langchain/vectorstores/chroma.py/Chroma/similarity_search_with_score
class Chroma:    def similarity_search_with_score(
        self,
        query: str,
        k: int = DEFAULT_K,
        filter: Optional[Dict[str, str]] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Run similarity search with Chroma with distance.

        Args:
            query (str): Query text to search for.
            k (int): Number of results to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

        Returns:
            List[Tuple[Document, float]]: List of documents most similar to the query
                text with distance in float.
        """
        if self._embedding_function is None:
            results = self.__query_collection(
                query_texts=[query], n_results=k, where=filter
            )
        else:
            query_embedding = self._embedding_function.embed_query(query)
            results = self.__query_collection(
                query_embeddings=[query_embedding], n_results=k, where=filter
            )

        return _results_to_docs_and_scores(results)