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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) |