import subprocess subprocess.run(["pip", "install", "--upgrade", "transformers[torch,sentencepiece]==4.34.1"]) import logging from pathlib import Path from time import perf_counter import gradio as gr from jinja2 import Environment, FileSystemLoader from backend.query_llm import generate_hf, generate_openai from backend.semantic_search import table, retriever, ranker proj_dir = Path(__file__).parent # Setting up the logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Set up the template environment with the templates directory env = Environment(loader=FileSystemLoader(proj_dir / 'templates')) # Load the templates directly from the environment template = env.get_template('template.j2') template_html = env.get_template('template_html.j2') # Examples examples = ['What is the capital of China?', 'Why is the sky blue?', 'Who won the mens world cup in 2014?', ] def add_text(history, text): history = [] if history is None else history history = history + [(text, None)] return history, gr.Textbox(value="", interactive=False) def bot(history, use_ranker, api_kind): top_k_rank = 4 top_k_retrieve = 40 query = history[-1][0] if not query: gr.Warning("Please submit a non-empty string as a prompt") raise ValueError("Empty string was submitted") logger.warning('Retrieving documents...') # Retrieve documents relevant to query document_start = perf_counter() if use_ranker: query_vec = retriever.encode(query) documents = table.search(query_vec, vector_column_name="embedding").limit(top_k_retrieve).to_list() documents = [doc["text"] for doc in documents] pairs = [(query, doc) for doc in documents] scores = ranker.predict(pairs, batch_size=8) documents = [doc for _, doc in sorted(zip(scores, documents))[-top_k_rank:]] else: query_vec = retriever.encode(query) documents = table.search(query_vec, vector_column_name="embedding").limit(top_k_rank).to_list() documents = [doc["text"] for doc in documents] document_time = perf_counter() - document_start logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...') # Create Prompt prompt = template.render(documents=documents, query=query) prompt_html = template_html.render(documents=documents, query=query) if api_kind == "HuggingFace": generate_fn = generate_hf elif api_kind == "OpenAI": generate_fn = generate_openai elif api_kind is None: gr.Warning("API name was not provided") raise ValueError("API name was not provided") else: gr.Warning(f"API {api_kind} is not supported") raise ValueError(f"API {api_kind} is not supported") history[-1][1] = "" for character in generate_fn(prompt, history[:-1]): history[-1][1] = character yield history, prompt_html with gr.Blocks() as demo: # with gr.Tab("RAGDemo"): chatbot = gr.Chatbot( [], elem_id="chatbot", avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg', 'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'), bubble_full_width=False, show_copy_button=True, show_share_button=True, ) with gr.Row(): txt = gr.Textbox( scale=3, show_label=False, placeholder="Enter text and press enter", container=False, ) txt_btn = gr.Button(value="Submit text", scale=1) cb = gr.Checkbox(label="Use cross-encoder", info="Rerank after retrieval?") api_kind = gr.Radio(choices=["HuggingFace", "OpenAI"], value="HuggingFace") prompt_html = gr.HTML() # Turn off interactivity while generating if you click txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( bot, [chatbot, cb, api_kind], [chatbot, prompt_html]) # Turn it back on txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False) # Turn off interactivity while generating if you hit enter txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( bot, [chatbot, cb, api_kind], [chatbot, prompt_html]) # Turn it back on txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False) # Examples gr.Examples(examples, txt) demo.queue() demo.launch(debug=True)