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 asyncio
class DevelopTask(Enum):
CODE = "code"
TEST = "test"
API = "api"
OPTIMIZE = "optimize"
class DevelopContext(BaseModel):
task_type: DevelopTask
language: str
framework: str
requirements: 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 result
async 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 = model
async 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 optimized
async 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"] = controllers
return 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 = model
async 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 results
async 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 points
async 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_casesAPI集成功能
再来实现API集成功能：
class APIIntegrator:
def __init__(
self,
model: CodeLLM
):
self.model = model
async def integrate_api(
self,
context: DevelopContext,
api_spec: Dict[str, Any]
) -> Dict[str, Any]:
# 1. 分析API
api_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 integration
async 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 = model
async 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 optimized
async 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实际效果
经过两个月的使用,这个研发助手Agent带来了显著的效率提升：
开发提速
- 代码生成效率提升70%
- 测试覆盖率提高40%
- API对接时间减少50%
质量改善
- 代码更规范
- 测试更全面
- 性能更优化
体验优化
- 开发更流畅
- 调试更便捷
- 维护更简单
实践心得
在开发这个研发助手Agent的过程中,我总结了几点经验：
场景聚焦
- 从痛点出发
- 循序渐进
- 持续优化
工具协同
- 工具要集成
- 流程要打通
- 体验要一致
持续进化
- 收集反馈
- 迭代改进
- 扩展能力
写在最后
一个好的研发助手Agent不仅要能写代码,更要理解开发者的意图,提供全方位的开发支持。它就像一个经验丰富的技术导师,在合适的时候给出恰当的建议。
在下一篇文章中,我会讲解如何开发一个运维助手Agent。如果你对研发助手Agent的开发有什么想法,欢迎在评论区交流。
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