_info))
+        self._file_batch_tasks[batch_id] = task
+
+        # Run cleanup if needed (throttled to once every 1 day)
+        current_time = int(time.time())
+        if current_time - self._last_file_batch_cleanup_time >= FILE_BATCH_CLEANUP_INTERVAL_SECONDS:
+            logger.info("Running throttled cleanup of expired file batches")
+            asyncio.create_task(self._cleanup_expired_file_batches())
+            self._last_file_batch_cleanup_time = current_time
+
+        return batch_object
+
+    async def _process_files_with_concurrency(
+        self,
+        file_ids: list[str],
+        vector_store_id: str,
+        attributes: dict[str, Any],
+        chunking_strategy_obj: Any,
+        batch_id: str,
+        batch_info: dict[str, Any],
+    ) -> None:
+        """Process files with controlled concurrency and chunking."""
+        semaphore = asyncio.Semaphore(MAX_CONCURRENT_FILES_PER_BATCH)
+
+        async def process_single_file(file_id: str) -> tuple[str, bool]:
+            """Process a single file with concurrency control."""
+            async with semaphore:
+                try:
+                    await self.openai_attach_file_to_vector_store(
+                        vector_store_id=vector_store_id,
+                        file_id=file_id,
+                        attributes=attributes,
+                        chunking_strategy=chunking_strategy_obj,
+                    )
+                    return file_id, True
+                except Exception as e:
+                    logger.error(f"Failed to process file {file_id} in batch {batch_id}: {e}")
+                    return file_id, False
+
+        # Process files in chunks to avoid creating too many tasks at once
+        total_files = len(file_ids)
+        for chunk_start in range(0, total_files, FILE_BATCH_CHUNK_SIZE):
+            chunk_end = min(chunk_start + FILE_BATCH_CHUNK_SIZE, total_files)
+            chunk = file_ids[chunk_start:chunk_end]
+
+            logger.info(
+                f"Processing chunk {chunk_start // FILE_BATCH_CHUNK_SIZE + 1} of {(total_files + FILE_BATCH_CHUNK_SIZE - 1) // FILE_BATCH_CHUNK_SIZE} ({len(chunk)} files)"
+            )
+
+            async with asyncio.TaskGroup() as tg:
+                chunk_tasks = [tg.create_task(process_single_file(file_id)) for file_id in chunk]
+
+            chunk_results = [task.result() for task in chunk_tasks]
+
+            # Update counts after each chunk for progressive feedback
+            for _, success in chunk_results:
+                self._update_file_counts(batch_info, success=success)
+
+            # Save progress after each chunk
+            await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+    def _update_file_counts(self, batch_info: dict[str, Any], success: bool) -> None:
+        """Update file counts based on processing result."""
+        if success:
+            batch_info["file_counts"]["completed"] += 1
+        else:
+            batch_info["file_counts"]["failed"] += 1
+        batch_info["file_counts"]["in_progress"] -= 1
+
+    def _update_batch_status(self, batch_info: dict[str, Any]) -> None:
+        """Update final batch status based on file processing results."""
+        if batch_info["file_counts"]["failed"] == 0:
+            batch_info["status"] = "completed"
+        elif batch_info["file_counts"]["completed"] == 0:
+            batch_info["status"] = "failed"
+        else:
+            batch_info["status"] = "completed"  # Partial success counts as completed
+
+    async def _process_file_batch_async(
+        self,
+        batch_id: str,
+        batch_info: dict[str, Any],
+    ) -> None:
+        """Process files in a batch asynchronously in the background."""
+        file_ids = batch_info["file_ids"]
+        attributes = batch_info["attributes"]
+        chunking_strategy = batch_info["chunking_strategy"]
+        vector_store_id = batch_info["vector_store_id"]
+        chunking_strategy_adapter: TypeAdapter[VectorStoreChunkingStrategy] = TypeAdapter(VectorStoreChunkingStrategy)
+        chunking_strategy_obj = chunking_strategy_adapter.validate_python(chunking_strategy)
+
+        try:
+            # Process all files with controlled concurrency
+            await self._process_files_with_concurrency(
+                file_ids=file_ids,
+                vector_store_id=vector_store_id,
+                attributes=attributes,
+                chunking_strategy_obj=chunking_strategy_obj,
+                batch_id=batch_id,
+                batch_info=batch_info,
+            )
+
+            # Update final batch status
+            self._update_batch_status(batch_info)
+            await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+            logger.info(f"File batch {batch_id} processing completed with status: {batch_info['status']}")
+
+        except asyncio.CancelledError:
+            logger.info(f"File batch {batch_id} processing was cancelled")
+            # Clean up task reference if it still exists
+            self._file_batch_tasks.pop(batch_id, None)
+            raise  # Re-raise to ensure proper cancellation propagation
+        finally:
+            # Always clean up task reference when processing ends
+            self._file_batch_tasks.pop(batch_id, None)
+
+    def _get_and_validate_batch(self, batch_id: str, vector_store_id: str) -> dict[str, Any]:
+        """Get and validate batch exists and belongs to vector store."""
+        if vector_store_id not in self.openai_vector_stores:
+            raise VectorStoreNotFoundError(vector_store_id)
+
+        if batch_id not in self.openai_file_batches:
+            raise ValueError(f"File batch {batch_id} not found")
+
+        batch_info = self.openai_file_batches[batch_id]
+
+        # Check if batch has expired (read-only check)
+        expires_at = batch_info.get("expires_at")
+        if expires_at:
+            current_time = int(time.time())
+            if current_time > expires_at:
+                raise ValueError(f"File batch {batch_id} has expired after 7 days from creation")
+
+        if batch_info["vector_store_id"] != vector_store_id:
+            raise ValueError(f"File batch {batch_id} does not belong to vector store {vector_store_id}")
+
+        return batch_info
+
+    def _paginate_objects(
+        self,
+        objects: list[Any],
+        limit: int | None = 20,
+        after: str | None = None,
+        before: str | None = None,
+    ) -> tuple[list[Any], bool, str | None, str | None]:
+        """Apply pagination to a list of objects with id fields."""
+        limit = min(limit or 20, 100)  # Cap at 100 as per OpenAI
+
+        # Find start index
+        start_idx = 0
+        if after:
+            for i, obj in enumerate(objects):
+                if obj.id == after:
+                    start_idx = i + 1
+                    break
+
+        # Find end index
+        end_idx = start_idx + limit
+        if before:
+            for i, obj in enumerate(objects[start_idx:], start_idx):
+                if obj.id == before:
+                    end_idx = i
+                    break
+
+        # Apply pagination
+        paginated_objects = objects[start_idx:end_idx]
+
+        # Determine pagination info
+        has_more = end_idx < len(objects)
+        first_id = paginated_objects[0].id if paginated_objects else None
+        last_id = paginated_objects[-1].id if paginated_objects else None
+
+        return paginated_objects, has_more, first_id, last_id
+
+    async def openai_retrieve_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+    ) -> VectorStoreFileBatchObject:
+        """Retrieve a vector store file batch."""
+        batch_info = self._get_and_validate_batch(batch_id, vector_store_id)
+        return VectorStoreFileBatchObject(**batch_info)
 
     async def openai_list_files_in_vector_store_file_batch(
         self,
@@ -841,15 +1135,39 @@ class OpenAIVectorStoreMixin(ABC):
         order: str | None = "desc",
     ) -> VectorStoreFilesListInBatchResponse:
         """Returns a list of vector store files in a batch."""
-        raise NotImplementedError("openai_list_files_in_vector_store_file_batch is not implemented yet")
+        batch_info = self._get_and_validate_batch(batch_id, vector_store_id)
+        batch_file_ids = batch_info["file_ids"]
 
-    async def openai_retrieve_vector_store_file_batch(
+        # Load file objects for files in this batch
-        self,
+        batch_file_objects = []
-        batch_id: str,
+
-        vector_store_id: str,
+        for file_id in batch_file_ids:
-    ) -> VectorStoreFileBatchObject:
+            try:
-        """Retrieve a vector store file batch."""
+                file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
-        raise NotImplementedError("openai_retrieve_vector_store_file_batch is not implemented yet")
+                file_object = VectorStoreFileObject(**file_info)
+
+                # Apply status filter if provided
+                if filter and file_object.status != filter:
+                    continue
+
+                batch_file_objects.append(file_object)
+            except Exception as e:
+                logger.warning(f"Could not load file {file_id} from batch {batch_id}: {e}")
+                continue
+
+        # Sort by created_at
+        reverse_order = order == "desc"
+        batch_file_objects.sort(key=lambda x: x.created_at, reverse=reverse_order)
+
+        # Apply pagination using helper
+        paginated_files, has_more, first_id, last_id = self._paginate_objects(batch_file_objects, limit, after, before)
+
+        return VectorStoreFilesListInBatchResponse(
+            data=paginated_files,
+            first_id=first_id,
+            last_id=last_id,
+            has_more=has_more,
+        )
 
     async def openai_cancel_vector_store_file_batch(
         self,
@@ -857,4 +1175,24 @@ class OpenAIVectorStoreMixin(ABC):
         vector_store_id: str,
     ) -> VectorStoreFileBatchObject:
         """Cancel a vector store file batch."""
-        raise NotImplementedError("openai_cancel_vector_store_file_batch is not implemented yet")
+        batch_info = self._get_and_validate_batch(batch_id, vector_store_id)
+
+        if batch_info["status"] not in ["in_progress"]:
+            raise ValueError(f"Cannot cancel batch {batch_id} with status {batch_info['status']}")
+
+        # Cancel the actual processing task if it exists
+        if batch_id in self._file_batch_tasks:
+            task = self._file_batch_tasks[batch_id]
+            if not task.done():
+                task.cancel()
+                logger.info(f"Cancelled processing task for file batch: {batch_id}")
+            # Remove from task tracking
+            del self._file_batch_tasks[batch_id]
+
+        batch_info["status"] = "cancelled"
+
+        await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+        updated_batch = VectorStoreFileBatchObject(**batch_info)
+
+        return updated_batch

tests/integration/vector_io/test_openai_vector_stores.py

@@ -902,3 +902,224 @@ def test_openai_vector_store_search_modes(llama_stack_client, client_with_models
         search_mode=search_mode,
     )
     assert search_response is not None
+
+
+def test_openai_vector_store_file_batch_create_and_retrieve(compat_client_with_empty_stores, client_with_models):
+    """Test creating and retrieving a vector store file batch."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_test_store")
+
+    # Create multiple files
+    file_ids = []
+    for i in range(3):
+        with BytesIO(f"This is batch test file {i}".encode()) as file_buffer:
+            file_buffer.name = f"batch_test_{i}.txt"
+            file = compat_client.files.create(file=file_buffer, purpose="assistants")
+        file_ids.append(file.id)
+
+    # Create a file batch
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+
+    assert batch is not None
+    assert batch.object == "vector_store.file_batch"
+    assert batch.vector_store_id == vector_store.id
+    assert batch.status in ["in_progress", "completed"]
+    assert batch.file_counts.total == len(file_ids)
+    assert hasattr(batch, "id")
+    assert hasattr(batch, "created_at")
+
+    # Wait for batch processing to complete
+    max_retries = 30  # 30 seconds max wait
+    retries = 0
+    retrieved_batch = None
+    while retries < max_retries:
+        retrieved_batch = compat_client.vector_stores.file_batches.retrieve(
+            vector_store_id=vector_store.id,
+            batch_id=batch.id,
+        )
+        if retrieved_batch.status in ["completed", "failed"]:
+            break
+        time.sleep(1)
+        retries += 1
+
+    assert retrieved_batch is not None
+    assert retrieved_batch.id == batch.id
+    assert retrieved_batch.vector_store_id == vector_store.id
+    assert retrieved_batch.object == "vector_store.file_batch"
+    assert retrieved_batch.file_counts.total == len(file_ids)
+    assert retrieved_batch.status == "completed"  # Should be completed after processing
+
+
+def test_openai_vector_store_file_batch_list_files(compat_client_with_empty_stores, client_with_models):
+    """Test listing files in a vector store file batch."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_list_test_store")
+
+    # Create multiple files
+    file_ids = []
+    for i in range(5):
+        with BytesIO(f"This is batch list test file {i}".encode()) as file_buffer:
+            file_buffer.name = f"batch_list_test_{i}.txt"
+            file = compat_client.files.create(file=file_buffer, purpose="assistants")
+        file_ids.append(file.id)
+
+    # Create a file batch
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+
+    # Wait for batch processing to complete
+    max_retries = 30  # 30 seconds max wait
+    retries = 0
+    while retries < max_retries:
+        retrieved_batch = compat_client.vector_stores.file_batches.retrieve(
+            vector_store_id=vector_store.id,
+            batch_id=batch.id,
+        )
+        if retrieved_batch.status in ["completed", "failed"]:
+            break
+        time.sleep(1)
+        retries += 1
+
+    # List all files in the batch
+    files_response = compat_client.vector_stores.file_batches.list_files(
+        vector_store_id=vector_store.id,
+        batch_id=batch.id,
+    )
+
+    assert files_response is not None
+    assert files_response.object == "list"
+    assert hasattr(files_response, "data")
+    assert len(files_response.data) == len(file_ids)
+
+    # Verify all files are in the response
+    response_file_ids = {file.id for file in files_response.data}
+    assert response_file_ids == set(file_ids)
+
+    # Test pagination with limit
+    limited_response = compat_client.vector_stores.file_batches.list_files(
+        vector_store_id=vector_store.id,
+        batch_id=batch.id,
+        limit=3,
+    )
+
+    assert len(limited_response.data) == 3
+    assert limited_response.has_more is True
+
+    # Test pagination with after cursor
+    first_page = compat_client.vector_stores.file_batches.list_files(
+        vector_store_id=vector_store.id,
+        batch_id=batch.id,
+        limit=2,
+    )
+
+    second_page = compat_client.vector_stores.file_batches.list_files(
+        vector_store_id=vector_store.id,
+        batch_id=batch.id,
+        limit=2,
+        after=first_page.data[-1].id,
+    )
+
+    assert len(first_page.data) == 2
+    assert len(second_page.data) <= 3  # Should be <= remaining files
+    # Ensure no overlap between pages
+    first_page_ids = {file.id for file in first_page.data}
+    second_page_ids = {file.id for file in second_page.data}
+    assert first_page_ids.isdisjoint(second_page_ids)
+
+
+def test_openai_vector_store_file_batch_cancel(compat_client_with_empty_stores, client_with_models):
+    """Test cancelling a vector store file batch."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_cancel_test_store")
+
+    # Create multiple files
+    file_ids = []
+    for i in range(3):
+        with BytesIO(f"This is batch cancel test file {i}".encode()) as file_buffer:
+            file_buffer.name = f"batch_cancel_test_{i}.txt"
+            file = compat_client.files.create(file=file_buffer, purpose="assistants")
+        file_ids.append(file.id)
+
+    # Create a file batch
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+    # Try to cancel the batch (may fail if already completed)
+    try:
+        cancelled_batch = compat_client.vector_stores.file_batches.cancel(
+            vector_store_id=vector_store.id,
+            batch_id=batch.id,
+        )
+
+        assert cancelled_batch is not None
+        assert cancelled_batch.id == batch.id
+        assert cancelled_batch.vector_store_id == vector_store.id
+        assert cancelled_batch.status == "cancelled"
+        assert cancelled_batch.object == "vector_store.file_batch"
+    except Exception as e:
+        # If cancellation fails because batch is already completed, that's acceptable
+        if "Cannot cancel" in str(e) or "already completed" in str(e):
+            pytest.skip(f"Batch completed too quickly to cancel: {e}")
+        else:
+            raise
+
+
+def test_openai_vector_store_file_batch_error_handling(compat_client_with_empty_stores, client_with_models):
+    """Test error handling for file batch operations."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_error_test_store")
+
+    # Test with invalid file IDs (should handle gracefully)
+    file_ids = ["invalid_file_id_1", "invalid_file_id_2"]
+
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+
+    assert batch is not None
+    assert batch.file_counts.total == len(file_ids)
+    # Invalid files should be marked as failed
+    assert batch.file_counts.failed >= 0  # Implementation may vary
+
+    # Determine expected errors based on client type
+    if isinstance(compat_client, LlamaStackAsLibraryClient):
+        errors = ValueError
+    else:
+        errors = (BadRequestError, OpenAIBadRequestError)
+
+    # Test retrieving non-existent batch
+    with pytest.raises(errors):  # Should raise an error for non-existent batch
+        compat_client.vector_stores.file_batches.retrieve(
+            vector_store_id=vector_store.id,
+            batch_id="non_existent_batch_id",
+        )
+
+    # Test operations on non-existent vector store
+    with pytest.raises(errors):  # Should raise an error for non-existent vector store
+        compat_client.vector_stores.file_batches.create(
+            vector_store_id="non_existent_vector_store",
+            file_ids=["any_file_id"],
+        )

tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py

@@ -6,16 +6,22 @@
 
 import json
 import time
-from unittest.mock import AsyncMock
+from unittest.mock import AsyncMock, patch
 
 import numpy as np
 import pytest
 
+from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
+from llama_stack.apis.vector_io import (
+    Chunk,
+    QueryChunksResponse,
+    VectorStoreChunkingStrategyAuto,
+    VectorStoreFileObject,
+)
 from llama_stack.providers.remote.vector_io.milvus.milvus import VECTOR_DBS_PREFIX
 
-# This test is a unit test for the inline VectoerIO providers. This should only contain
+# This test is a unit test for the inline VectorIO providers. This should only contain
 # tests which are specific to this class. More general (API-level) tests should be placed in
 # tests/integration/vector_io/
 #
@@ -25,6 +31,24 @@ from llama_stack.providers.remote.vector_io.milvus.milvus import VECTOR_DBS_PREF
 # -v -s --tb=short --disable-warnings --asyncio-mode=auto
 
 
+@pytest.fixture(autouse=True)
+def mock_resume_file_batches(request):
+    """Mock the resume functionality to prevent stale file batches from being processed during tests."""
+    # Skip mocking for tests that specifically test the resume functionality
+    if any(
+        test_name in request.node.name
+        for test_name in ["test_only_in_progress_batches_resumed", "test_file_batch_persistence_across_restarts"]
+    ):
+        yield
+        return
+
+    with patch(
+        "llama_stack.providers.utils.memory.openai_vector_store_mixin.OpenAIVectorStoreMixin._resume_incomplete_batches",
+        new_callable=AsyncMock,
+    ):
+        yield
+
+
 async def test_initialize_index(vector_index):
     await vector_index.initialize()
 
@@ -294,3 +318,673 @@ async def test_delete_openai_vector_store_file_from_storage(vector_io_adapter, t
     assert loaded_file_info == {}
     loaded_contents = await vector_io_adapter._load_openai_vector_store_file_contents(store_id, file_id)
     assert loaded_contents == []
+
+
+async def test_create_vector_store_file_batch(vector_io_adapter):
+    """Test creating a file batch."""
+    store_id = "vs_1234"
+    file_ids = ["file_1", "file_2", "file_3"]
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    # Mock attach method and batch processing to avoid actual processing
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+    vector_io_adapter._process_file_batch_async = AsyncMock()
+
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+
+    assert batch.vector_store_id == store_id
+    assert batch.status == "in_progress"
+    assert batch.file_counts.total == len(file_ids)
+    assert batch.file_counts.in_progress == len(file_ids)
+    assert batch.id in vector_io_adapter.openai_file_batches
+
+
+async def test_retrieve_vector_store_file_batch(vector_io_adapter):
+    """Test retrieving a file batch."""
+    store_id = "vs_1234"
+    file_ids = ["file_1", "file_2"]
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+
+    # Create batch first
+    created_batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+
+    # Retrieve batch
+    retrieved_batch = await vector_io_adapter.openai_retrieve_vector_store_file_batch(
+        batch_id=created_batch.id,
+        vector_store_id=store_id,
+    )
+
+    assert retrieved_batch.id == created_batch.id
+    assert retrieved_batch.vector_store_id == store_id
+    assert retrieved_batch.status == "in_progress"
+
+
+async def test_cancel_vector_store_file_batch(vector_io_adapter):
+    """Test cancelling a file batch."""
+    store_id = "vs_1234"
+    file_ids = ["file_1"]
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    # Mock both file attachment and batch processing to prevent automatic completion
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+    vector_io_adapter._process_file_batch_async = AsyncMock()
+
+    # Create batch
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+
+    # Cancel batch
+    cancelled_batch = await vector_io_adapter.openai_cancel_vector_store_file_batch(
+        batch_id=batch.id,
+        vector_store_id=store_id,
+    )
+
+    assert cancelled_batch.status == "cancelled"
+
+
+async def test_list_files_in_vector_store_file_batch(vector_io_adapter):
+    """Test listing files in a batch."""
+    store_id = "vs_1234"
+    file_ids = ["file_1", "file_2"]
+
+    # Setup vector store with files
+    files = {}
+    for i, file_id in enumerate(file_ids):
+        files[file_id] = VectorStoreFileObject(
+            id=file_id,
+            object="vector_store.file",
+            usage_bytes=1000,
+            created_at=int(time.time()) + i,
+            vector_store_id=store_id,
+            status="completed",
+            chunking_strategy=VectorStoreChunkingStrategyAuto(),
+        )
+
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": files,
+        "file_ids": file_ids,
+    }
+
+    # Mock file loading
+    vector_io_adapter._load_openai_vector_store_file = AsyncMock(
+        side_effect=lambda vs_id, f_id: files[f_id].model_dump()
+    )
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+
+    # Create batch
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+
+    # List files
+    response = await vector_io_adapter.openai_list_files_in_vector_store_file_batch(
+        batch_id=batch.id,
+        vector_store_id=store_id,
+    )
+
+    assert len(response.data) == len(file_ids)
+    assert response.first_id is not None
+    assert response.last_id is not None
+
+
+async def test_file_batch_validation_errors(vector_io_adapter):
+    """Test file batch validation errors."""
+    # Test nonexistent vector store
+    with pytest.raises(VectorStoreNotFoundError):
+        await vector_io_adapter.openai_create_vector_store_file_batch(
+            vector_store_id="nonexistent",
+            file_ids=["file_1"],
+        )
+
+    # Setup store for remaining tests
+    store_id = "vs_test"
+    vector_io_adapter.openai_vector_stores[store_id] = {"id": store_id, "files": {}, "file_ids": []}
+
+    # Test nonexistent batch
+    with pytest.raises(ValueError, match="File batch .* not found"):
+        await vector_io_adapter.openai_retrieve_vector_store_file_batch(
+            batch_id="nonexistent_batch",
+            vector_store_id=store_id,
+        )
+
+    # Test wrong vector store for batch
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=["file_1"],
+    )
+
+    # Create wrong_store so it exists but the batch doesn't belong to it
+    wrong_store_id = "wrong_store"
+    vector_io_adapter.openai_vector_stores[wrong_store_id] = {"id": wrong_store_id, "files": {}, "file_ids": []}
+
+    with pytest.raises(ValueError, match="does not belong to vector store"):
+        await vector_io_adapter.openai_retrieve_vector_store_file_batch(
+            batch_id=batch.id,
+            vector_store_id=wrong_store_id,
+        )
+
+
+async def test_file_batch_pagination(vector_io_adapter):
+    """Test file batch pagination."""
+    store_id = "vs_1234"
+    file_ids = ["file_1", "file_2", "file_3", "file_4", "file_5"]
+
+    # Setup vector store with multiple files
+    files = {}
+    for i, file_id in enumerate(file_ids):
+        files[file_id] = VectorStoreFileObject(
+            id=file_id,
+            object="vector_store.file",
+            usage_bytes=1000,
+            created_at=int(time.time()) + i,
+            vector_store_id=store_id,
+            status="completed",
+            chunking_strategy=VectorStoreChunkingStrategyAuto(),
+        )
+
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": files,
+        "file_ids": file_ids,
+    }
+
+    # Mock file loading
+    vector_io_adapter._load_openai_vector_store_file = AsyncMock(
+        side_effect=lambda vs_id, f_id: files[f_id].model_dump()
+    )
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+
+    # Create batch
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+
+    # Test pagination with limit
+    response = await vector_io_adapter.openai_list_files_in_vector_store_file_batch(
+        batch_id=batch.id,
+        vector_store_id=store_id,
+        limit=3,
+    )
+
+    assert len(response.data) == 3
+    assert response.has_more is True
+
+    # Test pagination with after cursor
+    first_page = await vector_io_adapter.openai_list_files_in_vector_store_file_batch(
+        batch_id=batch.id,
+        vector_store_id=store_id,
+        limit=2,
+    )
+
+    second_page = await vector_io_adapter.openai_list_files_in_vector_store_file_batch(
+        batch_id=batch.id,
+        vector_store_id=store_id,
+        limit=2,
+        after=first_page.last_id,
+    )
+
+    assert len(first_page.data) == 2
+    assert len(second_page.data) == 2
+    # Ensure no overlap between pages
+    first_page_ids = {file_obj.id for file_obj in first_page.data}
+    second_page_ids = {file_obj.id for file_obj in second_page.data}
+    assert first_page_ids.isdisjoint(second_page_ids)
+    # Verify we got all expected files across both pages (in desc order: file_5, file_4, file_3, file_2, file_1)
+    all_returned_ids = first_page_ids | second_page_ids
+    assert all_returned_ids == {"file_2", "file_3", "file_4", "file_5"}
+
+
+async def test_file_batch_status_filtering(vector_io_adapter):
+    """Test file batch status filtering."""
+    store_id = "vs_1234"
+    file_ids = ["file_1", "file_2", "file_3"]
+
+    # Setup vector store with files having different statuses
+    files = {}
+    statuses = ["completed", "in_progress", "completed"]
+    for i, (file_id, status) in enumerate(zip(file_ids, statuses, strict=False)):
+        files[file_id] = VectorStoreFileObject(
+            id=file_id,
+            object="vector_store.file",
+            usage_bytes=1000,
+            created_at=int(time.time()) + i,
+            vector_store_id=store_id,
+            status=status,
+            chunking_strategy=VectorStoreChunkingStrategyAuto(),
+        )
+
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": files,
+        "file_ids": file_ids,
+    }
+
+    # Mock file loading
+    vector_io_adapter._load_openai_vector_store_file = AsyncMock(
+        side_effect=lambda vs_id, f_id: files[f_id].model_dump()
+    )
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+
+    # Create batch
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+
+    # Test filtering by completed status
+    response = await vector_io_adapter.openai_list_files_in_vector_store_file_batch(
+        batch_id=batch.id,
+        vector_store_id=store_id,
+        filter="completed",
+    )
+
+    assert len(response.data) == 2  # Only 2 completed files
+    for file_obj in response.data:
+        assert file_obj.status == "completed"
+
+    # Test filtering by in_progress status
+    response = await vector_io_adapter.openai_list_files_in_vector_store_file_batch(
+        batch_id=batch.id,
+        vector_store_id=store_id,
+        filter="in_progress",
+    )
+
+    assert len(response.data) == 1  # Only 1 in_progress file
+    assert response.data[0].status == "in_progress"
+
+
+async def test_cancel_completed_batch_fails(vector_io_adapter):
+    """Test that cancelling completed batch fails."""
+    store_id = "vs_1234"
+    file_ids = ["file_1"]
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+
+    # Create batch
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+
+    # Manually update status to completed
+    batch_info = vector_io_adapter.openai_file_batches[batch.id]
+    batch_info["status"] = "completed"
+
+    # Try to cancel - should fail
+    with pytest.raises(ValueError, match="Cannot cancel batch .* with status completed"):
+        await vector_io_adapter.openai_cancel_vector_store_file_batch(
+            batch_id=batch.id,
+            vector_store_id=store_id,
+        )
+
+
+async def test_file_batch_persistence_across_restarts(vector_io_adapter):
+    """Test that in-progress file batches are persisted and resumed after restart."""
+    store_id = "vs_1234"
+    file_ids = ["file_1", "file_2"]
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    # Mock attach method and batch processing to avoid actual processing
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+    vector_io_adapter._process_file_batch_async = AsyncMock()
+
+    # Create batch
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+    batch_id = batch.id
+
+    # Verify batch is saved to persistent storage
+    assert batch_id in vector_io_adapter.openai_file_batches
+    saved_batch_key = f"openai_vector_stores_file_batches:v3::{batch_id}"
+    saved_batch = await vector_io_adapter.kvstore.get(saved_batch_key)
+    assert saved_batch is not None
+
+    # Verify the saved batch data contains all necessary information
+    saved_data = json.loads(saved_batch)
+    assert saved_data["id"] == batch_id
+    assert saved_data["status"] == "in_progress"
+    assert saved_data["file_ids"] == file_ids
+
+    # Simulate restart - clear in-memory cache and reload
+    vector_io_adapter.openai_file_batches.clear()
+
+    # Temporarily restore the real initialize_openai_vector_stores method
+    from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
+
+    real_method = OpenAIVectorStoreMixin.initialize_openai_vector_stores
+    await real_method(vector_io_adapter)
+
+    # Re-mock the processing method to prevent any resumed batches from processing
+    vector_io_adapter._process_file_batch_async = AsyncMock()
+
+    # Verify batch was restored
+    assert batch_id in vector_io_adapter.openai_file_batches
+    restored_batch = vector_io_adapter.openai_file_batches[batch_id]
+    assert restored_batch["status"] == "in_progress"
+    assert restored_batch["id"] == batch_id
+    assert vector_io_adapter.openai_file_batches[batch_id]["file_ids"] == file_ids
+
+
+async def test_cancelled_batch_persists_in_storage(vector_io_adapter):
+    """Test that cancelled batches persist in storage with updated status."""
+    store_id = "vs_1234"
+    file_ids = ["file_1", "file_2"]
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    # Mock attach method and batch processing to avoid actual processing
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+    vector_io_adapter._process_file_batch_async = AsyncMock()
+
+    # Create batch
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+    batch_id = batch.id
+
+    # Verify batch is initially saved to persistent storage
+    saved_batch_key = f"openai_vector_stores_file_batches:v3::{batch_id}"
+    saved_batch = await vector_io_adapter.kvstore.get(saved_batch_key)
+    assert saved_batch is not None
+
+    # Cancel the batch
+    cancelled_batch = await vector_io_adapter.openai_cancel_vector_store_file_batch(
+        batch_id=batch_id,
+        vector_store_id=store_id,
+    )
+
+    # Verify batch status is cancelled
+    assert cancelled_batch.status == "cancelled"
+
+    # Verify batch persists in storage with cancelled status
+    updated_batch = await vector_io_adapter.kvstore.get(saved_batch_key)
+    assert updated_batch is not None
+    batch_data = json.loads(updated_batch)
+    assert batch_data["status"] == "cancelled"
+
+    # Batch should remain in memory cache (matches vector store pattern)
+    assert batch_id in vector_io_adapter.openai_file_batches
+    assert vector_io_adapter.openai_file_batches[batch_id]["status"] == "cancelled"
+
+
+async def test_only_in_progress_batches_resumed(vector_io_adapter):
+    """Test that only in-progress batches are resumed for processing, but all batches are persisted."""
+    store_id = "vs_1234"
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    # Mock attach method and batch processing to prevent automatic completion
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+    vector_io_adapter._process_file_batch_async = AsyncMock()
+
+    # Create multiple batches
+    batch1 = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id, file_ids=["file_1"]
+    )
+    batch2 = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id, file_ids=["file_2"]
+    )
+
+    # Complete one batch (should persist with completed status)
+    batch1_info = vector_io_adapter.openai_file_batches[batch1.id]
+    batch1_info["status"] = "completed"
+    await vector_io_adapter._save_openai_vector_store_file_batch(batch1.id, batch1_info)
+
+    # Cancel the other batch (should persist with cancelled status)
+    await vector_io_adapter.openai_cancel_vector_store_file_batch(batch_id=batch2.id, vector_store_id=store_id)
+
+    # Create a third batch that stays in progress
+    batch3 = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id, file_ids=["file_3"]
+    )
+
+    # Simulate restart - first clear memory, then reload from persistence
+    vector_io_adapter.openai_file_batches.clear()
+
+    # Mock the processing method BEFORE calling initialize to capture the resume calls
+    mock_process = AsyncMock()
+    vector_io_adapter._process_file_batch_async = mock_process
+
+    # Temporarily restore the real initialize_openai_vector_stores method
+    from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
+
+    real_method = OpenAIVectorStoreMixin.initialize_openai_vector_stores
+    await real_method(vector_io_adapter)
+
+    # All batches should be restored from persistence
+    assert batch1.id in vector_io_adapter.openai_file_batches  # completed, persisted
+    assert batch2.id in vector_io_adapter.openai_file_batches  # cancelled, persisted
+    assert batch3.id in vector_io_adapter.openai_file_batches  # in-progress, restored
+
+    # Check their statuses
+    assert vector_io_adapter.openai_file_batches[batch1.id]["status"] == "completed"
+    assert vector_io_adapter.openai_file_batches[batch2.id]["status"] == "cancelled"
+    assert vector_io_adapter.openai_file_batches[batch3.id]["status"] == "in_progress"
+
+    # But only in-progress batches should have processing resumed (check mock was called)
+    mock_process.assert_called()
+
+
+async def test_cleanup_expired_file_batches(vector_io_adapter):
+    """Test that expired file batches are cleaned up properly."""
+    store_id = "vs_1234"
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    # Mock processing to prevent automatic completion
+    vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
+    vector_io_adapter._process_file_batch_async = AsyncMock()
+
+    # Create batches with different ages
+    import time
+
+    current_time = int(time.time())
+
+    # Create an old expired batch (10 days old)
+    old_batch_info = {
+        "id": "batch_old",
+        "vector_store_id": store_id,
+        "status": "completed",
+        "created_at": current_time - (10 * 24 * 60 * 60),  # 10 days ago
+        "expires_at": current_time - (3 * 24 * 60 * 60),  # Expired 3 days ago
+        "file_ids": ["file_1"],
+    }
+
+    # Create a recent valid batch
+    new_batch_info = {
+        "id": "batch_new",
+        "vector_store_id": store_id,
+        "status": "completed",
+        "created_at": current_time - (1 * 24 * 60 * 60),  # 1 day ago
+        "expires_at": current_time + (6 * 24 * 60 * 60),  # Expires in 6 days
+        "file_ids": ["file_2"],
+    }
+
+    # Store both batches in persistent storage
+    await vector_io_adapter._save_openai_vector_store_file_batch("batch_old", old_batch_info)
+    await vector_io_adapter._save_openai_vector_store_file_batch("batch_new", new_batch_info)
+
+    # Add to in-memory cache
+    vector_io_adapter.openai_file_batches["batch_old"] = old_batch_info
+    vector_io_adapter.openai_file_batches["batch_new"] = new_batch_info
+
+    # Verify both batches exist before cleanup
+    assert "batch_old" in vector_io_adapter.openai_file_batches
+    assert "batch_new" in vector_io_adapter.openai_file_batches
+
+    # Run cleanup
+    await vector_io_adapter._cleanup_expired_file_batches()
+
+    # Verify expired batch was removed from memory
+    assert "batch_old" not in vector_io_adapter.openai_file_batches
+    assert "batch_new" in vector_io_adapter.openai_file_batches
+
+    # Verify expired batch was removed from storage
+    old_batch_key = "openai_vector_stores_file_batches:v3::batch_old"
+    new_batch_key = "openai_vector_stores_file_batches:v3::batch_new"
+
+    old_stored = await vector_io_adapter.kvstore.get(old_batch_key)
+    new_stored = await vector_io_adapter.kvstore.get(new_batch_key)
+
+    assert old_stored is None  # Expired batch should be deleted
+    assert new_stored is not None  # Valid batch should remain
+
+
+async def test_expired_batch_access_error(vector_io_adapter):
+    """Test that accessing expired batches returns clear error message."""
+    store_id = "vs_1234"
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    # Create an expired batch
+    import time
+
+    current_time = int(time.time())
+
+    expired_batch_info = {
+        "id": "batch_expired",
+        "vector_store_id": store_id,
+        "status": "completed",
+        "created_at": current_time - (10 * 24 * 60 * 60),  # 10 days ago
+        "expires_at": current_time - (3 * 24 * 60 * 60),  # Expired 3 days ago
+        "file_ids": ["file_1"],
+    }
+
+    # Add to in-memory cache (simulating it was loaded before expiration)
+    vector_io_adapter.openai_file_batches["batch_expired"] = expired_batch_info
+
+    # Try to access expired batch
+    with pytest.raises(ValueError, match="File batch batch_expired has expired after 7 days from creation"):
+        vector_io_adapter._get_and_validate_batch("batch_expired", store_id)
+
+
+async def test_max_concurrent_files_per_batch(vector_io_adapter):
+    """Test that file batch processing respects MAX_CONCURRENT_FILES_PER_BATCH limit."""
+    import asyncio
+
+    store_id = "vs_1234"
+
+    # Setup vector store
+    vector_io_adapter.openai_vector_stores[store_id] = {
+        "id": store_id,
+        "name": "Test Store",
+        "files": {},
+        "file_ids": [],
+    }
+
+    active_files = 0
+
+    async def mock_attach_file_with_delay(vector_store_id: str, file_id: str, **kwargs):
+        """Mock that tracks concurrency and blocks indefinitely to test concurrency limit."""
+        nonlocal active_files
+        active_files += 1
+
+        # Block indefinitely to test concurrency limit
+        await asyncio.sleep(float("inf"))
+
+    # Replace the attachment method
+    vector_io_adapter.openai_attach_file_to_vector_store = mock_attach_file_with_delay
+
+    # Create a batch with more files than the concurrency limit
+    file_ids = [f"file_{i}" for i in range(8)]  # 8 files, but limit should be 5
+
+    batch = await vector_io_adapter.openai_create_vector_store_file_batch(
+        vector_store_id=store_id,
+        file_ids=file_ids,
+    )
+
+    # Give time for the semaphore logic to start processing files
+    await asyncio.sleep(0.2)
+
+    # Verify that only MAX_CONCURRENT_FILES_PER_BATCH files are processing concurrently
+    # The semaphore in _process_files_with_concurrency should limit this
+    from llama_stack.providers.utils.memory.openai_vector_store_mixin import MAX_CONCURRENT_FILES_PER_BATCH
+
+    assert active_files == MAX_CONCURRENT_FILES_PER_BATCH, (
+        f"Expected {MAX_CONCURRENT_FILES_PER_BATCH} active files, got {active_files}"
+    )
+
+    # Verify batch is in progress
+    assert batch.status == "in_progress"
+    assert batch.file_counts.total == 8
+    assert batch.file_counts.in_progress == 8