VERSION = "0.0.3 10-Dec-2024" print(f"Debate Simulator v{VERSION}") """ Created by Craig Shallahamer, craig@orapub.com / craig@viscosityna.com No warranties, express or implied. It's all on you. This is a simple debate simulator with between two simple agents. The agents are created with a role prompt and a message history. The message history is used to create a prompt for the LLM. The LLM is used to generate a response for the agent. The response is added to the message history. The debate continues until the maximum number of turns is reached. Note: The agents only access data from the LLM and do not have access to any external data or other systems. ------------------- Here is how to create the Python environment and launch the debate: 1. Create the config.py file with your Cohere API key This all you need in the config.py file: cohere_api_key = "abc123" 2. Download Miniconda at https://docs.conda.io/en/latest/miniconda.html 3. Create Python environment #conda deactivate #conda env list #conda env remove --name debate conda create --name debate python=3.10 conda activate debate pip install cohere asyncio 4. How To Launch The AI On Laptop cd "/Users/cshallah/OraPub/0-Blog-Wider-View/HTML-new/20241210/Agent-Debate/debate_simulator" conda activate debate python main.py """ # Settings to control the debate MAX_TURNS = 3 MAX_RESPONSE_LENGTH = 20 # Debate Topics and Roles option_number = 3 # Change this to select different debates (1, 2, or 3) if option_number == 1: # Cloud Computing VS On-premises Infrastructure INITIAL_PROMPT = """Discuss the following topic: Is Cloud Computing Better Than On-Premises Infrastructure? Present your perspective and respond to your opponent's arguments respectfully and factually. Do not repeat the same arguments.""" ADVOCATE = """You are a three-year CIO with an MBA from Harvard who strongly believes in the benefits of cloud computing. Cloud computing is the future: it provides unmatched scalability, cost efficiency, and accessibility. Companies can innovate faster without worrying about hardware maintenance. Security advancements from cloud providers often surpass on-premises solutions. It enables businesses to focus on growth while reducing operational overhead and achieving global reach seamlessly. Base your arguments on empirical data and public research.""" SKEPTIC = """You are a fifteen-year CIO with an MBA from USC who strongly believes in the benefits of On-premises infrastructure. Cloud computing sacrifices control and security for convenience. On-premises infrastructure ensures critical data stays in-house, reducing exposure to third-party risks. Long-term cloud costs can skyrocket, and downtime is at the mercy of providers. Businesses relying on cloud providers risk vendor lock-in and unpredictable service interruptions. Base your arguments on empirical data and public research.""" elif option_number == 2: # Airbnb Rating Dilemma INITIAL_PROMPT = """Discuss the following topic: Is it better to rate a shockingly average Airbnb stay with a 5 star review to help support an elderly person? Present your perspective and respond to your opponent's arguments respectfully and factually.""" ADVOCATE = """You are a frequent Airbnb guest who strongly believes in helping the elderly""" SKEPTIC = """You are a Airbnb host who strongly believes in honesty and integrity.""" elif option_number == 3: # AI Novel Solutions Debate INITIAL_PROMPT = """Discuss the following topic: Are novel solutions a distinctly human trait that AI can never match? Present your perspective and respond to your opponent's arguments respectfully and factually. Do not repeat the same arguments.""" ADVOCATE = """You firmly believe that novel solutions are a distinctly human trait that AI can never match. Based your arguments on demonstrations of AI's inability to solve novel problems that humans have solved.""" SKEPTIC = """You firmly believe that AI can match human novel solutions. Based your arguments on demonstrations of AI's ability to solve novel problems.""" # ----------------- # You should not need to change anything below this line # ----------------- # Settings llm_model_good = "command-r-plus-08-2024" PRE_PROMPT = f"Limit your response to {MAX_RESPONSE_LENGTH} words." # Import libraries import asyncio import cohere from dataclasses import dataclass from typing import List, Optional import textwrap import re try: from config import cohere_api_key except ImportError: raise ImportError("Please create a config.py file with your cohere_api_key") # Helper functions def clean_text(text: str) -> str: """Remove extra whitespace and newlines from text.""" # Replace multiple spaces with single space text = re.sub(r'\s+', ' ', text) # Remove spaces after newlines text = re.sub(r'\n\s+', '\n', text) return text.strip() def pretty_print(text: str, width: int = 80, prefix: str = "") -> None: """Print text with word wrapping and cleaned formatting.""" # Clean the text first text = clean_text(text) # Word wrap the text wrapped_lines = textwrap.wrap(text, width=width, initial_indent=prefix, subsequent_indent=prefix) # Print each wrapped line print() for line in wrapped_lines: print(line) # Main Classes and Functions @dataclass class Message: role: str content: str class DebateAgent: def __init__(self, name: str, role_prompt: str): self.name = name self.role_prompt = role_prompt self.message_history: List[Message] = [] self.co_async = cohere.AsyncClientV2(cohere_api_key) async def respond(self, opponent_message: Optional[str] = None) -> str: # Start with the initial prompt prompt = f"{INITIAL_PROMPT}\n\nYou are {self.name}. {self.role_prompt}\n\n" # Add message history for msg in self.message_history: prompt += f"{msg.role}: {msg.content}\n" # Add opponent's latest message if exists if opponent_message: prompt += f"\nOpponent's last message: {opponent_message}\n" prompt += "\nProvide your response:" super_prompt = f"{PRE_PROMPT}\n{prompt}" try: response = await self.co_async.chat( model=llm_model_good, temperature=0.7, # Add some variability messages=[{"role": "user", "content": super_prompt}] ) response_text = response.message.content[0].text.strip() self.message_history.append(Message("assistant", response_text)) return response_text except Exception as e: return f"Error generating response: {str(e)}" class DebateManager: def __init__(self, agents: List[DebateAgent]): self.agents = agents self.current_turn = 0 async def run_debate(self): pretty_print("\n=== Debate Starting ===\n") pretty_print(f"Topic: {INITIAL_PROMPT}\n") # Initial statements for agent in self.agents: response = await agent.respond() pretty_print(f"\n{agent.name}: {response}\n", prefix=" ") # Main debate loop while self.current_turn < MAX_TURNS: self.current_turn += 1 pretty_print(f"\n--- Turn {self.current_turn} ---\n") for i, agent in enumerate(self.agents): # Get the previous agent's last message prev_agent = self.agents[i-1] if prev_agent.message_history: prev_message = prev_agent.message_history[-1].content else: prev_message = None # Get response response = await agent.respond(prev_message) pretty_print(f"{agent.name}: {response}\n", prefix=" ") # Optional: Add a small delay between turns await asyncio.sleep(1) pretty_print("\n=== Debate Concluded ===\n") async def main(): # Create agents advocate = DebateAgent( name="Advocate", role_prompt=ADVOCATE ) skeptic = DebateAgent( name="Skeptic", role_prompt=SKEPTIC ) # Create and run debate debate = DebateManager([advocate, skeptic]) await debate.run_debate() if __name__ == "__main__": asyncio.run(main())