> For the complete documentation index, see [llms.txt](https://yeasy.gitbook.io/context_engineering_guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://yeasy.gitbook.io/context_engineering_guide/di-si-bu-fen-gong-cheng-shi-zhan-yu-wei-lai-yan-jin/12_production/12.2_optimization/12.2.7_cost_modeling_roi.md). # 12.2.7 成本建模与 ROI 分析框架 ## 12.2.7.1 引言：为什么需要成本建模 在上下文工程的实践中，一个常见的陷阱是“无限制优化”——不断增加上下文长度、提升检索精度、缓存更多数据，却没有对应的成本收益分析。结果往往是： * 基础设施成本飙升，但用户体验改善幅度有限 * 工程团队在边际收益递减的优化上耗费大量时间 * 无法与业务决策者有效沟通“这个优化值不值得做” **上下文工程的成本维度多样化**： 1. **Token 成本**：输入/输出 Token 的直接成本（最易量化） 2. **计算成本**：向量检索、重排序、压缩等的计算开销 3. **存储成本**：知识库存储、向量库、缓存的存储开销 4. **延迟成本**：响应时间影响的用户体验和转化率 5. **维护成本**：知识库更新、模型迭代、监控告警的人力成本 6. **合规成本**：数据安全、审计追踪、合规检查的额外投入 本节提供从基础 Token 计算到复杂 ROI 评估的完整框架。 ## 12.2.7.2 基础 Token 价格表 ```python from dataclasses import dataclass from typing import Dict, List from enum import Enum import json class ModelProvider(Enum): OPENAI = "openai" ANTHROPIC = "anthropic" GOOGLE = "google" OPEN_SOURCE = "open_source" @dataclass class ModelPricing: """模型定价信息""" provider: ModelProvider model_name: str input_price_per_1k_tokens: float # 美元 output_price_per_1k_tokens: float # 美元 cache_write_price_per_1k_tokens: float # 缓存写入价格（如果支持） cache_read_price_per_1k_tokens: float # 缓存读取价格（如果支持） def __post_init__(self): """验证价格数据""" assert self.input_price_per_1k_tokens >= 0 assert self.output_price_per_1k_tokens >= 0 # 通常缓存读取价格更便宜 if self.cache_read_price_per_1k_tokens > 0: assert self.cache_read_price_per_1k_tokens < self.input_price_per_1k_tokens # 教学示例价格，不代表任何供应商的实时定价。 # 生产环境必须使用具体模型 ID，并从供应商当前 Pricing 页或合同价同步。 PRICE_TABLE = [ ModelPricing( provider=ModelProvider.OPENAI, model_name="example-frontier", input_price_per_1k_tokens=0.0025, output_price_per_1k_tokens=0.015, cache_write_price_per_1k_tokens=0.0025 * 1.25, # 写入成本更高 cache_read_price_per_1k_tokens=0.0025 * 0.1, # 读取便宜 90% ), ModelPricing( provider=ModelProvider.OPENAI, model_name="example-mini", input_price_per_1k_tokens=0.00075, output_price_per_1k_tokens=0.0045, cache_write_price_per_1k_tokens=0.00075 * 1.25, cache_read_price_per_1k_tokens=0.00075 * 0.1, ), ModelPricing( provider=ModelProvider.ANTHROPIC, model_name="example-frontier", input_price_per_1k_tokens=0.005, output_price_per_1k_tokens=0.025, cache_write_price_per_1k_tokens=0.005 * 1.25, cache_read_price_per_1k_tokens=0.005 * 0.1, ), ModelPricing( provider=ModelProvider.ANTHROPIC, model_name="example-balanced", input_price_per_1k_tokens=0.003, output_price_per_1k_tokens=0.015, cache_write_price_per_1k_tokens=0.003 * 1.25, cache_read_price_per_1k_tokens=0.003 * 0.1, ), ] # 建立模型名称到价格的映射 # 价格表仅用于演示计算结构；生产环境请把它替换为经版本标注的当前价格快照。 PRICING_MAP = { f"{p.provider.value}:{p.model_name}": p for p in PRICE_TABLE } class TokenCostCalculator: """Token成本计算器""" def __init__(self, pricing_map: Dict[str, ModelPricing] = PRICING_MAP): self.pricing_map = pricing_map def calculate_single_request_cost( self, model_key: str, # 格式: "openai:example-frontier" input_tokens: int, output_tokens: int, use_cache: bool = False, cache_hit_ratio: float = 0.0 # 缓存命中率（0-1） ) -> Dict[str, float]: """ 计算单次请求的 Token成本。 返回字典包含： - input_cost: 输入成本 - output_cost: 输出成本 - cache_cost: 缓存相关成本（如适用） - total_cost: 总成本 """ if model_key not in self.pricing_map: raise ValueError(f"Unknown model: {model_key}") pricing = self.pricing_map[model_key] if not use_cache: # 无缓存场景：按正常价格计算 input_cost = (input_tokens / 1000) * pricing.input_price_per_1k_tokens output_cost = (output_tokens / 1000) * pricing.output_price_per_1k_tokens return { 'input_cost': input_cost, 'output_cost': output_cost, 'cache_cost': 0.0, 'total_cost': input_cost + output_cost, } else: # 缓存场景：部分命中缓存 cache_hit_tokens = int(input_tokens * cache_hit_ratio) cache_miss_tokens = input_tokens - cache_hit_tokens # 缓存命中：使用便宜的缓存读取价格 cache_hit_cost = (cache_hit_tokens / 1000) * pricing.cache_read_price_per_1k_tokens # 缓存未命中：写入缓存的 token 按 cache write 单价计费， # 不再额外叠加普通 input 单价 cache_write_cost = (cache_miss_tokens / 1000) * pricing.cache_write_price_per_1k_tokens # 输出成本不变 output_cost = (output_tokens / 1000) * pricing.output_price_per_1k_tokens total_input_cost = cache_hit_cost + cache_write_cost return { 'input_cost': total_input_cost, 'cache_hit_cost': cache_hit_cost, 'cache_miss_cost': cache_write_cost, 'cache_write_cost': cache_write_cost, 'cache_cost': total_input_cost, 'output_cost': output_cost, 'total_cost': total_input_cost + output_cost, } def calculate_batch_cost( self, model_key: str, requests: List[Dict], # 每个请求包含 input_tokens, output_tokens use_cache: bool = False, cache_hit_ratio: float = 0.0 ) -> Dict: """计算批量请求的总成本""" total_cost = { 'input_cost': 0.0, 'output_cost': 0.0, 'cache_cost': 0.0, 'total_cost': 0.0, 'num_requests': len(requests), } for req in requests: single_cost = self.calculate_single_request_cost( model_key, req['input_tokens'], req['output_tokens'], use_cache, cache_hit_ratio ) total_cost['input_cost'] += single_cost.get('input_cost', 0) total_cost['output_cost'] += single_cost.get('output_cost', 0) total_cost['cache_cost'] += single_cost.get('cache_cost', 0) total_cost['total_cost'] += single_cost['total_cost'] # 计算平均成本 total_cost['avg_cost_per_request'] = total_cost['total_cost'] / len(requests) if requests else 0 return total_cost def project_monthly_cost( self, model_key: str, daily_requests: int, avg_input_tokens_per_request: int, avg_output_tokens_per_request: int, use_cache: bool = False, cache_hit_ratio: float = 0.0 ) -> Dict: """ 根据日请求量和 Token数量，预测月度成本。 假设按工作日计算（20天/月）。 """ monthly_requests = daily_requests * 20 single_request_cost = self.calculate_single_request_cost( model_key, avg_input_tokens_per_request, avg_output_tokens_per_request, use_cache, cache_hit_ratio ) monthly_cost = single_request_cost['total_cost'] * monthly_requests return { 'daily_requests': daily_requests, 'monthly_requests': monthly_requests, 'avg_input_tokens': avg_input_tokens_per_request, 'avg_output_tokens': avg_output_tokens_per_request, 'cost_per_request': single_request_cost['total_cost'], 'monthly_cost': monthly_cost, 'monthly_cost_usd': f"${monthly_cost:.2f}", } ``` ## 12.2.7.3 长上下文成本分析 长上下文（extended context）是一把双刃剑： * **收益**：更多信息在单次请求中可用，减少多轮交互 * **成本**：输入 Token 数量增加，成本线性上升 ```python class ContextLengthCostAnalysis: """分析不同上下文长度的成本-效益权衡""" def __init__(self, calculator: TokenCostCalculator): self.calculator = calculator def compare_context_strategies( self, model_key: str, user_query_tokens: int = 100, output_tokens: int = 500, knowledge_base_sizes: List[int] = None # 上下文块的 token数 ) -> Dict: """ 比较不同上下文大小下的成本与预期收益。 knowledge_base_sizes: 要对比的上下文大小列表 例如：[500, 1000, 2000, 4000] 表示包含不同大小的知识块 """ if knowledge_base_sizes is None: knowledge_base_sizes = [500, 1000, 2000, 4000, 8000] analysis = { 'strategies': [], 'breakeven_point': None, } for ctx_size in knowledge_base_sizes: total_input_tokens = user_query_tokens + ctx_size # 假设更大的上下文能减少后续交互 # 这里用经验值：每增加 1000 tokens上下文，能减少 10%的后续轮次 # 必须同时钳上界：只 max(0, ...) 会让 ctx_size > 10500 时 # expected_follow_ups 变成负数，进而算出负成本， # 而 min() 选最优时就会一路选中最大的上下文 follow_up_reduction = min(1.0, max(0.0, (ctx_size - 500) / 1000 * 0.1)) expected_follow_ups = 1 - follow_up_reduction # 原期望平均 1次 follow-up # 成本计算 single_cost = self.calculator.calculate_single_request_cost( model_key, total_input_tokens, output_tokens ) # 假设需要 2轮对话来完成任务（初始查询 + follow-up） total_interaction_cost = single_cost['total_cost'] + (single_cost['total_cost'] * expected_follow_ups) strategy = { 'context_tokens': ctx_size, 'total_input_tokens': total_input_tokens, 'output_tokens': output_tokens, 'single_request_cost': single_cost['total_cost'], 'expected_follow_ups': expected_follow_ups, 'expected_total_interaction_cost': total_interaction_cost, } analysis['strategies'].append(strategy) # 找到最优点（成本最低的完成任务方式） optimal = min( analysis['strategies'], key=lambda s: s['expected_total_interaction_cost'] ) analysis['optimal_context_size'] = optimal['context_tokens'] analysis['optimal_cost'] = optimal['expected_total_interaction_cost'] return analysis def cost_quality_tradeoff( self, model_key: str, baseline_context_size: int = 2000, quality_scores: Dict[int, float] = None # 不同上下文大小下的质量评分 ) -> Dict: """ 分析成本与质量的权衡曲线。 quality_scores: 键为上下文 tokens数，值为预期的回答质量评分（0-100） """ if quality_scores is None: # 使用默认的质量函数：收益递减 quality_scores = { 500: 60, 1000: 75, 2000: 85, 4000: 90, 8000: 93, } analysis = { 'datapoints': [], } for ctx_size, quality_score in quality_scores.items(): cost_data = self.calculator.calculate_single_request_cost( model_key, ctx_size + 100, # 加上查询大小 500 # 假设输出 token固定 ) # 计算成本-效益指标：质量分数与成本的比值 quality_per_dollar = quality_score / max(cost_data['total_cost'], 0.0001) datapoint = { 'context_tokens': ctx_size, 'quality_score': quality_score, 'cost': cost_data['total_cost'], 'quality_per_dollar': quality_per_dollar, 'efficiency_ratio': quality_per_dollar / 100, # 仅缩放便于比较，量纲仍是「质量分/美元」，不是 0-1 } analysis['datapoints'].append(datapoint) # 找到效率最高的点 optimal_point = max(analysis['datapoints'], key=lambda p: p['quality_per_dollar']) analysis['optimal_point'] = optimal_point return analysis ``` ## 12.2.7.4 缓存策略的 ROI 分析 缓存是上下文工程中最重要的成本优化手段。 ```python class CacheROIAnalyzer: """缓存投资回报率分析""" def __init__(self, calculator: TokenCostCalculator): self.calculator = calculator def analyze_cache_investment( self, model_key: str, without_cache: Dict, # 不使用缓存的场景 with_cache: Dict, # 使用缓存的场景 cache_maintenance_cost_monthly: float = 100.0, # 美元/月 cache_setup_cost_one_time: float = 0.0 # 一次性建设成本，美元 ) -> Dict: """ 比较使用缓存前后的成本与收益。 without_cache: { 'daily_requests': 100, 'avg_input_tokens': 2000, 'avg_output_tokens': 500, } with_cache: { 'daily_requests': 100, 'avg_input_tokens': 2000, 'avg_output_tokens': 500, 'cache_hit_ratio': 0.6, # 60%的请求命中缓存 } """ # 计算无缓存场景的月成本 without_cache_cost = self.calculator.project_monthly_cost( model_key, without_cache['daily_requests'], without_cache['avg_input_tokens'], without_cache['avg_output_tokens'], use_cache=False ) # 计算有缓存场景的月成本 with_cache_cost = self.calculator.project_monthly_cost( model_key, with_cache['daily_requests'], with_cache['avg_input_tokens'], with_cache['avg_output_tokens'], use_cache=True, cache_hit_ratio=with_cache['cache_hit_ratio'] ) # 成本对比 cache_api_savings = ( without_cache_cost['monthly_cost'] - with_cache_cost['monthly_cost'] ) cache_net_savings = cache_api_savings - cache_maintenance_cost_monthly # 计算 ROI roi_percentage = ( (cache_net_savings / cache_maintenance_cost_monthly * 100) if cache_maintenance_cost_monthly > 0 else float('inf') ) # 计算一次性建设成本的回本周期（breakeven） months_to_breakeven = ( cache_setup_cost_one_time / cache_net_savings if cache_setup_cost_one_time > 0 and cache_net_savings > 0 else 0.0 if cache_setup_cost_one_time == 0 and cache_net_savings > 0 else float('inf') ) return { 'without_cache': { 'monthly_cost_usd': f"${without_cache_cost['monthly_cost']:.2f}", 'monthly_cost_value': without_cache_cost['monthly_cost'], }, 'with_cache': { 'monthly_cost_usd': f"${with_cache_cost['monthly_cost']:.2f}", 'monthly_cost_value': with_cache_cost['monthly_cost'], 'cache_hit_ratio': with_cache['cache_hit_ratio'], }, 'cache_investment': { 'monthly_maintenance_cost_usd': f"${cache_maintenance_cost_monthly:.2f}", 'monthly_maintenance_cost_value': cache_maintenance_cost_monthly, 'one_time_setup_cost_usd': f"${cache_setup_cost_one_time:.2f}", 'one_time_setup_cost_value': cache_setup_cost_one_time, }, 'roi': { 'api_savings_monthly_usd': f"${cache_api_savings:.2f}", 'api_savings_monthly_value': cache_api_savings, 'net_savings_monthly_usd': f"${cache_net_savings:.2f}", 'net_savings_monthly_value': cache_net_savings, 'roi_percentage': roi_percentage, 'months_to_breakeven': months_to_breakeven, } } def cache_strategy_comparison( self, model_key: str, baseline_scenario: Dict, strategy_configs: List[Dict] # 多个缓存策略配置 ) -> List[Dict]: """ 比较多个缓存策略的效果。 strategy_configs: [ {'name': '保守策略', 'cache_hit_ratio': 0.3}, {'name': '中等策略', 'cache_hit_ratio': 0.6}, {'name': '激进策略', 'cache_hit_ratio': 0.8}, ] """ comparison_results = [] for strategy in strategy_configs: scenario = baseline_scenario.copy() scenario['cache_hit_ratio'] = strategy['cache_hit_ratio'] roi = self.analyze_cache_investment( model_key, baseline_scenario, scenario ) roi['strategy_name'] = strategy['name'] roi['cache_hit_ratio'] = strategy['cache_hit_ratio'] comparison_results.append(roi) return comparison_results ``` ## 12.2.7.5 综合 ROI 评估框架 ```python from typing import Dict, List, Tuple from datetime import datetime class ComprehensiveROIAnalyzer: """综合的 ROI评估框架，包含多维成本与收益""" def __init__(self): self.token_calculator = TokenCostCalculator() self.cache_analyzer = CacheROIAnalyzer(self.token_calculator) def estimate_context_engineering_roi( self, project_name: str, baseline_metrics: Dict, # 优化前的指标 optimized_metrics: Dict, # 优化后的指标 optimization_cost: Dict, # 优化的成本投入 evaluation_period_months: int = 12 ) -> Dict: """ 计算整个上下文工程优化项目的 ROI。 baseline_metrics: { 'monthly_api_cost': 1000, 'avg_response_time_ms': 2000, 'user_satisfaction_score': 3.5, # 1-5分 'error_rate': 0.05, # 5% } optimized_metrics: { 'monthly_api_cost': 600, 'avg_response_time_ms': 800, 'user_satisfaction_score': 4.2, 'error_rate': 0.01, # 1% } optimization_cost: { 'engineering_hours': 100, 'hourly_rate': 150, 'infrastructure_one_time': 5000, 'monthly_maintenance': 500, } """ # 1. 直接成本节省 monthly_api_savings = ( baseline_metrics['monthly_api_cost'] - optimized_metrics['monthly_api_cost'] ) # 2. 性能改善带来的间接收益 # 假设响应时间每减少 1秒，转化率提升 0.5% response_time_improvement_sec = ( baseline_metrics['avg_response_time_ms'] - optimized_metrics['avg_response_time_ms'] ) / 1000 assumed_monthly_revenue = 100000 # 假设月收入 response_time_roi_multiplier = response_time_improvement_sec * 0.005 # 转化率提升 monthly_revenue_gain = assumed_monthly_revenue * response_time_roi_multiplier # 3. 用户满意度提升的价值 satisfaction_improvement = ( optimized_metrics['user_satisfaction_score'] - baseline_metrics['user_satisfaction_score'] ) # 假设满意度提升 1分，用户续约率提升 2% monthly_user_retention_gain = assumed_monthly_revenue * 0.02 * satisfaction_improvement # 4. 错误率改善的成本节省 # 假设每个错误造成的成本是处理费用+赔偿 monthly_error_cost_reduction = ( (baseline_metrics['error_rate'] - optimized_metrics['error_rate']) * assumed_monthly_revenue * 0.01 # 假设错误成本是收入的 1% ) # 5. 优化成本 engineering_cost = ( optimization_cost['engineering_hours'] * optimization_cost['hourly_rate'] ) monthly_maintenance = optimization_cost.get('monthly_maintenance', 0) # 6. 总的月度收益 total_monthly_benefit = ( monthly_api_savings + monthly_revenue_gain + monthly_user_retention_gain + monthly_error_cost_reduction ) # 7. 总的月度成本 total_monthly_cost = monthly_maintenance # 8. ROI计算 evaluation_period_cost = ( engineering_cost + optimization_cost.get('infrastructure_one_time', 0) + (monthly_maintenance * evaluation_period_months) ) evaluation_period_benefit = total_monthly_benefit * evaluation_period_months roi = { 'project_name': project_name, 'evaluation_period_months': evaluation_period_months, 'one_time_costs': { 'engineering': engineering_cost, 'infrastructure': optimization_cost.get('infrastructure_one_time', 0), }, 'monthly_costs': { 'maintenance': monthly_maintenance, }, 'monthly_benefits': { 'api_cost_savings': monthly_api_savings, 'response_time_roi': monthly_revenue_gain, 'user_satisfaction_roi': monthly_user_retention_gain, 'error_reduction_roi': monthly_error_cost_reduction, 'total': total_monthly_benefit, }, 'period_analysis': { 'total_cost': evaluation_period_cost, 'total_benefit': evaluation_period_benefit, 'net_benefit': evaluation_period_benefit - evaluation_period_cost, 'roi_percentage': ( (evaluation_period_benefit - evaluation_period_cost) / evaluation_period_cost * 100 if evaluation_period_cost > 0 else 0 ), }, 'metrics_comparison': { 'baseline': baseline_metrics, 'optimized': optimized_metrics, } } return roi def generate_roi_report( self, roi_analysis: Dict, output_format: str = 'text' ) -> str: """生成易读的 ROI报告""" # 结论必须由数据得出：写死一句「建议继续投入」会让 ROI 为负的项目 # 也拿到「继续投入」的结论，与本节「用数据决定投不投」的主张相反 roi_pct = roi_analysis['period_analysis']['roi_percentage'] if roi_pct >= 50: verdict = f"✓ 投资回报显著（ROI {roi_pct:.1f}%），建议继续投入" elif roi_pct > 0: verdict = f"→ 回报为正但不宽裕（ROI {roi_pct:.1f}%），建议缩小范围继续验证" else: verdict = f"✗ 当前口径下不回本（ROI {roi_pct:.1f}%），建议暂停并重新评估方案" if output_format == 'text': report = f""" ===== 上下文工程优化 ROI 报告 ===== 项目名称: {roi_analysis['project_name']} 评估周期: {roi_analysis['evaluation_period_months']} 个月 【成本总结】 一次性成本: - 工程成本: ${roi_analysis['one_time_costs']['engineering']:.2f} - 基础设施: ${roi_analysis['one_time_costs']['infrastructure']:.2f} 月度成本: - 运维: ${roi_analysis['monthly_costs']['maintenance']:.2f} 【收益总结】 月度收益分项: - API成本节省: ${roi_analysis['monthly_benefits']['api_cost_savings']:.2f} - 响应时间 ROI: ${roi_analysis['monthly_benefits']['response_time_roi']:.2f} - 满意度提升 ROI: ${roi_analysis['monthly_benefits']['user_satisfaction_roi']:.2f} - 错误率改善: ${roi_analysis['monthly_benefits']['error_reduction_roi']:.2f} - 月度总收益: ${roi_analysis['monthly_benefits']['total']:.2f} 【投资回报】 评估周期内: - 总成本: ${roi_analysis['period_analysis']['total_cost']:.2f} - 总收益: ${roi_analysis['period_analysis']['total_benefit']:.2f} - 净收益: ${roi_analysis['period_analysis']['net_benefit']:.2f} - ROI: {roi_analysis['period_analysis']['roi_percentage']:.1f}% 【关键指标对比】 | 优化前 | 优化后 | 改善 ----------------------------------- API成本 | ${roi_analysis['metrics_comparison']['baseline']['monthly_api_cost']:.0f} | ${roi_analysis['metrics_comparison']['optimized']['monthly_api_cost']:.0f} | {(roi_analysis['metrics_comparison']['baseline']['monthly_api_cost'] - roi_analysis['metrics_comparison']['optimized']['monthly_api_cost']) / roi_analysis['metrics_comparison']['baseline']['monthly_api_cost'] * 100:.1f}% 响应时间(ms) | {roi_analysis['metrics_comparison']['baseline']['avg_response_time_ms']} | {roi_analysis['metrics_comparison']['optimized']['avg_response_time_ms']} | {(roi_analysis['metrics_comparison']['baseline']['avg_response_time_ms'] - roi_analysis['metrics_comparison']['optimized']['avg_response_time_ms']) / roi_analysis['metrics_comparison']['baseline']['avg_response_time_ms'] * 100:.1f}% 满意度(1-5) | {roi_analysis['metrics_comparison']['baseline']['user_satisfaction_score']:.1f} | {roi_analysis['metrics_comparison']['optimized']['user_satisfaction_score']:.1f} | +{roi_analysis['metrics_comparison']['optimized']['user_satisfaction_score'] - roi_analysis['metrics_comparison']['baseline']['user_satisfaction_score']:.1f} 错误率 | {roi_analysis['metrics_comparison']['baseline']['error_rate']:.1%} | {roi_analysis['metrics_comparison']['optimized']['error_rate']:.1%} | {(roi_analysis['metrics_comparison']['baseline']['error_rate'] - roi_analysis['metrics_comparison']['optimized']['error_rate']) / roi_analysis['metrics_comparison']['baseline']['error_rate'] * 100:.1f}% 【建议】 {verdict} """ elif output_format == 'json': report = json.dumps(roi_analysis, indent=2, default=str) return report ``` ## 12.2.7.6 实际案例：电商客服系统的成本分析 ```python # 场景：电商平台集成了 AI客服，使用 RAG系统回答商品问题 # 优化前的基线 baseline = { 'daily_requests': 500, 'avg_input_tokens': 3000, # 包含长的产品描述和历史记录 'avg_output_tokens': 300, 'monthly_api_cost': 4500, 'avg_response_time_ms': 3000, 'user_satisfaction_score': 3.2, # 信息检索不准 'error_rate': 0.08, # 返回不相关信息 } # 优化后（实施了分块优化、向量检索、缓存） optimized = { 'daily_requests': 500, 'avg_input_tokens': 1500, # 上下文优化后减少一半 'avg_output_tokens': 300, 'monthly_api_cost': 2200, # 成本下降 51%（4500→2200） 'avg_response_time_ms': 800, # 响应时间快约 73%（3000→800ms） 'user_satisfaction_score': 4.3, # 满意度提升 'error_rate': 0.02, # 错误率从 8%降到 2% } # 优化成本 optimization_cost = { 'engineering_hours': 120, 'hourly_rate': 120, # $120/小时 'infrastructure_one_time': 3000, # 向量库搭建 'monthly_maintenance': 400, # 向量库维护 } # 执行分析 analyzer = ComprehensiveROIAnalyzer() roi_result = analyzer.estimate_context_engineering_roi( project_name='电商 AI客服系统优化', baseline_metrics=baseline, optimized_metrics=optimized, optimization_cost=optimization_cost, evaluation_period_months=12 ) print(analyzer.generate_roi_report(roi_result, output_format='text')) ``` 这个框架可以帮助团队做出有数据支撑的决策：是否投资某项上下文工程优化、投资多少、预期能获得多少回报。