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libs/partners/postgres/langchain_postgres/vectorstores.py

@@ -0,0 +1,994 @@
+"""Postgres vectorstore."""
+from __future__ import annotations
+
+import contextlib
+import enum
+import logging
+import uuid
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Type,
+)
+
+import sqlalchemy
+from langchain_core._api import warn_deprecated
+from sqlalchemy import SQLColumnExpression, cast, delete, func
+from sqlalchemy.dialects.postgresql import JSON, JSONB, JSONPATH, UUID
+from sqlalchemy.orm import Session, relationship
+
+try:
+    from sqlalchemy.orm import declarative_base
+except ImportError:
+    from sqlalchemy.ext.declarative import declarative_base
+
+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
+
+
+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"
+
+
+class BaseModel(Base):
+    """Base model for the SQL stores."""
+
+    __abstract__ = True
+    uuid = sqlalchemy.Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+
+
+_classes: Any = None
+
+COMPARISONS_TO_NATIVE = {
+    "$eq": "==",
+    "$ne": "!=",
+    "$lt": "<",
+    "$lte": "<=",
+    "$gt": ">",
+    "$gte": ">=",
+}
+
+SPECIAL_CASED_OPERATORS = {
+    "$in",
+    "$nin",
+    "$between",
+}
+
+TEXT_OPERATORS = {
+    "$like",
+    "$ilike",
+}
+
+LOGICAL_OPERATORS = {"$and", "$or"}
+
+SUPPORTED_OPERATORS = (
+    set(COMPARISONS_TO_NATIVE)
+    .union(TEXT_OPERATORS)
+    .union(LOGICAL_OPERATORS)
+    .union(SPECIAL_CASED_OPERATORS)
+)
+
+
+def _get_embedding_collection_store(vector_dimension: int) -> Any:
+    """Get the Embedding and Collection store classes."""
+    global _classes
+    if _classes is not None:
+        return _classes
+
+    from pgvector.sqlalchemy import Vector
+
+    class CollectionStore(BaseModel):
+        """Collection store."""
+
+        __tablename__ = "langchain_pg_collection"
+
+        name = sqlalchemy.Column(sqlalchemy.String)
+        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(cls.name == name).first()  # type: ignore
+
+        @classmethod
+        def get_or_create(
+            cls,
+            session: Session,
+            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 = 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
+
+    class EmbeddingStore(BaseModel):
+        """Embedding store."""
+
+        __tablename__ = "langchain_pg_embedding"
+
+        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)
+
+        # custom_id : any user defined id
+        custom_id = sqlalchemy.Column(sqlalchemy.String, 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]
+
+
+class PGVector(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.
+        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.
+        create_extension: If True, will create the vector extension if it doesn't exist.
+            disabling creation is useful when using ReadOnly Databases.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.vectorstores import PGVector
+            from langchain_community.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,
+                collection_name=COLLECTION_NAME,
+                connection_string=CONNECTION_STRING,
+            )
+    """
+
+    def __init__(
+        self,
+        connection_string: str,
+        embedding_function: Embeddings,
+        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,
+        *,
+        connection: Optional[sqlalchemy.engine.Connection] = None,
+        engine_args: Optional[dict[str, Any]] = None,
+        create_extension: bool = True,
+    ) -> None:
+        """Initialize the PGVector store."""
+        self.connection_string = connection_string
+        self.embedding_function = embedding_function
+        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_args = engine_args or {}
+        self._bind = connection if connection else self._create_engine()
+        self.create_extension = create_extension
+
+        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()
+
+    # def __del__(self) -> None:
+    #     if isinstance(self._bind, sqlalchemy.engine.Connection):
+    #         self._bind.close()
+
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
+    def _create_engine(self) -> sqlalchemy.engine.Engine:
+        return sqlalchemy.create_engine(url=self.connection_string, **self.engine_args)
+
+    def create_vector_extension(self) -> None:
+        try:
+            with Session(self._bind) as session:  # type: ignore[arg-type]
+                # The advisor lock fixes issue arising from concurrent
+                # creation of the vector extension.
+                # https://github.com/langchain-ai/langchain/issues/12933
+                # For more information see:
+                # https://www.postgresql.org/docs/16/explicit-locking.html#ADVISORY-LOCKS
+                statement = sqlalchemy.text(
+                    "BEGIN;"
+                    "SELECT pg_advisory_xact_lock(1573678846307946496);"
+                    "CREATE EXTENSION IF NOT EXISTS vector;"
+                    "COMMIT;"
+                )
+                session.execute(statement)
+                session.commit()
+        except Exception as e:
+            raise Exception(f"Failed to create vector extension: {e}") from e
+
+    def create_tables_if_not_exists(self) -> None:
+        with Session(self._bind) as session, session.begin():  # type: ignore[arg-type]
+            Base.metadata.create_all(session.get_bind())
+
+    def drop_tables(self) -> None:
+        with Session(self._bind) as session, session.begin():  # type: ignore[arg-type]
+            Base.metadata.drop_all(session.get_bind())
+
+    def create_collection(self) -> None:
+        if self.pre_delete_collection:
+            self.delete_collection()
+        with Session(self._bind) as session:  # type: ignore[arg-type]
+            self.CollectionStore.get_or_create(
+                session, self.collection_name, cmetadata=self.collection_metadata
+            )
+
+    def delete_collection(self) -> None:
+        self.logger.debug("Trying to delete collection")
+        with Session(self._bind) as session:  # type: ignore[arg-type]
+            collection = self.get_collection(session)
+            if not collection:
+                self.logger.warning("Collection not found")
+                return
+            session.delete(collection)
+            session.commit()
+
+    @contextlib.contextmanager
+    def _make_session(self) -> Generator[Session, None, None]:
+        """Create a context manager for the session, bind to _conn string."""
+        yield Session(self._bind)  # type: ignore[arg-type]
+
+    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 Session(self._bind) as session:  # type: ignore[arg-type]
+            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.custom_id.in_(ids))
+                session.execute(stmt)
+            session.commit()
+
+    def get_collection(self, session: Session) -> Any:
+        return self.CollectionStore.get_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_string: Optional[str] = None,
+        pre_delete_collection: bool = False,
+        **kwargs: Any,
+    ) -> PGVector:
+        if ids is None:
+            ids = [str(uuid.uuid1()) for _ in texts]
+
+        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,
+            collection_name=collection_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 ids is None:
+            ids = [str(uuid.uuid1()) for _ in texts]
+
+        if not metadatas:
+            metadatas = [{} for _ in texts]
+
+        with Session(self._bind) as session:  # type: ignore[arg-type]
+            collection = self.get_collection(session)
+            if not collection:
+                raise ValueError("Collection not found")
+            documents = []
+            for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
+                embedding_store = self.EmbeddingStore(
+                    embedding=embedding,
+                    document=text,
+                    cmetadata=metadata,
+                    custom_id=id,
+                    collection_id=collection.uuid,
+                )
+                documents.append(embedding_store)
+            session.bulk_save_objects(documents)
+            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.EmbeddingStore.document,
+                    metadata=result.EmbeddingStore.cmetadata,
+                ),
+                result.distance if self.embedding_function is not None else None,
+            )
+            for result in results
+        ]
+        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", "$nin"}:
+                for val in filter_value:
+                    if not isinstance(val, (str, int, float)):
+                        raise NotImplementedError(
+                            f"Unsupported type: {type(val)} for value: {val}"
+                        )
+
+            queried_field = self.EmbeddingStore.cmetadata[field].astext
+
+            if operator in {"$in"}:
+                return queried_field.in_([str(val) for val in filter_value])
+            elif operator in {"$nin"}:
+                return queried_field.nin_([str(val) for val in filter_value])
+            elif operator in {"$like"}:
+                return queried_field.like(filter_value)
+            elif operator in {"$ilike"}:
+                return queried_field.ilike(filter_value)
+            else:
+                raise NotImplementedError()
+        else:
+            raise NotImplementedError()
+
+    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 and $OR
+                # First check if an operator or a field
+                key, value = list(filters.items())[0]
+                if key.startswith("$"):
+                    # Then it's an operator
+                    if key.lower() not in ["$and", "$or"]:
+                        raise ValueError(
+                            f"Invalid filter condition. Expected $and or $or "
+                            f"but got: {key}"
+                        )
+                else:
+                    # Then it's a field
+                    return self._handle_field_filter(key, filters[key])
+
+                # Here we handle the $and and $or operators
+                if not isinstance(value, list):
+                    raise ValueError(
+                        f"Expected a list, but got {type(value)} for value: {value}"
+                    )
+                if key.lower() == "$and":
+                    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":
+                    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"
+                        )
+                else:
+                    raise ValueError(
+                        f"Invalid filter condition. Expected $and or $or "
+                        f"but got: {key}"
+                    )
+            elif len(filters) > 1:
+                # Then all keys have to be fields (they cannot be operators)
+                for key in filters.keys():
+                    if key.startswith("$"):
+                        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,
+    ) -> List[Any]:
+        """Query the collection."""
+        with Session(self._bind) 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:
+                filter_clauses = self._create_filter_clause(filter)
+                if filter_clauses is not None:
+                    filter_by.append(filter_clauses)
+
+            _type = self.EmbeddingStore
+
+            results: List[Any] = (
+                session.query(
+                    self.EmbeddingStore,
+                    self.distance_strategy(embedding).label("distance"),  # type: ignore
+                )
+                .filter(*filter_by)
+                .order_by(sqlalchemy.asc("distance"))
+                .join(
+                    self.CollectionStore,
+                    self.EmbeddingStore.collection_id == self.CollectionStore.uuid,
+                )
+                .limit(k)
+                .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.
+        """
+        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,
+        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:
+        """
+        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,
+            collection_name=collection_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,
+        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 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
+    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,
+        **kwargs: Any,
+    ) -> PGVector:
+        """
+        Get instance 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,
+            collection_name=collection_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[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,
+        **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_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,
+            collection_name=collection_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."
+            )

libs/partners/postgres/poetry.lock

@@ -194,6 +194,77 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
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+    {file = "SQLAlchemy-2.0.29-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:25664e18bef6dc45015b08f99c63952a53a0a61f61f2e48a9e70cec27e55f699"},
+    {file = "SQLAlchemy-2.0.29-cp39-cp39-win32.whl", hash = "sha256:77d29cb6c34b14af8a484e831ab530c0f7188f8efed1c6a833a2c674bf3c26ec"},
+    {file = "SQLAlchemy-2.0.29-cp39-cp39-win_amd64.whl", hash = "sha256:04c487305ab035a9548f573763915189fc0fe0824d9ba28433196f8436f1449c"},
+    {file = "SQLAlchemy-2.0.29-py3-none-any.whl", hash = "sha256:dc4ee2d4ee43251905f88637d5281a8d52e916a021384ec10758826f5cbae305"},
+    {file = "SQLAlchemy-2.0.29.tar.gz", hash = "sha256:bd9566b8e58cabd700bc367b60e90d9349cd16f0984973f98a9a09f9c64e86f0"},
+]
+
+[package.dependencies]
+greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
+typing-extensions = ">=4.6.0"
+
+[package.extras]
+aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
+aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
+aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
+asyncio = ["greenlet (!=0.4.17)"]
+asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
+mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
+mssql = ["pyodbc"]
+mssql-pymssql = ["pymssql"]
+mssql-pyodbc = ["pyodbc"]
+mypy = ["mypy (>=0.910)"]
+mysql = ["mysqlclient (>=1.4.0)"]
+mysql-connector = ["mysql-connector-python"]
+oracle = ["cx_oracle (>=8)"]
+oracle-oracledb = ["oracledb (>=1.0.1)"]
+postgresql = ["psycopg2 (>=2.7)"]
+postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
+postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
+postgresql-psycopg = ["psycopg (>=3.0.7)"]
+postgresql-psycopg2binary = ["psycopg2-binary"]
+postgresql-psycopg2cffi = ["psycopg2cffi"]
+postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
+pymysql = ["pymysql"]
+sqlcipher = ["sqlcipher3_binary"]
+
 [[package]]
 name = "tenacity"
 version = "8.2.3"
@@ -773,4 +1001,4 @@ zstd = ["zstandard (>=0.18.0)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "3ffcb2a37d4a25f6073fd59a7f2a14cdd89f03d847e651e9bcd8625426d28f50"
+content-hash = "3b883a965fbc147a1da57889401b3992dbc866945b0936236831d7acb807c9c8"

libs/partners/postgres/pyproject.toml

@@ -14,6 +14,8 @@ license = "MIT"
 python = ">=3.8.1,<4.0"
 langchain-core = "^0.1"
 psycopg = "^3.1.18"
+sqlalchemy = "^2.0.29"
+pgvector = "^0.2.5"
 
 [tool.poetry.group.test]
 optional = true

libs/partners/postgres/tests/integration_tests/fake_embeddings.py

@@ -0,0 +1,28 @@
+"""Copied from community."""
+from typing import List
+
+from langchain_core.embeddings import Embeddings
+
+fake_texts = ["foo", "bar", "baz"]
+
+
+class FakeEmbeddings(Embeddings):
+    """Fake embeddings functionality for testing."""
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Return simple embeddings.
+        Embeddings encode each text as its index."""
+        return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        return self.embed_documents(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """Return constant query embeddings.
+        Embeddings are identical to embed_documents(texts)[0].
+        Distance to each text will be that text's index,
+        as it was passed to embed_documents."""
+        return [float(1.0)] * 9 + [float(0.0)]
+
+    async def aembed_query(self, text: str) -> List[float]:
+        return self.embed_query(text)

libs/partners/postgres/tests/integration_tests/fixtures/filtering_test_cases.py

@@ -0,0 +1,218 @@
+"""Module needs to move to a stasndalone package."""
+from langchain_core.documents import Document
+
+metadatas = [
+    {
+        "name": "adam",
+        "date": "2021-01-01",
+        "count": 1,
+        "is_active": True,
+        "tags": ["a", "b"],
+        "location": [1.0, 2.0],
+        "id": 1,
+        "height": 10.0,  # Float column
+        "happiness": 0.9,  # Float column
+        "sadness": 0.1,  # Float column
+    },
+    {
+        "name": "bob",
+        "date": "2021-01-02",
+        "count": 2,
+        "is_active": False,
+        "tags": ["b", "c"],
+        "location": [2.0, 3.0],
+        "id": 2,
+        "height": 5.7,  # Float column
+        "happiness": 0.8,  # Float column
+        "sadness": 0.1,  # Float column
+    },
+    {
+        "name": "jane",
+        "date": "2021-01-01",
+        "count": 3,
+        "is_active": True,
+        "tags": ["b", "d"],
+        "location": [3.0, 4.0],
+        "id": 3,
+        "height": 2.4,  # Float column
+        "happiness": None,
+        # Sadness missing intentionally
+    },
+]
+texts = ["id {id}".format(id=metadata["id"]) for metadata in metadatas]
+
+DOCUMENTS = [
+    Document(page_content=text, metadata=metadata)
+    for text, metadata in zip(texts, metadatas)
+]
+
+
+TYPE_1_FILTERING_TEST_CASES = [
+    # These tests only involve equality checks
+    (
+        {"id": 1},
+        [1],
+    ),
+    # String field
+    (
+        # check name
+        {"name": "adam"},
+        [1],
+    ),
+    # Boolean fields
+    (
+        {"is_active": True},
+        [1, 3],
+    ),
+    (
+        {"is_active": False},
+        [2],
+    ),
+    # And semantics for top level filtering
+    (
+        {"id": 1, "is_active": True},
+        [1],
+    ),
+    (
+        {"id": 1, "is_active": False},
+        [],
+    ),
+]
+
+TYPE_2_FILTERING_TEST_CASES = [
+    # These involve equality checks and other operators
+    # like $ne, $gt, $gte, $lt, $lte, $not
+    (
+        {"id": 1},
+        [1],
+    ),
+    (
+        {"id": {"$ne": 1}},
+        [2, 3],
+    ),
+    (
+        {"id": {"$gt": 1}},
+        [2, 3],
+    ),
+    (
+        {"id": {"$gte": 1}},
+        [1, 2, 3],
+    ),
+    (
+        {"id": {"$lt": 1}},
+        [],
+    ),
+    (
+        {"id": {"$lte": 1}},
+        [1],
+    ),
+    # Repeat all the same tests with name (string column)
+    (
+        {"name": "adam"},
+        [1],
+    ),
+    (
+        {"name": "bob"},
+        [2],
+    ),
+    (
+        {"name": {"$eq": "adam"}},
+        [1],
+    ),
+    (
+        {"name": {"$ne": "adam"}},
+        [2, 3],
+    ),
+    # And also gt, gte, lt, lte relying on lexicographical ordering
+    (
+        {"name": {"$gt": "jane"}},
+        [],
+    ),
+    (
+        {"name": {"$gte": "jane"}},
+        [3],
+    ),
+    (
+        {"name": {"$lt": "jane"}},
+        [1, 2],
+    ),
+    (
+        {"name": {"$lte": "jane"}},
+        [1, 2, 3],
+    ),
+    (
+        {"is_active": {"$eq": True}},
+        [1, 3],
+    ),
+    (
+        {"is_active": {"$ne": True}},
+        [2],
+    ),
+    # Test float column.
+    (
+        {"height": {"$gt": 5.0}},
+        [1, 2],
+    ),
+    (
+        {"height": {"$gte": 5.0}},
+        [1, 2],
+    ),
+    (
+        {"height": {"$lt": 5.0}},
+        [3],
+    ),
+    (
+        {"height": {"$lte": 5.8}},
+        [2, 3],
+    ),
+]
+
+TYPE_3_FILTERING_TEST_CASES = [
+    # These involve usage of AND and OR operators
+    (
+        {"$or": [{"id": 1}, {"id": 2}]},
+        [1, 2],
+    ),
+    (
+        {"$or": [{"id": 1}, {"name": "bob"}]},
+        [1, 2],
+    ),
+    (
+        {"$and": [{"id": 1}, {"id": 2}]},
+        [],
+    ),
+    (
+        {"$or": [{"id": 1}, {"id": 2}, {"id": 3}]},
+        [1, 2, 3],
+    ),
+]
+
+TYPE_4_FILTERING_TEST_CASES = [
+    # These involve special operators like $in, $nin, $between
+    # Test between
+    (
+        {"id": {"$between": (1, 2)}},
+        [1, 2],
+    ),
+    (
+        {"id": {"$between": (1, 1)}},
+        [1],
+    ),
+    (
+        {"name": {"$in": ["adam", "bob"]}},
+        [1, 2],
+    ),
+]
+
+TYPE_5_FILTERING_TEST_CASES = [
+    # These involve special operators like $like, $ilike that
+    # may be specified to certain databases.
+    (
+        {"name": {"$like": "a%"}},
+        [1],
+    ),
+    (
+        {"name": {"$like": "%a%"}},  # adam and jane
+        [1, 3],
+    ),
+]

libs/partners/postgres/tests/integration_tests/test_vectorstore.py

@@ -0,0 +1,476 @@
+"""Test PGVector functionality."""
+import os
+from typing import Any, Dict, Generator, List
+
+import pytest
+import sqlalchemy
+from langchain_core.documents import Document
+from sqlalchemy.orm import Session
+
+from langchain_postgres.vectorstores import (
+    SUPPORTED_OPERATORS,
+    PGVector,
+)
+from tests.integration_tests.fake_embeddings import FakeEmbeddings
+from tests.integration_tests.fixtures.filtering_test_cases import (
+    DOCUMENTS,
+    TYPE_1_FILTERING_TEST_CASES,
+    TYPE_2_FILTERING_TEST_CASES,
+    TYPE_3_FILTERING_TEST_CASES,
+    TYPE_4_FILTERING_TEST_CASES,
+    TYPE_5_FILTERING_TEST_CASES,
+)
+
+# The connection string matches the default settings in the docker-compose file
+# located in the root of the repository: [root]/docker/docker-compose.yml
+# Non-standard ports are used to avoid conflicts with other local postgres
+# instances.
+# To spin up postgres with the pgvector extension:
+# cd [root]/docker/docker-compose.yml
+# docker compose up pgvector
+CONNECTION_STRING = PGVector.connection_string_from_db_params(
+    driver=os.environ.get("TEST_PGVECTOR_DRIVER", "psycopg"),
+    host=os.environ.get("TEST_PGVECTOR_HOST", "localhost"),
+    port=int(os.environ.get("TEST_PGVECTOR_PORT", "6024")),
+    database=os.environ.get("TEST_PGVECTOR_DATABASE", "langchain"),
+    user=os.environ.get("TEST_PGVECTOR_USER", "langchain"),
+    password=os.environ.get("TEST_PGVECTOR_PASSWORD", "langchain"),
+)
+
+ADA_TOKEN_COUNT = 1536
+
+
+class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
+    """Fake embeddings functionality for testing."""
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Return simple embeddings."""
+        return [
+            [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
+        ]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Return simple embeddings."""
+        return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
+
+
+def test_pgvector(pgvector: PGVector) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+def test_pgvector_embeddings() -> None:
+    """Test end to end construction with embeddings and search."""
+    texts = ["foo", "bar", "baz"]
+    text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts)
+    text_embedding_pairs = list(zip(texts, text_embeddings))
+    docsearch = PGVector.from_embeddings(
+        text_embeddings=text_embedding_pairs,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+def test_pgvector_with_metadatas() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo", metadata={"page": "0"})]
+
+
+def test_pgvector_with_metadatas_with_scores() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score("foo", k=1)
+    assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
+
+
+def test_pgvector_with_filter_match() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "0"})
+    assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
+
+
+def test_pgvector_with_filter_distant_match() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
+    assert output == [
+        (Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406)
+    ]
+
+
+def test_pgvector_with_filter_no_match() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "5"})
+    assert output == []
+
+
+def test_pgvector_collection_with_metadata() -> None:
+    """Test end to end collection construction"""
+    pgvector = PGVector(
+        collection_name="test_collection",
+        collection_metadata={"foo": "bar"},
+        embedding_function=FakeEmbeddingsWithAdaDimension(),
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    session = Session(pgvector._create_engine())
+    collection = pgvector.get_collection(session)
+    if collection is None:
+        assert False, "Expected a CollectionStore object but received None"
+    else:
+        assert collection.name == "test_collection"
+        assert collection.cmetadata == {"foo": "bar"}
+
+
+def test_pgvector_with_filter_in_set() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score(
+        "foo", k=2, filter={"page": {"IN": ["0", "2"]}}
+    )
+    assert output == [
+        (Document(page_content="foo", metadata={"page": "0"}), 0.0),
+        (Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406),
+    ]
+
+
+def test_pgvector_with_filter_nin_set() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score(
+        "foo", k=2, filter={"page": {"NIN": ["1"]}}
+    )
+    assert output == [
+        (Document(page_content="foo", metadata={"page": "0"}), 0.0),
+        (Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406),
+    ]
+
+
+def test_pgvector_delete_docs() -> None:
+    """Add and delete documents."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        ids=["1", "2", "3"],
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    docsearch.delete(["1", "2"])
+    with docsearch._make_session() as session:
+        records = list(session.query(docsearch.EmbeddingStore).all())
+        # ignoring type error since mypy cannot determine whether
+        # the list is sortable
+        assert sorted(record.custom_id for record in records) == ["3"]  # type: ignore
+
+    docsearch.delete(["2", "3"])  # Should not raise on missing ids
+    with docsearch._make_session() as session:
+        records = list(session.query(docsearch.EmbeddingStore).all())
+        # ignoring type error since mypy cannot determine whether
+        # the list is sortable
+        assert sorted(record.custom_id for record in records) == []  # type: ignore
+
+
+def test_pgvector_relevance_score() -> None:
+    """Test to make sure the relevance score is scaled to 0-1."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+
+    output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
+    assert output == [
+        (Document(page_content="foo", metadata={"page": "0"}), 1.0),
+        (Document(page_content="bar", metadata={"page": "1"}), 0.9996744261675065),
+        (Document(page_content="baz", metadata={"page": "2"}), 0.9986996093328621),
+    ]
+
+
+def test_pgvector_retriever_search_threshold() -> None:
+    """Test using retriever for searching with threshold."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+
+    retriever = docsearch.as_retriever(
+        search_type="similarity_score_threshold",
+        search_kwargs={"k": 3, "score_threshold": 0.999},
+    )
+    output = retriever.get_relevant_documents("summer")
+    assert output == [
+        Document(page_content="foo", metadata={"page": "0"}),
+        Document(page_content="bar", metadata={"page": "1"}),
+    ]
+
+
+def test_pgvector_retriever_search_threshold_custom_normalization_fn() -> None:
+    """Test searching with threshold and custom normalization function"""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+        relevance_score_fn=lambda d: d * 0,
+    )
+
+    retriever = docsearch.as_retriever(
+        search_type="similarity_score_threshold",
+        search_kwargs={"k": 3, "score_threshold": 0.5},
+    )
+    output = retriever.get_relevant_documents("foo")
+    assert output == []
+
+
+def test_pgvector_with_custom_connection() -> None:
+    """Test construction using a custom connection."""
+    texts = ["foo", "bar", "baz"]
+    engine = sqlalchemy.create_engine(CONNECTION_STRING)
+    with engine.connect() as connection:
+        docsearch = PGVector.from_texts(
+            texts=texts,
+            collection_name="test_collection",
+            embedding=FakeEmbeddingsWithAdaDimension(),
+            connection_string=CONNECTION_STRING,
+            pre_delete_collection=True,
+            connection=connection,
+        )
+        output = docsearch.similarity_search("foo", k=1)
+        assert output == [Document(page_content="foo")]
+
+
+def test_pgvector_with_custom_engine_args() -> None:
+    """Test construction using custom engine arguments."""
+    texts = ["foo", "bar", "baz"]
+    engine_args = {
+        "pool_size": 5,
+        "max_overflow": 10,
+        "pool_recycle": -1,
+        "pool_use_lifo": False,
+        "pool_pre_ping": False,
+        "pool_timeout": 30,
+    }
+    docsearch = PGVector.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+        engine_args=engine_args,
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+# We should reuse this test-case across other integrations
+# Add database fixture using pytest
+@pytest.fixture
+def pgvector() -> Generator[PGVector, None, None]:
+    """Create a PGVector instance."""
+    store = PGVector.from_documents(
+        documents=DOCUMENTS,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+        relevance_score_fn=lambda d: d * 0,
+        use_jsonb=True,
+    )
+    try:
+        yield store
+    # Do clean up
+    finally:
+        store.drop_tables()
+
+
+@pytest.mark.parametrize("test_filter, expected_ids", TYPE_1_FILTERING_TEST_CASES)
+def test_pgvector_with_with_metadata_filters_1(
+    pgvector: PGVector,
+    test_filter: Dict[str, Any],
+    expected_ids: List[int],
+) -> None:
+    """Test end to end construction and search."""
+    docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
+    assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
+
+
+@pytest.mark.parametrize("test_filter, expected_ids", TYPE_2_FILTERING_TEST_CASES)
+def test_pgvector_with_with_metadata_filters_2(
+    pgvector: PGVector,
+    test_filter: Dict[str, Any],
+    expected_ids: List[int],
+) -> None:
+    """Test end to end construction and search."""
+    docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
+    assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
+
+
+@pytest.mark.parametrize("test_filter, expected_ids", TYPE_3_FILTERING_TEST_CASES)
+def test_pgvector_with_with_metadata_filters_3(
+    pgvector: PGVector,
+    test_filter: Dict[str, Any],
+    expected_ids: List[int],
+) -> None:
+    """Test end to end construction and search."""
+    docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
+    assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
+
+
+@pytest.mark.parametrize("test_filter, expected_ids", TYPE_4_FILTERING_TEST_CASES)
+def test_pgvector_with_with_metadata_filters_4(
+    pgvector: PGVector,
+    test_filter: Dict[str, Any],
+    expected_ids: List[int],
+) -> None:
+    """Test end to end construction and search."""
+    docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
+    assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
+
+
+@pytest.mark.parametrize("test_filter, expected_ids", TYPE_5_FILTERING_TEST_CASES)
+def test_pgvector_with_with_metadata_filters_5(
+    pgvector: PGVector,
+    test_filter: Dict[str, Any],
+    expected_ids: List[int],
+) -> None:
+    """Test end to end construction and search."""
+    docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
+    assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
+
+
+@pytest.mark.parametrize(
+    "invalid_filter",
+    [
+        ["hello"],
+        {
+            "id": 2,
+            "$name": "foo",
+        },
+        {"$or": {}},
+        {"$and": {}},
+        {"$between": {}},
+        {"$eq": {}},
+    ],
+)
+def test_invalid_filters(pgvector: PGVector, invalid_filter: Any) -> None:
+    """Verify that invalid filters raise an error."""
+    with pytest.raises(ValueError):
+        pgvector._create_filter_clause(invalid_filter)
+
+
+def test_validate_operators() -> None:
+    """Verify that all operators have been categorized."""
+    assert sorted(SUPPORTED_OPERATORS) == [
+        "$and",
+        "$between",
+        "$eq",
+        "$gt",
+        "$gte",
+        "$ilike",
+        "$in",
+        "$like",
+        "$lt",
+        "$lte",
+        "$ne",
+        "$nin",
+        "$or",
+    ]