import streamlit as st from dotenv import load_dotenv from PyPDF2 import PdfReader from langchain.text_splitter import CharacterTextSplitter from langchain.text_splitter import RecursiveCharacterTextSplitter 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 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 #@st.cache_resource def split_texts(text, chunk_size, overlap, split_method): # Split texts # IN: text, chunk size, overlap, split_method # OUT: list of str splits st.info("`Splitting doc ...`") split_method = "RecursiveTextSplitter" text_splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size, chunk_overlap=overlap) splits = text_splitter.split_text(text) if not splits: st.error("Failed to split document") st.stop() return splits 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) chunks = split_texts(text, 1000, 200, "RecursiveCharacterTextSplitter") 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() 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 your meeting notes!", 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 your meeting notes! :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"): 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()