🔨 refactor(Vectara.py): reorganize function parameters for better readability and maintainability

🔧 chore(Vectara.py): add "embedding" parameter to the build function to support custom embeddings in VectaraComponent

src/backend/langflow/components/vectorstores/Vectara.py

@@ -5,6 +5,7 @@ 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):
@@ -21,14 +22,16 @@ 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(
         self,
-        documents: Optional[Document],
         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:
@@ -37,6 +40,7 @@ class VectaraComponent(CustomComponent):
                 vectara_customer_id=vectara_customer_id,
                 vectara_corpus_id=vectara_corpus_id,
                 vectara_api_key=vectara_api_key,
+                embedding=embedding,
             )
 
         return Vectara(