app.py

import os
import re
from pathlib import Path

import chromadb
import gradio as gr
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.vectorstores import Chroma
from unidecode import unidecode

list_llm = [
    "mistralai/Mistral-7B-Instruct-v0.2",
    "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "mistralai/Mistral-7B-Instruct-v0.1",
    "google/gemma-7b-it",
    "google/gemma-2b-it",
    "HuggingFaceH4/zephyr-7b-beta",
    "tiiuae/falcon-7b-instruct",
    "google/flan-t5-xxl",
]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]


def load_doc_and_create_splits(list_file_path, chunk_size, chunk_overlap):
    # Processing for one document only
    # loader = PyPDFLoader(file_path)
    # pages = loader.load()
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = []
    for loader in loaders:
        pages.extend(loader.load())
    # text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size, chunk_overlap=chunk_overlap
    )
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits


def create_vector_db(splits, collection_name):
    embedding = HuggingFaceEmbeddings()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
    )
    return vectordb


def initialize_llmchain(
    llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()
):
    progress(0.1, desc="Initializing HF Hub...")
    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model,
            temperature=temperature,
            max_new_tokens=max_tokens,
            top_k=top_k,
            load_in_8bit=True,
        )
    else:
        llm = HuggingFaceEndpoint(
            repo_id=llm_model,
            temperature=temperature,
            max_new_tokens=max_tokens,
            top_k=top_k,
        )

    progress(0.6, desc="Defining buffer memory...")
    memory = ConversationBufferMemory(
        memory_key="chat_history", output_key="answer", return_messages=True
    )
    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
    retriever = vector_db.as_retriever()

    progress(0.75, desc="Defining retrieval chain...")
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff",
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )

    progress(0.9, desc="Done!")
    return qa_chain


# Generate collection name for vector database
#  - Use filepath as input, ensuring unicode text
def create_collection_name(filepath):
    collection_name = Path(filepath).stem  # Extract filename without extension

    # Fix potential issues from naming convention
    collection_name = collection_name.replace(" ", "-")  # Remove space
    collection_name = unidecode(
        collection_name
    )  # ASCII transliterations of Unicode text
    collection_name = re.sub(
        "[^A-Za-z0-9]+", "-", collection_name
    )  # Remove special characters
    collection_name = collection_name[:50]  # Limit length to 50 characters

    # Minimum length of 3 characters
    if len(collection_name) < 3:
        collection_name = collection_name + "xyz"

    # Enforce start and end as alphanumeric character
    if not collection_name[0].isalnum():
        collection_name = "A" + collection_name[1:]
    if not collection_name[-1].isalnum():
        collection_name = collection_name[:-1] + "Z"

    print("Filepath: ", filepath)
    print("Collection name: ", collection_name)
    return collection_name


def initialize_database(
    list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()
):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    progress(0.1, desc="Creating collection name...")
    collection_name = create_collection_name(list_file_path[0])

    progress(0.25, desc="Loading document...")
    doc_splits = load_doc_and_create_splits(list_file_path, chunk_size, chunk_overlap)

    progress(0.5, desc="Generating vector database...")
    vector_db = create_vector_db(doc_splits, collection_name)

    progress(0.9, desc="Done!")
    return vector_db, collection_name, "Complete!"


def initialize_LLM(
    llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()
):
    llm_name = list_llm[llm_option]
    print("llm_name: ", llm_name)
    qa_chain = initialize_llmchain(
        llm_name, llm_temperature, max_tokens, top_k, vector_db, progress
    )
    return qa_chain, "Complete!"


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)

    # Generate response using QA chain
    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    if response_answer.find("Helpful Answer:") != -1:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    response_sources = response["source_documents"]
    # Langchain sources are zero-based
    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

    # Append user message and response to chat history
    new_history = history + [(message, response_answer)]
    # return gr.update(value=""), new_history, response_sources[0], response_sources[1]
    return (
        qa_chain,
        gr.update(value=""),
        new_history,
        response_source1,
        response_source1_page,
        response_source2,
        response_source2_page,
        response_source3,
        response_source3_page,
    )


def upload_file(file_obj):
    list_file_path = []
    for idx, file in enumerate(file_obj):
        file_path = file_obj.name
        list_file_path.append(file_path)
    return list_file_path


def demo():
    with gr.Blocks(theme="base") as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        collection_name = gr.State()

        gr.Markdown(
            """
            <center><h1>Chat with your PDF</center></h1>
            <center><h3>Ask any questions about your PDF documents</h3><center>
            """
        )
        # gr.Markdown(
        #    """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
        #    This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
        #    """
        # )

        with gr.Tab("Chatbot configuration"):
            gr.Markdown("1. Upload the PDF(s)")
            with gr.Row():
                document = gr.Files(
                    height=100,
                    file_count="multiple",
                    file_types=["pdf"],
                    interactive=True,
                    label="Upload your PDF documents (single or multiple)",
                )
                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)

            gr.Markdown("2. Configure the vector database")
            with gr.Row():
                with gr.Row():
                    db_btn = gr.Radio(
                        ["ChromaDB"],
                        label="Vector database type",
                        value="ChromaDB",
                        type="index",
                        info="Choose your vector database",
                    )
                with gr.Accordion(
                    "Advanced options - Document text splitter", open=False
                ):
                    with gr.Row():
                        slider_chunk_size = gr.Slider(
                            minimum=100,
                            maximum=1000,
                            value=600,
                            step=20,
                            label="Chunk size",
                            info="Chunk size",
                            interactive=True,
                        )
                    with gr.Row():
                        slider_chunk_overlap = gr.Slider(
                            minimum=10,
                            maximum=200,
                            value=40,
                            step=10,
                            label="Chunk overlap",
                            info="Chunk overlap",
                            interactive=True,
                        )
            with gr.Row():
                db_btn = gr.Button("Generate vector database", size="sm")
            with gr.Row():
                db_progress = gr.Textbox(
                    label="Vector database initialization", value="0% Configure the DB"
                )

            gr.Markdown("3. Configure the LLM model")
            with gr.Row():
                with gr.Row():
                    llm_btn = gr.Radio(
                        list_llm_simple,
                        label="LLM models",
                        value=list_llm_simple[0],
                        type="index",
                        info="Choose your LLM model",
                    )
                with gr.Accordion("Advanced options - LLM model", open=False):
                    with gr.Row():
                        slider_temperature = gr.Slider(
                            minimum=0.01,
                            maximum=1.0,
                            value=0.7,
                            step=0.1,
                            label="Temperature",
                            info="Model temperature",
                            interactive=True,
                        )
                    with gr.Row():
                        slider_maxtokens = gr.Slider(
                            minimum=224,
                            maximum=4096,
                            value=1024,
                            step=32,
                            label="Max Tokens",
                            info="Model max tokens",
                            interactive=True,
                        )
                    with gr.Row():
                        slider_topk = gr.Slider(
                            minimum=1,
                            maximum=10,
                            value=3,
                            step=1,
                            label="top-k samples",
                            info="Model top-k samples",
                            interactive=True,
                        )
            with gr.Row():
                qachain_btn = gr.Button(
                    "Initialize Question Answering chain", size="sm"
                )
            with gr.Row():
                llm_progress = gr.Textbox(
                    label="QA chain initialization", value="0% Configure the QA chain"
                )

        with gr.Tab("Chatbot"):
            chatbot = gr.Chatbot(height=300)
            with gr.Accordion("Advanced - Document references", open=False):
                with gr.Row():
                    doc_source1 = gr.Textbox(
                        label="Reference 1", lines=2, container=True, scale=20
                    )
                    source1_page = gr.Number(label="Page", scale=1)
                with gr.Row():
                    doc_source2 = gr.Textbox(
                        label="Reference 2", lines=2, container=True, scale=20
                    )
                    source2_page = gr.Number(label="Page", scale=1)
                with gr.Row():
                    doc_source3 = gr.Textbox(
                        label="Reference 3", lines=2, container=True, scale=20
                    )
                    source3_page = gr.Number(label="Page", scale=1)
            with gr.Row():
                msg = gr.Textbox(
                    placeholder="Type message (e.g. 'What is this document about?')",
                    container=True,
                )
            with gr.Row():
                submit_btn = gr.Button("Submit message")
                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")

        # Preprocessing events
        # upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
        db_btn.click(
            initialize_database,
            inputs=[document, slider_chunk_size, slider_chunk_overlap],
            outputs=[vector_db, collection_name, 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,
        )

        # Chatbot events
        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()