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| from omegaconf import OmegaConf | |
| from query import VectaraQuery | |
| import os | |
| import streamlit as st | |
| from PIL import Image | |
| def isTrue(x) -> bool: | |
| if isinstance(x, bool): | |
| return x | |
| return x.strip().lower() == 'true' | |
| def launch_bot(): | |
| def generate_response(question): | |
| response = vq.submit_query(question) | |
| return response | |
| def generate_streaming_response(question): | |
| response = vq.submit_query_streaming(question) | |
| return response | |
| if 'cfg' not in st.session_state: | |
| cfg = OmegaConf.create({ | |
| 'customer_id': str(os.environ['customer_id']), | |
| 'corpus_ids': list(str(eval(os.environ['corpus_ids']))), | |
| 'api_key': str(os.environ['api_key']), | |
| 'title': os.environ['title'], | |
| 'description': os.environ['description'], | |
| 'source_data_desc': os.environ['source_data_desc'], | |
| 'streaming': isTrue(os.environ.get('streaming', False)), | |
| 'questions': list(eval(os.environ['questions'])), | |
| 'prompt_name': os.environ.get('prompt_name', None) | |
| }) | |
| st.session_state.cfg = cfg | |
| st.session_state.vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids, 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.markdown(f"## Welcome to {cfg.title}\n\n" | |
| f"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"<center> <h2> Vectara chat demo: {cfg.title} </h2> </center>", unsafe_allow_html=True) | |
| st.markdown(f"<center> <h4> {cfg.description} <h4> </center>", unsafe_allow_html=True) | |
| if "messages" not in st.session_state.keys(): | |
| st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}] | |
| for question in cfg.questions: | |
| st.button(question, on_click=lambda q=question: submit_question(q)) | |
| # 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(): | |
| submit_question(prompt) | |
| def submit_question(question): | |
| st.session_state.messages.append({"role": "user", "content": question}) | |
| with st.chat_message("user"): | |
| st.write(question) | |
| generate_and_display_response(question) | |
| def generate_and_display_response(question): | |
| if cfg.streaming: | |
| stream = generate_streaming_response(question) | |
| response = st.write_stream(stream) | |
| else: | |
| with st.spinner("Thinking..."): | |
| response = generate_response(question) | |
| st.write(response) | |
| message = {"role": "assistant", "content": response} | |
| st.session_state.messages.append(message) | |
| if __name__ == "__main__": | |
| launch_bot() | |