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ADKLearning/google-adk-tutorial/code/eval_demo.py
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ADKLearning/google-adk-tutorial/code/eval_demo.py
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"""
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Google ADK 智能体评估完整示例
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展示评估集创建和评估运行方法
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对应教程：第08章 - 智能体评估
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"""
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# 导入 ADK 核心模块
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from google.adk.agents import Agent # Agent 类
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from google.adk.runners import Runner # 运行器
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from google.adk.sessions import InMemorySessionService # 会话服务
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from google.genai import types # 类型定义
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# 导入辅助模块
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import asyncio # 异步编程库
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import json # JSON 处理
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# ========================================
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# 定义被评估的 Agent
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# ========================================
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def get_weather(city: str) -> dict:
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"""
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获取天气信息
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Args:
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city (str): 城市名称
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Returns:
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dict: 天气信息
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"""
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weather_data = { # 天气数据
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"北京": {"temp": "25°C", "condition": "晴天"},
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"上海": {"temp": "28°C", "condition": "多云"},
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"广州": {"temp": "32°C", "condition": "雷阵雨"},
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}
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data = weather_data.get(city) # 查找数据
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if not data: # 如果找不到
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return {"status": "error", "error_message": f"未找到'{city}'的天气信息"}
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return {"status": "success", "city": city, **data} # 返回天气
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agent = Agent(
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name="weather_agent", # Agent 名称
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model="gemini-2.0-flash", # 模型
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instruction="你是天气助手，使用 get_weather 工具查询天气。用中文回答。", # 指令
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tools=[get_weather], # 工具
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)
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# ========================================
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# 示例一：创建评估集
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# ========================================
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def create_eval_set():
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"""
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创建评估集文件
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生成 .evalset.json 文件供 adk eval 使用
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"""
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# 定义评估用例
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eval_cases = [ # 用例列表
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{
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"case_id": "weather_beijing", # 用例 ID
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"description": "测试北京天气查询", # 描述
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"user_input": "北京今天天气怎么样？", # 用户输入
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"expected_keywords": ["北京", "天气"], # 期望关键词
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},
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{
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"case_id": "weather_shanghai", # 用例 ID
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"description": "测试上海天气查询", # 描述
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"user_input": "帮我查一下上海的天气", # 用户输入
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"expected_keywords": ["上海"], # 期望关键词
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},
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{
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"case_id": "greeting", # 用例 ID
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"description": "测试问候功能", # 描述
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"user_input": "你好！", # 用户输入
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"expected_keywords": ["你好"], # 期望关键词
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},
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{
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"case_id": "unknown_city", # 用例 ID
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"description": "测试未知城市处理", # 描述
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"user_input": "查询月球基地的天气", # 用户输入
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"expected_keywords": ["找不到", "无法", "不支持"], # 期望关键词
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},
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{
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"case_id": "multiple_cities", # 用例 ID
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"description": "测试多城市查询", # 描述
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"user_input": "北京和上海的天气对比", # 用户输入
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"expected_keywords": ["北京", "上海"], # 期望关键词
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},
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]
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# 构建评估集
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eval_set = { # 评估集对象
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"eval_cases": eval_cases, # 用例列表
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}
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# 写入文件
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filepath = "weather_agent_eval_set.evalset.json" # 文件路径
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with open(filepath, "w", encoding="utf-8") as f: # 打开文件
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json.dump( # 写入 JSON
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eval_set, # 数据
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f, # 文件对象
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ensure_ascii=False, # 允许中文
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indent=2, # 格式化
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)
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print(f"✅ 评估集已创建: {filepath}") # 打印成功信息
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print(f" 用例数量: {len(eval_cases)}") # 打印用例数
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return filepath # 返回文件路径
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# ========================================
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# 示例二：代码方式运行评估
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# ========================================
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async def evaluate_agent(test_cases: list) -> dict:
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"""
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评估 Agent
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Args:
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test_cases: 测试用例列表
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Returns:
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dict: 评估结果汇总
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"""
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# 初始化服务
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session_service = InMemorySessionService() # 会话服务
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runner = Runner( # 运行器
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agent=agent, # Agent
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app_name="eval_app", # 应用名称
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session_service=session_service, # 会话服务
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)
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results = [] # 结果列表
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passed = 0 # 通过计数
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for i, case in enumerate(test_cases): # 遍历用例
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case_id = case.get("case_id", f"case_{i}") # 用例 ID
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print(f"\n📋 测试用例: {case_id}") # 打印用例 ID
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print(f" 输入: {case['user_input']}") # 打印输入
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# 创建独立会话
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session_id = f"eval_session_{i}" # 会话 ID
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await session_service.create_session( # 创建会话
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app_name="eval_app", # 应用名称
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user_id="eval_user", # 用户 ID
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session_id=session_id, # 会话 ID
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)
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# 构造消息
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content = types.Content(
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role='user', # 角色
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parts=[types.Part(text=case["user_input"])], # 内容
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)
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# 运行 Agent
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events = runner.run_async(
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user_id="eval_user", # 用户 ID
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session_id=session_id, # 会话 ID
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new_message=content, # 消息
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)
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# 收集响应
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response_text = "" # 初始化响应
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async for event in events: # 遍历事件
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if event.is_final_response(): # 最终响应
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response_text = event.content.parts[0].text # 提取文本
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# 检查期望关键词
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case_passed = True # 默认通过
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failure_reason = "" # 失败原因
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if "expected_keywords" in case: # 如果有关键词要求
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missing = [] # 缺失的关键词
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for kw in case["expected_keywords"]: # 遍历关键词
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if kw not in response_text: # 如果缺失
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missing.append(kw) # 记录缺失
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if missing: # 如果有缺失
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case_passed = False # 标记失败
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failure_reason = f"缺少关键词: {missing}" # 失败原因
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# 检查不应出现的关键词
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if "not_expected_keywords" in case: # 如果有排除关键词
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for kw in case["not_expected_keywords"]: # 遍历
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if kw in response_text: # 如果出现
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case_passed = False # 标记失败
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failure_reason = f"不应包含关键词: '{kw}'" # 失败原因
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if case_passed: # 如果通过
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passed += 1 # 递增通过数
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print(f" ✅ 通过") # 打印通过
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else: # 如果失败
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print(f" ❌ 失败: {failure_reason}") # 打印失败原因
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# 记录结果
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results.append({ # 添加结果
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"case_id": case_id, # 用例 ID
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"status": "passed" if case_passed else "failed", # 状态
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"response": response_text[:200], # 响应（截断）
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"reason": failure_reason, # 失败原因
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})
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# 返回汇总
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return { # 返回结果
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"total": len(test_cases), # 总数
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"passed": passed, # 通过数
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"failed": len(test_cases) - passed, # 失败数
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"pass_rate": passed / len(test_cases) if test_cases else 0, # 通过率
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"results": results, # 详细结果
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}
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# ========================================
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# 示例三：打印评估报告
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# ========================================
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def print_report(result: dict):
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"""
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打印评估报告
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Args:
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result: 评估结果
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"""
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print("\n" + "=" * 60) # 分隔线
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print("📊 评估报告") # 标题
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print("=" * 60) # 分隔线
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print(f"总用例数: {result['total']}") # 总数
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print(f"通过: {result['passed']} ✅") # 通过数
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print(f"失败: {result['failed']} ❌") # 失败数
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print(f"通过率: {result['pass_rate']:.1%}") # 通过率
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print("-" * 60) # 分隔线
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for r in result["results"]: # 遍历详细结果
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icon = "✅" if r["status"] == "passed" else "❌" # 状态图标
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print(f" {icon} {r['case_id']}: {r['status']}") # 打印结果
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if r["reason"]: # 如果有失败原因
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print(f" 原因: {r['reason']}") # 打印原因
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if r["status"] == "passed": # 如果通过
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print(f" 响应: {r['response'][:100]}...") # 打印响应片段
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print("=" * 60) # 分隔线
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# ========================================
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# 主函数
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# ========================================
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async def main():
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"""主函数"""
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# 第一步：创建评估集
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filepath = create_eval_set() # 创建评估集文件
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# 第二步：加载评估用例
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with open(filepath, "r", encoding="utf-8") as f: # 读取文件
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eval_set = json.load(f) # 解析 JSON
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test_cases = eval_set["eval_cases"] # 获取用例列表
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# 第三步：运行评估
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result = await evaluate_agent(test_cases) # 执行评估
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# 第四步：打印报告
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print_report(result) # 打印评估报告
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if __name__ == "__main__": # 直接运行
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asyncio.run(main()) # 执行主函数
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