app.py

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("<center><h1>RAG PDF chatbot</h1><center>")
        gr.Markdown("""<b>Query your PDF documents!</b> 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("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
                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("<style>body { font-size: 16px; }</style><b>Select LLM and input parameters</b>")
                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("<b>Step 2 - Chat with your Document</b>")
                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()