langchain/vectorstores/pgvector.py/PGVector/similarity_search_by_vector
class PGVector:    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[dict] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

        Returns:
            List of Documents most similar to the query vector.
        """
        docs_and_scores = self.similarity_search_with_score_by_vector(
            embedding=embedding, k=k, filter=filter
        )
        return [doc for doc, _ in docs_and_scores]