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| import gradio as gr | |
| import os | |
| from langchain.document_loaders import PyPDFLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.llms import HuggingFacePipeline | |
| from langchain.chains import ConversationChain | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.llms import HuggingFaceHub | |
| from transformers import AutoTokenizer | |
| import transformers | |
| import torch | |
| import tqdm | |
| import accelerate | |
| default_persist_directory = './chroma_HF/' | |
| default_llm_name1 = "tiiuae/falcon-7b-instruct" | |
| default_llm_name2 = "google/flan-t5-xxl" | |
| default_llm_name3 = "mosaicml/mpt-7b-instruct" | |
| default_llm_name4 = "meta-llama/Llama-2-7b-chat-hf" | |
| default_llm_name5 = "mistralai/Mistral-7B-Instruct-v0.1" | |
| list_llm = [default_llm_name1, default_llm_name2, default_llm_name3, default_llm_name4, default_llm_name5] | |
| # Load PDF document and create doc splits | |
| def load_doc(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 | |
| # Create vector database | |
| def create_db(splits): | |
| embedding = HuggingFaceEmbeddings() | |
| vectordb = Chroma.from_documents( | |
| documents=splits, | |
| embedding=embedding, | |
| persist_directory=default_persist_directory | |
| ) | |
| return vectordb | |
| # Load vector database | |
| def load_db(): | |
| embedding = HuggingFaceEmbeddings() | |
| vectordb = Chroma( | |
| persist_directory=default_persist_directory, | |
| embedding_function=embedding) | |
| return vectordb | |
| # Initialize langchain LLM chain | |
| def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
| progress(0.1, desc="Initializing HF tokenizer...") | |
| # HuggingFacePipeline uses local model | |
| # Warning: it will download model locally... | |
| # tokenizer=AutoTokenizer.from_pretrained(llm_model) | |
| # progress(0.5, desc="Initializing HF pipeline...") | |
| # pipeline=transformers.pipeline( | |
| # "text-generation", | |
| # model=llm_model, | |
| # tokenizer=tokenizer, | |
| # torch_dtype=torch.bfloat16, | |
| # trust_remote_code=True, | |
| # device_map="auto", | |
| # # max_length=1024, | |
| # max_new_tokens=max_tokens, | |
| # do_sample=True, | |
| # top_k=top_k, | |
| # num_return_sequences=1, | |
| # eos_token_id=tokenizer.eos_token_id | |
| # ) | |
| # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature}) | |
| # HuggingFaceHub uses HF inference endpoints | |
| progress(0.5, desc="Initializing HF Hub...") | |
| llm = HuggingFaceHub( | |
| repo_id=llm_model, | |
| model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k} | |
| ) | |
| progress(0.5, desc="Defining buffer memory...") | |
| memory = ConversationBufferMemory( | |
| memory_key="chat_history", | |
| 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...") | |
| global qa_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=True, | |
| # verbose=True, | |
| ) | |
| progress(0.9, desc="Done!") | |
| # return qa_chain | |
| # Initialize all elements | |
| def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()): | |
| # Create list of documents (when valid) | |
| #file_path = file_obj.name | |
| list_file_path = [x.name for x in list_file_obj if x is not None] | |
| print('list_file_path', list_file_path) | |
| 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) | |
| progress(0.9, desc="Done!") | |
| return vector_db, "Complete!" | |
| #return qa_chain | |
| def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
| print("llm_option",llm_option) | |
| llm_name = list_llm[llm_option] | |
| print("llm_name",llm_name) | |
| initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress) | |
| return "Complete!" | |
| #return qa_chain | |
| 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(message, history): | |
| formatted_chat_history = format_chat_history(message, history) | |
| #print("formatted_chat_history",formatted_chat_history) | |
| # Generate response using QA chain | |
| response = qa_chain({"question": message, "chat_history": formatted_chat_history}) | |
| # return response['answer'] | |
| # Append user message and response to chat history | |
| new_history = history + [(message, response["answer"])] | |
| return gr.update(value=""), new_history | |
| 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) | |
| # print(file_path) | |
| # initialize_database(file_path, progress) | |
| return list_file_path | |
| def demo(): | |
| with gr.Blocks(theme="base") as demo: | |
| vector_db = gr.State() | |
| # qa_chain = gr.Variable() | |
| gr.Markdown( | |
| """<center><h2> Document-based chatbot</center></h2> | |
| <h3>Ask any questions about your PDF documents (single or multiple)</h3> | |
| <i>Note: chatbot performs question-answering using Langchain and LLMs</i> | |
| """) | |
| 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 PDF Documents") | |
| # 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", value = "ChromaDB", type="index", info="Choose your vector database") | |
| with gr.Accordion("Advanced options - 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=50, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True) | |
| with gr.Row(): | |
| db_progress = gr.Textbox(label="Database Initialization", value="None") | |
| with gr.Row(): | |
| db_btn = gr.Button("Generating vector database...") | |
| with gr.Tab("Step 2 - Initializing QA chain"): | |
| with gr.Row(): | |
| llm_btn = gr.Radio(["falcon-7b-instruct", "flan-t5-xxl", "mpt-7b-instruct", "Llama-2-7b-chat-hf", "Mistral-7B-Instruct-v0.1"], \ | |
| label="LLM", value = "falcon-7b-instruct", type="index", info="Choose your LLM model") | |
| with gr.Accordion("Advanced options - LLM", open=False): | |
| slider_temperature = gr.Slider(minimum = 0.0, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True) | |
| slider_maxtokens = gr.Slider(minimum = 256, maximum = 4096, value=1024, step=24, label="Max Tokens", info="Model max tokens", interactive=True) | |
| 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("QA chain Initialization...") | |
| with gr.Tab("Step 3 - Conversation"): | |
| chatbot = gr.Chatbot(height=600) | |
| 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, db_progress]) | |
| qachain_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], outputs=[llm_progress]).then(lambda: None, None, chatbot, queue=False) | |
| # Chatbot events | |
| msg.submit(conversation, [msg, chatbot], [msg, chatbot], queue=False) | |
| submit_btn.click(conversation, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False) | |
| clear_btn.click(lambda: None, None, chatbot, queue=False) | |
| demo.queue().launch(debug=True) | |
| if __name__ == "__main__": | |
| demo() | |