zxmfke
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Multi-step CoT Agent Example - Demonstrates how to use Chain of Thought mode in multi-turn conversations
|
|
"""
|
|
|
|
import asyncio
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
# Add project root directory to path
|
|
sys.path.append(str(Path(__file__).resolve().parent.parent))
|
|
|
|
from app.agent import CoTAgent
|
|
from app.logger import logger
|
|
from app.schema import Message
|
|
|
|
|
|
async def main():
|
|
# Create CoT agent
|
|
agent = CoTAgent(max_steps=3) # Set maximum steps to 3
|
|
|
|
# Initial question
|
|
initial_question = "As artificial intelligence technology develops, what ethical challenges might we face?"
|
|
|
|
logger.info(f"Initial question: {initial_question}")
|
|
|
|
# Add initial question to agent's memory
|
|
agent.memory.add_message(Message.user_message(initial_question))
|
|
|
|
# Step 1: Get initial thoughts
|
|
logger.info("Step 1: Initial thoughts")
|
|
response1 = await agent.step()
|
|
print(f"\nResponse:\n{response1}\n")
|
|
|
|
# Step 2: Ask follow-up question
|
|
follow_up_question = "Among the ethical challenges you mentioned, which one do you think is most urgent to address? Why?"
|
|
logger.info(f"Follow-up question: {follow_up_question}")
|
|
|
|
agent.memory.add_message(Message.user_message(follow_up_question))
|
|
response2 = await agent.step()
|
|
print(f"\nResponse:\n{response2}\n")
|
|
|
|
# Step 3: Ask final follow-up
|
|
final_question = "Can you suggest some specific solutions for addressing this most urgent ethical challenge?"
|
|
logger.info(f"Final question: {final_question}")
|
|
|
|
agent.memory.add_message(Message.user_message(final_question))
|
|
response3 = await agent.step()
|
|
print(f"\nResponse:\n{response3}\n")
|
|
|
|
logger.info("Multi-step CoT conversation complete!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|