forked from phoenix-oss/llama-stack-mirror
bb5fca9521
# What does this PR do? We added: * make sure docstrings are present with 'params' and 'returns' * fail if someone sets 'returns: None' * fix the failing APIs Signed-off-by: Sébastien Han <seb@redhat.com>
72 lines
2.3 KiB
Python
72 lines
2.3 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, Protocol, runtime_checkable
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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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from llama_stack.schema_utils import json_schema_type, webmethod
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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) -> VectorDB | None: ...
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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 | None = None
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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: int | None = None,
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) -> None:
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"""Insert chunks into a vector database.
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:param vector_db_id: The identifier of the vector database to insert the chunks into.
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:param chunks: The chunks to insert.
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:param ttl_seconds: The time to live of the chunks.
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"""
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...
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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: dict[str, Any] | None = None,
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) -> QueryChunksResponse:
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"""Query chunks from a vector database.
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:param vector_db_id: The identifier of the vector database to query.
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:param query: The query to search for.
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:param params: The parameters of the query.
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:returns: A QueryChunksResponse.
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"""
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...
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