forked from phoenix-oss/llama-stack-mirror
9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
57 lines
1.7 KiB
Python
57 lines
1.7 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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@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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