mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-09 21:18:38 +00:00
clean up
This commit is contained in:
Swapna Lekkala
parent
852c058806
commit
ca02ce0a98
3 changed files with 41 additions and 205 deletions
|
|
@ -55,9 +55,7 @@ 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_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:{VERSION}::"
|
||||
|
||||
|
||||
class OpenAIVectorStoreMixin(ABC):
|
||||
|
|
@ -74,9 +72,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
# KV store for persisting OpenAI vector store metadata
|
||||
kvstore: KVStore | None
|
||||
|
||||
async def _save_openai_vector_store(
|
||||
self, store_id: str, store_info: dict[str, Any]
|
||||
) -> None:
|
||||
async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
|
||||
"""Save vector store metadata to persistent storage."""
|
||||
assert self.kvstore
|
||||
key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
|
||||
|
|
@ -97,9 +93,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
stores[info["id"]] = info
|
||||
return stores
|
||||
|
||||
async def _update_openai_vector_store(
|
||||
self, store_id: str, store_info: dict[str, Any]
|
||||
) -> None:
|
||||
async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
|
||||
"""Update vector store metadata in persistent storage."""
|
||||
assert self.kvstore
|
||||
key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
|
||||
|
|
@ -126,26 +120,18 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
assert self.kvstore
|
||||
meta_key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.set(key=meta_key, value=json.dumps(file_info))
|
||||
contents_prefix = (
|
||||
f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}:"
|
||||
)
|
||||
contents_prefix = f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}:"
|
||||
for idx, chunk in enumerate(file_contents):
|
||||
await self.kvstore.set(
|
||||
key=f"{contents_prefix}{idx}", value=json.dumps(chunk)
|
||||
)
|
||||
await self.kvstore.set(key=f"{contents_prefix}{idx}", value=json.dumps(chunk))
|
||||
|
||||
async def _load_openai_vector_store_file(
|
||||
self, store_id: str, file_id: str
|
||||
) -> dict[str, Any]:
|
||||
async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]:
|
||||
"""Load vector store file metadata from persistent storage."""
|
||||
assert self.kvstore
|
||||
key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
stored_data = await self.kvstore.get(key)
|
||||
return json.loads(stored_data) if stored_data else {}
|
||||
|
||||
async def _load_openai_vector_store_file_contents(
|
||||
self, store_id: str, file_id: str
|
||||
) -> list[dict[str, Any]]:
|
||||
async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]:
|
||||
"""Load vector store file contents from persistent storage."""
|
||||
assert self.kvstore
|
||||
prefix = f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}:"
|
||||
|
|
@ -153,26 +139,20 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
raw_items = await self.kvstore.values_in_range(prefix, end_key)
|
||||
return [json.loads(item) for item in raw_items]
|
||||
|
||||
async def _update_openai_vector_store_file(
|
||||
self, store_id: str, file_id: str, file_info: dict[str, Any]
|
||||
) -> None:
|
||||
async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None:
|
||||
"""Update vector store file metadata in persistent storage."""
|
||||
assert self.kvstore
|
||||
key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.set(key=key, value=json.dumps(file_info))
|
||||
|
||||
async def _delete_openai_vector_store_file_from_storage(
|
||||
self, store_id: str, file_id: str
|
||||
) -> None:
|
||||
async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
|
||||
"""Delete vector store file metadata from persistent storage."""
|
||||
assert self.kvstore
|
||||
|
||||
meta_key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.delete(meta_key)
|
||||
|
||||
contents_prefix = (
|
||||
f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}:"
|
||||
)
|
||||
contents_prefix = f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}:"
|
||||
end_key = f"{contents_prefix}\xff"
|
||||
# load all stored chunk values (values_in_range is implemented by all backends)
|
||||
raw_items = await self.kvstore.values_in_range(contents_prefix, end_key)
|
||||
|
|
@ -185,9 +165,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
self.openai_vector_stores = await self._load_openai_vector_stores()
|
||||
|
||||
@abstractmethod
|
||||
async def delete_chunks(
|
||||
self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]
|
||||
) -> None:
|
||||
async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
|
||||
"""Delete chunks from a vector store."""
|
||||
pass
|
||||
|
||||
|
|
@ -298,10 +276,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
# Now that our vector store is created, attach any files that were provided
|
||||
file_ids = file_ids or []
|
||||
tasks = [
|
||||
self.openai_attach_file_to_vector_store(vector_db_id, file_id)
|
||||
for file_id in file_ids
|
||||
]
|
||||
tasks = [self.openai_attach_file_to_vector_store(vector_db_id, file_id) for file_id in file_ids]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
# Get the updated store info and return it
|
||||
|
|
@ -328,9 +303,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
# Apply cursor-based pagination
|
||||
if after:
|
||||
after_index = next(
|
||||
(i for i, store in enumerate(all_stores) if store["id"] == after), -1
|
||||
)
|
||||
after_index = next((i for i, store in enumerate(all_stores) if store["id"] == after), -1)
|
||||
if after_index >= 0:
|
||||
all_stores = all_stores[after_index + 1 :]
|
||||
|
||||
|
|
@ -419,9 +392,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
try:
|
||||
await self.unregister_vector_db(vector_store_id)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Failed to delete underlying vector DB {vector_store_id}: {e}"
|
||||
)
|
||||
logger.warning(f"Failed to delete underlying vector DB {vector_store_id}: {e}")
|
||||
|
||||
return VectorStoreDeleteResponse(
|
||||
id=vector_store_id,
|
||||
|
|
@ -446,9 +417,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
# Validate search_mode
|
||||
valid_modes = {"keyword", "vector", "hybrid"}
|
||||
if search_mode not in valid_modes:
|
||||
raise ValueError(
|
||||
f"search_mode must be one of {valid_modes}, got {search_mode}"
|
||||
)
|
||||
raise ValueError(f"search_mode must be one of {valid_modes}, got {search_mode}")
|
||||
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise VectorStoreNotFoundError(vector_store_id)
|
||||
|
|
@ -516,9 +485,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
next_page=None,
|
||||
)
|
||||
|
||||
def _matches_filters(
|
||||
self, metadata: dict[str, Any], filters: dict[str, Any]
|
||||
) -> bool:
|
||||
def _matches_filters(self, metadata: dict[str, Any], filters: dict[str, Any]) -> bool:
|
||||
"""Check if metadata matches the provided filters."""
|
||||
if not filters:
|
||||
return True
|
||||
|
|
@ -638,9 +605,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
try:
|
||||
file_response = await self.files_api.openai_retrieve_file(file_id)
|
||||
mime_type, _ = mimetypes.guess_type(file_response.filename)
|
||||
content_response = await self.files_api.openai_retrieve_file_content(
|
||||
file_id
|
||||
)
|
||||
content_response = await self.files_api.openai_retrieve_file_content(file_id)
|
||||
|
||||
content = content_from_data_and_mime_type(content_response.body, mime_type)
|
||||
|
||||
|
|
@ -679,9 +644,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
# Save vector store file to persistent storage (provider-specific)
|
||||
dict_chunks = [c.model_dump() for c in chunks]
|
||||
# This should be updated to include chunk_id
|
||||
await self._save_openai_vector_store_file(
|
||||
vector_store_id, file_id, file_info, dict_chunks
|
||||
)
|
||||
await self._save_openai_vector_store_file(vector_store_id, file_id, file_info, dict_chunks)
|
||||
|
||||
# Update file_ids and file_counts in vector store metadata
|
||||
store_info = self.openai_vector_stores[vector_store_id].copy()
|
||||
|
|
@ -717,9 +680,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
file_objects: list[VectorStoreFileObject] = []
|
||||
for file_id in store_info["file_ids"]:
|
||||
file_info = await self._load_openai_vector_store_file(
|
||||
vector_store_id, file_id
|
||||
)
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
file_object = VectorStoreFileObject(**file_info)
|
||||
if filter and file_object.status != filter:
|
||||
continue
|
||||
|
|
@ -731,9 +692,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
# Apply cursor-based pagination
|
||||
if after:
|
||||
after_index = next(
|
||||
(i for i, file in enumerate(file_objects) if file.id == after), -1
|
||||
)
|
||||
after_index = next((i for i, file in enumerate(file_objects) if file.id == after), -1)
|
||||
if after_index >= 0:
|
||||
file_objects = file_objects[after_index + 1 :]
|
||||
|
||||
|
|
@ -770,9 +729,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
store_info = self.openai_vector_stores[vector_store_id]
|
||||
if file_id not in store_info["file_ids"]:
|
||||
raise ValueError(
|
||||
f"File {file_id} not found in vector store {vector_store_id}"
|
||||
)
|
||||
raise ValueError(f"File {file_id} not found in vector store {vector_store_id}")
|
||||
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
return VectorStoreFileObject(**file_info)
|
||||
|
|
@ -787,9 +744,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
raise VectorStoreNotFoundError(vector_store_id)
|
||||
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
dict_chunks = await self._load_openai_vector_store_file_contents(
|
||||
vector_store_id, file_id
|
||||
)
|
||||
dict_chunks = await self._load_openai_vector_store_file_contents(vector_store_id, file_id)
|
||||
chunks = [Chunk.model_validate(c) for c in dict_chunks]
|
||||
content = []
|
||||
for chunk in chunks:
|
||||
|
|
@ -813,9 +768,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
store_info = self.openai_vector_stores[vector_store_id]
|
||||
if file_id not in store_info["file_ids"]:
|
||||
raise ValueError(
|
||||
f"File {file_id} not found in vector store {vector_store_id}"
|
||||
)
|
||||
raise ValueError(f"File {file_id} not found in vector store {vector_store_id}")
|
||||
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
file_info["attributes"] = attributes
|
||||
|
|
@ -831,9 +784,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise VectorStoreNotFoundError(vector_store_id)
|
||||
|
||||
dict_chunks = await self._load_openai_vector_store_file_contents(
|
||||
vector_store_id, file_id
|
||||
)
|
||||
dict_chunks = await self._load_openai_vector_store_file_contents(vector_store_id, file_id)
|
||||
chunks = [Chunk.model_validate(c) for c in dict_chunks]
|
||||
|
||||
# Create ChunkForDeletion objects with both chunk_id and document_id
|
||||
|
|
@ -844,15 +795,9 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
c.chunk_metadata.document_id if c.chunk_metadata else None
|
||||
)
|
||||
if document_id:
|
||||
chunks_for_deletion.append(
|
||||
ChunkForDeletion(
|
||||
chunk_id=str(c.chunk_id), document_id=document_id
|
||||
)
|
||||
)
|
||||
chunks_for_deletion.append(ChunkForDeletion(chunk_id=str(c.chunk_id), document_id=document_id))
|
||||
else:
|
||||
logger.warning(
|
||||
f"Chunk {c.chunk_id} has no document_id, skipping deletion"
|
||||
)
|
||||
logger.warning(f"Chunk {c.chunk_id} has no document_id, skipping deletion")
|
||||
|
||||
if chunks_for_deletion:
|
||||
await self.delete_chunks(vector_store_id, chunks_for_deletion)
|
||||
|
|
@ -860,9 +805,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
store_info = self.openai_vector_stores[vector_store_id].copy()
|
||||
|
||||
file = await self.openai_retrieve_vector_store_file(vector_store_id, file_id)
|
||||
await self._delete_openai_vector_store_file_from_storage(
|
||||
vector_store_id, file_id
|
||||
)
|
||||
await self._delete_openai_vector_store_file_from_storage(vector_store_id, file_id)
|
||||
|
||||
# Update in-memory cache
|
||||
store_info["file_ids"].remove(file_id)
|
||||
|
|
@ -919,11 +862,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
}
|
||||
|
||||
# Start background processing of files
|
||||
asyncio.create_task(
|
||||
self._process_file_batch_async(
|
||||
batch_id, file_ids, attributes, chunking_strategy
|
||||
)
|
||||
)
|
||||
asyncio.create_task(self._process_file_batch_async(batch_id, file_ids, attributes, chunking_strategy))
|
||||
|
||||
return batch_object
|
||||
|
||||
|
|
@ -954,9 +893,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
batch_object.file_counts.in_progress -= 1
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Failed to process file {file_id} in batch {batch_id}: {e}"
|
||||
)
|
||||
logger.error(f"Failed to process file {file_id} in batch {batch_id}: {e}")
|
||||
batch_object.file_counts.failed += 1
|
||||
batch_object.file_counts.in_progress -= 1
|
||||
|
||||
|
|
@ -968,9 +905,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
else:
|
||||
batch_object.status = "completed" # Partial success counts as completed
|
||||
|
||||
logger.info(
|
||||
f"File batch {batch_id} processing completed with status: {batch_object.status}"
|
||||
)
|
||||
logger.info(f"File batch {batch_id} processing completed with status: {batch_object.status}")
|
||||
|
||||
def _get_and_validate_batch(
|
||||
self, batch_id: str, vector_store_id: str
|
||||
|
|
@ -986,9 +921,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
batch_object = batch_info["batch_object"]
|
||||
|
||||
if batch_object.vector_store_id != vector_store_id:
|
||||
raise ValueError(
|
||||
f"File batch {batch_id} does not belong to vector store {vector_store_id}"
|
||||
)
|
||||
raise ValueError(f"File batch {batch_id} does not belong to vector store {vector_store_id}")
|
||||
|
||||
return batch_info, batch_object
|
||||
|
||||
|
|
@ -1056,9 +989,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
for file_id in batch_file_ids:
|
||||
try:
|
||||
file_info = await self._load_openai_vector_store_file(
|
||||
vector_store_id, file_id
|
||||
)
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
file_object = VectorStoreFileObject(**file_info)
|
||||
|
||||
# Apply status filter if provided
|
||||
|
|
@ -1067,9 +998,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
batch_file_objects.append(file_object)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Could not load file {file_id} from batch {batch_id}: {e}"
|
||||
)
|
||||
logger.warning(f"Could not load file {file_id} from batch {batch_id}: {e}")
|
||||
continue
|
||||
|
||||
# Sort by created_at
|
||||
|
|
@ -1077,9 +1006,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
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
|
||||
)
|
||||
paginated_files, has_more, first_id, last_id = self._paginate_objects(batch_file_objects, limit, after, before)
|
||||
|
||||
return VectorStoreFilesListInBatchResponse(
|
||||
data=paginated_files,
|
||||
|
|
@ -1094,15 +1021,11 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
vector_store_id: str,
|
||||
) -> VectorStoreFileBatchObject:
|
||||
"""Cancel a vector store file batch."""
|
||||
batch_info, batch_object = self._get_and_validate_batch(
|
||||
batch_id, vector_store_id
|
||||
)
|
||||
batch_info, batch_object = self._get_and_validate_batch(batch_id, vector_store_id)
|
||||
|
||||
# Only allow cancellation if batch is in progress
|
||||
if batch_object.status not in ["in_progress"]:
|
||||
raise ValueError(
|
||||
f"Cannot cancel batch {batch_id} with status {batch_object.status}"
|
||||
)
|
||||
raise ValueError(f"Cannot cancel batch {batch_id} with status {batch_object.status}")
|
||||
|
||||
# Create updated batch object with cancelled status
|
||||
updated_batch = VectorStoreFileBatchObject(
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue