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| 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 huggingface_hub import snapshot_download | |
| from llama_cpp import Llama | |
| 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() | |
| #llm = HuggingFaceHub(repo_id="tinkoff-ai/ruDialoGPT-medium", model_kwargs={"temperature": 0.1, | |
| #"top_k": 10, | |
| # "top_p": 0.95,}) | |
| #"max_length": 512}) | |
| repo_name = "IlyaGusev/saiga2_7b_gguf" | |
| model_name = "model-q2_K.gguf" | |
| snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name) | |
| llm = Llama(model_path=model_name, n_ctx=2000, n_parts=1) | |
| 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"): | |
| 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) | |
| main() | |