app.py

import gradio as gr
import os
api_token = os.getenv("HF_TOKEN")

from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import TextLoader
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 langchain.prompts import PromptTemplate

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]

# Custom prompt template
CUSTOM_PROMPT_TEMPLATE = """
**Response Instructions:**
- Write a detailed, coherent, and insightful article that fully addresses the query based on the provided context.
- Adhere to the following principles:
  1. **Define the Core Subject**: Introduce and build the discussion logically around the main topic.
  2. **Establish Connections**: Highlight relationships between ideas and concepts with reasoning and examples.
  3. **Elaborate on Key Points**: Provide in-depth explanations and emphasize the significance of concepts.
  4. **Maintain Objectivity**: Use only the context provided, avoiding speculation or external knowledge.
  5. **Ensure Structure and Clarity**: Present information sequentially for a smooth narrative flow.
  6. **Engage with Content**: Explore implicit meanings, resolve doubts, and address counterpoints logically.
  7. **Provide Examples and Insights**: Use examples to clarify abstract ideas and offer actionable steps if applicable.
  8. **Logical Depth**: Draw inferences, explain purposes, and refute opposing ideas when necessary.

Context: {context}
Question: {question}
Chat History: {chat_history}

Craft the response as a seamless, thorough, and authoritative explanation that naturally integrates all aspects of the query.
"""

# Load and split text documents
def load_doc(list_file_path):
    pages = []
    for file_path in list_file_path:
        if file_path.endswith('.txt'):
            loader = TextLoader(file_path)
            pages.extend(loader.load())
    
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1024,
        chunk_overlap=64
    )  
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits

# Create vector database
def create_db(splits):
    embeddings = HuggingFaceEmbeddings()
    vectordb = FAISS.from_documents(splits, embeddings)
    return vectordb

# Initialize langchain LLM chain with custom prompt
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
    )

    # Create custom prompt
    custom_prompt = PromptTemplate(
        template=CUSTOM_PROMPT_TEMPLATE,
        input_variables=["context", "question", "chat_history"]
    )

    retriever = vector_db.as_retriever()
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff",
        memory=memory,
        return_source_documents=True,
        verbose=False,
        combine_docs_chain_kwargs={"prompt": custom_prompt}
    )
    return qa_chain

# Initialize database
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, "Text database created!"

# 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

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"]
    
    # Get sources (with fallback for when there are fewer than 3 sources)
    sources_content = []
    sources_pages = []
    for i in range(3):
        if i < len(response_sources):
            sources_content.append(response_sources[i].page_content.strip())
            sources_pages.append(0)  # For text files, we don't have page numbers
        else:
            sources_content.append("")
            sources_pages.append(0)
    
    new_history = history + [(message, response_answer)]
    return (qa_chain, gr.update(value=""), new_history, 
            sources_content[0], sources_pages[0],
            sources_content[1], sources_pages[1],
            sources_content[2], sources_pages[2])

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 Text Document Chatbot</h1><center>")
        gr.Markdown("""<b>Query your text documents!</b> This AI agent performs retrieval augmented generation (RAG) on TXT documents. 
        <b>Please do not upload confidential documents.</b>
        """)
        
        with gr.Row():
            with gr.Column(scale=86):
                gr.Markdown("<b>Step 1 - Upload Text Files and Initialize RAG pipeline</b>")
                with gr.Row():
                    document = gr.Files(height=300, file_count="multiple", 
                                      file_types=["txt"], interactive=True, 
                                      label="Upload TXT documents")
                with gr.Row():
                    db_btn = gr.Button("Create text database")
                with gr.Row():
                    db_progress = gr.Textbox(value="Not initialized", show_label=False)
                
                gr.Markdown("<b>Select Large Language Model (LLM) and input parameters</b>")
                with gr.Row():
                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", 
                                      value=list_llm_simple[0], type="index")
                with gr.Row():
                    with gr.Accordion("LLM input parameters", open=False):
                        with gr.Row():
                            slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.5, 
                                                         step=0.1, label="Temperature")
                        with gr.Row():
                            slider_maxtokens = gr.Slider(minimum=128, maximum=9192, value=4096, 
                                                       step=128, label="Max New Tokens")
                        with gr.Row():
                            slider_topk = gr.Slider(minimum=1, maximum=10, value=3, 
                                                  step=1, label="top-k")
                with gr.Row():
                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
                with gr.Row():
                    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):
                    for i in range(1, 4):
                        with gr.Row():
                            doc_source = gr.Textbox(label=f"Reference {i}", lines=2, 
                                                  container=True, scale=20)
                            source_page = gr.Number(label="Line Range", scale=1, visible=False)
                with gr.Row():
                    msg = gr.Textbox(placeholder="Ask a question", container=True)
                with gr.Row():
                    submit_btn = gr.Button("Submit")
                    clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
        
        # Event handlers
        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],
                             outputs=[chatbot] + [doc for i in range(1,4) for doc in [globals()[f"doc_source{i}"], globals()[f"source_page{i}"]]],
                             queue=False)

        msg.submit(conversation, inputs=[qa_chain, msg, chatbot],
                  outputs=[qa_chain, msg, chatbot] + [globals()[f"doc_source{i}"] for i in range(1,4)] + [globals()[f"source_page{i}"] for i in range(1,4)]],
                  queue=False)
        submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot],
                       outputs=[qa_chain, msg, chatbot] + [globals()[f"doc_source{i}"] for i in range(1,4)] + [globals()[f"source_page{i}"] for i in range(1,4)]],
                       queue=False)
        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0],
                       outputs=[chatbot] + [doc for i in range(1,4) for doc in [globals()[f"doc_source{i}"], globals()[f"source_page{i}"]]],
                       queue=False)
    
    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()