forked from phoenix-oss/llama-stack-mirror
a63a43c646
Making a few small naming changes as per feedback: - RAGToolRuntime methods are called `insert` and `query` to keep them more general - The tool names are changed to non-namespaced forms `insert_into_memory` and `query_from_memory` - The REST endpoints are more REST-ful
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
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from llama_models.schema_utils import json_schema_type, webmethod
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from pydantic import BaseModel, Field
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from llama_stack.apis.inference import InterleavedContent
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
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class Chunk(BaseModel):
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content: InterleavedContent
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metadata: Dict[str, Any] = Field(default_factory=dict)
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@json_schema_type
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class QueryChunksResponse(BaseModel):
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chunks: List[Chunk]
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scores: List[float]
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class VectorDBStore(Protocol):
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def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]: ...
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@runtime_checkable
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@trace_protocol
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class VectorIO(Protocol):
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vector_db_store: VectorDBStore
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# this will just block now until chunks are inserted, but it should
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# probably return a Job instance which can be polled for completion
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@webmethod(route="/vector-io/insert", method="POST")
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async def insert_chunks(
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self,
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vector_db_id: str,
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chunks: List[Chunk],
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ttl_seconds: Optional[int] = None,
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) -> None: ...
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@webmethod(route="/vector-io/query", method="POST")
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async def query_chunks(
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self,
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vector_db_id: str,
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query: InterleavedContent,
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params: Optional[Dict[str, Any]] = None,
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) -> QueryChunksResponse: ...
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