forked from phoenix-oss/llama-stack-mirror
3ae8585b65
See https://github.com/meta-llama/llama-stack/issues/827 for the broader design. This is the first part: - delete other kinds of memory banks (keyvalue, keyword, graph) for now; we will introduce a keyvalue store API as part of this design but not use it in the RAG tool yet. - renaming of the APIs
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
|
|
|
|
from llama_models.schema_utils import json_schema_type, webmethod
|
|
from pydantic import BaseModel, Field
|
|
|
|
from llama_stack.apis.inference import InterleavedContent
|
|
from llama_stack.apis.vector_dbs import VectorDB
|
|
from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
|
|
|
|
|
|
class Chunk(BaseModel):
|
|
content: InterleavedContent
|
|
metadata: Dict[str, Any] = Field(default_factory=dict)
|
|
|
|
|
|
@json_schema_type
|
|
class QueryChunksResponse(BaseModel):
|
|
chunks: List[Chunk]
|
|
scores: List[float]
|
|
|
|
|
|
class VectorDBStore(Protocol):
|
|
def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]: ...
|
|
|
|
|
|
@runtime_checkable
|
|
@trace_protocol
|
|
class VectorIO(Protocol):
|
|
vector_db_store: VectorDBStore
|
|
|
|
# this will just block now until documents are inserted, but it should
|
|
# probably return a Job instance which can be polled for completion
|
|
@webmethod(route="/vector-io/insert", method="POST")
|
|
async def insert_chunks(
|
|
self,
|
|
vector_db_id: str,
|
|
chunks: List[Chunk],
|
|
ttl_seconds: Optional[int] = None,
|
|
) -> None: ...
|
|
|
|
@webmethod(route="/vector-io/query", method="POST")
|
|
async def query_chunks(
|
|
self,
|
|
vector_db_id: str,
|
|
query: InterleavedContent,
|
|
params: Optional[Dict[str, Any]] = None,
|
|
) -> QueryChunksResponse: ...
|