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Compact accumulate

CompactAndAccumulate #

Bases: Accumulate

在紧凑的文本块中累积响应。

Source code in llama_index/core/response_synthesizers/compact_and_accumulate.py 
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class CompactAndAccumulate(Accumulate):
    """在紧凑的文本块中累积响应。"""

    async def aget_response(
        self,
        query_str: str,
        text_chunks: Sequence[str],
        separator: str = "\n---------------------\n",
        **response_kwargs: Any,
    ) -> RESPONSE_TEXT_TYPE:
        """获取紧凑的响应。"""
        # use prompt helper to fix compact text_chunks under the prompt limitation
        text_qa_template = self._text_qa_template.partial_format(query_str=query_str)

        with temp_set_attrs(self._prompt_helper):
            new_texts = self._prompt_helper.repack(text_qa_template, text_chunks)

            return await super().aget_response(
                query_str=query_str,
                text_chunks=new_texts,
                separator=separator,
                **response_kwargs,
            )

    def get_response(
        self,
        query_str: str,
        text_chunks: Sequence[str],
        separator: str = "\n---------------------\n",
        **response_kwargs: Any,
    ) -> RESPONSE_TEXT_TYPE:
        """获取紧凑的响应。"""
        # use prompt helper to fix compact text_chunks under the prompt limitation
        text_qa_template = self._text_qa_template.partial_format(query_str=query_str)

        with temp_set_attrs(self._prompt_helper):
            new_texts = self._prompt_helper.repack(text_qa_template, text_chunks)

            return super().get_response(
                query_str=query_str,
                text_chunks=new_texts,
                separator=separator,
                **response_kwargs,
            )

aget_response async #

aget_response(
    query_str: str,
    text_chunks: Sequence[str],
    separator: str = "\n---------------------\n",
    **response_kwargs: Any
) -> RESPONSE_TEXT_TYPE

获取紧凑的响应。

Source code in llama_index/core/response_synthesizers/compact_and_accumulate.py 
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async def aget_response(
    self,
    query_str: str,
    text_chunks: Sequence[str],
    separator: str = "\n---------------------\n",
    **response_kwargs: Any,
) -> RESPONSE_TEXT_TYPE:
    """获取紧凑的响应。"""
    # use prompt helper to fix compact text_chunks under the prompt limitation
    text_qa_template = self._text_qa_template.partial_format(query_str=query_str)

    with temp_set_attrs(self._prompt_helper):
        new_texts = self._prompt_helper.repack(text_qa_template, text_chunks)

        return await super().aget_response(
            query_str=query_str,
            text_chunks=new_texts,
            separator=separator,
            **response_kwargs,
        )

get_response #

get_response(
    query_str: str,
    text_chunks: Sequence[str],
    separator: str = "\n---------------------\n",
    **response_kwargs: Any
) -> RESPONSE_TEXT_TYPE

获取紧凑的响应。

Source code in llama_index/core/response_synthesizers/compact_and_accumulate.py 
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def get_response(
    self,
    query_str: str,
    text_chunks: Sequence[str],
    separator: str = "\n---------------------\n",
    **response_kwargs: Any,
) -> RESPONSE_TEXT_TYPE:
    """获取紧凑的响应。"""
    # use prompt helper to fix compact text_chunks under the prompt limitation
    text_qa_template = self._text_qa_template.partial_format(query_str=query_str)

    with temp_set_attrs(self._prompt_helper):
        new_texts = self._prompt_helper.repack(text_qa_template, text_chunks)

        return super().get_response(
            query_str=query_str,
            text_chunks=new_texts,
            separator=separator,
            **response_kwargs,
        )