"""Filter down documents based on their relevance to the query."""
        filtered_docs = []
        for doc in documents:
            _input = self.get_input(query, doc)
            include_doc = self.llm_chain.predict_and_parse(
                **_input, callbacks=callbacks
            )
            if include_doc:
                filtered_docs.append(doc)
        return filtered_docs
[docs]    async def acompress_documents(
        self,
        documents: Sequence[Document],
        query: str,
        callbacks: Optional[Callbacks] = None,
    ) -> Sequence[Document]:
        """Filter down documents."""
        raise NotImplementedError()
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        prompt: Optional[BasePromptTemplate] = None,
        **kwargs: Any
    ) -> "LLMChainFilter":
        """Create a LLMChainFilter from a language model.
        Args:
            llm: The language model to use for filtering.
            prompt: The prompt to use for the filter.
            **kwargs: Additional arguments to pass to the constructor.
        Returns:
            A LLMChainFilter that uses the given language model.
        """
        _prompt = prompt if prompt is not None else _get_default_chain_prompt()
        llm_chain = LLMChain(llm=llm, prompt=_prompt)
        return cls(llm_chain=llm_chain, **kwargs)