def _get_doc_with_query(
        self, query: str, store: Chroma, retrieve_k: int=None, score_threshold: float=None,
    ) -> List[Tuple[Document, float]]:
        """Using the given `query` to query documents from the given vector store `store`.

        Returns:
            List[Tuple[Document, float]]: each item is a pair of (document, relevance score).
        """
        if retrieve_k is None:
            retrieve_k = self.retrieve_k
        if score_threshold is None:
            score_threshold = self.retrieve_score_threshold

        infos: List[Tuple[Document, float]] = store.similarity_search_with_relevance_scores(
            query=query,
            k=retrieve_k,
            score_threshold=score_threshold,
        )

        filtered_docs = [(doc, score) for doc, score in infos if score >= score_threshold]
        sorted_docs = sorted(filtered_docs, key=lambda x: x[1], reverse=True)

        return sorted_docs
Using the given `query` to query documents from the given vector store `store`.

        Returns:
            List[Tuple[Document, float]]: each item is a pair of (document, relevance score).
        
pikerag/knowledge_retrievers/mixins/chroma_mixin.py