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feat(api): Add vector store file batches api (#3642)
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# 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
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"object": "embedding"
}
],
"model": "all-minilm:l6-v2",
"object": "list",
"usage": {
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"total_tokens": 10
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}
},
"is_streaming": false
}
}

20
tests/integration/vector_io/recordings/ffea5475c2625b87e302ec419cc536f34da3ce7e80eba86bec16d231aa347d00.json Normal file
View file

@ -0,0 +1,20 @@
{
"test_id": "tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_file_batch_create_and_retrieve[client_with_models-ollama/llama3.2:3b-instruct-fp16-None-ollama/all-minilm:l6-v2-None-384]",
"request": {
"method": "POST",
"url": "http://localhost:11434/api/ps",
"headers": {},
"body": {},
"endpoint": "/api/ps",
"model": ""
},
"response": {
"body": {
"__type__": "ollama._types.ProcessResponse",
"__data__": {
"models": []
}
},
"is_streaming": false
}
}

287
tests/integration/vector_io/test_openai_vector_stores.py
View file

@ -902,3 +902,290 @@ 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(2):
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 = 60 # 60 seconds max wait (increased for file processing delays)
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(2):
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 = 60 # 60 seconds max wait (increased for file processing delays)
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) == 2
assert limited_response.has_more is False
# 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 a batch to test cancellation
file_ids = []
for i in range(2): # Batch size that allows time for cancellation
with BytesIO(f"This is batch cancel test file {i} with substantial content".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:
# Cancel the batch immediately after creation
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:
# If cancellation fails (e.g., batch completed too quickly),
# verify the batch reached completion instead
final_batch = compat_client.vector_stores.file_batches.retrieve(
vector_store_id=vector_store.id,
batch_id=batch.id,
)
assert final_batch.status in ["completed", "cancelled"]
def test_openai_vector_store_file_batch_retrieve_contents(compat_client_with_empty_stores, client_with_models):
"""Test retrieving file contents after file batch processing."""
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_contents_test_store")
# Create multiple files with known content
file_data = [
("test_file_1.txt", b"This is the content of test file 1"),
("test_file_2.txt", b"This is the content of test file 2"),
]
file_ids = []
for filename, content in file_data:
with BytesIO(content) as file_buffer:
file_buffer.name = filename
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 = 60 # 60 seconds max wait (increased for file processing delays)
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
assert retrieved_batch.status == "completed"
# Retrieve file contents for each file in the batch
for i, file_id in enumerate(file_ids):
file_contents = compat_client.vector_stores.files.content(
vector_store_id=vector_store.id,
file_id=file_id,
)
assert file_contents is not None
assert file_contents.filename == file_data[i][0]
assert len(file_contents.content) > 0
# Verify the content matches what we uploaded
content_text = (
file_contents.content[0].text
if hasattr(file_contents.content[0], "text")
else file_contents.content[0]["text"]
)
assert file_data[i][1].decode("utf-8") in content_text
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"],
)