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| """ | |
| CollaborativeAI Component for Codette | |
| Handles multi-agent collaboration and consensus building | |
| """ | |
| import logging | |
| from typing import Dict, List, Any, Optional | |
| from datetime import datetime | |
| import asyncio | |
| try: | |
| import numpy as np | |
| except Exception: | |
| np = None | |
| logger = logging.getLogger(__name__) | |
| class CollaborativeAI: | |
| """Manages collaborative AI processes for Codette""" | |
| def __init__(self, | |
| consensus_threshold: float = 0.7, | |
| max_rounds: int = 5, | |
| min_agents: int = 2): | |
| """Initialize the collaborative AI system""" | |
| self.consensus_threshold = consensus_threshold | |
| self.max_rounds = max_rounds | |
| self.min_agents = min_agents | |
| # Initialize state | |
| self.active_agents = {} | |
| self.collaboration_history = [] | |
| self.current_state = { | |
| "round": 0, | |
| "consensus_level": 0.0, | |
| "active_collaborations": 0, | |
| "agent_states": {} | |
| } | |
| logger.info("CollaborativeAI system initialized") | |
| async def collaborate(self, | |
| task: Dict[str, Any], | |
| agents: List[str], | |
| context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: | |
| """Coordinate collaboration between multiple agents""" | |
| try: | |
| if len(agents) < self.min_agents: | |
| return { | |
| "status": "error", | |
| "message": f"Need at least {self.min_agents} agents" | |
| } | |
| # Initialize collaboration | |
| collab_id = self._init_collaboration(task, agents) | |
| # Run collaboration rounds | |
| final_result = await self._run_collaboration_rounds(collab_id, task, agents, context) | |
| # Update history | |
| self._update_history(collab_id, final_result) | |
| return final_result | |
| except Exception as e: | |
| logger.error(f"Error in collaboration: {e}") | |
| return {"status": "error", "message": str(e)} | |
| def _init_collaboration(self, | |
| task: Dict[str, Any], | |
| agents: List[str]) -> str: | |
| """Initialize a new collaboration session""" | |
| try: | |
| # Generate collaboration ID | |
| collab_id = f"collab_{datetime.now().timestamp()}" | |
| # Initialize agent states | |
| for agent in agents: | |
| self.active_agents[agent] = { | |
| "status": "ready", | |
| "contributions": [], | |
| "consensus_votes": {}, | |
| "last_update": datetime.now().isoformat() | |
| } | |
| # Reset current state | |
| self.current_state.update({ | |
| "round": 0, | |
| "consensus_level": 0.0, | |
| "active_collaborations": len(self.active_agents), | |
| "agent_states": { | |
| agent: "ready" for agent in agents | |
| } | |
| }) | |
| logger.info(f"Initialized collaboration {collab_id}") | |
| return collab_id | |
| except Exception as e: | |
| logger.error(f"Error initializing collaboration: {e}") | |
| raise | |
| async def _run_collaboration_rounds(self, | |
| collab_id: str, | |
| task: Dict[str, Any], | |
| agents: List[str], | |
| context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: | |
| """Run multiple rounds of collaboration""" | |
| try: | |
| round_results = [] | |
| consensus_reached = False | |
| for round_num in range(self.max_rounds): | |
| self.current_state["round"] = round_num + 1 | |
| # Collect individual contributions | |
| contributions = await self._collect_contributions(task, agents, context) | |
| # Synthesize contributions | |
| synthesis = self._synthesize_contributions(contributions) | |
| # Check for consensus | |
| consensus_level = await self._evaluate_consensus(synthesis, agents) | |
| self.current_state["consensus_level"] = consensus_level | |
| round_results.append({ | |
| "round": round_num + 1, | |
| "contributions": contributions, | |
| "synthesis": synthesis, | |
| "consensus_level": consensus_level | |
| }) | |
| if consensus_level >= self.consensus_threshold: | |
| consensus_reached = True | |
| break | |
| # Update task with synthesis for next round | |
| task = self._update_task_with_synthesis(task, synthesis) | |
| return self._prepare_final_result( | |
| collab_id, | |
| round_results, | |
| consensus_reached | |
| ) | |
| except Exception as e: | |
| logger.error(f"Error in collaboration rounds: {e}") | |
| return {"status": "error", "message": str(e)} | |
| async def _collect_contributions(self, | |
| task: Dict[str, Any], | |
| agents: List[str], | |
| context: Optional[Dict[str, Any]] = None) -> List[Dict[str, Any]]: | |
| """Collect contributions from all agents""" | |
| contributions = [] | |
| try: | |
| # Create tasks for each agent | |
| agent_tasks = [ | |
| self._get_agent_contribution(agent, task, context) | |
| for agent in agents | |
| ] | |
| # Gather contributions asynchronously | |
| results = await asyncio.gather(*agent_tasks, return_exceptions=True) | |
| for agent, result in zip(agents, results): | |
| if isinstance(result, Exception): | |
| logger.error(f"Error getting contribution from {agent}: {result}") | |
| continue | |
| contributions.append({ | |
| "agent": agent, | |
| "contribution": result, | |
| "timestamp": datetime.now().isoformat() | |
| }) | |
| return contributions | |
| except Exception as e: | |
| logger.error(f"Error collecting contributions: {e}") | |
| return [] | |
| async def _get_agent_contribution(self, | |
| agent: str, | |
| task: Dict[str, Any], | |
| context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: | |
| """Get contribution from a single agent""" | |
| try: | |
| # Update agent state | |
| self.active_agents[agent]["status"] = "working" | |
| # Simulate agent processing | |
| # In real implementation, this would call the actual agent's processing | |
| await asyncio.sleep(0.1) # Simulate processing time | |
| contribution = { | |
| "task_id": task.get("id"), | |
| "agent_id": agent, | |
| "content": self._generate_contribution(task, agent), | |
| "confidence": float(np.random.uniform(0.5, 1.0)) if np is not None else float(0.75), | |
| "timestamp": datetime.now().isoformat() | |
| } | |
| # Store contribution | |
| self.active_agents[agent]["contributions"].append(contribution) | |
| self.active_agents[agent]["status"] = "contributed" | |
| return contribution | |
| except Exception as e: | |
| logger.error(f"Error getting agent contribution: {e}") | |
| raise | |
| def _synthesize_contributions(self, | |
| contributions: List[Dict[str, Any]]) -> Dict[str, Any]: | |
| """Synthesize multiple contributions into a single result""" | |
| try: | |
| if not contributions: | |
| return {"status": "error", "message": "No contributions to synthesize"} | |
| # Extract content and confidence | |
| contents = [] | |
| confidences = [] | |
| for contrib in contributions: | |
| content = contrib.get("contribution", {}).get("content") | |
| confidence = contrib.get("contribution", {}).get("confidence", 0.5) | |
| if content: | |
| contents.append(content) | |
| confidences.append(confidence) | |
| # Weight contents by confidence | |
| if not contents: | |
| return {"status": "error", "message": "No valid content to synthesize"} | |
| # Simple weighted combination for demonstration | |
| # In real implementation, this would use more sophisticated methods | |
| synthesis = { | |
| "content": self._combine_contents(contents, confidences), | |
| "confidence_level": float(np.mean(confidences)) if np is not None else float(sum(confidences)/len(confidences)) if confidences else 0.0, | |
| "num_contributors": len(contents), | |
| "timestamp": datetime.now().isoformat() | |
| } | |
| return synthesis | |
| except Exception as e: | |
| logger.error(f"Error synthesizing contributions: {e}") | |
| return {"status": "error", "message": str(e)} | |
| async def _evaluate_consensus(self, | |
| synthesis: Dict[str, Any], | |
| agents: List[str]) -> float: | |
| """Evaluate consensus level among agents""" | |
| try: | |
| # Collect votes from agents | |
| votes = await self._collect_consensus_votes(synthesis, agents) | |
| if not votes: | |
| return 0.0 | |
| # Calculate consensus level | |
| positive_votes = sum(1 for vote in votes if vote.get("agreement", 0) > 0.5) | |
| consensus_level = positive_votes / len(votes) | |
| return consensus_level | |
| except Exception as e: | |
| logger.error(f"Error evaluating consensus: {e}") | |
| return 0.0 | |
| async def _collect_consensus_votes(self, | |
| synthesis: Dict[str, Any], | |
| agents: List[str]) -> List[Dict[str, Any]]: | |
| """Collect consensus votes from all agents""" | |
| votes = [] | |
| try: | |
| # Create voting tasks | |
| vote_tasks = [ | |
| self._get_agent_vote(agent, synthesis) | |
| for agent in agents | |
| ] | |
| # Collect votes asynchronously | |
| results = await asyncio.gather(*vote_tasks, return_exceptions=True) | |
| for agent, result in zip(agents, results): | |
| if isinstance(result, Exception): | |
| logger.error(f"Error getting vote from {agent}: {result}") | |
| continue | |
| votes.append(result) | |
| return votes | |
| except Exception as e: | |
| logger.error(f"Error collecting votes: {e}") | |
| return [] | |
| async def _get_agent_vote(self, | |
| agent: str, | |
| synthesis: Dict[str, Any]) -> Dict[str, Any]: | |
| """Get consensus vote from a single agent""" | |
| try: | |
| # Simulate agent voting | |
| # In real implementation, this would use actual agent evaluation | |
| await asyncio.sleep(0.1) # Simulate processing time | |
| agreement = np.random.uniform(0.5, 1.0) # Simulate agreement level | |
| vote = { | |
| "agent": agent, | |
| "agreement": agreement, | |
| "timestamp": datetime.now().isoformat() | |
| } | |
| # Store vote | |
| self.active_agents[agent]["consensus_votes"] = vote | |
| return vote | |
| except Exception as e: | |
| logger.error(f"Error getting agent vote: {e}") | |
| raise | |
| def _update_task_with_synthesis(self, | |
| task: Dict[str, Any], | |
| synthesis: Dict[str, Any]) -> Dict[str, Any]: | |
| """Update task for next round using synthesis results""" | |
| try: | |
| updated_task = task.copy() | |
| # Add synthesis results to task context | |
| if "context" not in updated_task: | |
| updated_task["context"] = {} | |
| updated_task["context"]["previous_synthesis"] = synthesis | |
| updated_task["context"]["round"] = self.current_state["round"] | |
| return updated_task | |
| except Exception as e: | |
| logger.error(f"Error updating task: {e}") | |
| return task | |
| def _prepare_final_result(self, | |
| collab_id: str, | |
| round_results: List[Dict[str, Any]], | |
| consensus_reached: bool) -> Dict[str, Any]: | |
| """Prepare final collaboration result""" | |
| try: | |
| final_synthesis = round_results[-1]["synthesis"] if round_results else {} | |
| return { | |
| "status": "success" if consensus_reached else "partial", | |
| "collaboration_id": collab_id, | |
| "rounds_completed": len(round_results), | |
| "consensus_reached": consensus_reached, | |
| "final_consensus_level": self.current_state["consensus_level"], | |
| "final_result": final_synthesis, | |
| "round_history": round_results, | |
| "metrics": { | |
| "total_contributions": sum( | |
| len(r["contributions"]) for r in round_results | |
| ), | |
| "average_consensus": float(np.mean([ | |
| r["consensus_level"] for r in round_results | |
| ])) if np is not None and round_results else float(sum(r["consensus_level"] for r in round_results)/len(round_results)) if round_results else 0.0, | |
| "collaboration_duration": len(round_results) | |
| }, | |
| "timestamp": datetime.now().isoformat() | |
| } | |
| except Exception as e: | |
| logger.error(f"Error preparing final result: {e}") | |
| return {"status": "error", "message": str(e)} | |
| def _generate_contribution(self, | |
| task: Dict[str, Any], | |
| agent: str) -> Dict[str, Any]: | |
| """Generate a contribution for an agent""" | |
| try: | |
| # This is a placeholder implementation | |
| # In real system, this would use the agent's actual processing | |
| return { | |
| "type": "contribution", | |
| "agent": agent, | |
| "task_id": task.get("id"), | |
| "content": f"Contribution from {agent} for task {task.get('id')}", | |
| "timestamp": datetime.now().isoformat() | |
| } | |
| except Exception as e: | |
| logger.error(f"Error generating contribution: {e}") | |
| return {} | |
| def _combine_contents(self, | |
| contents: List[Dict[str, Any]], | |
| weights: List[float]) -> Dict[str, Any]: | |
| """Combine multiple contents with weights""" | |
| try: | |
| # This is a placeholder implementation | |
| # In real system, this would use more sophisticated combination methods | |
| return { | |
| "type": "synthesis", | |
| "components": len(contents), | |
| "weighted_combination": "Combined result", | |
| "timestamp": datetime.now().isoformat() | |
| } | |
| except Exception as e: | |
| logger.error(f"Error combining contents: {e}") | |
| return {} | |
| def _update_history(self, collab_id: str, result: Dict[str, Any]): | |
| """Update collaboration history""" | |
| try: | |
| self.collaboration_history.append({ | |
| "id": collab_id, | |
| "result": result, | |
| "timestamp": datetime.now().isoformat() | |
| }) | |
| except Exception as e: | |
| logger.error(f"Error updating history: {e}") | |
| def get_state(self) -> Dict[str, Any]: | |
| """Get current state of the collaborative system""" | |
| return self.current_state.copy() | |
| def get_history(self) -> List[Dict[str, Any]]: | |
| """Get collaboration history""" | |
| return self.collaboration_history.copy() | |
| def get_agent_state(self, agent: str) -> Optional[Dict[str, Any]]: | |
| """Get state of a specific agent""" | |
| return self.active_agents.get(agent) |