vectorstores.py

# pylint: disable=too-many-lines
from __future__ import annotations

import contextlib
import enum
import logging
import uuid
from typing import (
    Any,
    AsyncGenerator,
    Callable,
    Dict,
    Generator,
    Iterable,
    List,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
)
from typing import (
    cast as typing_cast,
)

import numpy as np
import sqlalchemy
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils import get_from_dict_or_env
from langchain_core.vectorstores import VectorStore
from sqlalchemy import SQLColumnExpression, cast, create_engine, delete, func, select
from sqlalchemy.dialects.postgresql import JSON, JSONB, JSONPATH, UUID, insert
from sqlalchemy.engine import Connection, Engine
from sqlalchemy.ext.asyncio import (
    AsyncEngine,
    AsyncSession,
    async_sessionmaker,
    create_async_engine,
)
from sqlalchemy.orm import (
    Session,
    declarative_base,
    relationship,
    scoped_session,
    sessionmaker,
)

from langchain_postgres._utils import maximal_marginal_relevance


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"


_classes: Any = None

COMPARISONS_TO_NATIVE = {
    "$eq": "==",
    "$ne": "!=",
    "$lt": "<",
    "$lte": "<=",
    "$gt": ">",
    "$gte": ">=",
}

SPECIAL_CASED_OPERATORS = {
    "$in",
    "$nin",
    "$between",
    "$exists",
}

TEXT_OPERATORS = {
    "$like",
    "$ilike",
}

LOGICAL_OPERATORS = {"$and", "$or", "$not"}

SUPPORTED_OPERATORS = (
    set(COMPARISONS_TO_NATIVE)
    .union(TEXT_OPERATORS)
    .union(LOGICAL_OPERATORS)
    .union(SPECIAL_CASED_OPERATORS)
)


def _get_embedding_collection_store(vector_dimension: Optional[int] = None) -> Any:
    global _classes
    if _classes is not None:
        return _classes

    from pgvector.sqlalchemy import Vector  # type: ignore

    class CollectionStore(Base):
        """Collection store."""

        __tablename__ = "langchain_pg_collection"

        uuid = sqlalchemy.Column(
            UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
        )
        name = sqlalchemy.Column(sqlalchemy.String, nullable=False, unique=True)
        cmetadata = sqlalchemy.Column(JSON)

        embeddings = relationship(
            "EmbeddingStore",
            back_populates="collection",
            passive_deletes=True,
        )

        @classmethod
        def get_by_name(
            cls, session: Session, name: str
        ) -> Optional["CollectionStore"]:
            return (
                session.query(cls)
                .filter(typing_cast(sqlalchemy.Column, cls.name) == name)
                .first()
            )

        @classmethod
        async def aget_by_name(
            cls, session: AsyncSession, name: str
        ) -> Optional["CollectionStore"]:
            return (
                (
                    await session.execute(
                        select(CollectionStore).where(
                            typing_cast(sqlalchemy.Column, cls.name) == name
                        )
                    )
                )
                .scalars()
                .first()
            )

        @classmethod
        def get_or_create(
            cls,
            session: Session,
            name: str,
            cmetadata: Optional[dict] = None,
        ) -> Tuple["CollectionStore", bool]:
            """Get or create a collection.
            Returns:
                 Where the bool is True if the collection was created.
            """  # noqa: E501
            created = False
            collection = cls.get_by_name(session, name)
            if collection:
                return collection, created

            collection = cls(name=name, cmetadata=cmetadata)
            session.add(collection)
            session.commit()
            created = True
            return collection, created

        @classmethod
        async def aget_or_create(
            cls,
            session: AsyncSession,
            name: str,
            cmetadata: Optional[dict] = None,
        ) -> Tuple["CollectionStore", bool]:
            """
            Get or create a collection.
            Returns [Collection, bool] where the bool is True if the collection was created.
            """  # noqa: E501
            created = False
            collection = await cls.aget_by_name(session, name)
            if collection:
                return collection, created

            collection = cls(name=name, cmetadata=cmetadata)
            session.add(collection)
            await session.commit()
            created = True
            return collection, created

    class EmbeddingStore(Base):
        """Embedding store."""

        __tablename__ = "langchain_pg_embedding"

        id = sqlalchemy.Column(
            sqlalchemy.String, nullable=True, primary_key=True, index=True, unique=True
        )

        collection_id = sqlalchemy.Column(
            UUID(as_uuid=True),
            sqlalchemy.ForeignKey(
                f"{CollectionStore.__tablename__}.uuid",
                ondelete="CASCADE",
            ),
        )
        collection = relationship(CollectionStore, back_populates="embeddings")

        embedding: Vector = sqlalchemy.Column(Vector(vector_dimension))
        document = sqlalchemy.Column(sqlalchemy.String, nullable=True)
        cmetadata = sqlalchemy.Column(JSONB, nullable=True)

        __table_args__ = (
            sqlalchemy.Index(
                "ix_cmetadata_gin",
                "cmetadata",
                postgresql_using="gin",
                postgresql_ops={"cmetadata": "jsonb_path_ops"},
            ),
        )

    _classes = (EmbeddingStore, CollectionStore)

    return _classes


def _results_to_docs(docs_and_scores: Any) -> List[Document]:
    """Return docs from docs and scores."""
    return [doc for doc, _ in docs_and_scores]


def _create_vector_extension(conn: Connection) -> None:
    statement = sqlalchemy.text(
        "SELECT pg_advisory_xact_lock(1573678846307946496);"
        "CREATE EXTENSION IF NOT EXISTS vector;"
    )
    conn.execute(statement)
    conn.commit()


DBConnection = Union[sqlalchemy.engine.Engine, str]


class PGVector(VectorStore):
    """Postgres vector store integration.

    Setup:
        Install ``langchain_postgres`` and run the docker container.

        .. code-block:: bash

            pip install -qU langchain-postgres
            docker run --name pgvector-container -e POSTGRES_USER=langchain -e POSTGRES_PASSWORD=langchain -e POSTGRES_DB=langchain -p 6024:5432 -d pgvector/pgvector:pg16

    Key init args — indexing params:
        collection_name: str
            Name of the collection.
        embeddings: Embeddings
            Embedding function to use.

    Key init args — client params:
        connection: Union[None, DBConnection, Engine, AsyncEngine, str]
            Connection string or engine.

    Instantiate:
        .. code-block:: python

            from langchain_postgres import PGVector
            from langchain_postgres.vectorstores import PGVector
            from langchain_openai import OpenAIEmbeddings

            # See docker command above to launch a postgres instance with pgvector enabled.
            connection = "postgresql+psycopg://langchain:langchain@localhost:6024/langchain"  # Uses psycopg3!
            collection_name = "my_docs"

            vector_store = PGVector(
                embeddings=OpenAIEmbeddings(model="text-embedding-3-large"),
                collection_name=collection_name,
                connection=connection,
                use_jsonb=True,
            )

    Add Documents:
        .. code-block:: python

            from langchain_core.documents import Document

            document_1 = Document(page_content="foo", metadata={"baz": "bar"})
            document_2 = Document(page_content="thud", metadata={"bar": "baz"})
            document_3 = Document(page_content="i will be deleted :(")

            documents = [document_1, document_2, document_3]
            ids = ["1", "2", "3"]
            vector_store.add_documents(documents=documents, ids=ids)

    Delete Documents:
        .. code-block:: python

            vector_store.delete(ids=["3"])

    Search:
        .. code-block:: python

            results = vector_store.similarity_search(query="thud",k=1)
            for doc in results:
                print(f"* {doc.page_content} [{doc.metadata}]")

        .. code-block:: python

            * thud [{'bar': 'baz'}]

    Search with filter:
        .. code-block:: python

            results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
            for doc in results:
                print(f"* {doc.page_content} [{doc.metadata}]")

        .. code-block:: python

            * thud [{'bar': 'baz'}]

    Search with score:
        .. code-block:: python

            results = vector_store.similarity_search_with_score(query="qux",k=1)
            for doc, score in results:
                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

        .. code-block:: python

            * [SIM=0.499243] foo [{'baz': 'bar'}]

    Async:
        .. code-block:: python

            # add documents
            # await vector_store.aadd_documents(documents=documents, ids=ids)

            # delete documents
            # await vector_store.adelete(ids=["3"])

            # search
            # results = vector_store.asimilarity_search(query="thud",k=1)

            # search with score
            results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
            for doc,score in results:
                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

        .. code-block:: python

            * [SIM=0.499243] foo [{'baz': 'bar'}]

    Use as Retriever:
        .. code-block:: python

            retriever = vector_store.as_retriever(
                search_type="mmr",
                search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
            )
            retriever.invoke("thud")

        .. code-block:: python

            [Document(metadata={'bar': 'baz'}, page_content='thud')]

    """  # noqa: E501

    def __init__(
        self,
        embeddings: Embeddings,
        *,
        connection: Union[None, DBConnection, Engine, AsyncEngine, str] = None,
        embedding_length: Optional[int] = None,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        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,
        engine_args: Optional[dict[str, Any]] = None,
        use_jsonb: bool = True,
        create_extension: bool = True,
        async_mode: bool = False,
    ) -> None:
        """Initialize the PGVector store.
        For an async version, use `PGVector.acreate()` instead.

        Args:
            connection: Postgres connection string or (async)engine.
            embeddings: Any embedding function implementing
                `langchain.embeddings.base.Embeddings` interface.
            embedding_length: The length of the embedding vector. (default: None)
                NOTE: This is not mandatory. Defining it will prevent vectors of
                any other size to be added to the embeddings table but, without it,
                the embeddings can't be indexed.
            collection_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.
            engine_args: SQLAlchemy's create engine arguments.
            use_jsonb: Use JSONB instead of JSON for metadata. (default: True)
                Strongly discouraged from using JSON as it's not as efficient
                for querying.
                It's provided here for backwards compatibility with older versions,
                and will be removed in the future.
            create_extension: If True, will create the vector extension if it
                doesn't exist. disabling creation is useful when using ReadOnly
                Databases.
        """
        self.async_mode = async_mode
        self.embedding_function = embeddings
        self._embedding_length = embedding_length
        self.collection_name = collection_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._engine: Optional[Engine] = None
        self._async_engine: Optional[AsyncEngine] = None
        self._async_init = False

        if isinstance(connection, str):
            if async_mode:
                self._async_engine = create_async_engine(
                    connection, **(engine_args or {})
                )
            else:
                self._engine = create_engine(url=connection, **(engine_args or {}))
        elif isinstance(connection, Engine):
            self.async_mode = False
            self._engine = connection
        elif isinstance(connection, AsyncEngine):
            self.async_mode = True
            self._async_engine = connection
        else:
            raise ValueError(
                "connection should be a connection string or an instance of "
                "sqlalchemy.engine.Engine or sqlalchemy.ext.asyncio.engine.AsyncEngine"
            )
        self.session_maker: Union[scoped_session, async_sessionmaker]
        if self.async_mode:
            self.session_maker = async_sessionmaker(bind=self._async_engine)
        else:
            self.session_maker = scoped_session(sessionmaker(bind=self._engine))

        self.use_jsonb = use_jsonb
        self.create_extension = create_extension

        if not use_jsonb:
            # Replace with a deprecation warning.
            raise NotImplementedError("use_jsonb=False is no longer supported.")
        if not self.async_mode:
            self.__post_init__()

    def __post_init__(
        self,
    ) -> None:
        """Initialize the store."""
        if self.create_extension:
            self.create_vector_extension()

        EmbeddingStore, CollectionStore = _get_embedding_collection_store(
            self._embedding_length
        )
        self.CollectionStore = CollectionStore
        self.EmbeddingStore = EmbeddingStore
        self.create_tables_if_not_exists()
        self.create_collection()

    async def __apost_init__(
        self,
    ) -> None:
        """Async initialize the store (use lazy approach)."""
        if self._async_init:  # Warning: possible race condition
            return
        self._async_init = True

        EmbeddingStore, CollectionStore = _get_embedding_collection_store(
            self._embedding_length
        )
        self.CollectionStore = CollectionStore
        self.EmbeddingStore = EmbeddingStore
        if self.create_extension:
            await self.acreate_vector_extension()

        await self.acreate_tables_if_not_exists()
        await self.acreate_collection()

    @property
    def embeddings(self) -> Embeddings:
        return self.embedding_function

    def create_vector_extension(self) -> None:
        assert self._engine, "engine not found"
        try:
            with self._engine.connect() as conn:
                _create_vector_extension(conn)
        except Exception as e:
            raise Exception(f"Failed to create vector extension: {e}") from e

    async def acreate_vector_extension(self) -> None:
        assert self._async_engine, "_async_engine not found"

        async with self._async_engine.begin() as conn:
            await conn.run_sync(_create_vector_extension)

    def create_tables_if_not_exists(self) -> None:
        with self._make_sync_session() as session:
            Base.metadata.create_all(session.get_bind())
            session.commit()

    async def acreate_tables_if_not_exists(self) -> None:
        assert self._async_engine, "This method must be called with async_mode"
        async with self._async_engine.begin() as conn:
            await conn.run_sync(Base.metadata.create_all)

    def drop_tables(self) -> None:
        with self._make_sync_session() as session:
            Base.metadata.drop_all(session.get_bind())
            session.commit()

    async def adrop_tables(self) -> None:
        assert self._async_engine, "This method must be called with async_mode"
        await self.__apost_init__()  # Lazy async init
        async with self._async_engine.begin() as conn:
            await conn.run_sync(Base.metadata.drop_all)

    def create_collection(self) -> None:
        if self.pre_delete_collection:
            self.delete_collection()
        with self._make_sync_session() as session:
            self.CollectionStore.get_or_create(
                session, self.collection_name, cmetadata=self.collection_metadata
            )
            session.commit()

    async def acreate_collection(self) -> None:
        await self.__apost_init__()  # Lazy async init
        async with self._make_async_session() as session:
            if self.pre_delete_collection:
                await self._adelete_collection(session)
            await self.CollectionStore.aget_or_create(
                session, self.collection_name, cmetadata=self.collection_metadata
            )
            await session.commit()

    def _delete_collection(self, session: Session) -> None:
        collection = self.get_collection(session)
        if not collection:
            self.logger.warning("Collection not found")
            return
        session.delete(collection)

    async def _adelete_collection(self, session: AsyncSession) -> None:
        collection = await self.aget_collection(session)
        if not collection:
            self.logger.warning("Collection not found")
            return
        await session.delete(collection)

    def delete_collection(self) -> None:
        with self._make_sync_session() as session:
            collection = self.get_collection(session)
            if not collection:
                self.logger.warning("Collection not found")
                return
            session.delete(collection)
            session.commit()

    async def adelete_collection(self) -> None:
        await self.__apost_init__()  # Lazy async init
        async with self._make_async_session() as session:
            collection = await self.aget_collection(session)
            if not collection:
                self.logger.warning("Collection not found")
                return
            await session.delete(collection)
            await session.commit()

    def delete(
        self,
        ids: Optional[List[str]] = None,
        collection_only: bool = False,
        **kwargs: Any,
    ) -> None:
        """Delete vectors by ids or uuids.

        Args:
            ids: List of ids to delete.
            collection_only: Only delete ids in the collection.
        """
        with self._make_sync_session() 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)

                if collection_only:
                    collection = self.get_collection(session)
                    if not collection:
                        self.logger.warning("Collection not found")
                        return

                    stmt = stmt.where(
                        self.EmbeddingStore.collection_id == collection.uuid
                    )

                stmt = stmt.where(self.EmbeddingStore.id.in_(ids))
                session.execute(stmt)
            session.commit()

    async def adelete(
        self,
        ids: Optional[List[str]] = None,
        collection_only: bool = False,
        **kwargs: Any,
    ) -> None:
        """Async delete vectors by ids or uuids.

        Args:
            ids: List of ids to delete.
            collection_only: Only delete ids in the collection.
        """
        await self.__apost_init__()  # Lazy async init
        async with self._make_async_session() 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)

                if collection_only:
                    collection = await self.aget_collection(session)
                    if not collection:
                        self.logger.warning("Collection not found")
                        return

                    stmt = stmt.where(
                        self.EmbeddingStore.collection_id == collection.uuid
                    )

                stmt = stmt.where(self.EmbeddingStore.id.in_(ids))
                await session.execute(stmt)
            await session.commit()

    def get_collection(self, session: Session) -> Any:
        assert not self._async_engine, "This method must be called without async_mode"
        return self.CollectionStore.get_by_name(session, self.collection_name)

    async def aget_collection(self, session: AsyncSession) -> Any:
        assert self._async_engine, "This method must be called with async_mode"
        await self.__apost_init__()  # Lazy async init
        return await self.CollectionStore.aget_by_name(session, self.collection_name)

    @classmethod
    def __from(
        cls,
        texts: List[str],
        embeddings: List[List[float]],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        connection: Optional[str] = None,
        pre_delete_collection: bool = False,
        *,
        use_jsonb: bool = True,
        **kwargs: Any,
    ) -> PGVector:
        if ids is None:
            ids = [str(uuid.uuid4()) for _ in texts]

        if not metadatas:
            metadatas = [{} for _ in texts]

        store = cls(
            connection=connection,
            collection_name=collection_name,
            embeddings=embedding,
            distance_strategy=distance_strategy,
            pre_delete_collection=pre_delete_collection,
            use_jsonb=use_jsonb,
            **kwargs,
        )

        store.add_embeddings(
            texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
        )

        return store

    @classmethod
    async def __afrom(
        cls,
        texts: List[str],
        embeddings: List[List[float]],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        connection: Optional[str] = None,
        pre_delete_collection: bool = False,
        *,
        use_jsonb: bool = True,
        **kwargs: Any,
    ) -> PGVector:
        if ids is None:
            ids = [str(uuid.uuid1()) for _ in texts]

        if not metadatas:
            metadatas = [{} for _ in texts]

        store = cls(
            connection=connection,
            collection_name=collection_name,
            embeddings=embedding,
            distance_strategy=distance_strategy,
            pre_delete_collection=pre_delete_collection,
            use_jsonb=use_jsonb,
            async_mode=True,
            **kwargs,
        )

        await store.aadd_embeddings(
            texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
        )

        return store

    def add_embeddings(
        self,
        texts: Sequence[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.
            ids: Optional list of ids for the documents.
                 If not provided, will generate a new id for each document.
            kwargs: vectorstore specific parameters
        """
        assert not self._async_engine, "This method must be called with sync_mode"
        if ids is None:
            ids_ = [str(uuid.uuid4()) for _ in texts]
        else:
            ids_ = [id if id is not None else str(uuid.uuid4()) for id in ids]

        if not metadatas:
            metadatas = [{} for _ in texts]

        with self._make_sync_session() as session:  # type: ignore[arg-type]
            collection = self.get_collection(session)
            if not collection:
                raise ValueError("Collection not found")
            data = [
                {
                    "id": id,
                    "collection_id": collection.uuid,
                    "embedding": embedding,
                    "document": text,
                    "cmetadata": metadata or {},
                }
                for text, metadata, embedding, id in zip(
                    texts, metadatas, embeddings, ids_
                )
            ]
            stmt = insert(self.EmbeddingStore).values(data)
            on_conflict_stmt = stmt.on_conflict_do_update(
                index_elements=["id"],
                # Conflict detection based on these columns
                set_={
                    "embedding": stmt.excluded.embedding,
                    "document": stmt.excluded.document,
                    "cmetadata": stmt.excluded.cmetadata,
                },
            )
            session.execute(on_conflict_stmt)
            session.commit()

        return ids_

    async def aadd_embeddings(
        self,
        texts: Sequence[str],
        embeddings: List[List[float]],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Async 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.
            ids: Optional list of ids for the texts.
                 If not provided, will generate a new id for each text.
            kwargs: vectorstore specific parameters
        """
        await self.__apost_init__()  # Lazy async init

        if ids is None:
            ids_ = [str(uuid.uuid4()) for _ in texts]
        else:
            ids_ = [id if id is not None else str(uuid.uuid4()) for id in ids]

        if not metadatas:
            metadatas = [{} for _ in texts]

        async with self._make_async_session() as session:  # type: ignore[arg-type]
            collection = await self.aget_collection(session)
            if not collection:
                raise ValueError("Collection not found")
            data = [
                {
                    "id": id,
                    "collection_id": collection.uuid,
                    "embedding": embedding,
                    "document": text,
                    "cmetadata": metadata or {},
                }
                for text, metadata, embedding, id in zip(
                    texts, metadatas, embeddings, ids_
                )
            ]
            stmt = insert(self.EmbeddingStore).values(data)
            on_conflict_stmt = stmt.on_conflict_do_update(
                index_elements=["id"],
                # Conflict detection based on these columns
                set_={
                    "embedding": stmt.excluded.embedding,
                    "document": stmt.excluded.document,
                    "cmetadata": stmt.excluded.cmetadata,
                },
            )
            await session.execute(on_conflict_stmt)
            await 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.
            ids: Optional list of ids for the texts.
                 If not provided, will generate a new id for each text.
            kwargs: vectorstore specific parameters

        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        assert not self._async_engine, "This method must be called without async_mode"
        texts_ = list(texts)
        embeddings = self.embedding_function.embed_documents(texts_)
        return self.add_embeddings(
            texts=texts_,
            embeddings=list(embeddings),
            metadatas=list(metadatas) if metadatas else None,
            ids=list(ids) if ids else None,
            **kwargs,
        )

    async def aadd_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.
            ids: Optional list of ids for the texts.
                 If not provided, will generate a new id for each text.
            kwargs: vectorstore specific parameters

        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        await self.__apost_init__()  # Lazy async init
        texts_ = list(texts)
        embeddings = await self.embedding_function.aembed_documents(texts_)
        return await self.aadd_embeddings(
            texts=texts_,
            embeddings=list(embeddings),
            metadatas=list(metadatas) if metadatas else None,
            ids=list(ids) if ids else None,
            **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.
        """
        assert not self._async_engine, "This method must be called without async_mode"
        embedding = self.embeddings.embed_query(query)
        return self.similarity_search_by_vector(
            embedding=embedding,
            k=k,
            filter=filter,
        )

    async def asimilarity_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.
        """
        await self.__apost_init__()  # Lazy async init
        embedding = await self.embeddings.aembed_query(query)
        return await self.asimilarity_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.
        """
        assert not self._async_engine, "This method must be called without async_mode"
        embedding = self.embeddings.embed_query(query)
        docs = self.similarity_search_with_score_by_vector(
            embedding=embedding, k=k, filter=filter
        )
        return docs

    async def asimilarity_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.
        """
        await self.__apost_init__()  # Lazy async init
        embedding = await self.embeddings.aembed_query(query)
        docs = await self.asimilarity_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]]:
        assert not self._async_engine, "This method must be called without async_mode"
        results = self.__query_collection(embedding=embedding, k=k, filter=filter)

        return self._results_to_docs_and_scores(results)

    async def asimilarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[dict] = None,
    ) -> List[Tuple[Document, float]]:
        await self.__apost_init__()  # Lazy async init
        async with self._make_async_session() as session:  # type: ignore[arg-type]
            results = await self.__aquery_collection(
                session=session, 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 = []
        for result in results:
            metadata = result.EmbeddingStore.cmetadata

            # Attempt to convert metadata to a dict
            try:
                if isinstance(metadata, dict):
                    pass  # Already a dict
                elif isinstance(metadata, str):
                    metadata = json.loads(metadata)
                elif hasattr(metadata, 'buf'):
                    # For Fragment types (e.g., from asyncpg)
                    metadata_bytes = metadata.buf
                    metadata_str = metadata_bytes.decode('utf-8')
                    metadata = json.loads(metadata_str)
                elif hasattr(metadata, 'decode'):
                    # For other byte-like types
                    metadata_str = metadata.decode('utf-8')
                    metadata = json.loads(metadata_str)
                else:
                    metadata = {}  # Default to empty dict if unknown type
            except Exception as e:
                self.logger.warning(f"Failed to deserialize metadata: {e}")
                metadata = {}

            doc = Document(
                id=str(result.EmbeddingStore.id),
                page_content=result.EmbeddingStore.document,
                metadata=metadata,
            )
            score = result.distance if self.embeddings is not None else None
            docs.append((doc, score))
        return docs

    def _handle_field_filter(
        self,
        field: str,
        value: Any,
    ) -> SQLColumnExpression:
        """Create a filter for a specific field.

        Args:
            field: name of field
            value: value to filter
                If provided as is then this will be an equality filter
                If provided as a dictionary then this will be a filter, the key
                will be the operator and the value will be the value to filter by

        Returns:
            sqlalchemy expression
        """
        if not isinstance(field, str):
            raise ValueError(
                f"field should be a string but got: {type(field)} with value: {field}"
            )

        if field.startswith("$"):
            raise ValueError(
                f"Invalid filter condition. Expected a field but got an operator: "
                f"{field}"
            )

        # Allow [a-zA-Z0-9_], disallow $ for now until we support escape characters
        if not field.isidentifier():
            raise ValueError(
                f"Invalid field name: {field}. Expected a valid identifier."
            )

        if isinstance(value, dict):
            # This is a filter specification
            if len(value) != 1:
                raise ValueError(
                    "Invalid filter condition. Expected a value which "
                    "is a dictionary with a single key that corresponds to an operator "
                    f"but got a dictionary with {len(value)} keys. The first few "
                    f"keys are: {list(value.keys())[:3]}"
                )
            operator, filter_value = list(value.items())[0]
            # Verify that that operator is an operator
            if operator not in SUPPORTED_OPERATORS:
                raise ValueError(
                    f"Invalid operator: {operator}. "
                    f"Expected one of {SUPPORTED_OPERATORS}"
                )
        else:  # Then we assume an equality operator
            operator = "$eq"
            filter_value = value

        if operator in COMPARISONS_TO_NATIVE:
            # Then we implement an equality filter
            # native is trusted input
            native = COMPARISONS_TO_NATIVE[operator]
            return func.jsonb_path_match(
                self.EmbeddingStore.cmetadata,
                cast(f"$.{field} {native} $value", JSONPATH),
                cast({"value": filter_value}, JSONB),
            )
        elif operator == "$between":
            # Use AND with two comparisons
            low, high = filter_value

            lower_bound = func.jsonb_path_match(
                self.EmbeddingStore.cmetadata,
                cast(f"$.{field} >= $value", JSONPATH),
                cast({"value": low}, JSONB),
            )
            upper_bound = func.jsonb_path_match(
                self.EmbeddingStore.cmetadata,
                cast(f"$.{field} <= $value", JSONPATH),
                cast({"value": high}, JSONB),
            )
            return sqlalchemy.and_(lower_bound, upper_bound)
        elif operator in {"$in", "$nin", "$like", "$ilike"}:
            # We'll do force coercion to text
            if operator in {"$in", "$nin"}:
                for val in filter_value:
                    if not isinstance(val, (str, int, float)):
                        raise NotImplementedError(
                            f"Unsupported type: {type(val)} for value: {val}"
                        )

                    if isinstance(val, bool):  # b/c bool is an instance of int
                        raise NotImplementedError(
                            f"Unsupported type: {type(val)} for value: {val}"
                        )

            queried_field = self.EmbeddingStore.cmetadata[field].astext

            if operator in {"$in"}:
                return queried_field.in_([str(val) for val in filter_value])
            elif operator in {"$nin"}:
                return ~queried_field.in_([str(val) for val in filter_value])
            elif operator in {"$like"}:
                return queried_field.like(filter_value)
            elif operator in {"$ilike"}:
                return queried_field.ilike(filter_value)
            else:
                raise NotImplementedError()
        elif operator == "$exists":
            if not isinstance(filter_value, bool):
                raise ValueError(
                    "Expected a boolean value for $exists "
                    f"operator, but got: {filter_value}"
                )
            condition = func.jsonb_exists(
                self.EmbeddingStore.cmetadata,
                field,
            )
            return condition if filter_value else ~condition
        else:
            raise NotImplementedError()

    def _create_filter_clause_deprecated(self, key, value):  # type: ignore[no-untyped-def]
        """Deprecated functionality.

        This is for backwards compatibility with the JSON based schema for metadata.
        It uses incorrect operator syntax (operators are not prefixed with $).

        This implementation is not efficient, and has bugs associated with
        the way that it handles numeric filter clauses.
        """
        IN, NIN, BETWEEN, GT, LT, NE = "in", "nin", "between", "gt", "lt", "ne"
        EQ, LIKE, CONTAINS, OR, AND = "eq", "like", "contains", "or", "and"

        value_case_insensitive = {k.lower(): v for k, v in value.items()}
        if IN in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.in_(
                value_case_insensitive[IN]
            )
        elif NIN in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.not_in(
                value_case_insensitive[NIN]
            )
        elif BETWEEN in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.between(
                str(value_case_insensitive[BETWEEN][0]),
                str(value_case_insensitive[BETWEEN][1]),
            )
        elif GT in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext > str(
                value_case_insensitive[GT]
            )
        elif LT in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext < str(
                value_case_insensitive[LT]
            )
        elif NE in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext != str(
                value_case_insensitive[NE]
            )
        elif EQ in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext == str(
                value_case_insensitive[EQ]
            )
        elif LIKE in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.like(
                value_case_insensitive[LIKE]
            )
        elif CONTAINS in map(str.lower, value):
            filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.contains(
                value_case_insensitive[CONTAINS]
            )
        elif OR in map(str.lower, value):
            or_clauses = [
                self._create_filter_clause(key, sub_value)
                for sub_value in value_case_insensitive[OR]
            ]
            filter_by_metadata = sqlalchemy.or_(*or_clauses)
        elif AND in map(str.lower, value):
            and_clauses = [
                self._create_filter_clause(key, sub_value)
                for sub_value in value_case_insensitive[AND]
            ]
            filter_by_metadata = sqlalchemy.and_(*and_clauses)

        else:
            filter_by_metadata = None

        return filter_by_metadata

    def _create_filter_clause_json_deprecated(
        self, filter: Any
    ) -> List[SQLColumnExpression]:
        """Convert filters from IR to SQL clauses.

        **DEPRECATED** This functionality will be deprecated in the future.

        It implements translation of filters for a schema that uses JSON
        for metadata rather than the JSONB field which is more efficient
        for querying.
        """
        filter_clauses = []
        for key, value in filter.items():
            if isinstance(value, dict):
                filter_by_metadata = self._create_filter_clause_deprecated(key, value)

                if filter_by_metadata is not None:
                    filter_clauses.append(filter_by_metadata)
            else:
                filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext == str(
                    value
                )
                filter_clauses.append(filter_by_metadata)
        return filter_clauses

    def _create_filter_clause(self, filters: Any) -> Any:
        """Convert LangChain IR filter representation to matching SQLAlchemy clauses.

        At the top level, we still don't know if we're working with a field
        or an operator for the keys. After we've determined that we can
        call the appropriate logic to handle filter creation.

        Args:
            filters: Dictionary of filters to apply to the query.

        Returns:
            SQLAlchemy clause to apply to the query.
        """
        if isinstance(filters, dict):
            if len(filters) == 1:
                # The only operators allowed at the top level are $AND, $OR, and $NOT
                # First check if an operator or a field
                key, value = list(filters.items())[0]
                if key.startswith("$"):
                    # Then it's an operator
                    if key.lower() not in ["$and", "$or", "$not"]:
                        raise ValueError(
                            f"Invalid filter condition. Expected $and, $or or $not "
                            f"but got: {key}"
                        )
                else:
                    # Then it's a field
                    return self._handle_field_filter(key, filters[key])

                if key.lower() == "$and":
                    if not isinstance(value, list):
                        raise ValueError(
                            f"Expected a list, but got {type(value)} for value: {value}"
                        )
                    and_ = [self._create_filter_clause(el) for el in value]
                    if len(and_) > 1:
                        return sqlalchemy.and_(*and_)
                    elif len(and_) == 1:
                        return and_[0]
                    else:
                        raise ValueError(
                            "Invalid filter condition. Expected a dictionary "
                            "but got an empty dictionary"
                        )
                elif key.lower() == "$or":
                    if not isinstance(value, list):
                        raise ValueError(
                            f"Expected a list, but got {type(value)} for value: {value}"
                        )
                    or_ = [self._create_filter_clause(el) for el in value]
                    if len(or_) > 1:
                        return sqlalchemy.or_(*or_)
                    elif len(or_) == 1:
                        return or_[0]
                    else:
                        raise ValueError(
                            "Invalid filter condition. Expected a dictionary "
                            "but got an empty dictionary"
                        )
                elif key.lower() == "$not":
                    if isinstance(value, list):
                        not_conditions = [
                            self._create_filter_clause(item) for item in value
                        ]
                        not_ = sqlalchemy.and_(
                            *[
                                sqlalchemy.not_(condition)
                                for condition in not_conditions
                            ]
                        )
                        return not_
                    elif isinstance(value, dict):
                        not_ = self._create_filter_clause(value)
                        return sqlalchemy.not_(not_)
                    else:
                        raise ValueError(
                            f"Invalid filter condition. Expected a dictionary "
                            f"or a list but got: {type(value)}"
                        )
                else:
                    raise ValueError(
                        f"Invalid filter condition. Expected $and, $or or $not "
                        f"but got: {key}"
                    )
            elif len(filters) > 1:
                # Then all keys have to be fields (they cannot be operators)
                for key in filters.keys():
                    if key.startswith("$"):
                        raise ValueError(
                            f"Invalid filter condition. Expected a field but got: {key}"
                        )
                # These should all be fields and combined using an $and operator
                and_ = [self._handle_field_filter(k, v) for k, v in filters.items()]
                if len(and_) > 1:
                    return sqlalchemy.and_(*and_)
                elif len(and_) == 1:
                    return and_[0]
                else:
                    raise ValueError(
                        "Invalid filter condition. Expected a dictionary "
                        "but got an empty dictionary"
                    )
            else:
                raise ValueError("Got an empty dictionary for filters.")
        else:
            raise ValueError(
                f"Invalid type: Expected a dictionary but got type: {type(filters)}"
            )

    def __query_collection(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
    ) -> Sequence[Any]:
        """Query the collection."""
        with self._make_sync_session() as session:  # type: ignore[arg-type]
            collection = self.get_collection(session)
            if not collection:
                raise ValueError("Collection not found")

            filter_by = [self.EmbeddingStore.collection_id == collection.uuid]
            if filter:
                if self.use_jsonb:
                    filter_clauses = self._create_filter_clause(filter)
                    if filter_clauses is not None:
                        filter_by.append(filter_clauses)
                else:
                    # Old way of doing things
                    filter_clauses = self._create_filter_clause_json_deprecated(filter)
                    filter_by.extend(filter_clauses)

            _type = self.EmbeddingStore

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

        return results

    async def __aquery_collection(
        self,
        session: AsyncSession,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
    ) -> Sequence[Any]:
        """Query the collection."""
        async with self._make_async_session() as session:  # type: ignore[arg-type]
            collection = await self.aget_collection(session)
            if not collection:
                raise ValueError("Collection not found")

            filter_by = [self.EmbeddingStore.collection_id == collection.uuid]
            if filter:
                if self.use_jsonb:
                    filter_clauses = self._create_filter_clause(filter)
                    if filter_clauses is not None:
                        filter_by.append(filter_clauses)
                else:
                    # Old way of doing things
                    filter_clauses = self._create_filter_clause_json_deprecated(filter)
                    filter_by.extend(filter_clauses)

            _type = self.EmbeddingStore

            stmt = (
                select(
                    self.EmbeddingStore,
                    self.distance_strategy(embedding).label("distance"),
                )
                .filter(*filter_by)
                .order_by(sqlalchemy.asc("distance"))
                .join(
                    self.CollectionStore,
                    self.EmbeddingStore.collection_id == self.CollectionStore.uuid,
                )
                .limit(k)
            )

            results: Sequence[Any] = (await session.execute(stmt)).all()

            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.
        """
        assert not self._async_engine, "This method must be called without async_mode"
        docs_and_scores = self.similarity_search_with_score_by_vector(
            embedding=embedding, k=k, filter=filter
        )
        return _results_to_docs(docs_and_scores)

    async def asimilarity_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.
        """
        assert self._async_engine, "This method must be called with async_mode"
        await self.__apost_init__()  # Lazy async init
        docs_and_scores = await self.asimilarity_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,
        *,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        ids: Optional[List[str]] = None,
        pre_delete_collection: bool = False,
        use_jsonb: bool = True,
        **kwargs: Any,
    ) -> PGVector:
        """Return VectorStore initialized from documents and embeddings."""
        embeddings = embedding.embed_documents(list(texts))

        return cls.__from(
            texts,
            embeddings,
            embedding,
            metadatas=metadatas,
            ids=ids,
            collection_name=collection_name,
            distance_strategy=distance_strategy,
            pre_delete_collection=pre_delete_collection,
            use_jsonb=use_jsonb,
            **kwargs,
        )

    @classmethod
    async def afrom_texts(
        cls: Type[PGVector],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        ids: Optional[List[str]] = None,
        pre_delete_collection: bool = False,
        *,
        use_jsonb: bool = True,
        **kwargs: Any,
    ) -> PGVector:
        """Return VectorStore initialized from documents and embeddings."""
        embeddings = await embedding.aembed_documents(list(texts))
        return await cls.__afrom(
            texts,
            embeddings,
            embedding,
            metadatas=metadatas,
            ids=ids,
            collection_name=collection_name,
            distance_strategy=distance_strategy,
            pre_delete_collection=pre_delete_collection,
            use_jsonb=use_jsonb,
            **kwargs,
        )

    @classmethod
    def from_embeddings(
        cls,
        text_embeddings: List[Tuple[str, List[float]]],
        embedding: Embeddings,
        *,
        metadatas: Optional[List[dict]] = None,
        collection_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 embeddings.

        Args:
            text_embeddings: List of tuples of text and embeddings.
            embedding: Embeddings object.
            metadatas: Optional list of metadatas associated with the texts.
            collection_name: Name of the collection.
            distance_strategy: Distance strategy to use.
            ids: Optional list of ids for the documents.
                 If not provided, will generate a new id for each document.
            pre_delete_collection: If True, will delete the collection if it exists.
                **Attention**: This will delete all the documents in the existing
                collection.
            kwargs: Additional arguments.

        Returns:
            PGVector: PGVector instance.

        Example:
            .. code-block:: python

                from langchain_postgres.vectorstores import PGVector
                from langchain_openai.embeddings import OpenAIEmbeddings

                embeddings = OpenAIEmbeddings()
                text_embeddings = embeddings.embed_documents(texts)
                text_embedding_pairs = list(zip(texts, text_embeddings))
                vectorstore = 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,
            collection_name=collection_name,
            distance_strategy=distance_strategy,
            pre_delete_collection=pre_delete_collection,
            **kwargs,
        )

    @classmethod
    async def afrom_embeddings(
        cls,
        text_embeddings: List[Tuple[str, List[float]]],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        collection_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_community.vectorstores import PGVector
                from langchain_community.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 await cls.__afrom(
            texts,
            embeddings,
            embedding,
            metadatas=metadatas,
            ids=ids,
            collection_name=collection_name,
            distance_strategy=distance_strategy,
            pre_delete_collection=pre_delete_collection,
            **kwargs,
        )

    @classmethod
    def from_existing_index(
        cls: Type[PGVector],
        embedding: Embeddings,
        *,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        pre_delete_collection: bool = False,
        connection: Optional[DBConnection] = None,
        **kwargs: Any,
    ) -> PGVector:
        """
        Get instance of an existing PGVector store.This method will
        return the instance of the store without inserting any new
        embeddings
        """
        store = cls(
            connection=connection,
            collection_name=collection_name,
            embeddings=embedding,
            distance_strategy=distance_strategy,
            pre_delete_collection=pre_delete_collection,
            **kwargs,
        )

        return store

    @classmethod
    async def afrom_existing_index(
        cls: Type[PGVector],
        embedding: Embeddings,
        *,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        pre_delete_collection: bool = False,
        connection: Optional[DBConnection] = None,
        **kwargs: Any,
    ) -> PGVector:
        """
        Get instance of an existing PGVector store.This method will
        return the instance of the store without inserting any new
        embeddings
        """
        store = PGVector(
            connection=connection,
            collection_name=collection_name,
            embeddings=embedding,
            distance_strategy=distance_strategy,
            pre_delete_collection=pre_delete_collection,
            async_mode=True,
            **kwargs,
        )

        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",
            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[PGVector],
        documents: List[Document],
        embedding: Embeddings,
        *,
        connection: Optional[DBConnection] = None,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        ids: Optional[List[str]] = None,
        pre_delete_collection: bool = False,
        use_jsonb: bool = True,
        **kwargs: Any,
    ) -> PGVector:
        """Return VectorStore initialized from documents and embeddings."""

        texts = [d.page_content for d in documents]
        metadatas = [d.metadata for d in documents]

        return cls.from_texts(
            texts=texts,
            pre_delete_collection=pre_delete_collection,
            embedding=embedding,
            distance_strategy=distance_strategy,
            metadatas=metadatas,
            connection=connection,
            ids=ids,
            collection_name=collection_name,
            use_jsonb=use_jsonb,
            **kwargs,
        )

    @classmethod
    async def afrom_documents(
        cls: Type[PGVector],
        documents: List[Document],
        embedding: Embeddings,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        ids: Optional[List[str]] = None,
        pre_delete_collection: bool = False,
        *,
        use_jsonb: bool = True,
        **kwargs: Any,
    ) -> PGVector:
        """
        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"] = connection_string

        return await cls.afrom_texts(
            texts=texts,
            pre_delete_collection=pre_delete_collection,
            embedding=embedding,
            distance_strategy=distance_strategy,
            metadatas=metadatas,
            ids=ids,
            collection_name=collection_name,
            use_jsonb=use_jsonb,
            **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."""
        if driver != "psycopg":
            raise NotImplementedError("Only psycopg3 driver is supported")
        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.
        """
        assert not self._async_engine, "This method must be called without async_mode"
        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]

    async def amax_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.
        """
        await self.__apost_init__()  # Lazy async init
        async with self._make_async_session() as session:
            results = await self.__aquery_collection(
                session=session, 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.embeddings.embed_query(query)
        return self.max_marginal_relevance_search_by_vector(
            embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            **kwargs,
        )

    async def amax_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.
        """
        await self.__apost_init__()  # Lazy async init
        embedding = await self.embeddings.aembed_query(query)
        return await self.amax_marginal_relevance_search_by_vector(
            embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            **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.embeddings.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

    async def amax_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.
        """
        await self.__apost_init__()  # Lazy async init
        embedding = await self.embeddings.aembed_query(query)
        docs = await self.amax_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
            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.
        """
        await self.__apost_init__()  # Lazy async init
        docs_and_scores = (
            await self.amax_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)

    @contextlib.contextmanager
    def _make_sync_session(self) -> Generator[Session, None, None]:
        """Make an async session."""
        if self.async_mode:
            raise ValueError(
                "Attempting to use a sync method in when async mode is turned on. "
                "Please use the corresponding async method instead."
            )
        with self.session_maker() as session:
            yield typing_cast(Session, session)

    @contextlib.asynccontextmanager
    async def _make_async_session(self) -> AsyncGenerator[AsyncSession, None]:
        """Make an async session."""
        if not self.async_mode:
            raise ValueError(
                "Attempting to use an async method in when sync mode is turned on. "
                "Please use the corresponding async method instead."
            )
        async with self.session_maker() as session:
            yield typing_cast(AsyncSession, session)

    def get_by_ids(self, ids: Sequence[str], /) -> List[Document]:
        """Get documents by ids."""
        documents = []
        with self._make_sync_session() as session:
            collection = self.get_collection(session)
            filter_by = [self.EmbeddingStore.collection_id == collection.uuid]
            stmt = (
                select(
                    self.EmbeddingStore,
                )
                .where(self.EmbeddingStore.id.in_(ids))
                .filter(*filter_by)
            )

            for result in session.execute(stmt).scalars().all():
                documents.append(
                    Document(
                        id=result.id,
                        page_content=result.document,
                        metadata=result.cmetadata,
                    )
                )
        return documents

    async def aget_by_ids(self, ids: Sequence[str], /) -> List[Document]:
        """Get documents by ids."""
        documents = []
        async with self._make_async_session() as session:
            collection = await self.aget_collection(session)
            filter_by = [self.EmbeddingStore.collection_id == collection.uuid]

            stmt = (
                select(
                    self.EmbeddingStore,
                )
                .where(self.EmbeddingStore.id.in_(ids))
                .filter(*filter_by)
            )

            results: Sequence[Any] = (await session.execute(stmt)).scalars().all()

            for result in results:
                documents.append(
                    Document(
                        id=str(result.id),
                        page_content=result.document,
                        metadata=result.cmetadata,
                    )
                )
        return documents