langchain/vectorstores/qdrant.py/Qdrant/similarity_search_with_score
class Qdrant:    def similarity_search_with_score(
        self, query: str, k: int = 4, filter: Optional[MetadataFilter] = None
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query.

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.

        Returns:
            List of Documents most similar to the query and score for each.
        """

        results = self.client.search(
            collection_name=self.collection_name,
            query_vector=self._embed_query(query),
            query_filter=self._qdrant_filter_from_dict(filter),
            with_payload=True,
            limit=k,
        )
        return [
            (
                self._document_from_scored_point(
                    result, self.content_payload_key, self.metadata_payload_key
                ),
                result.score,
            )
            for result in results
        ]