feat: Override PGVector with custom source

custom_pgvector.py

@@ -0,0 +1,789 @@
+from __future__ import annotations
+
+import asyncio
+import contextlib
+import enum
+import logging
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Type,
+)
+
+import numpy as np
+import sqlalchemy
+from langchain.docstore.document import Document
+from langchain.schema.embeddings import Embeddings
+from langchain.utils import get_from_dict_or_env
+from langchain.vectorstores.base import VectorStore
+from langchain.vectorstores.pgvector import BaseModel
+from langchain.vectorstores.utils import maximal_marginal_relevance
+from pgvector.sqlalchemy import Vector
+from sqlalchemy import delete
+from sqlalchemy.orm import Session, declarative_base, relationship
+
+if TYPE_CHECKING:
+    from langchain.vectorstores._pgvector_data_models import CollectionStore
+
+
+class DistanceStrategy(str, enum.Enum):
+    """Enumerator of the Distance strategies."""
+
+    EUCLIDEAN = "l2"
+    COSINE = "cosine"
+    MAX_INNER_PRODUCT = "inner"
+
+
+DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.COSINE
+
+Base = declarative_base()  # type: Any
+
+
+_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
+
+
+def _results_to_docs(docs_and_scores: Any) -> List[Document]:
+    """Return docs from docs and scores."""
+    return [doc for doc, _ in docs_and_scores]
+
+
+class Article(Base):
+    """Embedding store."""
+
+    __tablename__ = "article"
+
+    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, nullable=False)
+    title = sqlalchemy.Column(sqlalchemy.String, nullable=True)
+    abstract = sqlalchemy.Column(sqlalchemy.String, nullable=True)
+    embedding: Vector = sqlalchemy.Column("abstract_embedding", Vector(None))
+    doi = sqlalchemy.Column(sqlalchemy.String, nullable=True)
+
+
+class CustomPGVector(VectorStore):
+    """`Postgres`/`PGVector` vector store.
+
+    To use, you should have the ``pgvector`` python package installed.
+
+    Args:
+        connection_string: Postgres connection string.
+        embedding_function: Any embedding function implementing
+            `langchain.embeddings.base.Embeddings` interface.
+        table_name: The name of the collection to use. (default: langchain)
+            NOTE: This is not the name of the table, but the name of the collection.
+            The tables will be created when initializing the store (if not exists)
+            So, make sure the user has the right permissions to create tables.
+        distance_strategy: The distance strategy to use. (default: COSINE)
+        pre_delete_collection: If True, will delete the collection if it exists.
+            (default: False). Useful for testing.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import PGVector
+            from langchain.embeddings.openai import OpenAIEmbeddings
+
+            CONNECTION_STRING = "postgresql+psycopg2://hwc@localhost:5432/test3"
+            COLLECTION_NAME = "state_of_the_union_test"
+            embeddings = OpenAIEmbeddings()
+            vectorestore = PGVector.from_documents(
+                embedding=embeddings,
+                documents=docs,
+                table_name=COLLECTION_NAME,
+                connection_string=CONNECTION_STRING,
+            )
+
+
+    """
+
+    def __init__(
+        self,
+        connection_string: str,
+        embedding_function: Embeddings,
+        table_name: str,
+        column_name: str,
+        collection_metadata: Optional[dict] = None,
+        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
+        pre_delete_collection: bool = False,
+        logger: Optional[logging.Logger] = None,
+        relevance_score_fn: Optional[Callable[[float], float]] = None,
+    ) -> None:
+        self.connection_string = connection_string
+        self.embedding_function = embedding_function
+        self.table_name = table_name
+        self.column_name = column_name
+        self.collection_metadata = collection_metadata
+        self._distance_strategy = distance_strategy
+        self.pre_delete_collection = pre_delete_collection
+        self.logger = logger or logging.getLogger(__name__)
+        self.override_relevance_score_fn = relevance_score_fn
+        self.__post_init__()
+
+    def __post_init__(
+        self,
+    ) -> None:
+        """
+        Initialize the store.
+        """
+        self._conn = self.connect()
+        self.create_vector_extension()
+
+        self.EmbeddingStore = Article
+
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
+    def connect(self) -> sqlalchemy.engine.Connection:
+        engine = sqlalchemy.create_engine(self.connection_string)
+        conn = engine.connect()
+        return conn
+
+    def create_vector_extension(self) -> None:
+        try:
+            with Session(self._conn) as session:
+                statement = sqlalchemy.text("CREATE EXTENSION IF NOT EXISTS vector")
+                session.execute(statement)
+                session.commit()
+        except Exception as e:
+            self.logger.exception(e)
+
+    def drop_tables(self) -> None:
+        with self._conn.begin():
+            Base.metadata.drop_all(self._conn)
+
+    @contextlib.contextmanager
+    def _make_session(self) -> Generator[Session, None, None]:
+        """Create a context manager for the session, bind to _conn string."""
+        yield Session(self._conn)
+
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Delete vectors by ids.
+
+        Args:
+            ids: List of ids to delete.
+        """
+        with Session(self._conn) as session:
+            if ids is not None:
+                self.logger.debug(
+                    "Trying to delete vectors by ids (represented by the model "
+                    "using the custom ids field)"
+                )
+                stmt = delete(self.EmbeddingStore).where(
+                    self.EmbeddingStore.custom_id.in_(ids)
+                )
+                session.execute(stmt)
+            session.commit()
+
+    @classmethod
+    def __from(
+        cls,
+        texts: List[str],
+        embeddings: List[List[float]],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        table_name: str = "article",
+        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
+        connection_string: Optional[str] = None,
+        pre_delete_collection: bool = False,
+        **kwargs: Any,
+    ) -> CustomPGVector:
+        if not metadatas:
+            metadatas = [{} for _ in texts]
+        if connection_string is None:
+            connection_string = cls.get_connection_string(kwargs)
+
+        store = cls(
+            connection_string=connection_string,
+            table_name=table_name,
+            embedding_function=embedding,
+            distance_strategy=distance_strategy,
+            pre_delete_collection=pre_delete_collection,
+            **kwargs,
+        )
+
+        store.add_embeddings(
+            texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
+        )
+
+        return store
+
+    def add_embeddings(
+        self,
+        texts: Iterable[str],
+        embeddings: List[List[float]],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Add embeddings to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            embeddings: List of list of embedding vectors.
+            metadatas: List of metadatas associated with the texts.
+            kwargs: vectorstore specific parameters
+        """
+        if not metadatas:
+            metadatas = [{} for _ in texts]
+
+        with Session(self._conn) as session:
+            # collection = self.get_collection(session)
+            # if not collection:
+            #     raise ValueError("Collection not found")
+            for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
+                embedding_store = self.EmbeddingStore(
+                    embedding=embedding,
+                    document=text,
+                    cmetadata=metadata,
+                    custom_id=id,
+                )
+                session.add(embedding_store)
+            session.commit()
+
+        return ids
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+            kwargs: vectorstore specific parameters
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        embeddings = self.embedding_function.embed_documents(list(texts))
+        return self.add_embeddings(
+            texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
+        )
+
+    def similarity_search(
+        self,
+        query: str,
+        k: int = 4,
+        filter: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Run similarity search with PGVector 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 of Documents most similar to the query.
+        """
+        embedding = self.embedding_function.embed_query(text=query)
+        return self.similarity_search_by_vector(
+            embedding=embedding,
+            k=k,
+            filter=filter,
+        )
+
+    def similarity_search_with_score(
+        self,
+        query: str,
+        k: int = 4,
+        filter: Optional[dict] = 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 (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
+
+        Returns:
+            List of Documents most similar to the query and score for each.
+        """
+        embedding = self.embedding_function.embed_query(query)
+        docs = self.similarity_search_with_score_by_vector(
+            embedding=embedding, k=k, filter=filter
+        )
+        return docs
+
+    @property
+    def distance_strategy(self) -> Any:
+        if self._distance_strategy == DistanceStrategy.EUCLIDEAN:
+            return self.EmbeddingStore.embedding.l2_distance
+        elif self._distance_strategy == DistanceStrategy.COSINE:
+            return self.EmbeddingStore.embedding.cosine_distance
+        elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
+            return self.EmbeddingStore.embedding.max_inner_product
+        else:
+            raise ValueError(
+                f"Got unexpected value for distance: {self._distance_strategy}. "
+                f"Should be one of {', '.join([ds.value for ds in DistanceStrategy])}."
+            )
+
+    def similarity_search_with_score_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        filter: Optional[dict] = None,
+    ) -> List[Tuple[Document, float]]:
+        results = self.__query_collection(embedding=embedding, k=k, filter=filter)
+
+        return self._results_to_docs_and_scores(results)
+
+    def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, float]]:
+        """Return docs and scores from results."""
+        docs = [
+            (
+                Document(
+                    page_content=result.Article.abstract,
+                    # metadata={"title": result.Article.title},
+                ),
+                result.distance if self.embedding_function is not None else None,
+            )
+            for result in results
+        ]
+        return docs
+
+    def __query_collection(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        filter: Optional[Dict[str, str]] = None,
+    ) -> List[Any]:
+        """Query the collection."""
+        with Session(self._conn) as session:
+            results: List[Any] = (
+                session.query(
+                    self.EmbeddingStore,
+                    self.distance_strategy(embedding).label("distance"),  # type: ignore
+                )
+                .order_by(sqlalchemy.asc("distance"))
+                .limit(k)
+                .all()
+            )
+        print(results)
+        return results
+
+    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 _results_to_docs(docs_and_scores)
+
+    @classmethod
+    def from_texts(
+        cls: Type[PGVector],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        table_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
+        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
+        ids: Optional[List[str]] = None,
+        pre_delete_collection: bool = False,
+        **kwargs: Any,
+    ) -> PGVector:
+        """
+        Return VectorStore initialized from texts and embeddings.
+        Postgres connection string is required
+        "Either pass it as a parameter
+        or set the PGVECTOR_CONNECTION_STRING environment variable.
+        """
+        embeddings = embedding.embed_documents(list(texts))
+
+        return cls.__from(
+            texts,
+            embeddings,
+            embedding,
+            metadatas=metadatas,
+            ids=ids,
+            table_name=table_name,
+            distance_strategy=distance_strategy,
+            pre_delete_collection=pre_delete_collection,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_embeddings(
+        cls,
+        text_embeddings: List[Tuple[str, List[float]]],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        table_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
+        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
+        ids: Optional[List[str]] = None,
+        pre_delete_collection: bool = False,
+        **kwargs: Any,
+    ) -> PGVector:
+        """Construct PGVector wrapper from raw documents and pre-
+        generated embeddings.
+
+        Return VectorStore initialized from documents and embeddings.
+        Postgres connection string is required
+        "Either pass it as a parameter
+        or set the PGVECTOR_CONNECTION_STRING environment variable.
+
+        Example:
+            .. code-block:: python
+
+                from langchain.vectorstores import PGVector
+                from langchain.embeddings import OpenAIEmbeddings
+                embeddings = OpenAIEmbeddings()
+                text_embeddings = embeddings.embed_documents(texts)
+                text_embedding_pairs = list(zip(texts, text_embeddings))
+                faiss = PGVector.from_embeddings(text_embedding_pairs, embeddings)
+        """
+        texts = [t[0] for t in text_embeddings]
+        embeddings = [t[1] for t in text_embeddings]
+
+        return cls.__from(
+            texts,
+            embeddings,
+            embedding,
+            metadatas=metadatas,
+            ids=ids,
+            table_name=table_name,
+            distance_strategy=distance_strategy,
+            pre_delete_collection=pre_delete_collection,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_existing_index(
+        cls: Type[PGVector],
+        embedding: Embeddings,
+        table_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
+        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
+        pre_delete_collection: bool = False,
+        **kwargs: Any,
+    ) -> PGVector:
+        """
+        Get intsance of an existing PGVector store.This method will
+        return the instance of the store without inserting any new
+        embeddings
+        """
+
+        connection_string = cls.get_connection_string(kwargs)
+
+        store = cls(
+            connection_string=connection_string,
+            table_name=table_name,
+            embedding_function=embedding,
+            distance_strategy=distance_strategy,
+            pre_delete_collection=pre_delete_collection,
+        )
+
+        return store
+
+    @classmethod
+    def get_connection_string(cls, kwargs: Dict[str, Any]) -> str:
+        connection_string: str = get_from_dict_or_env(
+            data=kwargs,
+            key="connection_string",
+            env_key="PGVECTOR_CONNECTION_STRING",
+        )
+
+        if not connection_string:
+            raise ValueError(
+                "Postgres connection string is required"
+                "Either pass it as a parameter"
+                "or set the PGVECTOR_CONNECTION_STRING environment variable."
+            )
+
+        return connection_string
+
+    @classmethod
+    def from_documents(
+        cls: Type[CustomPGVector],
+        documents: List[Document],
+        embedding: Embeddings,
+        table_name: str = "article",
+        column_name: str = "embeding",
+        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
+        ids: Optional[List[str]] = None,
+        pre_delete_collection: bool = False,
+        **kwargs: Any,
+    ) -> CustomPGVector:
+        """
+        Return VectorStore initialized from documents and embeddings.
+        Postgres connection string is required
+        "Either pass it as a parameter
+        or set the PGVECTOR_CONNECTION_STRING environment variable.
+        """
+
+        texts = [d.page_content for d in documents]
+        metadatas = [d.metadata for d in documents]
+        connection_string = cls.get_connection_string(kwargs)
+
+        kwargs["connection_string"] = connection_string
+
+        return cls.from_texts(
+            texts=texts,
+            pre_delete_collection=pre_delete_collection,
+            embedding=embedding,
+            distance_strategy=distance_strategy,
+            metadatas=metadatas,
+            ids=ids,
+            table_name=table_name,
+            column_name=column_name,
+            **kwargs,
+        )
+
+    @classmethod
+    def connection_string_from_db_params(
+        cls,
+        driver: str,
+        host: str,
+        port: int,
+        database: str,
+        user: str,
+        password: str,
+    ) -> str:
+        """Return connection string from database parameters."""
+        return f"postgresql+{driver}://{user}:{password}@{host}:{port}/{database}"
+
+    def _select_relevance_score_fn(self) -> Callable[[float], float]:
+        """
+        The 'correct' relevance function
+        may differ depending on a few things, including:
+        - the distance / similarity metric used by the VectorStore
+        - the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
+        - embedding dimensionality
+        - etc.
+        """
+        if self.override_relevance_score_fn is not None:
+            return self.override_relevance_score_fn
+
+        # Default strategy is to rely on distance strategy provided
+        # in vectorstore constructor
+        if self._distance_strategy == DistanceStrategy.COSINE:
+            return self._cosine_relevance_score_fn
+        elif self._distance_strategy == DistanceStrategy.EUCLIDEAN:
+            return self._euclidean_relevance_score_fn
+        elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
+            return self._max_inner_product_relevance_score_fn
+        else:
+            raise ValueError(
+                "No supported normalization function"
+                f" for distance_strategy of {self._distance_strategy}."
+                "Consider providing relevance_score_fn to PGVector constructor."
+            )
+
+    def max_marginal_relevance_search_with_score_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return docs selected using the maximal marginal relevance with score
+            to embedding vector.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+            among selected documents.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k (int): Number of Documents to return. Defaults to 4.
+            fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
+                Defaults to 20.
+            lambda_mult (float): Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding
+                to maximum diversity and 1 to minimum diversity.
+                Defaults to 0.5.
+            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
+
+        Returns:
+            List[Tuple[Document, float]]: List of Documents selected by maximal marginal
+                relevance to the query and score for each.
+        """
+        results = self.__query_collection(embedding=embedding, k=fetch_k, filter=filter)
+
+        embedding_list = [result.EmbeddingStore.embedding for result in results]
+
+        mmr_selected = maximal_marginal_relevance(
+            np.array(embedding, dtype=np.float32),
+            embedding_list,
+            k=k,
+            lambda_mult=lambda_mult,
+        )
+
+        candidates = self._results_to_docs_and_scores(results)
+
+        return [r for i, r in enumerate(candidates) if i in mmr_selected]
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+            among selected documents.
+
+        Args:
+            query (str): Text to look up documents similar to.
+            k (int): Number of Documents to return. Defaults to 4.
+            fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
+                Defaults to 20.
+            lambda_mult (float): Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding
+                to maximum diversity and 1 to minimum diversity.
+                Defaults to 0.5.
+            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
+
+        Returns:
+            List[Document]: List of Documents selected by maximal marginal relevance.
+        """
+        embedding = self.embedding_function.embed_query(query)
+        return self.max_marginal_relevance_search_by_vector(
+            embedding,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            **kwargs,
+        )
+
+    def max_marginal_relevance_search_with_score(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return docs selected using the maximal marginal relevance with score.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+            among selected documents.
+
+        Args:
+            query (str): Text to look up documents similar to.
+            k (int): Number of Documents to return. Defaults to 4.
+            fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
+                Defaults to 20.
+            lambda_mult (float): Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding
+                to maximum diversity and 1 to minimum diversity.
+                Defaults to 0.5.
+            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
+
+        Returns:
+            List[Tuple[Document, float]]: List of Documents selected by maximal marginal
+                relevance to the query and score for each.
+        """
+        embedding = self.embedding_function.embed_query(query)
+        docs = self.max_marginal_relevance_search_with_score_by_vector(
+            embedding=embedding,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            filter=filter,
+            **kwargs,
+        )
+        return docs
+
+    def max_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance
+            to embedding vector.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+            among selected documents.
+
+        Args:
+            embedding (str): Text to look up documents similar to.
+            k (int): Number of Documents to return. Defaults to 4.
+            fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
+                Defaults to 20.
+            lambda_mult (float): Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding
+                to maximum diversity and 1 to minimum diversity.
+                Defaults to 0.5.
+            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
+
+        Returns:
+            List[Document]: List of Documents selected by maximal marginal relevance.
+        """
+        docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector(
+            embedding,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            filter=filter,
+            **kwargs,
+        )
+
+        return _results_to_docs(docs_and_scores)
+
+    async def amax_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance."""
+
+        # This is a temporary workaround to make the similarity search
+        # asynchronous. The proper solution is to make the similarity search
+        # asynchronous in the vector store implementations.
+        func = partial(
+            self.max_marginal_relevance_search_by_vector,
+            embedding,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            filter=filter,
+            **kwargs,
+        )
+        return await asyncio.get_event_loop().run_in_executor(None, func)