langchain/vectorstores/pgvector.py/PGVector/similarity_search_with_score_by_vector
class PGVector:    def similarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[dict] = None,
    ) -> List[Tuple[Document, float]]:
        with Session(self._conn) as session:
            collection = self.get_collection(session)
            if not collection:
                raise ValueError("Collection not found")

            filter_by = EmbeddingStore.collection_id == collection.uuid

            if filter is not None:
                filter_clauses = []
                for key, value in filter.items():
                    IN = "in"
                    if isinstance(value, dict) and IN in map(str.lower, value):
                        value_case_insensitive = {
                            k.lower(): v for k, v in value.items()
                        }
                        filter_by_metadata = EmbeddingStore.cmetadata[key].astext.in_(
                            value_case_insensitive[IN]
                        )
                        filter_clauses.append(filter_by_metadata)
                    else:
                        filter_by_metadata = EmbeddingStore.cmetadata[
                            key
                        ].astext == str(value)
                        filter_clauses.append(filter_by_metadata)

                filter_by = sqlalchemy.and_(filter_by, *filter_clauses)

            results: List[QueryResult] = (
                session.query(
                    EmbeddingStore,
                    self.distance_strategy(embedding).label("distance"),  # type: ignore
                )
                .filter(filter_by)
                .order_by(sqlalchemy.asc("distance"))
                .join(
                    CollectionStore,
                    EmbeddingStore.collection_id == CollectionStore.uuid,
                )
                .limit(k)
                .all()
            )

        docs = [
            (
                Document(
                    page_content=result.EmbeddingStore.document,
                    metadata=result.EmbeddingStore.cmetadata,
                ),
                result.distance if self.embedding_function is not None else None,
            )
            for result in results
        ]
        return docs