构建一个研发助手Agent：提升开发效率的实践

在上一篇文章中,我们讨论了如何构建一个文档助手Agent。今天,我想分享另一个实际项目:如何构建一个研发助手Agent。这个项目源于我们团队的真实需求 - 提升研发效率,降低开发成本。

从开发痛点说起

记得和研发团队讨论时的场景：

小张：每天要写很多重复的代码,很浪费时间
小李：是啊,而且经常要查API文档,切换上下文很烦
我：主要是哪些开发场景？
小张：CRUD、单元测试、接口对接这些
我：这些场景很适合用AI Agent来协助

经过需求分析,我们确定了几个核心功能：

代码生成
测试辅助
API集成
性能优化

技术方案设计

首先是整体架构：

from typing import List, Dict, Any, Optional
from enum import Enum
from pydantic import BaseModel
import asyncioclass DevelopTask(Enum):CODE = "code"TEST = "test"API = "api"OPTIMIZE = "optimize"class DevelopContext(BaseModel):task_type: DevelopTasklanguage: strframework: strrequirements: Dict[str, Any]code_context: Optional[str]class DevelopAssistant:def __init__(self,config: Dict[str, Any]):# 1. 初始化代码模型self.code_model = CodeLLM(model="codellama-34b",temperature=0.2,context_length=8000)# 2. 初始化工具集self.tools = {"generator": CodeGenerator(),"tester": TestHelper(),"api": APIIntegrator(),"optimizer": CodeOptimizer()}# 3. 初始化知识库self.knowledge_base = VectorStore(embeddings=CodeEmbeddings(),collection="dev_knowledge")async def process_task(self,context: DevelopContext) -> Dict[str, Any]:# 1. 理解需求requirements = await self._understand_requirements(context)# 2. 准备上下文dev_context = await self._prepare_context(context,requirements)# 3. 生成方案plan = await self._generate_plan(requirements,dev_context)# 4. 执行任务result = await self._execute_task(plan,context)return resultasync def _understand_requirements(self,context: DevelopContext) -> Dict[str, Any]:# 1. 分析任务类型task_analysis = await self._analyze_task(context.task_type,context.requirements)# 2. 提取关键需求key_requirements = await self._extract_requirements(context.requirements)# 3. 确定技术栈tech_stack = await self._determine_tech_stack(context,key_requirements)return {"task": task_analysis,"requirements": key_requirements,"tech_stack": tech_stack}

代码生成功能

首先实现代码生成功能：

class CodeGenerator:def __init__(self,model: CodeLLM):self.model = modelasync def generate_code(self,context: DevelopContext) -> Dict[str, Any]:# 1. 分析需求specs = await self._analyze_specs(context)# 2. 生成代码code = await self._generate_implementation(specs)# 3. 优化代码optimized = await self._optimize_code(code,context)return optimizedasync def _analyze_specs(self,context: DevelopContext) -> Dict[str, Any]:# 1. 提取功能点features = await self._extract_features(context.requirements)# 2. 识别依赖dependencies = await self._identify_dependencies(features,context)# 3. 设计接口interfaces = await self._design_interfaces(features,context)return {"features": features,"dependencies": dependencies,"interfaces": interfaces}async def _generate_implementation(self,specs: Dict[str, Any]) -> Dict[str, Any]:implementations = {}# 1. 生成模型models = await self._generate_models(specs["features"])implementations["models"] = models# 2. 生成服务services = await self._generate_services(specs["features"],models)implementations["services"] = services# 3. 生成控制器controllers = await self._generate_controllers(specs["interfaces"],services)implementations["controllers"] = controllersreturn implementations

测试辅助功能

接下来是测试辅助功能：

class TestHelper:def __init__(self,model: CodeLLM):self.model = modelasync def assist_testing(self,context: DevelopContext,code: Dict[str, Any]) -> Dict[str, Any]:# 1. 分析测试点test_points = await self._analyze_test_points(code,context)# 2. 生成测试用例test_cases = await self._generate_test_cases(test_points)# 3. 执行测试results = await self._run_tests(test_cases,code)return resultsasync def _analyze_test_points(self,code: Dict[str, Any],context: DevelopContext) -> List[Dict[str, Any]]:points = []# 1. 功能测试点functional = await self._identify_functional_points(code)points.extend(functional)# 2. 边界测试点boundary = await self._identify_boundary_points(code)points.extend(boundary)# 3. 异常测试点exception = await self._identify_exception_points(code)points.extend(exception)return pointsasync def _generate_test_cases(self,test_points: List[Dict[str, Any]]) -> List[Dict[str, Any]]:test_cases = []for point in test_points:# 1. 准备测试数据test_data = await self._prepare_test_data(point)# 2. 生成测试代码test_code = await self._generate_test_code(point,test_data)# 3. 添加断言assertions = await self._add_assertions(test_code,point)test_cases.append({"point": point,"data": test_data,"code": test_code,"assertions": assertions})return test_cases

API集成功能

再来实现API集成功能：

class APIIntegrator:def __init__(self,model: CodeLLM):self.model = modelasync def integrate_api(self,context: DevelopContext,api_spec: Dict[str, Any]) -> Dict[str, Any]:# 1. 分析APIapi_info = await self._analyze_api(api_spec)# 2. 生成客户端client = await self._generate_client(api_info,context)# 3. 实现集成integration = await self._implement_integration(client,context)return integrationasync def _analyze_api(self,api_spec: Dict[str, Any]) -> Dict[str, Any]:# 1. 解析接口定义endpoints = await self._parse_endpoints(api_spec)# 2. 分析数据模型models = await self._analyze_models(api_spec)# 3. 识别认证方式auth = await self._identify_auth(api_spec)return {"endpoints": endpoints,"models": models,"auth": auth}async def _implement_integration(self,client: Dict[str, Any],context: DevelopContext) -> Dict[str, Any]:# 1. 生成调用代码calls = await self._generate_api_calls(client["endpoints"])# 2. 处理响应handlers = await self._generate_response_handlers(client["models"])# 3. 错误处理error_handlers = await self._generate_error_handlers(client["endpoints"])return {"calls": calls,"handlers": handlers,"error_handlers": error_handlers}

性能优化功能

最后是性能优化功能：

class CodeOptimizer:def __init__(self,model: CodeLLM):self.model = modelasync def optimize_code(self,context: DevelopContext,code: Dict[str, Any]) -> Dict[str, Any]:# 1. 性能分析profile = await self._analyze_performance(code)# 2. 识别瓶颈bottlenecks = await self._identify_bottlenecks(profile)# 3. 优化实现optimized = await self._apply_optimizations(code,bottlenecks)return optimizedasync def _analyze_performance(self,code: Dict[str, Any]) -> Dict[str, Any]:# 1. 静态分析static = await self._static_analysis(code)# 2. 复杂度分析complexity = await self._complexity_analysis(code)# 3. 资源使用分析resources = await self._resource_analysis(code)return {"static": static,"complexity": complexity,"resources": resources}async def _apply_optimizations(self,code: Dict[str, Any],bottlenecks: List[Dict[str, Any]]) -> Dict[str, Any]:optimized = code.copy()for bottleneck in bottlenecks:# 1. 生成优化方案solution = await self._generate_optimization(bottleneck)# 2. 应用优化optimized = await self._apply_optimization(optimized,solution)# 3. 验证效果verified = await self._verify_optimization(optimized,bottleneck)if not verified:optimized = await self._rollback_optimization(optimized,solution)return optimized