from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.document_loaders import UnstructuredFileLoader from langchain.vectorstores.faiss import FAISS from langchain.embeddings import OpenAIEmbeddings import pickle # Load Data loader = UnstructuredFileLoader("state_of_the_union.txt") raw_documents = loader.load() # Split text text_splitter = RecursiveCharacterTextSplitter() documents = text_splitter.split_documents(raw_documents) # Load Data to vectorstore embeddings = OpenAIEmbeddings() vectorstore = FAISS.from_documents(documents, embeddings) query = "What is Sales Handoff?" docs = vectorstore.similarity_search(query) db = FAISS.from_documents(docs, embeddings) print(docs[0].page_content) # Save vectorstore # with open("vectorstore.pkl", "wb") as f: # pickle.dump(vectorstore, f)