from omegaconf import OmegaConf import streamlit as st import os from PIL import Image import re from translate import Translator from llama_index.indices.managed.vectara import VectaraIndex from llama_index.core.agent import ReActAgent from llama_index.llms.openai import OpenAI from llama_index.core.tools import QueryEngineTool, ToolMetadata from llama_index.core.utils import print_text from llama_index.core.agent.react.formatter import ReActChatFormatter from prompts import prompt_template learning_styles = ['traditional', 'Inquiry-based', 'Socratic'] languages = {'English': 'en', 'Spanish': 'es', 'French': 'fr', 'German': 'de', 'Arabic': 'ar', 'Chinese': 'zh-cn', 'Hebrew': 'he', 'Hindi': 'hi', 'Italian': 'it', 'Japanese': 'ja', 'Korean': 'ko', 'Portuguese': 'pt'} initial_prompt = "How can I help you today?" def launch_bot(): def reset(): cfg = st.session_state.cfg llm = OpenAI(model="gpt-4o", temperature=0) tr_prompt = Translator(to_lang=languages[cfg.language]).translate(initial_prompt) st.session_state.messages = [{"role": "assistant", "content": tr_prompt, "avatar": "🦖"}] st.session_state.thinking_prompt = Translator(to_lang=languages[cfg.language]).translate("Thinking...") vectara = VectaraIndex(vectara_api_key=cfg.api_key, vectara_customer_id=cfg.customer_id, vectara_corpus_id=cfg.corpus_id) # Create the Vectara Tool vectara_tool = QueryEngineTool( query_engine = vectara.as_query_engine(summary_enabled = True, summary_num_results = 10, summary_response_lang = languages[cfg.language], summary_prompt_name = "vectara-summary-ext-24-05-large", vectara_query_mode = "mmr", rerank_k = 50, mmr_diversity_bias = 0.1, n_sentence_before = 5, n_sentence_after = 5), metadata = ToolMetadata(name="vectara", description=""" A tool that is able to answer questions about the justice, morality, politics and related topics. Based on transcripts of recordings from the Justice Harvard class that includes a lot of content on these topics. When using the tool it's best to ask simple short questions. """), ) # Create the agent prompt = prompt_template.replace("{style}", cfg.style) \ .replace("{language}", cfg.language) \ .replace("{student_age}", str(cfg.student_age)) print(prompt) st.session_state.agent = ReActAgent.from_tools( tools=[vectara_tool], llm=llm, verbose=True, react_chat_formatter = ReActChatFormatter(system_header=prompt) ) if 'cfg' not in st.session_state: cfg = OmegaConf.create({ 'customer_id': str(os.environ['VECTARA_CUSTOMER_ID']), 'corpus_id': str(os.environ['VECTARA_CORPUS_ID']), 'api_key': str(os.environ['VECTARA_API_KEY']), 'style': learning_styles[0], 'language': 'English', 'student_age': 18 }) st.session_state.cfg = cfg st.session_state.style = cfg.style st.session_state.language = cfg.language st.session_state.student_age = cfg.student_age reset() cfg = st.session_state.cfg st.set_page_config(page_title="Teaching Assistant", layout="wide") # left side content with st.sidebar: image = Image.open('Vectara-logo.png') st.image(image, width=250) st.markdown("## Welcome to the Justice Harvard e-learning assistant demo.\n\n\n") st.markdown("\n") cfg.style = st.selectbox('Learning Style:', learning_styles) if st.session_state.style != cfg.style: st.session_state.style = cfg.style reset() st.markdown("\n") cfg.language = st.selectbox('Language:', languages.keys()) if st.session_state.language != cfg.language: st.session_state.langage = cfg.language reset() st.markdown("\n") cfg.student_age = st.number_input( 'Student age:', min_value=13, value=cfg.student_age, step=1, format='%i' ) if st.session_state.student_age != cfg.student_age: st.session_state.student_age = cfg.student_age reset() st.markdown("\n\n") if st.button('Start Over'): reset() st.markdown("---") st.markdown( "## How this works?\n" "This app was built with [Vectara](https://vectara.com).\n\n" "It demonstrates the use of Agentic Chat functionality with Vectara" ) st.markdown("---") 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"]) # User-provided prompt if prompt := st.chat_input(): st.session_state.messages.append({"role": "user", "content": prompt, "avatar": '🧑‍💻'}) with st.chat_message("user", avatar='🧑‍💻'): print_text(f"Starting new question: {prompt}\n", color='green') 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", avatar='🤖'): with st.spinner(st.session_state.thinking_prompt): res = st.session_state.agent.chat(prompt) cleaned = re.sub(r'\[\d+\]', '', res.response) st.write(cleaned) message = {"role": "assistant", "content": cleaned, "avatar": '🤖'} st.session_state.messages.append(message) if __name__ == "__main__": launch_bot()