from omegaconf import OmegaConf from query import VectaraQuery import os from PIL import Image import uuid import streamlit as st from streamlit_pills import pills from streamlit_feedback import streamlit_feedback from utils import thumbs_feedback, send_amplitude_data, escape_dollars_outside_latex from dotenv import load_dotenv load_dotenv(override=True) max_examples = 6 languages = {'English': 'eng', 'Spanish': 'spa', 'French': 'fra', 'Chinese': 'zho', 'German': 'deu', 'Hindi': 'hin', 'Arabic': 'ara', 'Portuguese': 'por', 'Italian': 'ita', 'Japanese': 'jpn', 'Korean': 'kor', 'Russian': 'rus', 'Turkish': 'tur', 'Persian (Farsi)': 'fas', 'Vietnamese': 'vie', 'Thai': 'tha', 'Hebrew': 'heb', 'Dutch': 'nld', 'Indonesian': 'ind', 'Polish': 'pol', 'Ukrainian': 'ukr', 'Romanian': 'ron', 'Swedish': 'swe', 'Czech': 'ces', 'Greek': 'ell', 'Bengali': 'ben', 'Malay (or Malaysian)': 'msa', 'Urdu': 'urd'} # Setup for HTTP API Calls to Amplitude Analytics if 'device_id' not in st.session_state: st.session_state.device_id = str(uuid.uuid4()) if "feedback_key" not in st.session_state: st.session_state.feedback_key = 0 def isTrue(x) -> bool: if isinstance(x, bool): return x return x.strip().lower() == 'true' def launch_bot(): def reset(): st.session_state.messages = [{"role": "assistant", "content": "How may I help you?", "avatar": '🤖'}] st.session_state.ex_prompt = None st.session_state.first_turn = True def generate_response(question): response = vq.submit_query(question, languages[st.session_state.language]) return response def generate_streaming_response(question): response = vq.submit_query_streaming(question, languages[st.session_state.language]) return response def show_example_questions(): if len(st.session_state.example_messages) > 0 and st.session_state.first_turn: selected_example = pills("Queries to Try:", st.session_state.example_messages, index=None) if selected_example: st.session_state.ex_prompt = selected_example st.session_state.first_turn = False return True return False if 'cfg' not in st.session_state: corpus_keys = str(os.environ['corpus_keys']).split(',') cfg = OmegaConf.create({ 'corpus_keys': corpus_keys, 'api_key': str(os.environ['api_key']), 'title': os.environ['title'], 'source_data_desc': os.environ['source_data_desc'], 'streaming': isTrue(os.environ.get('streaming', False)), 'prompt_name': os.environ.get('prompt_name', None), 'examples': os.environ.get('examples', None), 'language': 'English' }) st.session_state.cfg = cfg st.session_state.ex_prompt = None st.session_state.first_turn = True st.session_state.language = cfg.language example_messages = [example.strip() for example in cfg.examples.split(",")] st.session_state.example_messages = [em for em in example_messages if len(em)>0][:max_examples] st.session_state.vq = VectaraQuery(cfg.api_key, cfg.corpus_keys, cfg.prompt_name) cfg = st.session_state.cfg vq = st.session_state.vq st.set_page_config(page_title=cfg.title, layout="wide") # left side content with st.sidebar: image = Image.open('Vectara-logo.png') st.image(image, width=175) st.markdown(f"## About\n\n" f"This demo uses Vectara RAG to ask questions about {cfg.source_data_desc}\n") cfg.language = st.selectbox('Language:', languages.keys()) if st.session_state.language != cfg.language: st.session_state.language = cfg.language reset() st.rerun() st.markdown("\n") bc1, _ = st.columns([1, 1]) with bc1: if st.button('Start Over'): reset() st.rerun() st.markdown("---") st.markdown( "## How this works?\n" "This app was built with [Vectara](https://vectara.com).\n" "This app uses Vectara [Chat API](https://docs.vectara.com/docs/console-ui/vectara-chat-overview) to query the corpus and present the results to you, answering your question.\n\n" ) st.markdown(f"

Vectara AI Assistant: {cfg.title}

", unsafe_allow_html=True) if "messages" not in st.session_state.keys(): reset() # Display chat messages for message in st.session_state.messages: with st.chat_message(message["role"], avatar=message["avatar"]): st.write(message["content"]) example_container = st.empty() with example_container: if show_example_questions(): example_container.empty() st.rerun() # select prompt from example question or user provided input if st.session_state.ex_prompt: prompt = st.session_state.ex_prompt else: prompt = st.chat_input() if prompt: st.session_state.messages.append({"role": "user", "content": prompt, "avatar": '🧑‍💻'}) with st.chat_message("user", avatar="🧑‍💻"): st.write(prompt) st.session_state.ex_prompt = None # Generate a new response if last message is not from assistant if st.session_state.messages[-1]["role"] != "assistant": with st.chat_message("assistant", avatar="🤖"): if cfg.streaming: stream = generate_streaming_response(prompt) response = st.write_stream(stream) else: with st.spinner("Thinking..."): response = generate_response(prompt) st.write(response) response = escape_dollars_outside_latex(response) message = {"role": "assistant", "content": response, "avatar": '🤖'} st.session_state.messages.append(message) # Send query and response to Amplitude Analytics send_amplitude_data( user_query=st.session_state.messages[-2]["content"], chat_response=st.session_state.messages[-1]["content"], demo_name=cfg["title"], language=st.session_state.language ) st.rerun() if (st.session_state.messages[-1]["role"] == "assistant") & (st.session_state.messages[-1]["content"] != "How may I help you?"): streamlit_feedback(feedback_type="thumbs", on_submit = thumbs_feedback, key = st.session_state.feedback_key, kwargs = {"user_query": st.session_state.messages[-2]["content"], "chat_response": st.session_state.messages[-1]["content"], "demo_name": cfg["title"], "response_language": st.session_state.language}) if __name__ == "__main__": launch_bot()