diff --git a/langchain/vectorstores/_pgvector_data_models.py b/langchain/vectorstores/_pgvector_data_models.py
index 1be27e5533f..f44bd2e3527 100644
--- a/langchain/vectorstores/_pgvector_data_models.py
+++ b/langchain/vectorstores/_pgvector_data_models.py
@@ -1,9 +1,50 @@
+from typing import Optional, Tuple
+
 import sqlalchemy
 from pgvector.sqlalchemy import Vector
 from sqlalchemy.dialects.postgresql import JSON, UUID
-from sqlalchemy.orm import relationship
+from sqlalchemy.orm import Session, relationship
 
-from langchain.vectorstores.pgvector import BaseModel, CollectionStore
+from langchain.vectorstores.pgvector import BaseModel
+
+
+class CollectionStore(BaseModel):
+    __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.
+        """
+        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):
diff --git a/langchain/vectorstores/pgvector.py b/langchain/vectorstores/pgvector.py
index e3fcb3f04a5..b0958b78fa5 100644
--- a/langchain/vectorstores/pgvector.py
+++ b/langchain/vectorstores/pgvector.py
@@ -4,17 +4,30 @@ from __future__ import annotations
 import enum
 import logging
 import uuid
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Type,
+)
 
 import sqlalchemy
-from sqlalchemy.dialects.postgresql import JSON, UUID
-from sqlalchemy.orm import Session, declarative_base, relationship
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import Session, declarative_base
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
 from langchain.utils import get_from_dict_or_env
 from langchain.vectorstores.base import VectorStore
 
+if TYPE_CHECKING:
+    from langchain.vectorstores._pgvector_data_models import CollectionStore
+
 
 class DistanceStrategy(str, enum.Enum):
     """Enumerator of the Distance strategies."""
@@ -37,45 +50,6 @@ class BaseModel(Base):
     uuid = sqlalchemy.Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
 
 
-class CollectionStore(BaseModel):
-    __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.
-        """
-        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 PGVector(VectorStore):
     """VectorStore implementation using Postgres and pgvector.
 
@@ -141,8 +115,12 @@ class PGVector(VectorStore):
         """
         self._conn = self.connect()
         # self.create_vector_extension()
-        from langchain.vectorstores._pgvector_data_models import EmbeddingStore
+        from langchain.vectorstores._pgvector_data_models import (
+            CollectionStore,
+            EmbeddingStore,
+        )
 
+        self.CollectionStore = CollectionStore
         self.EmbeddingStore = EmbeddingStore
         self.create_tables_if_not_exists()
         self.create_collection()
@@ -173,7 +151,7 @@ class PGVector(VectorStore):
         if self.pre_delete_collection:
             self.delete_collection()
         with Session(self._conn) as session:
-            CollectionStore.get_or_create(
+            self.CollectionStore.get_or_create(
                 session, self.collection_name, cmetadata=self.collection_metadata
             )
 
@@ -188,7 +166,7 @@ class PGVector(VectorStore):
             session.commit()
 
     def get_collection(self, session: Session) -> Optional["CollectionStore"]:
-        return CollectionStore.get_by_name(session, self.collection_name)
+        return self.CollectionStore.get_by_name(session, self.collection_name)
 
     @classmethod
     def __from(
@@ -200,6 +178,7 @@ class PGVector(VectorStore):
         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:
@@ -208,7 +187,8 @@ class PGVector(VectorStore):
 
         if not metadatas:
             metadatas = [{} for _ in texts]
-        connection_string = cls.get_connection_string(kwargs)
+        if connection_string is None:
+            connection_string = cls.get_connection_string(kwargs)
 
         store = cls(
             connection_string=connection_string,
@@ -389,8 +369,8 @@ class PGVector(VectorStore):
                 .filter(filter_by)
                 .order_by(sqlalchemy.asc("distance"))
                 .join(
-                    CollectionStore,
-                    self.EmbeddingStore.collection_id == CollectionStore.uuid,
+                    self.CollectionStore,
+                    self.EmbeddingStore.collection_id == self.CollectionStore.uuid,
                 )
                 .limit(k)
                 .all()