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| """ | |
| Credit to Derek Thomas, derek@huggingface.co | |
| """ | |
| import os | |
| 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 retrieve | |
| from backend.reranker import rerank_documents | |
| TOP_K = int(os.getenv("TOP_K", 4)) | |
| 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') | |
| 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, chunk_table, embedding_model, llm_model, cross_encoder, top_k_param, rerank_topk ): | |
| top_k_param = int(top_k_param) | |
| query = history[-1][0] | |
| logger.info("bot launched ...") | |
| logger.info(f"embedding model: {embedding_model}") | |
| logger.info(f"LLM model: {llm_model}") | |
| logger.info(f"Cross encoder model: {cross_encoder}") | |
| logger.info(f"TopK: {top_k_param}") | |
| logger.info(f"ReRank TopK: {rerank_topk}") | |
| if not query: | |
| raise gr.Warning("Please submit a non-empty string as a prompt") | |
| logger.info('Retrieving documents...') | |
| # Retrieve documents relevant to query | |
| document_start = perf_counter() | |
| #documents = retrieve(query, TOP_K) | |
| documents = retrieve(query, top_k_param, chunk_table, embedding_model) | |
| logger.info(f'Retrived document count: {len(documents)}') | |
| if cross_encoder != "None" and len(documents) > 1: | |
| documents = rerank_documents(cross_encoder, documents, query, top_k_rerank=rerank_topk) | |
| #"cross-encoder/ms-marco-MiniLM-L-6-v2" | |
| logger.info(f'ReRank done, document count: {len(documents)}') | |
| document_time = perf_counter() - document_start | |
| logger.info(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 llm_model == "mistralai/Mistral-7B-Instruct-v0.2": | |
| generate_fn = generate_hf | |
| if llm_model == "mistralai/Mistral-7B-v0.1": | |
| generate_fn = generate_hf | |
| if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1": | |
| generate_fn = generate_hf | |
| if llm_model == "gpt-3.5-turbo": | |
| generate_fn = generate_openai | |
| if llm_model == "gpt-4-turbo-preview": | |
| generate_fn = generate_openai | |
| #if api_kind == "HuggingFace": | |
| # generate_fn = generate_hf | |
| #elif api_kind == "OpenAI": | |
| # generate_fn = generate_openai | |
| #else: | |
| # raise gr.Error(f"API {api_kind} is not supported") | |
| logger.info(f'Complition started. llm_model: {llm_model}, prompt: {prompt}') | |
| history[-1][1] = "" | |
| for character in generate_fn(prompt, history[:-1], llm_model): | |
| history[-1][1] = character | |
| yield history, prompt_html | |
| with gr.Blocks() as demo: | |
| 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) | |
| #api_kind = gr.Radio(choices=["HuggingFace", | |
| # "OpenAI"], value="HuggingFace") | |
| chunk_table = gr.Radio(choices=["BGE_CharacterTextSplitter", | |
| "BGE_FixedSizeSplitter", | |
| "BGE_RecursiveCharacterTextSplitter", | |
| "MiniLM_CharacterTextSplitter", | |
| "MiniLM_FixedSizeSplitter", | |
| "MiniLM_RecursiveCharacterSplitter" | |
| ], | |
| value="MiniLM_CharacterTextSplitter", | |
| label="Chunk table") | |
| embedding_model = gr.Radio( | |
| choices=[ | |
| "BAAI/bge-large-en-v1.5", | |
| "sentence-transformers/all-MiniLM-L6-v2", | |
| ], | |
| value="sentence-transformers/all-MiniLM-L6-v2", | |
| label='Embedding model' | |
| ) | |
| llm_model = gr.Radio( | |
| choices=[ | |
| "mistralai/Mistral-7B-Instruct-v0.2", | |
| "gpt-3.5-turbo", | |
| "gpt-4-turbo-preview", | |
| "mistralai/Mistral-7B-v0.1", | |
| "mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| ], | |
| value="mistralai/Mistral-7B-Instruct-v0.2", | |
| label='LLM' | |
| ) | |
| cross_encoder = gr.Radio( | |
| choices=[ | |
| "None", | |
| "BAAI/bge-reranker-large", | |
| "cross-encoder/ms-marco-MiniLM-L-6-v2", | |
| ], | |
| value="None", | |
| label='Cross-encoder model' | |
| ) | |
| top_k_param = gr.Radio( | |
| choices=[ | |
| "5", | |
| "10", | |
| "20", | |
| "50", | |
| ], | |
| value="5", | |
| label='top-K' | |
| ) | |
| rerank_topk = gr.Radio( | |
| choices=[ | |
| "5", | |
| "10", | |
| "20", | |
| "50", | |
| ], | |
| value="5", | |
| label='rerank-top-K' | |
| ) | |
| 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, chunk_table, embedding_model, llm_model, cross_encoder, top_k_param, rerank_topk], [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, chunk_table, embedding_model, llm_model, cross_encoder, top_k_param, rerank_topk], [chatbot, prompt_html]) | |
| # Turn it back on | |
| txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False) | |
| demo.queue() | |
| demo.launch(debug=True) | |