import streamlit as st from dotenv import load_dotenv from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter from langchain.vectorstores import FAISS from langchain.embeddings import HuggingFaceEmbeddings # General embeddings from HuggingFace models. from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain from htmlTemplates import css, bot_template, user_template from langchain.llms import LlamaCpp # For loading transformer models. from langchain.document_loaders import PyPDFLoader, TextLoader, JSONLoader, CSVLoader import tempfile import os from huggingface_hub import hf_hub_download def get_pdf_text(pdf_docs): temp_dir = tempfile.TemporaryDirectory() temp_filepath = os.path.join(temp_dir.name, pdf_docs.name) with open(temp_filepath, "wb") as f: f.write(pdf_docs.getvalue()) pdf_loader = PyPDFLoader(temp_filepath) pdf_doc = pdf_loader.load() return pdf_doc def get_text_file(docs): with NamedTemporaryFile() as temp_file: temp_file.write(docs.getvalue()) temp_file.seek(0) text_loader = TextLoader(temp_file.name) text_doc = text_loader.load() return text_doc def get_csv_file(docs): with NamedTemporaryFile() as temp_file: temp_file.write(docs.getvalue()) temp_file.seek(0) text_loader = CSVLoader(temp_file.name) text_doc = text_loader.load() return text_doc def get_json_file(docs): with NamedTemporaryFile() as temp_file: temp_file.write(docs.getvalue()) temp_file.seek(0) json_loader = JSONLoader(temp_file.name, jq_schema='.scans[].relationships', text_content=False) json_doc = json_loader.load() return json_doc def get_text_chunks(documents): text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200, length_function=len ) documents = text_splitter.split_documents(documents) return documents def get_vectorstore(text_chunks): # Load the desired embeddings model. embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2', model_kwargs={'device': 'cpu'}) vectorstore = FAISS.from_documents(text_chunks, embeddings) return vectorstore def get_conversation_chain(vectorstore): model_name_or_path = 'TheBloke/Llama-2-7B-chat-GGUF' model_basename = 'llama-2-7b-chat.Q2_K.gguf' model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename) llm = LlamaCpp(model_path=model_path, n_ctx=4086, input={"temperature": 0.75, "max_length": 2000, "top_p": 1}, verbose=True, ) 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): print('user_question => ', 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") 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 doc_list = [] for file in docs: print('file - type : ', file.type) if file.type == 'text/plain': # file is .txt doc_list.extend(get_text_file(file)) elif file.type in ['application/octet-stream', 'application/pdf']: # file is .pdf doc_list.extend(get_pdf_text(file)) elif file.type == 'text/csv': # file is .csv doc_list.extend(get_csv_file(file)) elif file.type == 'application/json': # file is .json doc_list.extend(get_json_file(file)) # get the text chunks text_chunks = get_text_chunks(doc_list) # create vector store vectorstore = get_vectorstore(text_chunks) # create conversation chain st.session_state.conversation = get_conversation_chain( vectorstore) if __name__ == '__main__': main()