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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
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": "π¦"}]
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": '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(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": 'π€'}
st.session_state.messages.append(message)
if __name__ == "__main__":
launch_bot()
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