检索器

基类: BaseQueryEngine

检索器查询引擎。

参数

名称

类型	描述	默认值	BaseRetriever
检索器	一个检索器对象。	必填	response_synthesizer
可选[BaseSynthesizer]	一个 BaseSynthesizer 对象。	callback_manager	无
可选[CallbackManager]	一个回调管理器。	源代码位于 llama-index-core/llama_index/core/query_engine/retriever_query_engine.py	无

retriever 属性

class RetrieverQueryEngine(BaseQueryEngine):
    """
    Retriever query engine.

    Args:
        retriever (BaseRetriever): A retriever object.
        response_synthesizer (Optional[BaseSynthesizer]): A BaseSynthesizer
            object.
        callback_manager (Optional[CallbackManager]): A callback manager.

    """

    def __init__(
        self,
        retriever: BaseRetriever,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
        callback_manager: Optional[CallbackManager] = None,
    ) -> None:
        self._retriever = retriever
        self._response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=Settings.llm,
            callback_manager=callback_manager or Settings.callback_manager,
        )

        self._node_postprocessors = node_postprocessors or []
        callback_manager = (
            callback_manager or self._response_synthesizer.callback_manager
        )
        for node_postprocessor in self._node_postprocessors:
            node_postprocessor.callback_manager = callback_manager
        super().__init__(callback_manager=callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {"response_synthesizer": self._response_synthesizer}

    @classmethod
    def from_args(
        cls,
        retriever: BaseRetriever,
        llm: Optional[LLM] = None,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
        callback_manager: Optional[CallbackManager] = None,
        # response synthesizer args
        response_mode: ResponseMode = ResponseMode.COMPACT,
        text_qa_template: Optional[BasePromptTemplate] = None,
        refine_template: Optional[BasePromptTemplate] = None,
        summary_template: Optional[BasePromptTemplate] = None,
        simple_template: Optional[BasePromptTemplate] = None,
        output_cls: Optional[Type[BaseModel]] = None,
        use_async: bool = False,
        streaming: bool = False,
        **kwargs: Any,
    ) -> "RetrieverQueryEngine":
        """
        Initialize a RetrieverQueryEngine object.".

        Args:
            retriever (BaseRetriever): A retriever object.
            llm (Optional[LLM]): An instance of an LLM.
            response_synthesizer (Optional[BaseSynthesizer]): An instance of a response
                synthesizer.
            node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of
                node postprocessors.
            callback_manager (Optional[CallbackManager]): A callback manager.
            response_mode (ResponseMode): A ResponseMode object.
            text_qa_template (Optional[BasePromptTemplate]): A BasePromptTemplate
                object.
            refine_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
            summary_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
            simple_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
            output_cls (Optional[Type[BaseModel]]): The pydantic model to pass to the
                response synthesizer.
            use_async (bool): Whether to use async.
            streaming (bool): Whether to use streaming.

        """
        llm = llm or Settings.llm

        response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=llm,
            text_qa_template=text_qa_template,
            refine_template=refine_template,
            summary_template=summary_template,
            simple_template=simple_template,
            response_mode=response_mode,
            output_cls=output_cls,
            use_async=use_async,
            streaming=streaming,
        )

        callback_manager = callback_manager or Settings.callback_manager

        return cls(
            retriever=retriever,
            response_synthesizer=response_synthesizer,
            callback_manager=callback_manager,
            node_postprocessors=node_postprocessors,
        )

    def _apply_node_postprocessors(
        self, nodes: List[NodeWithScore], query_bundle: QueryBundle
    ) -> List[NodeWithScore]:
        for node_postprocessor in self._node_postprocessors:
            nodes = node_postprocessor.postprocess_nodes(
                nodes, query_bundle=query_bundle
            )
        return nodes

    def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        nodes = self._retriever.retrieve(query_bundle)
        return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)

    async def aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        nodes = await self._retriever.aretrieve(query_bundle)
        return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)

    def with_retriever(self, retriever: BaseRetriever) -> "RetrieverQueryEngine":
        return RetrieverQueryEngine(
            retriever=retriever,
            response_synthesizer=self._response_synthesizer,
            callback_manager=self.callback_manager,
            node_postprocessors=self._node_postprocessors,
        )

    def synthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        return self._response_synthesizer.synthesize(
            query=query_bundle,
            nodes=nodes,
            additional_source_nodes=additional_source_nodes,
        )

    async def asynthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        return await self._response_synthesizer.asynthesize(
            query=query_bundle,
            nodes=nodes,
            additional_source_nodes=additional_source_nodes,
        )

    @dispatcher.span
    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            nodes = self.retrieve(query_bundle)
            response = self._response_synthesizer.synthesize(
                query=query_bundle,
                nodes=nodes,
            )
            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    @dispatcher.span
    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            nodes = await self.aretrieve(query_bundle)

            response = await self._response_synthesizer.asynthesize(
                query=query_bundle,
                nodes=nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    @property
    def retriever(self) -> BaseRetriever:
        """Get the retriever object."""
        return self._retriever

获取检索器对象。

retriever: BaseRetriever

from_args 类方法

初始化一个 RetrieverQueryEngine 对象。”。

from_args(retriever: BaseRetriever, llm: Optional[LLM] = None, response_synthesizer: Optional[BaseSynthesizer] = None, node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, callback_manager: Optional[CallbackManager] = None, response_mode: ResponseMode = COMPACT, text_qa_template: Optional[BasePromptTemplate] = None, refine_template: Optional[BasePromptTemplate] = None, summary_template: Optional[BasePromptTemplate] = None, simple_template: Optional[BasePromptTemplate] = None, output_cls: Optional[Type[BaseModel]] = None, use_async: bool = False, streaming: bool = False, **kwargs: Any) -> RetrieverQueryEngine

llm

名称

类型	描述	默认值	BaseRetriever
检索器	一个检索器对象。	必填	response_synthesizer
可选[LLM]	LLM 的一个实例。	响应合成器的一个实例。	无
可选[BaseSynthesizer]	一个 BaseSynthesizer 对象。	node_postprocessors	无
可选[列表[BaseNodePostprocessor]]	节点后处理器列表。	response_mode	无
可选[CallbackManager]	一个回调管理器。	源代码位于 llama-index-core/llama_index/core/query_engine/retriever_query_engine.py	无
ResponseMode	一个 ResponseMode 对象。	COMPACT	text_qa_template
可选[BasePromptTemplate]	一个 BasePromptTemplate 对象。	refine_template	无
summary_template	一个 BasePromptTemplate 对象。	refine_template	无
simple_template	一个 BasePromptTemplate 对象。	refine_template	无
output_cls	一个 BasePromptTemplate 对象。	refine_template	无
可选[类型[BaseModel]]	要传递给响应合成器的 pydantic 模型。	use_async	无
bool	是否使用异步。	False	streaming
是否使用流式传输。	是否使用异步。	返回顶部	streaming

retriever 属性

@classmethod
def from_args(
    cls,
    retriever: BaseRetriever,
    llm: Optional[LLM] = None,
    response_synthesizer: Optional[BaseSynthesizer] = None,
    node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
    callback_manager: Optional[CallbackManager] = None,
    # response synthesizer args
    response_mode: ResponseMode = ResponseMode.COMPACT,
    text_qa_template: Optional[BasePromptTemplate] = None,
    refine_template: Optional[BasePromptTemplate] = None,
    summary_template: Optional[BasePromptTemplate] = None,
    simple_template: Optional[BasePromptTemplate] = None,
    output_cls: Optional[Type[BaseModel]] = None,
    use_async: bool = False,
    streaming: bool = False,
    **kwargs: Any,
) -> "RetrieverQueryEngine":
    """
    Initialize a RetrieverQueryEngine object.".

    Args:
        retriever (BaseRetriever): A retriever object.
        llm (Optional[LLM]): An instance of an LLM.
        response_synthesizer (Optional[BaseSynthesizer]): An instance of a response
            synthesizer.
        node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of
            node postprocessors.
        callback_manager (Optional[CallbackManager]): A callback manager.
        response_mode (ResponseMode): A ResponseMode object.
        text_qa_template (Optional[BasePromptTemplate]): A BasePromptTemplate
            object.
        refine_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
        summary_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
        simple_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
        output_cls (Optional[Type[BaseModel]]): The pydantic model to pass to the
            response synthesizer.
        use_async (bool): Whether to use async.
        streaming (bool): Whether to use streaming.

    """
    llm = llm or Settings.llm

    response_synthesizer = response_synthesizer or get_response_synthesizer(
        llm=llm,
        text_qa_template=text_qa_template,
        refine_template=refine_template,
        summary_template=summary_template,
        simple_template=simple_template,
        response_mode=response_mode,
        output_cls=output_cls,
        use_async=use_async,
        streaming=streaming,
    )

    callback_manager = callback_manager or Settings.callback_manager

    return cls(
        retriever=retriever,
        response_synthesizer=response_synthesizer,
        callback_manager=callback_manager,
        node_postprocessors=node_postprocessors,
    )