Google ADK（Agent Development Kit）简要示例说明

一、环境准备与依赖安装

1.1 系统

硬件： GPU NVIDIA 3070加速模型推理，内存64GB
软件：
- Python 3.11
- Docker 28.04（用于容器化部署）
- Kubernetes 1.25（可选，用于集群管理）

1.2 安装 ADK 工具链

# 安装 Google ADK Python SDK
pip install google-adk# 安装依赖库
pip install tensorflow-serving-api==2.13.0 \torch==2.1.0+cu121 \pinecone-client==2.2.0

1.3 初始化项目结构

mkdir my_agent_project && cd my_agent_project
adk init --template=multi-agent  # 选择多智能体模板

二、智能体开发核心步骤

2.1 定义智能体能力（Agent Card）

创建 agent_card.json：

{"name": "financial-analyzer","version": "v1.0.0","description": "金融数据分析智能体，支持财报分析、风险评估","skills": ["financial-report", "risk-assessment", "market-trend"],"endpoints": {"query": "http://localhost:8080/query","stream": "http://localhost:8080/stream"},"authentication": {"type": "oauth2","client_id": "your_client_id","scopes": ["financial-data:read"]}
}

2.2 实现智能体逻辑（Python 代码）

# agents/financial_analyzer.py
from google.adk.agents import Agent
from google.adk.tasks import Task
from google.adk.memory import LongTermMemoryclass FinancialAnalyzer(Agent):def __init__(self):super().__init__(name="financial-analyzer")self.memory = LongTermMemory(pinecone_api_key="your_pinecone_key")self.models = {"risk_model": load_model("models/risk_assessment.pth"),"trend_model": load_model("models/market_trend.pth")}async def handle_task(self, task: Task):# 处理用户请求if task.type == "financial-report":return await self.analyze_financial_report(task.payload)elif task.type == "risk-assessment":return await self.assess_risk(task.payload)async def analyze_financial_report(self, data: dict):# 调用外部 API 获取财报数据financial_data = await self.invoke_tool("fetch-financial-data", data)# 模型推理analysis = self.models["risk_model"].predict(financial_data)# 存储到长期记忆self.memory.save("financial-analysis", analysis)return analysisasync def assess_risk(self, data: dict):# 结合历史分析结果history = self.memory.retrieve("financial-analysis")# 多模型融合risk_score = self.models["trend_model"].predict({**data, **history})return {"risk_score": risk_score}

2.3 配置工具链

在 toolchain.yaml 中定义工具：

tools:- name: fetch-financial-datatype: apiendpoint: https://api.finance.com/reportmethod: POSTheaders:Authorization: Bearer ${FINANCE_API_KEY}

三、多智能体协同开发

3.1 定义任务流程（BPMN 2.0）

使用 FlowStudio 设计工作流：

<process id="financial-workflow"><startEvent id="start"/><sequenceFlow sourceRef="start" targetRef="analyze-report"/><serviceTask id="analyze-report" name="分析财报" agentRef="financial-analyzer" taskType="financial-report"/><sequenceFlow sourceRef="analyze-report" targetRef="assess-risk"/><serviceTask id="assess-risk" name="风险评估" agentRef="financial-analyzer" taskType="risk-assessment"/><endEvent id="end"/>
</process>

3.2 实现任务编排

# workflows/financial_workflow.py
from google.adk.orchestration import WorkflowEngineclass FinancialWorkflow(WorkflowEngine):def __init__(self):super().__init__(name="financial-workflow")self.register_agent("financial-analyzer", FinancialAnalyzer())async def execute(self, user_request: dict):# 启动工作流task = Task(type="financial-report",payload=user_request,workflow_id=self.id)result = await self.dispatch(task)return result

四、调试与测试

4.1 本地调试

# 启动本地服务
adk run --port 8080# 测试请求
curl -X POST http://localhost:8080/query \-H "Content-Type: application/json" \-d '{"query": "分析苹果公司2024年Q3财报","task_type": "financial-report"}'

4.2 单元测试

# tests/test_financial_analyzer.py
from agents.financial_analyzer import FinancialAnalyzerdef test_financial_analysis():agent = FinancialAnalyzer()mock_data = {"company": "Apple", "quarter": "2024Q3"}result = agent.analyze_financial_report(mock_data)assert "risk_score" in result

五、性能优化

5.1 模型量化

# 量化模型
import tensorflow as tf
from tensorflow_model_optimization.sparsity import kerasdef quantize_model(model):converter = tf.lite.TFLiteConverter.from_keras_model(model)converter.optimizations = [tf.lite.Optimize.DEFAULT]converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]converter.inference_input_type = tf.int8converter.inference_output_type = tf.int8quantized_model = converter.convert()return quantized_model

5.2 硬件加速配置

# deployment/gpu_config.yaml
resources:limits:nvidia.com/gpu: 1requests:nvidia.com/gpu: 1

六、容器化部署

6.1 Dockerfile

FROM tensorflow/tensorflow:latest-gpuWORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txtCOPY . .CMD ["adk", "serve", "--config", "config.yaml"]

6.2 Kubernetes 部署

# kubernetes/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:name: financial-agent
spec:replicas: 3selector:matchLabels:app: financial-agenttemplate:metadata:labels:app: financial-agentspec:containers:- name: financial-agentimage: financial-agent:v1.0ports:- containerPort: 8080resources:limits:nvidia.com/gpu: 1

七、安全与合规

7.1 身份验证

# security/auth.py
from google.auth.transport.requests import Request
from google.oauth2 import id_tokendef verify_token(token):try:idinfo = id_token.verify_oauth2_token(token, Request())if idinfo["aud"] != "your_client_id":raise ValueError("Invalid audience")return idinfoexcept ValueError:return None

7.2 数据加密

# security/encryption.py
from cryptography.fernet import Fernetclass DataEncryptor:def __init__(self, key):self.cipher_suite = Fernet(key)def encrypt(self, data):return self.cipher_suite.encrypt(data.encode())def decrypt(self, encrypted_data):return self.cipher_suite.decrypt(encrypted_data).decode()

八、监控与运维

8.1 Prometheus 指标采集

# monitoring/metrics.py
from prometheus_client import Counter, GaugeREQUESTS_TOTAL = Counter('agent_requests_total', 'Total requests processed')
LATENCY = Gauge('agent_latency_seconds', 'Request latency')@LATENCY.time()
@REQUESTS_TOTAL.count_exceptions()
async def handle_request():# 业务逻辑pass

8.2 日志配置

# logging_config.py
import logging
from pythonjsonlogger import jsonloggerlogger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)handler = logging.StreamHandler()
formatter = jsonlogger.JsonFormatter("%(asctime)s %(levelname)s %(name)s %(message)s"
)
handler.setFormatter(formatter)
logger.addHandler(handler)

九、联邦学习集成示例

# federated_learning.py
import tensorflow_federated as tffclass FederatedTrainer:def __init__(self):self.model = create_model()self.client_datasets = load_client_datasets()def train(self):iterative_process = tff.learning.build_federated_averaging_process(model_fn=lambda: self.model,client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.01))state = iterative_process.initialize()for _ in range(10):state, metrics = iterative_process.next(state, self.client_datasets)return state.model

十、性能测试与优化

10.1 压力测试

# 使用 locust 进行压测
locust -f locustfile.py --host=http://localhost:8080

10.2 性能优化策略

优化方向	方法	预期效果
模型推理	量化感知训练（QAT）	推理速度提升3倍
并发处理	异步任务队列	吞吐量提升至1000 req/s
内存管理	显存动态分配	内存占用降低40%
网络传输	gRPC流式传输	端到端延迟控制在100ms以内