import streamlit as st from langchain.llms import OpenAI from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.chains import RetrievalQA def generate_response(uploaded_file, openai_api_key, query_text): # Load document if file is uploaded if uploaded_file is not None: documents = [uploaded_file.read().decode()] # Split documents into chunks text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.create_documents(documents) # Select embeddings embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) # Create a vectorstore from documents db = Chroma.from_documents(texts, embeddings) # Create retriever interface retriever = db.as_retriever() # Create QA chain qa = RetrievalQA.from_chain_type(llm=OpenAI(openai_api_key=openai_api_key), chain_type='stuff', retriever=retriever) return qa.run(query_text) # Page title st.set_page_config(page_title='🦜🔗 Ask the Doc App') st.title('🦜🔗 Ask the Doc App') # File upload uploaded_file = st.file_uploader('Upload an article', type='txt') # Query text query_text = st.text_input('Enter your question:', placeholder = 'Please provide a short summary.', disabled=not uploaded_file) # Form input and query result = [] with st.form('myform', clear_on_submit=True): openai_api_key = st.text_input('OpenAI API Key', type='password', disabled=not (uploaded_file and query_text)) submitted = st.form_submit_button('Submit', disabled=not(uploaded_file and query_text)) if submitted and openai_api_key.startswith('sk-'): with st.spinner('Calculating...'): response = generate_response(uploaded_file, openai_api_key, query_text) result.append(response) del openai_api_key if len(result): st.info(response)