langchain/retrievers/self_query/base.py/SelfQueryRetriever/get_relevant_documents
class SelfQueryRetriever:    def get_relevant_documents(self, query: str) -> List[Document]:
        """Get documents relevant for a query.

        Args:
            query: string to find relevant documents for

        Returns:
            List of relevant documents
        """
        inputs = self.llm_chain.prep_inputs({"query": query})
        structured_query = cast(
            StructuredQuery, self.llm_chain.predict_and_parse(callbacks=None, **inputs)
        )
        if self.verbose:
            print(structured_query)
        new_query, new_kwargs = self.structured_query_translator.visit_structured_query(
            structured_query
        )
        if structured_query.limit is not None:
            new_kwargs["k"] = structured_query.limit

        search_kwargs = {**self.search_kwargs, **new_kwargs}
        docs = self.vectorstore.search(new_query, self.search_type, **search_kwargs)
        return docs