diff --git a/langchain/vectorstores/pgvector.py b/langchain/vectorstores/pgvector.py
index 64595230..161e8e2c 100644
--- a/langchain/vectorstores/pgvector.py
+++ b/langchain/vectorstores/pgvector.py
@@ -192,6 +192,72 @@ class PGVector(VectorStore):
     def get_collection(self, session: Session) -> Optional["CollectionStore"]:
         return 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 = DistanceStrategy.COSINE,
+        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]
+
+        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,
+        )
+
+        store.add_embeddings(
+            texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
+        )
+
+        return store
+
+    def add_embeddings(
+        self,
+        texts: List[str],
+        embeddings: List[List[float]],
+        metadatas: List[dict],
+        ids: List[str],
+        **kwargs: Any,
+    ) -> None:
+        """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
+        """
+        with Session(self._conn) as session:
+            collection = self.get_collection(session)
+            if not collection:
+                raise ValueError("Collection not found")
+            for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
+                embedding_store = EmbeddingStore(
+                    embedding=embedding,
+                    document=text,
+                    cmetadata=metadata,
+                    custom_id=id,
+                )
+                collection.embeddings.append(embedding_store)
+                session.add(embedding_store)
+            session.commit()
+
     def add_texts(
         self,
         texts: Iterable[str],
@@ -380,19 +446,64 @@ class PGVector(VectorStore):
         "Either pass it as a parameter
         or set the PGVECTOR_CONNECTION_STRING environment variable.
         """
+        embeddings = embedding.embed_documents(list(texts))
 
-        connection_string = cls.get_connection_string(kwargs)
-
-        store = cls(
-            connection_string=connection_string,
+        return cls.__from(
+            texts,
+            embeddings,
+            embedding,
+            metadatas=metadatas,
+            ids=ids,
             collection_name=collection_name,
-            embedding_function=embedding,
             distance_strategy=distance_strategy,
             pre_delete_collection=pre_delete_collection,
+            **kwargs,
         )
 
-        store.add_texts(texts=texts, metadatas=metadatas, ids=ids, **kwargs)
-        return store
+    @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 = DistanceStrategy.COSINE,
+        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 import PGVector
+                from langchain.embeddings import OpenAIEmbeddings
+                embeddings = OpenAIEmbeddings()
+                text_embeddings = embeddings.embed_documents(texts)
+                text_embedding_pairs = list(zip(texts, text_embeddings))
+                faiss = PGVector.from_embeddings(text_embedding_pairs, embeddings)
+        """
+        texts = [t[0] for t in text_embeddings]
+        embeddings = [t[1] for t in text_embeddings]
+
+        return cls.__from(
+            texts,
+            embeddings,
+            embedding,
+            metadatas=metadatas,
+            ids=ids,
+            collection_name=collection_name,
+            distance_strategy=distance_strategy,
+            pre_delete_collection=pre_delete_collection,
+            **kwargs,
+        )
 
     @classmethod
     def get_connection_string(cls, kwargs: Dict[str, Any]) -> str:
diff --git a/tests/integration_tests/vectorstores/test_pgvector.py b/tests/integration_tests/vectorstores/test_pgvector.py
index 8ad7f1bc..b8d3314d 100644
--- a/tests/integration_tests/vectorstores/test_pgvector.py
+++ b/tests/integration_tests/vectorstores/test_pgvector.py
@@ -49,6 +49,22 @@ def test_pgvector() -> None:
     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"]