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| 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"<center> <h2> Vectara demo app: {cfg.title} </h2> </center>", unsafe_allow_html=True) | |
| st.markdown(f"<center> <h4> {cfg.description} <h4> </center>", 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("""<h5>Search</h5>""", 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("<h5>Response</h5>", 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("<h5 style='text-align:center; color: red'> Sample questions </h5>", 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() | |