app.py

import gradio as gr
import os
from getpass import getpass
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings 
from langchain_community.llms import HuggingFacePipeline
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_anthropic import ChatAnthropic

from pathlib import Path
import chromadb
from unidecode import unidecode

from transformers import AutoTokenizer
import transformers
import torch
import tqdm 
import accelerate
import re

# Load PDF document and create doc splits
def load_doc(list_file_path, chunk_size, chunk_overlap):
    # Processing for one document only
    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

# Create vector database
def create_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


# Load vector database
def load_db():
    embedding = HuggingFaceEmbeddings()
    vectordb = Chroma(
        embedding_function=embedding)
    return vectordb


# Initialize langchain LLM chain
def initialize_llmchain(key, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    progress(0.1, desc="Initializing...")
    
    llm = ChatAnthropic(model_name="claude-3-opus-20240229",
                        temperature=temperature, 
                        anthropic_api_key=key
                        # max_new_tokens = max_tokens,
                        # top_k = top_k,
                        )
    
    progress(0.75, 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(0.8, desc="Defining retrieval chain...")
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff", 
        memory=memory,
        # combine_docs_chain_kwargs={"prompt": your_prompt})
        return_source_documents=True,
        #return_generated_question=False,
        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):
    # Extract filename without extension
    collection_name = Path(filepath).stem
    # Fix potential issues from naming convention
    ## Remove space
    collection_name = collection_name.replace(" ","-") 
    ## ASCII transliterations of Unicode text
    collection_name = unidecode(collection_name)
    ## Remove special characters
    #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
    ## Limit length to 50 characters
    collection_name = collection_name[:50]
    ## 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


# Initialize database
def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
    # Create list of documents (when valid)
    list_file_path = [x.name for x in list_file_obj if x is not None]
    # Create collection_name for vector database
    progress(0.1, desc="Creating collection name...")
    collection_name = create_collection_name(list_file_path[0])
    progress(0.25, desc="Loading document...")
    # Load document and create splits
    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
    # Create or load vector database
    progress(0.5, desc="Generating vector database...")
    # global vector_db
    vector_db = create_db(doc_splits, collection_name)
    progress(0.9, desc="Done!")
    return vector_db, collection_name, "Complete!"


def initialize_LLM( key, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    qa_chain = initialize_llmchain( key, 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"]
    response_source1 = response_sources[0].page_content.strip()
    response_source2 = response_sources[1].page_content.strip()
    response_source3 = response_sources[2].page_content.strip()

    # Langchain sources are zero-based
    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 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><h2>PDF-based chatbot (powered by LangChain and Anthropic Claude-3)</center></h2>
        <h3>Ask any questions about your PDF documents, along with follow-ups</h3>
        <b>Note:</b> This AI assistant performs retrieval-augmented generation from your PDF documents. \
        When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.</i>
        <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate an output.<br>
        """)
        with gr.Tab("Step 1 - Document pre-processing"):
            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)
            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_progress = gr.Textbox(label="Vector database initialization", value="None")
            with gr.Row():
                db_btn = gr.Button("Generate vector database...")
            
        with gr.Tab("Step 2 - Claude QA chain initialization"):
            with gr.Row():
              gr.Markdown(
                """<h3>To use Anthropic models, you will need to set the ANTHROPIC_API_KEY environment variable. You can get an Anthropic API key <a href="https://console.anthropic.com/settings/keys">here</a></h3>""")
            with gr.Row():
                claude_key = gr.Textbox(placeholder="Enter your Anthropic API Key...", container=True,label="Anthropic API Key")
            with gr.Accordion("Advanced options - LLM model", open=False):
                with gr.Row():
                    slider_temperature = gr.Slider(minimum = 0.0, 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():
                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
            with gr.Row():
                qachain_btn = gr.Button("Initialize question-answering chain...")

        with gr.Tab("Step 3 - Conversation with 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", container=True)
            with gr.Row():
                submit_btn = gr.Button("Submit")
                clear_btn = gr.ClearButton([msg, chatbot])
            
        # 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=[ claude_key, 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()