add concurrent file attaching

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

@@ -207,6 +207,7 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
         self.kvstore: KVStore | None = None
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
         self._last_file_batch_cleanup_time = 0
 
     async def initialize(self) -> None:

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

@@ -416,6 +416,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
         self.cache: dict[str, VectorDBWithIndex] = {}
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
         self._last_file_batch_cleanup_time = 0
         self.kvstore: KVStore | None = None
 

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

@@ -167,6 +167,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
             self.client = chromadb.PersistentClient(path=self.config.db_path)
         self.openai_vector_stores = await self._load_openai_vector_stores()
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
         self._last_file_batch_cleanup_time = 0
 
     async def shutdown(self) -> None:

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

@@ -318,6 +318,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self.vector_db_store = None
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
         self._last_file_batch_cleanup_time = 0
         self.metadata_collection_name = "openai_vector_stores_metadata"
 

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

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import asyncio
 import heapq
 from typing import Any
 
@@ -354,6 +355,7 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
         self.vector_db_store = None
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
         self._last_file_batch_cleanup_time = 0
         self.metadata_collection_name = "openai_vector_stores_metadata"
 

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

@@ -171,6 +171,7 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self.kvstore: KVStore | None = None
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
         self._last_file_batch_cleanup_time = 0
         self._qdrant_lock = asyncio.Lock()
 

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

@@ -3,6 +3,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+import asyncio
 import json
 from typing import Any
 
@@ -293,6 +294,7 @@ class WeaviateVectorIOAdapter(
         self.vector_db_store = None
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
         self._last_file_batch_cleanup_time = 0
         self.metadata_collection_name = "openai_vector_stores_metadata"
 

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -53,6 +53,8 @@ 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}::"
@@ -77,6 +79,8 @@ class OpenAIVectorStoreMixin(ABC):
     kvstore: KVStore | None
     # Track last cleanup time to throttle cleanup operations
     _last_file_batch_cleanup_time: int
+    # Track running file batch processing tasks
+    _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."""
@@ -224,12 +228,14 @@ class OpenAIVectorStoreMixin(ABC):
             if batch_info["status"] == "in_progress":
                 logger.info(f"Resuming incomplete file batch: {batch_id}")
                 # Restart the background processing task
-                asyncio.create_task(self._process_file_batch_async(batch_id, batch_info))
+                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 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
 
@@ -935,7 +941,8 @@ class OpenAIVectorStoreMixin(ABC):
         await self._save_openai_vector_store_file_batch(batch_id, batch_info)
 
         # Start background processing of files
-        asyncio.create_task(self._process_file_batch_async(batch_id, batch_info))
+        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())
@@ -946,6 +953,72 @@ class OpenAIVectorStoreMixin(ABC):
 
         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,
@@ -956,40 +1029,34 @@ class OpenAIVectorStoreMixin(ABC):
         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)
 
-        for file_id in file_ids:
-            try:
-                chunking_strategy_adapter: TypeAdapter[VectorStoreChunkingStrategy] = TypeAdapter(
-                    VectorStoreChunkingStrategy
-                )
-                chunking_strategy_obj = chunking_strategy_adapter.validate_python(chunking_strategy)
-                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,
-                )
+        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 counts atomically
-                batch_info["file_counts"]["completed"] += 1
-                batch_info["file_counts"]["in_progress"] -= 1
+            # Update final batch status
+            self._update_batch_status(batch_info)
+            await self._save_openai_vector_store_file_batch(batch_id, batch_info)
 
-            except Exception as e:
-                logger.error(f"Failed to process file {file_id} in batch {batch_id}: {e}")
-                batch_info["file_counts"]["failed"] += 1
-                batch_info["file_counts"]["in_progress"] -= 1
+            logger.info(f"File batch {batch_id} processing completed with status: {batch_info['status']}")
 
-        # Update final status when all files are processed
-        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
-
-        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."""
@@ -1114,6 +1181,15 @@ class OpenAIVectorStoreMixin(ABC):
         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)

tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py

@@ -6,7 +6,7 @@
 
 import json
 import time
-from unittest.mock import AsyncMock
+from unittest.mock import AsyncMock, patch
 
 import numpy as np
 import pytest
@@ -31,6 +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()
 
@@ -918,3 +936,55 @@ async def test_expired_batch_access_error(vector_io_adapter):
     # 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