import sys import toml from omegaconf import OmegaConf from query import VectaraQuery import os import streamlit as st from PIL import Image def launch_bot(): def generate_response(question): response = vq.submit_query(question) return response if 'cfg' not in st.session_state: corpus_ids = str(os.environ['corpus_ids']).split(',') questions = list(eval(os.environ['examples'])) cfg = OmegaConf.create({ 'customer_id': str(os.environ['customer_id']), 'corpus_ids': corpus_ids, 'api_key': str(os.environ['api_key']), 'title': os.environ['title'], 'description': os.environ['description'], 'examples': questions, 'source_data_desc': os.environ['source_data_desc'] }) st.session_state.cfg = cfg st.session_state.vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids) 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.markdown(f"## Welcome to {cfg.title}\n\n" f"With this demo uses Retrieval Augmented Generation to ask questions about {cfg.source_data_desc}\n\n") st.markdown("---") st.markdown( "## How this works?\n" "This app was built with [Vectara](https://vectara.com).\n" "Vectara's [Indexing API](https://docs.vectara.com/docs/api-reference/indexing-apis/indexing) was used to ingest the data into a Vectara corpus (or index).\n\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("---") st.image(image, width=250) st.markdown(f"

Vectara chat demo: {cfg.title}

", unsafe_allow_html=True) st.markdown(f"

{cfg.description}

", unsafe_allow_html=True) if "messages" not in st.session_state.keys(): st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}] # Display chat messages for message in st.session_state.messages: with st.chat_message(message["role"]): st.write(message["content"]) # User-provided prompt if prompt := st.chat_input(): st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.write(prompt) # Generate a new response if last message is not from assistant if st.session_state.messages[-1]["role"] != "assistant": with st.chat_message("assistant"): with st.spinner("Thinking..."): response = generate_response(prompt) st.write(response) message = {"role": "assistant", "content": response} st.session_state.messages.append(message) if __name__ == "__main__": launch_bot()