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 HuggingFaceHub from pathlib import Path import chromadb llm_names = ["mistralai/Mixtral-8x7B-Instruct-v0.1"] llm_names_simple = [os.path.basename(llm) for llm in llm_names] def load_doc(list_file_path, chunk_size, chunk_overlap): loaders = [PyPDFLoader(x) for x in list_file_path] pages = [loader.load() for loader in loaders] text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) doc_splits = text_splitter.split_documents(pages) return doc_splits 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 def load_db(): embedding = HuggingFaceEmbeddings() vectordb = Chroma(embedding_function=embedding) return vectordb def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): progress(0.1, desc="Initializing HF tokenizer...") progress(0.5, desc="Initializing HF Hub...") model_kwargs = {"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k} if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1": model_kwargs["load_in_8bit"] = True llm = HuggingFaceHub(repo_id=llm_model, model_kwargs=model_kwargs) progress(0.75, desc="Defining buffer memory...") memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True) 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, return_source_documents=True, ) progress(0.9, desc="Done!") return qa_chain 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] collection_name = Path(list_file_path[0]).stem progress(0.25, desc="Loading document...") doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap) progress(0.5, desc="Generating vector database...") vector_db = create_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 = llm_names[llm_option] 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 = [f"User: {user_message}\nAssistant: {bot_message}" for user_message, bot_message in chat_history] return formatted_chat_history def conversation(qa_chain, message, history): formatted_chat_history = format_chat_history(message, history) response = qa_chain({"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_source1_page = response_sources[0].metadata["page"] + 1 response_source2_page = response_sources[1].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 def upload_file(file_obj): list_file_path = [file_obj.name for _ in file_obj] 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("""

ChatPDF

""") with gr.Tab("Step 1 - Selezione PDF"): document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)") 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): slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True) slider_chunk_overlap = gr.Slider(minimum=10, maximum=200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True) db_progress = gr.Textbox(label="Vector database initialization", value="None") db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress]) with gr.Tab("Step 2 - Inizializzazione QA"): llm_btn = gr.Radio(llm_names_simple, label="LLM models", value=llm_names_simple[0], type="index", info="Choose your LLM model") with gr.Accordion("Advanced options - LLM model", 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=224, maximum=4096, value=1024, step=32, 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) llm_progress = gr.Textbox(value="None", label="QA chain initialization") qachain_btn = gr.Button("Initialize question-answering chain...") 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], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], queue=False) with gr.Tab("Step 3 - Conversazione con Chatbot"): chatbot = gr.Chatbot(height=300) with gr.Accordion("Advanced - Document references", open=True): 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) msg = gr.Textbox(placeholder="Type message", container=True) submit_btn = gr.Button("Submit") clear_btn = gr.ClearButton([msg, chatbot]) msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_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], queue=False) clear_btn.click(lambda: [None, "", 0, "", 0], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], queue=False) demo.queue().launch(debug=True) if __name__ == "__main__": demo()