diff --git a/src/backend/langflow/components/vectorstores/FAISS.py b/src/backend/langflow/components/vectorstores/FAISS.py
index dec14f6db..13e5bba35 100644
--- a/src/backend/langflow/components/vectorstores/FAISS.py
+++ b/src/backend/langflow/components/vectorstores/FAISS.py
@@ -3,24 +3,35 @@ from typing import List, Union
 from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.faiss import FAISS
+
 from langflow import CustomComponent
 from langflow.field_typing import Document, Embeddings
 
 
 class FAISSComponent(CustomComponent):
     display_name = "FAISS"
-    description = "Construct FAISS wrapper from raw documents."
+    description = "Ingest documents into FAISS Vector Store."
     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.",
+            },
         }
 
     def build(
         self,
         embedding: Embeddings,
         documents: List[Document],
+        folder_path: str,
+        index_name: str = "langflow_index",
     ) -> Union[VectorStore, FAISS, BaseRetriever]:
-        return FAISS.from_documents(documents=documents, embedding=embedding)
+        vector_store = FAISS.from_documents(documents=documents, embedding=embedding)
+        if not folder_path:
+            raise ValueError("Folder path is required to save the FAISS index.")
+        path = self.resolve_path(folder_path)
+        vector_store.save_local(str(path), index_name)