import gradio as gr import os from langchain_community.vectorstores import FAISS from langchain_community.document_loaders import PyPDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chains import ConversationalRetrievalChain from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.llms import HuggingFacePipeline from langchain.memory import ConversationBufferMemory import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline # Lista de modelos públicos e leves list_llm = ["EleutherAI/gpt-neo-125m", "distilbert/distilgpt2"] list_llm_simple = [os.path.basename(llm) for llm in list_llm] # Carregar e dividir documento PDF def load_doc(list_file_path): loaders = [PyPDFLoader(x) for x in list_file_path] pages = [] for loader in loaders: pages.extend(loader.load()) text_splitter = RecursiveCharacterTextSplitter( chunk_size=512, # Reduzido para acelerar a busca chunk_overlap=32 # Menor sobreposição para menos processamento ) doc_splits = text_splitter.split_documents(pages) return doc_splits # Criar banco de vetores def create_db(splits): embeddings = HuggingFaceEmbeddings() vectordb = FAISS.from_documents(splits, embeddings) return vectordb # Inicializar o chain LLM local def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): tokenizer = AutoTokenizer.from_pretrained(llm_model) model = AutoModelForCausalLM.from_pretrained( llm_model, device_map="auto", # Usa GPU se disponível torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, # Otimiza para GPU trust_remote_code=True ) # Pipeline otimizado pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=max_tokens, temperature=temperature, top_k=top_k, do_sample=False, # Greedy decoding para mais velocidade repetition_penalty=1.1, return_full_text=False ) llm = HuggingFacePipeline(pipeline=pipe) memory = ConversationBufferMemory( memory_key="chat_history", output_key="answer", return_messages=True ) retriever = vector_db.as_retriever(search_kwargs={"k": 2}) # Reduzir número de documentos retornados qa_chain = ConversationalRetrievalChain.from_llm( llm, retriever=retriever, chain_type="stuff", memory=memory, return_source_documents=True, verbose=False, ) return qa_chain # Inicializar banco de dados 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, "Database created!" # Inicializar LLM 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, "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"] 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 = "" # Menos referências para acelerar response_source1_page = response_sources[0].metadata["page"] + 1 response_source2_page = response_sources[1].metadata["page"] + 1 response_source3_page = 0 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=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo: vector_db = gr.State() qa_chain = gr.State() gr.HTML("

RAG PDF chatbot

") gr.Markdown("""Query your PDF documents! This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. Optimized for speed without an API token. \ Please do not upload confidential documents. """) with gr.Row(): with gr.Column(scale=86): gr.Markdown("Step 1 - Upload PDF documents and Initialize RAG pipeline") with gr.Row(): document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents") with gr.Row(): db_btn = gr.Button("Create vector database") with gr.Row(): db_progress = gr.Textbox(value="Not initialized", show_label=False) gr.Markdown("Select Large Language Model (LLM) and input parameters") 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", info="Controls randomness (ignored with greedy decoding)", interactive=True) with gr.Row(): slider_maxtokens = gr.Slider(minimum=64, maximum=512, value=128, step=64, label="Max New Tokens", info="Maximum number of tokens to be generated", interactive=True) with gr.Row(): slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k", info="Number of tokens to select (ignored with greedy decoding)", interactive=True) 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("Step 2 - Chat with your Document") chatbot = gr.Chatbot(height=505) with gr.Accordion("Relevant context from the source document", 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="Ask a question", container=True) with gr.Row(): submit_btn = gr.Button("Submit") clear_btn = gr.ClearButton([msg, chatbot], value="Clear") # Eventos de pré-processamento 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], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False ) # Eventos do chatbot 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()