| import streamlit as st | |
| # from langchain.vectorstores import FAISS | |
| from langchain_community.vectorstores import FAISS | |
| # from langchain.chat_models import ChatOpenAI | |
| # from langchain.memory import ConversationBufferMemory | |
| # from langchain.chains import ConversationalRetrievalChain | |
| # Assuming this function encodes the question into a vector representation | |
| def encode_question(question,embeddings): | |
| # embeddings = HuggingFaceInstructEmbeddings() # Instantiate the embeddings model | |
| question_vector = embeddings.embed_query(question) # Encode the question into a vector | |
| return question_vector | |
| def save_vector_store(text_chunks,embeddings): | |
| # embeddings = OpenAIEmbeddings() | |
| # model = INSTRUCTOR('hkunlp/instructor-base') | |
| # embeddings = model.encode(raw_text) | |
| # embeddings = HuggingFaceInstructEmbeddings() | |
| vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| new_db = FAISS.load_local("faiss_index_V2", embeddings,allow_dangerous_deserialization=True) | |
| new_db.merge_from(vectorstore) | |
| new_db.save_local('faiss_index_V2') | |
| return st.write("vector Store is Saved") |