import streamlit as st from dotenv import load_dotenv from PyPDF2 import PdfReader from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings from langchain.vectorstores import FAISS from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain from htmlTemplates import css, bot_template, user_template from langchain.llms import HuggingFaceHub from langchain.callbacks import get_openai_callback def get_pdf_text(pdf_docs): text = "" for pdf in pdf_docs: pdf_reader = PdfReader(pdf) for page in pdf_reader.pages: text += page.extract_text() return text def get_text_chunks(text): text_splitter = CharacterTextSplitter( separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len ) chunks = text_splitter.split_text(text) return chunks def get_vectorstore(text_chunks): # embeddings = OpenAIEmbeddings() embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) return vectorstore def get_conversation_chain(vectorstore): # llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k") llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512}) memory = ConversationBufferMemory( memory_key='chat_history', return_messages=True) conversation_chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=vectorstore.as_retriever(), memory=memory ) return conversation_chain def handle_userinput(user_question): response = st.session_state.conversation({'question': user_question}) st.session_state.chat_history = response['chat_history'] for i, message in enumerate(st.session_state.chat_history): if i % 2 == 0: st.write(user_template.replace( "{{MSG}}", message.content), unsafe_allow_html=True) else: st.write(bot_template.replace( "{{MSG}}", message.content), unsafe_allow_html=True) def main(): load_dotenv() st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:") st.write(css, unsafe_allow_html=True) if "conversation" not in st.session_state: st.session_state.conversation = None if "chat_history" not in st.session_state: st.session_state.chat_history = None st.header("Chat with multiple PDFs :books:") user_question = st.text_input("Ask a question about your documents:") if user_question: handle_userinput(user_question) with st.sidebar: st.subheader("Your documents") pdf_docs = st.file_uploader( "Upload your PDFs here and click on 'Process'", accept_multiple_files=True) if st.button("Process"): if(len(pdf_docs) == 0): st.error("Please upload at least one PDF") else: with st.spinner("Processing"): # get pdf text raw_text = get_pdf_text(pdf_docs) # get the text chunks text_chunks = get_text_chunks(raw_text) # create vector store vectorstore = get_vectorstore(text_chunks) # create conversation chain st.session_state.conversation = get_conversation_chain( vectorstore) if __name__ == '__main__': main() # import os # import getpass # import streamlit as st # from langchain.document_loaders import PyPDFLoader # from langchain.text_splitter import RecursiveCharacterTextSplitter # from langchain.embeddings import HuggingFaceEmbeddings # from langchain.vectorstores import Chroma # from langchain import HuggingFaceHub # from langchain.chains import RetrievalQA # # __import__('pysqlite3') # # import sys # # sys.modules['sqlite3'] = sys.modules.pop('pysqlite3') # # load huggingface api key # hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"] # # use streamlit file uploader to ask user for file # # file = st.file_uploader("Upload PDF") # path = "Geeta.pdf" # loader = PyPDFLoader(path) # pages = loader.load() # # st.write(pages) # splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20) # docs = splitter.split_documents(pages) # embeddings = HuggingFaceEmbeddings() # doc_search = Chroma.from_documents(docs, embeddings) # repo_id = "tiiuae/falcon-7b" # llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000}) # from langchain.schema import retriever # retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever()) # if query := st.chat_input("Enter a question: "): # with st.chat_message("assistant"): # st.write(retireval_chain.run(query))