import sys import toml from omegaconf import OmegaConf from query import VectaraQuery import os import streamlit as st from PIL import Image from functools import partial def set_query(q: str): st.session_state['query'] = q def launch_bot(): def get_answer(question): response = vq.submit_query(question) return response corpus_ids = list(eval(os.environ['corpus_ids'])) questions = list(eval(os.environ['examples'])) cfg = OmegaConf.create({ 'customer_id': os.environ['customer_id'], 'corpus_ids': corpus_ids, 'api_key': os.environ['api_key'], 'title': os.environ['title'], 'description': os.environ['description'], 'examples': questions, 'source_data_desc': os.environ['source_data_desc'] }) vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids) 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 Retieval 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 API 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 demo app: {cfg.title}

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

{cfg.description}

", unsafe_allow_html=True) # Setup a split column layout main_col, questions_col = st.columns([4, 2], gap="medium") with main_col: cols = st.columns([1, 8], gap="small") cols[0].markdown("""
Search
""", unsafe_allow_html=True) cols[1].text_input(label="search", key='query', max_chars=256, label_visibility='collapsed', help="Enter your question here") st.markdown("
Response
", unsafe_allow_html=True) response_text = st.empty() response_text.text_area(f" ", placeholder="The answer will appear here.", disabled=True, key="response", height=1, label_visibility='collapsed') with questions_col: st.markdown("
Sample questions
", unsafe_allow_html=True) for q in list(cfg.examples): st.button(q, on_click=partial(set_query, q), use_container_width=True) # run the main flow if st.session_state.get('query'): query = st.session_state['query'] response = get_answer(query) response_text.markdown(response) if __name__ == "__main__": launch_bot()