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import os
from openai import AsyncOpenAI
import chainlit as cl
from chainlit.prompt import Prompt, PromptMessage
from chainlit.playground.providers import ChatOpenAI
from dotenv import load_dotenv
load_dotenv()
# Template for LLM system vibe checking
user_template = """You are an assistant helping to perform quick evaluations of LLM-powered systems. Your role is to:
1. Help identify potential critical failure points in the system
2. Assess basic functionality and obvious issues
3. Look for significant problems that would be immediately noticeable
4. Provide a cursory but meaningful evaluation
5. Focus on crucial functions where failure would be severe
Frame your assessment in these sections:
- Basic Functionality Check
- Critical Issues Assessment
- Obvious Failure Points
- Quick Recommendations
System or component to evaluate: {input}
Key areas to examine:
- Core functionality problems
- Obvious response issues
- Critical safety concerns
- Basic performance problems
- User-facing issues
Provide an informal but insightful evaluation focusing on major concerns.
"""
@cl.on_chat_start
async def start_chat():
# Welcome message with LLM system vibe check introduction
await cl.Message(
content="👋 Welcome to the LLM System Vibe Check Assistant! I'll help you perform quick evaluations "
"of LLM-powered systems. Share any component or behavior you want to evaluate, such as:\n\n"
"1. Response quality or consistency\n"
"2. Safety mechanism effectiveness\n"
"3. Basic functionality issues\n"
"4. User interaction problems\n"
"5. Critical system behaviors\n\n"
"Remember: This is meant to be a cursory check for obvious issues, not a comprehensive evaluation."
).send()
settings = {
"model": "o1-mini",
}
cl.user_session.set("settings", settings)
@cl.on_message
async def main(message: cl.Message):
settings = cl.user_session.get("settings")
client = AsyncOpenAI()
prompt = Prompt(
provider=ChatOpenAI.id,
messages=[
PromptMessage(
role="user",
template=user_template,
formatted=user_template.format(input=message.content),
),
],
inputs={"input": message.content},
settings=settings,
)
msg = cl.Message(content="")
async for stream_resp in await client.chat.completions.create(
messages=[m.to_openai() for m in prompt.messages],
stream=True,
**settings
):
token = stream_resp.choices[0].delta.content
if token is not None:
await msg.stream_token(token)
# Update the prompt object with the completion
prompt.completion = msg.content
msg.prompt = prompt
# Add indicator emoji based on evaluation result
#if any(word in msg.content.lower() for word in ['critical', 'severe', 'serious', 'failing']):
# await msg.stream_token(" 🚨") # Critical issues found
#elif any(word in msg.content.lower() for word in ['minor', 'small', 'minimal']):
# await msg.stream_token(" ⚠️") # Minor issues found
#else:
# await msg.stream_token(" ✅") # No obvious issues
await msg.send()
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