diff --git a/src/backend/langflow/components/vectorstores/FAISS.py b/src/backend/langflow/components/vectorstores/FAISS.py
index 13e5bba35..0cecab8e7 100644
--- a/src/backend/langflow/components/vectorstores/FAISS.py
+++ b/src/backend/langflow/components/vectorstores/FAISS.py
@@ -21,6 +21,7 @@ class FAISSComponent(CustomComponent):
                 "display_name": "Folder Path",
                 "info": "Path to save the FAISS index. It will be relative to where Langflow is running.",
             },
+            "index_name": {"display_name": "Index Name"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/vectorstores/FAISSSearch.py b/src/backend/langflow/components/vectorstores/FAISSSearch.py
new file mode 100644
index 000000000..dbc63faac
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/FAISSSearch.py
@@ -0,0 +1,45 @@
+from typing import List
+
+from langchain_community.vectorstores.faiss import FAISS
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.field_typing import Embeddings
+from langflow.schema import Record
+
+
+class FAISSSearchComponent(LCVectorStoreComponent):
+    display_name = "FAISS Search"
+    description = "Search a FAISS Vector Store for similar documents."
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "folder_path": {
+                "display_name": "Folder Path",
+                "info": "Path to save the FAISS index. It will be relative to where Langflow is running.",
+            },
+            "input_value": {"display_name": "Input"},
+            "index_name": {"display_name": "Index Name"},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        folder_path: str,
+        index_name: str = "langflow_index",
+    ) -> List[Record]:
+        if not folder_path:
+            raise ValueError("Folder path is required to save the FAISS index.")
+        path = self.resolve_path(folder_path)
+        vector_store = FAISS.load_local(
+            folder_path=str(path), embeddings=embedding, index_name=index_name
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the FAISS index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type="similarity"
+        )
diff --git a/src/backend/langflow/components/vectorstores/pgvectorSearch.py b/src/backend/langflow/components/vectorstores/pgvectorSearch.py
new file mode 100644
index 000000000..f525611bd
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/pgvectorSearch.py
@@ -0,0 +1,75 @@
+from typing import List, Optional
+
+from langchain.embeddings.base import Embeddings
+from langchain_community.vectorstores.pgvector import PGVector
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.schema import Record
+
+
+class PGVectorSearchComponent(LCVectorStoreComponent):
+    """
+    A custom component for implementing a Vector Store using PostgreSQL.
+    """
+
+    display_name: str = "PGVector Search"
+    description: str = "Search a PGVector Store for similar documents."
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
+    )
+
+    def build_config(self):
+        """
+        Builds the configuration for the component.
+
+        Returns:
+        - dict: A dictionary containing the configuration options for the component.
+        """
+        return {
+            "code": {"show": False},
+            "embedding": {"display_name": "Embedding"},
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "pg_server_url": {
+                "display_name": "PostgreSQL Server Connection String",
+                "advanced": False,
+            },
+            "collection_name": {"display_name": "Table", "advanced": False},
+            "input_value": {"display_name": "Input"},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        pg_server_url: str,
+        collection_name: str,
+        search_type: Optional[str] = None,
+    ) -> List[Record]:
+        """
+        Builds the Vector Store or BaseRetriever object.
+
+        Args:
+        - input_value (str): The input value to search for.
+        - embedding (Embeddings): The embeddings to use for the Vector Store.
+        - collection_name (str): The name of the PG table.
+        - pg_server_url (str): The URL for the PG server.
+
+        Returns:
+        - VectorStore: The Vector Store object.
+        """
+
+        try:
+            vector_store = PGVector.from_existing_index(
+                embedding=embedding,
+                collection_name=collection_name,
+                connection_string=pg_server_url,
+            )
+
+        except Exception as e:
+            raise RuntimeError(f"Failed to build PGVector: {e}")
+        return self.search_with_vector_store(
+            input_value=input_value, search_type=search_type, vector_store=vector_store
+        )