Spaces:
Runtime error
Runtime error
| from typing import Dict, Any, List | |
| from loguru import logger | |
| from utils.llm_orchestrator import LLMOrchestrator | |
| class ReasoningAgent: | |
| def __init__(self, llm_api_key: str): | |
| """Initialize the Reasoning Agent.""" | |
| logger.info("Initializing ReasoningAgent") | |
| self.llm_orchestrator = LLMOrchestrator(llm_api_key) | |
| self.capabilities = [ | |
| "step_by_step_reasoning", | |
| "context_management", | |
| "agent_coordination", | |
| "result_aggregation" | |
| ] | |
| self.setup_logger() | |
| def setup_logger(self): | |
| """Configure logging for the agent.""" | |
| logger.add("logs/reasoning_agent.log", rotation="500 MB") | |
| async def perform_reasoning( | |
| self, goal: str, available_agents: List[Dict[str, Any]]) -> Dict[str, Any]: | |
| """Perform step-by-step reasoning to achieve a goal.""" | |
| logger.info(f"Performing reasoning for goal: {goal}") | |
| try: | |
| context = { | |
| "goal": goal, | |
| "available_agents": available_agents, | |
| "steps": [] | |
| } | |
| # Example of a simple reasoning process with 2 steps. | |
| # This can be made more sophisticated based on the specific needs. | |
| for step in range(2): | |
| prompt = self.generate_reasoning_prompt(context) | |
| response = await self.llm_orchestrator.generate_completion(prompt) | |
| logger.info(f"Reasoning step {step + 1}: {response}") | |
| # Placeholder for action execution based on reasoning | |
| action = self.extract_action(response) | |
| if action and action["agent"] != "reasoning_agent": | |
| # Here we simulate executing an action with another agent | |
| # In a real scenario, this would involve calling the | |
| # appropriate agent | |
| action_result = await self.execute_agent_action(action, context) | |
| context["steps"].append({ | |
| "step": step + 1, | |
| "prompt": prompt, | |
| "response": response, | |
| "action": action, | |
| "action_result": action_result | |
| }) | |
| else: | |
| context["steps"].append({ | |
| "step": step + 1, | |
| "prompt": prompt, | |
| "response": response, | |
| "action": action | |
| }) | |
| return { | |
| "status": "success", | |
| "reasoning_process": context["steps"], | |
| "result": "Reasoning process completed." # Placeholder for final result | |
| } | |
| except Exception as e: | |
| logger.error(f"Error during reasoning: {str(e)}") | |
| return { | |
| "status": "error", | |
| "message": str(e) | |
| } | |
| def generate_reasoning_prompt(self, context: Dict[str, Any]) -> str: | |
| """Generate a prompt for the LLM to guide the reasoning process.""" | |
| prompt = f""" | |
| Goal: {context['goal']} | |
| Available Agents: {', '.join([agent['name'] for agent in context['available_agents']])} | |
| Reasoning Steps: | |
| """ | |
| for step in context["steps"]: | |
| prompt += f"- Step {step['step']}: {step['response']}\n" | |
| if "action" in step and step["action"]: | |
| prompt += f" Action: {step['action']}\n" | |
| if "action_result" in step and step["action_result"]: | |
| prompt += f" Result: {step['action_result']}\n" | |
| prompt += "What is the next logical step to achieve the goal? Explain your reasoning." | |
| return prompt | |
| def extract_action(self, response: str) -> Dict[str, Any]: | |
| """Extract the next action to be taken based on the LLM's response.""" | |
| # Basic implementation: Assume the last line of the response contains | |
| # the action | |
| lines = response.strip().split("\n") | |
| last_line = lines[-1] | |
| if ":" in last_line: | |
| parts = last_line.split(":") | |
| agent = parts[0].strip() | |
| parameters = parts[1].strip() if len(parts) > 1 else "" | |
| return { | |
| "agent": agent, | |
| "parameters": parameters | |
| } | |
| else: | |
| return None | |
| async def execute_agent_action( | |
| self, action: Dict[str, Any], context: Dict[str, Any]) -> str: | |
| """Simulate executing an action with another agent.""" | |
| # This is a placeholder for actual agent execution | |
| # In a real scenario, this method would call the appropriate agent based on action["agent"] | |
| # and pass the necessary parameters from action["parameters"] | |
| # Find the agent in the available agents list | |
| agent_info = next( | |
| (agent for agent in context["available_agents"] | |
| if agent["name"] == action["agent"]), | |
| None) | |
| if agent_info: | |
| logger.info( | |
| f"Executing action with agent: {action['agent']} with parameters: {action['parameters']}") | |
| # Simulate an action result | |
| return f"Result of action with {action['agent']}: Success" | |
| else: | |
| logger.error(f"Agent {action['agent']} not found.") | |
| return "Error: Agent not found" | |