hubian
05950a3c84
核心组件： - Orchestrator: 意图理解、任务拆分、竞标管理、结果验证 - Worker: 竞标任务、执行交付 - TaskBoard: 状态管理、信息存储 - BidEvaluator: 竞标评估算法 - ExecutionMonitor: 执行监控、超时处理 - LLMClient: 大模型接口调用 功能特性： - 竞标机制：Agent主动竞争任务 - 动态调度：串行/并行任务智能调度 - 智能容错：超时切换、验证重试 - 质量保证：结果验证、历史追踪 Web界面：首页、请求列表、任务列表、Agent管理 API接口：请求/任务/Agent管理、测试接口 端口：19015
155 lines
4.4 KiB
Python
155 lines
4.4 KiB
Python
"""
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竞标评估器 - 评估Agent竞标，选择最佳执行者
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"""
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from typing import Dict, List
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from .models import Bid, Task, AgentProfile
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class BidEvaluator:
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"""竞标评估器"""
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def __init__(self):
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self.weights = {
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'capability': 0.3, # 能力匹配度
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'confidence': 0.2, # 自信度
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'time_efficiency': 0.2, # 时间效率
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'approach_quality': 0.2, # 方案质量
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'historical': 0.1 # 历史表现
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}
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def evaluate_bid(self, bid: Bid, agent: AgentProfile, max_time: int = 300) -> float:
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"""
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评估单个竞标
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Args:
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bid: 竞标书
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agent: Agent档案
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max_time: 最大可接受时间
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Returns:
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综合得分 0-1
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"""
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scores = {}
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# 1. 能力匹配度
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scores['capability'] = bid.capability_match
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# 2. 自信度
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scores['confidence'] = bid.confidence
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# 3. 时间效率（时间越短得分越高）
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scores['time_efficiency'] = max(0, 1 - bid.estimated_time / max_time)
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# 4. 方案质量
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scores['approach_quality'] = self._rate_approach(bid.approach)
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# 5. 历史表现
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scores['historical'] = agent.success_rate
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# 加权求和
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final_score = sum(
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self.weights[k] * v
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for k, v in scores.items()
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)
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return final_score
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def _rate_approach(self, approach: str) -> float:
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"""评估方案质量"""
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score = 0.5
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# 有备选方案加分
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if '备选' in approach or 'alternative' in approach.lower():
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score += 0.1
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# 有错误处理加分
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if '错误' in approach or '异常' in approach or 'error' in approach.lower():
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score += 0.1
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# 有详细描述加分
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if len(approach) > 100:
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score += 0.1
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# 有步骤分解加分
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if '步骤' in approach or 'step' in approach.lower():
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score += 0.1
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return min(score, 1.0)
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def select_best_bid(
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self,
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bids: List[Bid],
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agents: Dict[str, AgentProfile]
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) -> tuple:
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"""
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选择最佳竞标
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Args:
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bids: 竞标列表
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agents: Agent档案字典
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Returns:
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(best_bid, best_agent, scores_dict)
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"""
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if not bids:
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return (None, None, {})
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scores = {}
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for bid in bids:
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agent = agents.get(bid.agent_id)
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if agent:
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score = self.evaluate_bid(bid, agent)
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scores[bid.id] = {
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'score': score,
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'bid': bid,
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'agent': agent,
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'details': {
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'capability': bid.capability_match,
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'confidence': bid.confidence,
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'time_efficiency': max(0, 1 - bid.estimated_time / 300),
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'approach_quality': self._rate_approach(bid.approach),
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'historical': agent.success_rate
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}
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}
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if not scores:
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return (None, None, {})
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# 找最高分
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best_bid_id = max(scores.keys(), key=lambda k: scores[k]['score'])
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best = scores[best_bid_id]
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return (best['bid'], best['agent'], scores)
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def get_backup_agents(
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self,
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bids: List[Bid],
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agents: Dict[str, AgentProfile],
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selected_agent_id: str
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) -> List[AgentProfile]:
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"""
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获取备选Agent列表（按得分排序）
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Args:
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bids: 竞标列表
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agents: Agent档案字典
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selected_agent_id: 已选择的AgentID
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Returns:
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备选Agent列表
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"""
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backup = []
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for bid in bids:
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if bid.agent_id != selected_agent_id:
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agent = agents.get(bid.agent_id)
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if agent:
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score = self.evaluate_bid(bid, agent)
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backup.append((agent, score))
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# 按得分降序排序
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backup.sort(key=lambda x: x[1], reverse=True)
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return [a for a, s in backup] |