diff --git a/src/backend/base/langflow/components/vectorstores/pgvector.py b/src/backend/base/langflow/components/vectorstores/pgvector.py
index 95808c70c..a078d67cf 100644
--- a/src/backend/base/langflow/components/vectorstores/pgvector.py
+++ b/src/backend/base/langflow/components/vectorstores/pgvector.py
@@ -11,24 +11,19 @@ from langflow.schema import Data
 class PGVectorStoreComponent(LCVectorStoreComponent):
     display_name = "PGVector"
     description = "PGVector Vector Store with search capabilities"
-    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pgvector"
+    documentation = "https://python.langchain.com/v0.2/docs/integrations/vectorstores/pgvector/"
     icon = "PGVector"
 
     inputs = [
         SecretStrInput(name="pg_server_url", display_name="PostgreSQL Server Connection String", required=True),
         StrInput(name="collection_name", display_name="Table", required=True),
-        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        MultilineInput(name="search_query", display_name="Search Query"),
         DataInput(
-            name="vector_store_inputs",
-            display_name="Vector Store Inputs",
+            name="ingest_data",
+            display_name="Ingestion Data",
             is_list=True,
         ),
-        BoolInput(
-            name="add_to_vector_store",
-            display_name="Add to Vector Store",
-            info="If true, the Vector Store Inputs will be added to the Vector Store.",
-        ),
-        MultilineInput(name="search_input", display_name="Search Input"),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
         IntInput(
             name="number_of_results",
             display_name="Number of Results",
@@ -36,33 +31,27 @@ class PGVectorStoreComponent(LCVectorStoreComponent):
             value=4,
             advanced=True,
         ),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
     ]
 
     def build_vector_store(self) -> PGVector:
         return self._build_pgvector()
 
     def _build_pgvector(self) -> PGVector:
-        if self.add_to_vector_store:
-            documents = []
-            for _input in self.vector_store_inputs or []:
-                if isinstance(_input, Data):
-                    documents.append(_input.to_lc_document())
-                else:
-                    documents.append(_input)
-
-            if documents:
-                pgvector = PGVector.from_documents(
-                    embedding=self.embedding,
-                    documents=documents,
-                    collection_name=self.collection_name,
-                    connection_string=self.pg_server_url,
-                )
+        documents = []
+        for _input in self.ingest_data or []:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
             else:
-                pgvector = PGVector.from_existing_index(
-                    embedding=self.embedding,
-                    collection_name=self.collection_name,
-                    connection_string=self.pg_server_url,
-                )
+                documents.append(_input)
+
+        if documents:
+            pgvector = PGVector.from_documents(
+                embedding=self.embedding,
+                documents=documents,
+                collection_name=self.collection_name,
+                connection_string=self.pg_server_url,
+            )
         else:
             pgvector = PGVector.from_existing_index(
                 embedding=self.embedding,
@@ -75,9 +64,9 @@ class PGVectorStoreComponent(LCVectorStoreComponent):
     def search_documents(self) -> List[Data]:
         vector_store = self._build_pgvector()
 
-        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+        if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():
             docs = vector_store.similarity_search(
-                query=self.search_input,
+                query=self.search_query,
                 k=self.number_of_results,
             )