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84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
"""Embedding retriever operator."""
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from functools import reduce
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from typing import List, Optional, Union
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from dbgpt.core import Chunk
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from dbgpt.core.interface.operators.retriever import RetrieverOperator
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from dbgpt.storage.vector_store.connector import VectorStoreConnector
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from ..assembler.embedding import EmbeddingAssembler
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from ..chunk_manager import ChunkParameters
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from ..knowledge import Knowledge
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from ..retriever.embedding import EmbeddingRetriever
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from ..retriever.rerank import Ranker
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from ..retriever.rewrite import QueryRewrite
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from .assembler import AssemblerOperator
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class EmbeddingRetrieverOperator(RetrieverOperator[Union[str, List[str]], List[Chunk]]):
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"""The Embedding Retriever Operator."""
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def __init__(
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self,
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vector_store_connector: VectorStoreConnector,
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top_k: int,
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score_threshold: float = 0.3,
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query_rewrite: Optional[QueryRewrite] = None,
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rerank: Optional[Ranker] = None,
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**kwargs
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):
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"""Create a new EmbeddingRetrieverOperator."""
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super().__init__(**kwargs)
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self._score_threshold = score_threshold
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self._retriever = EmbeddingRetriever(
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vector_store_connector=vector_store_connector,
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top_k=top_k,
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query_rewrite=query_rewrite,
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rerank=rerank,
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)
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def retrieve(self, query: Union[str, List[str]]) -> List[Chunk]:
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"""Retrieve the candidates."""
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if isinstance(query, str):
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return self._retriever.retrieve_with_scores(query, self._score_threshold)
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elif isinstance(query, list):
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candidates = [
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self._retriever.retrieve_with_scores(q, self._score_threshold)
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for q in query
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]
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return reduce(lambda x, y: x + y, candidates)
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class EmbeddingAssemblerOperator(AssemblerOperator[Knowledge, List[Chunk]]):
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"""The Embedding Assembler Operator."""
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def __init__(
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self,
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vector_store_connector: VectorStoreConnector,
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chunk_parameters: Optional[ChunkParameters] = ChunkParameters(
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chunk_strategy="CHUNK_BY_SIZE"
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),
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**kwargs
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):
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"""Create a new EmbeddingAssemblerOperator.
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Args:
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vector_store_connector (VectorStoreConnector): The vector store connector.
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chunk_parameters (Optional[ChunkParameters], optional): The chunk
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parameters. Defaults to ChunkParameters(chunk_strategy="CHUNK_BY_SIZE").
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"""
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self._chunk_parameters = chunk_parameters
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self._vector_store_connector = vector_store_connector
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super().__init__(**kwargs)
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def assemble(self, knowledge: Knowledge) -> List[Chunk]:
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"""Assemble knowledge for input value."""
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assembler = EmbeddingAssembler.load_from_knowledge(
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knowledge=knowledge,
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chunk_parameters=self._chunk_parameters,
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vector_store_connector=self._vector_store_connector,
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)
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assembler.persist()
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return assembler.get_chunks()
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