As I have already post my documents in corpus via API, how do I build vectara vectorestore ?
A normal procedure for example
from langchain.vectorstores import Vectara loader = TextLoader(“state_of_the_union.txt”) documents = loader.load() vectorstore= Vectara.from_documents(documents, embedding = None) retriever = Vectara.as_retriever(vectorstore)
I believe I do not need the codes from_documents() for loading documents because of my case.
Is there sort of way like Vectara.from_documents but from corpus directly for building vectore store so I don’t have to change my ongoing langchain pipeline as where retriever is build by Vectara.as_retriever(vectorstore) ?
Thanks for any hints.