import streamlit as st import pickle import os import torch from tqdm.auto import tqdm from langchain.text_splitter import RecursiveCharacterTextSplitter # from langchain.vectorstores import Chroma from langchain.vectorstores import FAISS from langchain.embeddings import HuggingFaceInstructEmbeddings from langchain import HuggingFacePipeline from langchain.chains import RetrievalQA st.set_page_config( page_title = 'aitGPT', page_icon = '✅') st.markdown("# Hello") with open("ait-web-document", "rb") as fp: ait_web_documents = pickle.load(fp) text_splitter = RecursiveCharacterTextSplitter( # Set a really small chunk size, just to show. chunk_size = 500, chunk_overlap = 100, length_function = len, ) chunked_text = text_splitter.create_documents([doc for doc in tqdm(ait_web_documents)]) st.markdown(f"Number of Documents: {len(ait_web_documents)}") st.markdown(f"Number of chunked texts: {len(chunked_text)}") embedding_model = HuggingFaceInstructEmbeddings(model_name='hkunlp/instructor-base', model_kwargs = {'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu')}) vector_database = FAISS.load_local("faiss_index", embedding_model) print("load done") query_input = st.text_input(label= 'your question') def retrieve_document(query_input): related_doc = vector_database.similarity_search(query_input) return related_doc output = st.text_area(label = "Here is the relevant documents", value = retrieve_document(query_input))