From 7fbb1078da6a30da60510bf68b1514b418185d91 Mon Sep 17 00:00:00 2001
From: Ofer Mendelevitch <ofer@vectara.com>
Date: Sat, 14 Oct 2023 03:03:30 -0700
Subject: [PATCH] update of Vectara component

---
 src/backend/langflow/components/vectorstores/Vectara.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/src/backend/langflow/components/vectorstores/Vectara.py b/src/backend/langflow/components/vectorstores/Vectara.py
index 6edc69822..509d12a99 100644
--- a/src/backend/langflow/components/vectorstores/Vectara.py
+++ b/src/backend/langflow/components/vectorstores/Vectara.py
@@ -5,7 +5,6 @@ from langchain.vectorstores import Vectara
 from langchain.schema import Document
 from langchain.vectorstores.base import VectorStore
 from langchain.schema import BaseRetriever
-from langchain.embeddings.base import Embeddings
 
 
 class VectaraComponent(CustomComponent):
@@ -22,7 +21,6 @@ class VectaraComponent(CustomComponent):
         "vectara_api_key": {"display_name": "Vectara API Key", "password": True},
         "code": {"show": False},
         "documents": {"display_name": "Documents"},
-        "embedding": {"display_name": "Embedding"},
     }
 
     def build(
@@ -30,21 +28,21 @@ class VectaraComponent(CustomComponent):
         vectara_customer_id: str,
         vectara_corpus_id: str,
         vectara_api_key: str,
-        embedding: Optional[Embeddings] = None,
         documents: Optional[Document] = None,
     ) -> Union[VectorStore, BaseRetriever]:
         # If documents, then we need to create a Vectara instance using .from_documents
-        if documents is not None and embedding is not None:
+        if documents is not None:
             return Vectara.from_documents(
                 documents=documents,  # type: ignore
                 vectara_customer_id=vectara_customer_id,
                 vectara_corpus_id=vectara_corpus_id,
                 vectara_api_key=vectara_api_key,
-                embedding=embedding,
+                source='langflow',
             )
 
         return Vectara(
             vectara_customer_id=vectara_customer_id,
             vectara_corpus_id=vectara_corpus_id,
             vectara_api_key=vectara_api_key,
+            source='langflow',
         )