class StorageQualityEvaluator:
    """存储质量评估器"""
    
    def __init__(self, embed_model, llm_client):
        self.embed_model = embed_model
        self.llm = llm_client
    
    async def evaluate_completeness(self, original_text, stored_memory):
        """评估信息完整性"""
        # 方法1: Embedding 相似度
        emb_original = await self.embed_model.embed(original_text)
        emb_stored = await self.embed_model.embed(stored_memory)
        cosine_sim = cosine_similarity(emb_original, emb_stored)
        
        # 方法2: LLM-based 评估
        prompt = f"""
        评估以下记忆是否完整保留了原始信息的关键内容：
        
        原始文本: {original_text}
        存储记忆: {stored_memory}
        
        评分标准 (0-1):
        1.0 - 完整保留所有关键信息
        0.8 - 保留大部分关键信息，细节有损失
        0.6 - 保留部分关键信息
        0.4 - 关键信息不完整
        0.2 - 严重信息丢失
        
        输出评分和理由。
        """
        
        llm_score = await self.llm.evaluate(prompt)
        
        # 综合评分
        return {
            'embedding_similarity': cosine_sim,
            'llm_score': llm_score['score'],
            'composite_score': 0.4 * cosine_sim + 0.6 * llm_score['score'],
            'reasoning': llm_score['reasoning']
        }

from ragas import evaluate
from ragas.metrics import context_precision, context_recall, context_relevancy

class RetrievalQualityEvaluator:
    """检索质量评估器"""
    
    def __init__(self, memory_system):
        self.memory = memory_system
    
    async def evaluate_with_ragas(self, test_dataset):
        """使用 RAGAS 评估检索质量"""
        
        # 准备数据
        eval_data = {
            'question': test_dataset['queries'],
            'contexts': test_dataset['retrieved_memories'],
            'ground_truths': test_dataset['relevant_memories']
        }
        
        # RAGAS 评估
        results = evaluate(
            eval_data,
            metrics=[
                context_precision,      # 检索精确度
                context_recall,         # 检索召回率
                context_relevancy       # 检索相关性
            ]
        )
        
        return {
            'precision': results['context_precision'],
            'recall': results['context_recall'],
            'relevancy': results['context_relevancy']
        }
    
    async def evaluate_latency(self, queries, top_k=10):
        """评估检索延迟"""
        latencies = []
        
        for query in queries:
            start = time.time()
            _ = await self.memory.retrieve(query, top_k=top_k)
            latency = time.time() - start
            latencies.append(latency)
        
        return {
            'avg_latency_ms': np.mean(latencies) * 1000,
            'p95_latency_ms': np.percentile(latencies, 95) * 1000,
            'p99_latency_ms': np.percentile(latencies, 99) * 1000
        }

class MemoryManagementEvaluator:
    """记忆管理评估器"""
    
    async def evaluate_forgetting(self, memory_system, test_period_days=30):
        """评估遗忘机制"""
        # 识别候选遗忘记忆
        candidates = await self.identify_forget_candidates(memory_system)
        
        results = {
            'candidates_count': len(candidates),
            'correct_forgettings': 0,
            'incorrect_forgettings': 0,
            'examples': []
        }
        
        for candidate in candidates[:10]:  # 抽查10个
            # 人工验证是否应该遗忘
            should_forget = await self.human_verify_forgetting(candidate)
            
            if should_forget:
                results['correct_forgettings'] += 1
            else:
                results['incorrect_forgettings'] += 1
                results['examples'].append({
                    'memory': candidate,
                    'reason': "不应被遗忘"
                })
        
        results['accuracy'] = results['correct_forgettings'] / min(len(candidates[:10]), 1)
        
        return results
    
    async def evaluate_conflict_resolution(self, memory_system, conflict_cases):
        """评估冲突解决能力"""
        results = {
            'total_conflicts': len(conflict_cases),
            'resolved': 0,
            'correct_resolution': 0
        }
        
        for case in conflict_cases:
            # 尝试解决冲突
            resolution = await memory_system.resolve_conflict(
                case['memory_a'],
                case['memory_b']
            )
            
            # 验证解决方案
            if resolution:
                results['resolved'] += 1
                
                # 检查是否正确
                if await self.verify_resolution(resolution, case['ground_truth']):
                    results['correct_resolution'] += 1
        
        results['resolution_rate'] = results['resolved'] / results['total_conflicts']
        results['accuracy'] = results['correct_resolution'] / results['resolved'] if results['resolved'] > 0 else 0
        
        return results

class LongTermLearningEvaluator:
    """长期学习评估器"""
    
    async def evaluate_knowledge_growth(self, memory_system, start_date, end_date):
        """评估知识增长"""
        # 获取时间段内的记忆增长
        growth_data = await memory_system.get_growth_statistics(start_date, end_date)
        
        # 评估新增知识的质量
        new_memories = await memory_system.get_memories_added_after(start_date)
        quality_scores = []
        
        for memory in new_memories[:100]:  # 抽样评估
            # 评估知识的实用性
            usefulness = await self.evaluate_memory_usefulness(memory)
            quality_scores.append(usefulness)
        
        return {
            'total_new_memories': len(new_memories),
            'avg_quality': np.mean(quality_scores),
            'high_quality_rate': len([s for s in quality_scores if s > 0.7]) / len(quality_scores),
            'growth_rate': growth_data['growth_rate']
        }
    
    async def evaluate_experience_transfer(self, memory_system, test_scenarios):
        """评估经验迁移能力"""
        results = []
        
        for scenario in test_scenarios:
            # 场景1: 使用 Memory 系统
            agent_with_memory = await self.run_scenario_with_memory(
                memory_system,
                scenario
            )
            
            # 场景2: 不使用 Memory 系统
            agent_without_memory = await self.run_scenario_without_memory(
                scenario
            )
            
            # 对比结果
            improvement = self.calculate_improvement(
                agent_with_memory,
                agent_without_memory
            )
            
            results.append({
                'scenario': scenario['name'],
                'improvement': improvement,
                'memory_used': agent_with_memory['memories_accessed']
            })
        
        return {
            'avg_improvement': np.mean([r['improvement'] for r in results]),
            'transfer_success_rate': len([r for r in results if r['improvement'] > 0]) / len(results),
            'detailed_results': results
        }

class MemoryEvaluationPipeline:
    """Memory 系统完整评估流水线"""
    
    def __init__(self, memory_system):
        self.memory = memory_system
        
        # 初始化评估器
        self.storage_evaluator = StorageQualityEvaluator()
        self.retrieval_evaluator = RetrievalQualityEvaluator()
        self.management_evaluator = MemoryManagementEvaluator()
        self.learning_evaluator = LongTermLearningEvaluator()
    
    async def run_full_evaluation(self, test_cases):
        """运行完整评估"""
        results = {}
        
        # 1. 存储质量评估
        print("1. 评估存储质量...")
        results['storage'] = await self.evaluate_storage_quality(test_cases)
        
        # 2. 检索性能评估
        print("2. 评估检索性能...")
        results['retrieval'] = await self.evaluate_retrieval_performance(test_cases)
        
        # 3. 记忆管理评估
        print("3. 评估记忆管理...")
        results['management'] = await self.evaluate_memory_management()
        
        # 4. 长期学习评估
        print("4. 评估长期学习...")
        results['learning'] = await self.evaluate_long_term_learning()
        
        # 5. 生成综合报告
        report = self.generate_report(results)
        
        return report
    
    async def evaluate_storage_quality(self, test_cases):
        """存储质量评估"""
        results = {
            'completeness_scores': [],
            'compression_ratios': [],
            'coverage_rate': None,
            'deduplication_rate': None
        }
        
        # 评估信息完整性
        for case in test_cases[:100]:
            completeness = await self.storage_evaluator.evaluate_completeness(
                case['original'],
                case['stored']
            )
            results['completeness_scores'].append(completeness)
        
        # 评估压缩效率
        for case in test_cases[:100]:
            original_size = len(case['original'])
            stored_size = len(case['stored'])
            info_retention = completeness['composite_score']
            
            compression = self.storage_evaluator.evaluate_compression_efficiency(
                original_size,
                stored_size,
                info_retention
            )
            results['compression_ratios'].append(compression)
        
        # 评估索引覆盖率
        coverage = await self.storage_evaluator.evaluate_index_coverage(
            self.memory.get_all_memories(),
            self.memory.get_indexed_memories()
        )
        results['coverage_rate'] = coverage['coverage_rate']
        
        # 评估去重率
        dedup = await self.storage_evaluator.evaluate_deduplication(
            self.memory.get_all_memories()
        )
        results['deduplication_rate'] = dedup['deduplication_rate']
        
        return results
    
    async def evaluate_retrieval_performance(self, test_cases):
        """检索性能评估"""
        # 使用 RAGAS 评估检索质量
        quality_results = await self.retrieval_evaluator.evaluate_with_ragas(
            test_cases
        )
        
        # 评估检索延迟
        queries = [case['query'] for case in test_cases]
        latency_results = await self.retrieval_evaluator.evaluate_latency(
            queries
        )
        
        return {
            'quality': quality_results,
            'latency': latency_results
        }
    
    def generate_report(self, results):
        """生成评估报告"""
        report = {
            'timestamp': datetime.now().isoformat(),
            'summary': {},
            'details': results,
            'recommendations': []
        }
        
        # 生成摘要
        report['summary'] = {
            'storage_quality': {
                'avg_completeness': np.mean([s['composite_score'] for s in results['storage']['completeness_scores']]),
                'avg_compression': np.mean([c['efficiency_score'] for c in results['storage']['compression_ratios']]),
                'coverage_rate': results['storage']['coverage_rate'],
                'dedup_rate': results['storage']['deduplication_rate']
            },
            'retrieval_performance': {
                'precision': results['retrieval']['quality']['precision'],
                'recall': results['retrieval']['quality']['recall'],
                'p95_latency_ms': results['retrieval']['latency']['p95_latency_ms']
            },
            'management': {
                'forgetting_accuracy': results['management']['forgetting']['accuracy'],
                'conflict_resolution_rate': results['management']['conflict']['resolution_rate']
            },
            'learning': {
                'knowledge_growth_rate': results['learning']['growth']['growth_rate'],
                'experience_transfer_rate': results['learning']['transfer']['transfer_success_rate']
            }
        }
        
        # 生成建议
        report['recommendations'] = self.generate_recommendations(results)
        
        return report
    
    def generate_recommendations(self, results):
        """基于评估结果生成优化建议"""
        recommendations = []
        
        # 检索性能优化
        if results['retrieval']['quality']['precision'] < 0.80:
            recommendations.append({
                'priority': 'HIGH',
                'category': 'Retrieval',
                'issue': '检索准确率低',
                'recommendation': '考虑升级 Embedding 模型或引入重排序机制',
                'expected_impact': '准确率提升 10-15%'
            })
        
        # 延迟优化
        if results['retrieval']['latency']['p95_latency_ms'] > 150:
            recommendations.append({
                'priority': 'HIGH',
                'category': 'Performance',
                'issue': '检索延迟高',
                'recommendation': '引入缓存层或优化向量数据库索引',
                'expected_impact': '延迟降低 40-60%'
            })
        
        # 存储优化
        if results['storage']['coverage_rate'] < 0.95:
            recommendations.append({
                'priority': 'MEDIUM',
                'category': 'Storage',
                'issue': '索引覆盖率不足',
                'recommendation': '检查索引策略/确保所有记忆被索引',
                'expected_impact': '召回率提升 5-10%'
            })
        
        return recommendations

class MemoryMonitoringSystem:
    """Memory 系统持续监控"""
    
    def __init__(self, memory_system, alert_config):
        self.memory = memory_system
        self.alerts = alert_config
        self.metrics_history = []
    
    async def start_monitoring(self, interval_minutes=5):
        """开始持续监控"""
        while True:
            # 收集实时指标
            metrics = await self.collect_real_time_metrics()
            
            # 检查告警条件
            alerts = self.check_alert_conditions(metrics)
            
            # 记录历史数据
            self.metrics_history.append({
                'timestamp': datetime.now(),
                'metrics': metrics,
                'alerts': alerts
            })
            
            # 发送告警
            if alerts:
                await self.send_alerts(alerts)
            
            # 等待下一次采样
            await asyncio.sleep(interval_minutes * 60)
    
    async def collect_real_time_metrics(self):
        """收集实时指标"""
        return {
            'storage': await self.collect_storage_metrics(),
            'retrieval': await self.collect_retrieval_metrics(),
            'management': await self.collect_management_metrics()
        }
    
    async def collect_retrieval_metrics(self):
        """收集检索指标"""
        # 最近 100 次检索的统计
        recent_searches = await self.memory.get_recent_searches(limit=100)
        
        latencies = [s['latency_ms'] for s in recent_searches]
        
        return {
            'search_count_1h': len(recent_searches),
            'avg_latency_ms': np.mean(latencies),
            'p95_latency_ms': np.percentile(latencies, 95),
            'cache_hit_rate': await self.memory.get_cache_hit_rate()
        }
    
    def check_alert_conditions(self, metrics):
        """检查告警条件"""
        alerts = []
        
        # 检索延迟告警
        if metrics['retrieval']['p95_latency_ms'] > self.alerts['latency_threshold_ms']:
            alerts.append({
                'type': 'HIGH_LATENCY',
                'severity': 'WARNING',
                'message': f"P95 延迟 {metrics['retrieval']['p95_latency_ms']:.0f}ms 超过阈值 {self.alerts['latency_threshold_ms']:.0f}ms",
                'timestamp': datetime.now()
            })
        
        # 存储空间告警
        if metrics['storage']['usage_ratio'] > self.alerts['storage_threshold']:
            alerts.append({
                'type': 'HIGH_STORAGE',
                'severity': 'CRITICAL',
                'message': f"存储使用率 {metrics['storage']['usage_ratio']:.1%} 超过阈值 {self.alerts['storage_threshold']:.1%}",
                'timestamp': datetime.now()
            })
        
        return alerts

completion_rate = successful_tasks / total_tasks

class ErrorRateCalculator:
    def calculate_hallucination_rate(self, responses, ground_truths):
        """
        计算幻觉率
        responses: Agent 生成的回答
        ground_truths: 真实答案
        """
        hallucinations = 0
        for response, truth in zip(responses, ground_truths):
            # 使用 LLM 判断是否存在幻觉
            if self.llm_detects_hallucination(response, truth):
                hallucinations += 1
        
        return hallucinations / len(responses)

# 某企业 Agent 成本优化前后对比
优化前:
  - 平均每任务成本: $0.08
  - 每月总成本: $24,000 (30万任务)

优化后 (通过缓存 + 提示词优化):
  - 平均每任务成本: $0.03
  - 每月总成本: $9,000
  - 节约成本: 62.5%

availability = (total_time - downtime) / total_time * 100%

cost_per_task = (
    api_call_cost + 
    compute_cost / tasks_processed +
    storage_cost / tasks_processed +
    human_intervention_cost * human_intervention_rate
)

ROI = (收益 - 成本) / 成本 × 100%

# 示例
投资成本: $100,000 (开发 + 部署)
运营成本: $10,000/月
自动化节约: $50,000/月
收入增量: $20,000/月

第一年 ROI = ((50,000 + 20,000) × 12 - 100,000 - 10,000 × 12) / (100,000 + 10,000 × 12) × 100%
          = (840,000 - 220,000) / 220,000 × 100%
          = 281%

class CustomerServiceAgentEvaluator:
    """客服 Agent 评估器"""
    
    def evaluate(self, conversation_logs):
        """综合评估"""
        return {
            'resolution_rate': self.calculate_resolution_rate(conversation_logs),
            'user_satisfaction': self.extract_satisfaction_scores(conversation_logs),
            'response_time': self.calculate_response_times(conversation_logs),
            'accuracy': self.validate_responses(conversation_logs),
            'cost_per_conversation': self.calculate_costs(conversation_logs)
        }
    
    def calculate_resolution_rate(self, logs):
        """计算自动化解决率"""
        auto_resolved = sum(1 for log in logs if log['auto_resolved'])
        return auto_resolved / len(logs)
    
    def validate_responses(self, logs):
        """验证回答准确性"""
        correct = 0
        for log in logs:
            # 方法1: 规则检查
            if self.rule_based_check(log['response']):
                correct += 1
            # 方法2: LLM 评估
            elif self.llm_judge(log['response'], log['ground_truth']):
                correct += 1
        
        return correct / len(logs)

class RetrievalEvaluator:
    """检索质量评估"""
    
    def evaluate_retrieval(self, query, retrieved_docs, ground_truth):
        """评估检索效果"""
        return {
            'precision@5': self.precision_at_k(retrieved_docs, ground_truth, k=5),
            'recall@5': self.recall_at_k(retrieved_docs, ground_truth, k=5),
            'mrr': self.mean_reciprocal_rank(retrieved_docs, ground_truth),
            'ndcg': self.ndcg_at_k(retrieved_docs, ground_truth, k=5)
        }
    
    def precision_at_k(self, retrieved, relevant, k):
        """Top-K 精确率"""
        retrieved_k = set(retrieved[:k])
        relevant_set = set(relevant)
        return len(retrieved_k & relevant_set) / k

from ragas import evaluate
from ragas.metrics import (
    faithfulness,
    answer_relevancy,
    context_precision,
    context_recall
)

# 评估 RAG 系统
results = evaluate(
    dataset=your_test_dataset,
    metrics=[
        faithfulness,        # 忠实度: 答案是否基于检索内容
        answer_relevancy,    # 答案相关性
        context_precision,   # 上下文精确度
        context_recall       # 上下文召回率
    ]
)

print(f"Faithfulness: {results['faithfulness']:.2f}")
print(f"Answer Relevancy: {results['answer_relevancy']:.2f}")

class CodeQualityEvaluator:
    """代码质量评估器"""
    
    def evaluate_code(self, generated_code, test_cases):
        """评估生成的代码"""
        return {
            'syntax_correctness': self.check_syntax(generated_code),
            'lint_pass_rate': self.run_linter(generated_code),
            'test_pass_rate': self.run_tests(generated_code, test_cases),
            'security_issues': self.security_scan(generated_code),
            'code_complexity': self.calculate_complexity(generated_code)
        }

class CoreAgentMetrics:
    """通用 Agent 核心指标"""
    
    # 1. 任务完成率
    task_completion_rate = Metric(
        name="Task Completion Rate",
        description="任务完成率",
        target=0.95,
        measurement="成功任务数 / 总任务数",
        frequency="实时"
    )
    
    # 2. 准确率
    accuracy = Metric(
        name="Accuracy",
        description="输出准确率",
        target=0.90,
        measurement="正确输出数 / 总输出数",
        frequency="每次交互"
    )
    
    # 3. 响应时间 P95
    response_time_p95 = Metric(
        name="Response Time P95",
        description="95% 响应时间",
        target=5.0,  # 秒
        measurement="响应时间分布",
        frequency="实时"
    )
    
    # 4. 用户满意度
    user_satisfaction = Metric(
        name="User Satisfaction Score",
        description="用户满意度评分",
        target=4.0,  # 5分制
        measurement="用户反馈平均分",
        frequency="每日"
    )
    
    # 5. 单任务成本
    cost_per_task = Metric(
        name="Cost Per Task",
        description="单任务成本",
        target=0.05,  # 美元
        measurement="总成本 / 任务数",
        frequency="每小时"
    )

class SimpleEvaluationPipeline:
    """简化版评估流水线"""
    
    def __init__(self):
        self.metrics_history = []
    
    async def evaluate(self, agent, test_cases):
        """评估 Agent"""
        results = []
        
        for test_case in test_cases:
            start_time = time.time()
            
            # 执行任务
            response = await agent.execute(test_case['input'])
            
            # 计算指标
            result = {
                'task_id': test_case['id'],
                'completion': self.check_completion(response, test_case['expected']),
                'accuracy': self.calculate_accuracy(response, test_case['expected']),
                'response_time': time.time() - start_time,
                'cost': self.calculate_cost(response)
            }
            
            results.append(result)
        
        # 汇总统计
        summary = {
            'task_completion_rate': np.mean([r['completion'] for r in results]),
            'accuracy': np.mean([r['accuracy'] for r in results]),
            'response_time_p50': np.percentile([r['response_time'] for r in results], 50),
            'response_time_p95': np.percentile([r['response_time'] for r in results], 95),
            'avg_cost_per_task': np.mean([r['cost'] for r in results])
        }
        
        return summary
    
    def check_completion(self, response, expected):
        """检查任务是否完成"""
        # 简单规则检查
        if response and not response.get('error'):
            return 1.0
        return 0.0
    
    def calculate_accuracy(self, response, expected):
        """计算准确率"""
        # 使用 LLM 评估或规则匹配
        if self.exact_match(response, expected):
            return 1.0
        elif self.semantic_match(response, expected):
            return 0.8
        return 0.0

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy

# 准备测试数据
test_data = {
    'question': ["What is AI?", "Explain machine learning"],
    'answer': ["AI is...", "Machine learning is..."],
    'contexts': [["AI definition document"], ["ML textbook"]]
}

# 自动评估
results = evaluate(
    test_data,
    metrics=[faithfulness, answer_relevancy]
)

print(f"Faithfulness: {results['faithfulness']:.2f}")
print(f"Answer Relevancy: {results['answer_relevancy']:.2f}")

class LLMBasedJudge:
    """使用强模型评估弱模型"""
    
    async def evaluate_response(self, query, response, criteria):
        """使用 GPT-4 评估 GPT-3.5 的输出"""
        prompt = f"""
        评估以下回答的质量:
        
        问题: {query}
        回答: {response}
        
        评估标准:
        - 准确性: {criteria['accuracy']}
        - 相关性: {criteria['relevance']}
        - 完整性: {criteria['completeness']}
        
        请给出 1-5 分的评分和理由。
        """
        
        evaluation = await self.strong_llm.generate(prompt)
        return self.parse_evaluation(evaluation)

class OnlineEvaluator:
    """在线实时评估"""
    
    def __init__(self):
        self.streaming_metrics = StreamingMetricsCollector()
        self.alert_system = AlertSystem()
    
    async def real_time_evaluation(self, interaction):
        """实时评估每次交互"""
        # 收集实时指标
        metrics = await self.streaming_metrics.collect(interaction)
        
        # 检查是否触发告警
        if self.should_alert(metrics):
            await self.alert_system.send_alert(metrics)
        
        # 记录到时序数据库
        await self.store_metrics(metrics)
        
        return metrics

class EnergyEfficiencyMetrics:
    """能源效率指标"""
    
    energy_per_task: float  # kWh/任务
    carbon_intensity: float  # kg CO2/千次任务
    green_energy_ratio: float  # 绿色能源占比

class SecurityEvaluator:
    """安全性评估器"""
    
    def evaluate_adversarial_robustness(self, agent, adversarial_test_cases):
        """评估对抗攻击鲁棒性"""
        successful_attacks = 0
        
        for test_case in adversarial_test_cases:
            response = agent.execute(test_case['malicious_input'])
            if self.is_harmful_response(response):
                successful_attacks += 1
        
        robustness = 1 - (successful_attacks / len(adversarial_test_cases))
        return robustness