import gradio as gr import os from langchain_community.vectorstores import FAISS, Chroma from langchain_community.document_loaders import PyPDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chains import ConversationalRetrievalChain from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.llms import HuggingFaceEndpoint from langchain.memory import ConversationBufferMemory from pathlib import Path # Set environment variable for Hugging Face API token api_token = os.getenv("HF_TOKEN") # LLM model options list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"] list_llm_simple = [os.path.basename(llm) for llm in list_llm] # Set directory for persistent storage default_persist_directory = './chroma_database/' # Ensure directory exists # Load and split PDF document def load_doc(list_file_path): loaders = [PyPDFLoader(x) for x in list_file_path] pages = [] for loader in loaders: pages.extend(loader.load()) text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64) doc_splits = text_splitter.split_documents(pages) return doc_splits # Create or update vector database with Chroma and persistence def create_db(splits): embeddings = HuggingFaceEmbeddings() vectordb = Chroma.from_documents( documents=splits, embedding=embeddings, persist_directory=default_persist_directory # Set persistence directory ) vectordb.persist() # Ensure data is saved to chroma.sqlite3 return vectordb # Initialize langchain LLM chain def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): llm = HuggingFaceEndpoint( repo_id=llm_model, huggingfacehub_api_token=api_token, temperature=temperature, max_new_tokens=max_tokens, top_k=top_k ) memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True) retriever = vector_db.as_retriever() qa_chain = ConversationalRetrievalChain.from_llm( llm, retriever=retriever, chain_type="stuff", memory=memory, return_source_documents=True, verbose=False ) return qa_chain # Initialize database with persistence def initialize_database(list_file_obj, progress=gr.Progress()): list_file_path = [x.name for x in list_file_obj if x is not None] doc_splits = load_doc(list_file_path) vector_db = create_db(doc_splits) return vector_db, "Database created and persisted!" # Initialize LLM def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): llm_name = list_llm[llm_option] qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress) return qa_chain, "QA chain initialized. Chatbot is ready!" def format_chat_history(message, chat_history): formatted_chat_history = [] for user_message, bot_message in chat_history: formatted_chat_history.append(f"User: {user_message}") formatted_chat_history.append(f"Assistant: {bot_message}") return formatted_chat_history # Conversation handling def conversation(qa_chain, message, history): formatted_chat_history = format_chat_history(message, history) response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history}) response_answer = response["answer"] response_sources = response["source_documents"] response_source1 = response_sources[0].page_content.strip() response_source2 = response_sources[1].page_content.strip() response_source3 = response_sources[2].page_content.strip() response_source1_page = response_sources[0].metadata["page"] + 1 response_source2_page = response_sources[1].metadata["page"] + 1 response_source3_page = response_sources[2].metadata["page"] + 1 new_history = history + [(message, response_answer)] return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page # Gradio UI setup def demo(): with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo: vector_db = gr.State() qa_chain = gr.State() gr.HTML("

RAG PDF chatbot

") gr.Markdown("""Query your PDF documents! This AI agent performs retrieval augmented generation (RAG) on PDF documents. \ Please do not upload confidential documents.""") with gr.Row(): with gr.Column(scale=86): gr.Markdown("Step 1 - Upload PDF documents and Initialize RAG pipeline") document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents") db_btn = gr.Button("Create vector database") db_progress = gr.Textbox(value="Not initialized", show_label=False) gr.Markdown("Select LLM and input parameters") llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index") with gr.Accordion("LLM input parameters", open=False): slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.1, label="Temperature") slider_maxtokens = gr.Slider(minimum=128, maximum=9192, value=4096, step=128, label="Max New Tokens") slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k") qachain_btn = gr.Button("Initialize Question Answering Chatbot") llm_progress = gr.Textbox(value="Not initialized", show_label=False) with gr.Column(scale=200): gr.Markdown("Step 2 - Chat with your Document") chatbot = gr.Chatbot(height=505) with gr.Accordion("Relevant context from the source document", open=False): doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20) source1_page = gr.Number(label="Page", scale=1) doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20) source2_page = gr.Number(label="Page", scale=1) doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20) source3_page = gr.Number(label="Page", scale=1) msg = gr.Textbox(placeholder="Ask a question", container=True) submit_btn = gr.Button("Submit") clear_btn = gr.ClearButton([msg, chatbot], value="Clear") # Set up Gradio events db_btn.click(initialize_database, inputs=[document], outputs=[vector_db, db_progress]) qachain_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], outputs=[qa_chain, llm_progress]).then(lambda: [None, "", 0, "", 0, "", 0], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False) msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False) submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False) clear_btn.click(lambda: [None, "", 0, "", 0, "", 0], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False) demo.queue().launch(debug=True) if __name__ == "__main__": demo()