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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 | |
learning_styles = ['traditional', 'inquiry based'] | |
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) | |
print(tr_prompt) | |
st.session_state.messages = [{"role": "assistant", "content": tr_prompt, "avatar": "π¦"}] | |
vectara = VectaraIndex(vectara_api_key=cfg.api_key, | |
vectara_customer_id=cfg.customer_id, | |
vectara_corpus_id=cfg.corpus_id) | |
vectara_tool = QueryEngineTool( | |
query_engine = vectara.as_query_engine(summary_enabled=True, summary_num_results=5, summary_response_lang = languages[cfg.language], | |
summary_prompt_name="vectara-summary-ext-24-05-large"), | |
metadata = ToolMetadata(name="Vectara", | |
description="Vectara Query Engine that is able to answer any questions about the Justice Harvard class."), | |
) | |
llm = OpenAI(model="gpt-4o", temperature=0) | |
st.session_state.agent = ReActAgent.from_tools( | |
tools=[vectara_tool], llm=llm, | |
context = f''' | |
You are a teacher assistant at Justice Harvard course. You are helping a student with his questions. | |
The student is student who is {cfg.student_age} years old, you personalize your assistance to the student's age, | |
and rephrase your answer if needed to fit the {cfg.style} learning style. | |
''', | |
verbose=True | |
) | |
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': 21 | |
}) | |
st.session_state.cfg = cfg | |
st.session_state.style = learning_styles[0] | |
st.session_state.language = 'English' | |
st.session_state.student_age = 21 | |
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(f"## Welcome to Justice Harvard.\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": 'π§βπ»'}) | |
with st.chat_message("user", avatar='π§βπ»'): | |
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='π€'): | |
with st.spinner('Thinking...'): | |
res = st.session_state.agent.chat(prompt) | |
cleaned = re.sub(r'\[\d+\]', '', res.response) | |
st.write(cleaned) | |
message = {"role": "assistant", "content": cleaned, "avatar": 'π€'} | |
st.session_state.messages.append(message) | |
if __name__ == "__main__": | |
launch_bot() | |