CodeBuddy全新升级：体验Craft智能体的对话式编程革命

# 示例：使用Craft API生成Python排序代码
from codebuddy.craft import CodeGenerator

# 初始化Craft客户端
craft = CodeGenerator(api_key="your_api_key")

# 用自然语言描述需求
response = craft.generate_code(
    language="python",
    description="实现一个快速排序算法，对整数列表进行升序排列"
)

# 输出生成的代码
print(response.code)
"""
def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr)//2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quick_sort(left) + middle + quick_sort(right)
"""

// 示例：对话式调试过程
// 开发者: 为什么我的React组件渲染不出来？
// Craft: 请检查以下几点：
// 1. 组件是否正确定义和导出
// 2. 父组件是否正确导入和使用了该组件
// 3. 是否有控制台错误信息

// 开发者提供错误信息后
// Craft: 根据错误"Uncaught TypeError: Cannot read property 'map' of undefined"，
// 建议在数据加载前添加空值检查：

function UserList({ users }) {
    if (!users) return <div>Loading...</div>;
    return (
        <ul>
            {users.map(user => (
                <li key={user.id}>{user.name}</li>
            ))}
        </ul>
    );
}

// 伪代码展示核心处理流程
public class CraftEngine {
    private NLUModule nlu; // 自然语言理解模块
    private CodeGenerator codeGen; // 代码生成模块
    private ContextAnalyzer contextAnalyzer; // 上下文分析器
    
    public Response processRequest(Request request) {
        // 1. 理解用户意图
        Intent intent = nlu.understand(request.getInput());
        
        // 2. 分析代码上下文
        CodeContext context = contextAnalyzer.analyze(request.getCodeContext());
        
        // 3. 生成或修改代码
        CodeSuggestion suggestion = codeGen.generate(intent, context);
        
        // 4. 验证代码质量
        ValidationResult validation = validate(suggestion);
        
        return new Response(suggestion, validation);
    }
}

# 安装CodeBuddy CLI工具
npm install -g codebuddy-cli

# 登录CodeBuddy账户
codebuddy login

# 初始化新项目
codebuddy init my-project --template=react-ts

# 启动Craft交互模式
codebuddy craft --interactive

开发者: 我需要一个React用户列表页面，包含搜索和分页功能

Craft: 我将帮你创建一个完整的用户列表组件。首先，需要以下API端点：
GET /api/users?page=1&search=term

这是初始组件结构：

// 生成的初始代码
interface User {
    id: number;
    name: string;
    email: string;
    role: string;
}

export function UserList() {
    const [users, setUsers] = useState<User[]>([]);
    const [searchTerm, setSearchTerm] = useState('');
    const [currentPage, setCurrentPage] = useState(1);
    
    // TODO: 实现数据获取逻辑
    // TODO: 实现搜索处理
    // TODO: 实现分页控制
    
    return (
        <div className="user-list">
            <input 
                type="text" 
                placeholder="Search users..."
                value={searchTerm}
                onChange={(e) => setSearchTerm(e.target.value)}
            />
            {/* 用户列表将在这里渲染 */}
        </div>
    );
}

开发者: 请添加数据获取逻辑，使用axios

Craft: 已添加数据获取逻辑，并处理了加载和错误状态：

// 更新后的代码
import axios from 'axios';

export function UserList() {
    // ...原有状态...
    const [isLoading, setIsLoading] = useState(false);
    const [error, setError] = useState<string | null>(null);
    
    useEffect(() => {
        const fetchUsers = async () => {
            setIsLoading(true);
            try {
                const response = await axios.get(`/api/users`, {
                    params: { page: currentPage, search: searchTerm }
                });
                setUsers(response.data);
            } catch (err) {
                setError('Failed to load users');
            } finally {
                setIsLoading(false);
            }
        };
        
        fetchUsers();
    }, [currentPage, searchTerm]);
    
    // ...渲染逻辑...
}

# 原始代码
def calculate_stats(data):
    total = 0
    count = 0
    for item in data:
        total += item
        count += 1
    avg = total / count
    
    squared_diff = 0
    for item in data:
        squared_diff += (item - avg) ** 2
    std_dev = (squared_diff / count) ** 0.5
    
    return {"average": avg, "std_dev": std_dev}

# Craft优化建议：
"""
1. 可以合并循环减少时间复杂度
2. 使用统计库简化计算
3. 添加空输入检查

优化后代码：
"""
import statistics

def calculate_stats(data):
    if not data:
        return None
    return {
        "average": statistics.mean(data),
        "std_dev": statistics.stdev(data) if len(data) > 1 else 0
    }

# craft-config.yaml
performance:
  max_file_size: 500KB  # 限制分析文件大小
  cache_ttl: 3600       # 缓存持续时间(秒)
  concurrency: 5        # 最大并行请求数

features:
  enable_deep_analysis: false  # 大型项目中关闭深度分析
  prioritize_speed: true       # 优先响应速度

// Jenkinsfile示例
pipeline {
    agent any
    stages {
        stage('Code Review') {
            steps {
                script {
                    def craftReport = sh(script: 'codebuddy craft analyze --output=json', returnStdout: true)
                    archiveArtifacts artifacts: 'craft-report.json'
                    
                    // 根据报告质量门禁
                    if (craftReport.contains('CRITICAL_ISSUES')) {
                        error('Critical code issues detected')
                    }
                }
            }
        }
    }
}

# 示例：检测SQL注入漏洞
# 开发者请求：生成用户登录验证代码
response = craft.generate_code(
    language="python",
    description="实现用户登录功能，使用Flask和MySQL"
)

# Craft生成的代码包含安全防护
"""
@app.route('/login', methods=['POST'])
def login():
    username = request.form['username']
    password = request.form['password']
    
    # 使用参数化查询防止SQL注入
    cursor = db.cursor(prepared=True)
    query = "SELECT * FROM users WHERE username = ? AND password = ?"
    cursor.execute(query, (username, hashlib.sha256(password.encode()).hexdigest()))
    
    user = cursor.fetchone()
    if user:
        return jsonify({"status": "success"})
    else:
        return jsonify({"status": "fail"}), 401
"""

// 医疗健康应用的数据处理合规检查
public class PatientDataProcessor {
    @CraftComplianceCheck(standard="HIPAA")
    public void processPatientData(PatientRecord record) {
        // Craft自动添加的合规性注释
        /* HIPAA合规要求：
           - 数据加密传输
           - 访问日志记录
           - 最小权限原则 */
           
        // 自动生成的合规代码
        String encryptedData = EncryptionUtils.hippaEncrypt(record.toString());
        AuditLogger.logAccess("DATA_ACCESS", getUser());
        // ...其他处理逻辑
    }
}

# craft-enterprise-config.yaml
knowledge_base:
  internal_repos:
    - url: git@internal.company.com:core-libs.git
      branch: main
      patterns: ["*.md", "best_practices/*.yaml"]
  api_docs:
    - url: https://internal.api.company.com/v3/docs
      auth:
        type: bearer
        token: ${API_DOCS_TOKEN}

// 金融交易DSL示例
craft.learnDSL("""
domain FinancialTrade {
    entity Order {
        string orderId
        string symbol
        decimal quantity
        enum side (BUY, SELL)
        datetime executionTime
    }
    
    rule "Large Order Alert" {
        when: quantity > 10000
        then: sendAlert("Large order detected")
    }
}
""")

// 生成的监控代码
class OrderMonitor {
    fun checkLargeOrders(order: Order) {
        if (order.quantity > 10000.0) {
            AlertService.send("Large order detected: ${order.orderId}")
            // 自动添加的合规检查
            ComplianceChecker.verifyLargeTrade(order)
        }
    }
}

// Craft核心引擎的内存管理片段
class MemoryAwareGenerator : public CodeGeneratorBase {
private:
    static constexpr size_t MAX_MEM_USAGE = 1024 * 1024 * 512; // 512MB
    
    void ensureMemoryAvailability() {
        if (currentMemoryUsage() > MAX_MEM_USAGE * 0.8) {
            // 触发内存优化策略
            cache_.purgeLRUItems();
            model_.releaseTempResources();
            
            // 如果仍然不足，则抛出异常
            if (currentMemoryUsage() > MAX_MEM_USAGE) {
                throw MemoryLimitExceeded("Reached maximum memory limit");
            }
        }
    }
    
public:
    GeneratedCode generate(InputSpec spec) override {
        ensureMemoryAvailability();
        // ...生成逻辑...
    }
};

def auto_retry_generation(prompt, max_retries=3):
    retry_count = 0
    last_error = None
    
    while retry_count < max_retries:
        try:
            response = craft.generate_code(
                prompt=prompt,
                fallback_model="gpt-3.5-turbo" if retry_count > 0 else None
            )
            return response
        except CraftError as e:
            last_error = e
            retry_count += 1
            if e.should_retry():
                log.warning(f"Attempt {retry_count} failed, retrying...")
                continue
            break
    
    # 所有重试失败后的处理
    return {
        "error": "Generation failed",
        "reason": str(last_error),
        "suggestion": "Please try simplifying your request"
    }

// 输入设计图后生成的React代码
function Dashboard({ metrics }) {
    return (
        <div className="dashboard">
            <header>
                <h1>Sales Dashboard</h1>
                <DatePicker />
            </header>
            
            <div className="metrics-grid">
                {metrics.map(metric => (
                    <MetricCard 
                        key={metric.id}
                        title={metric.name}
                        value={metric.value}
                        trend={metric.trend}
                        // 自动从设计图提取的颜色
                        color="#4F46E5" 
                    />
                ))}
            </div>
            
            <div className="chart-container">
                <LineChart data={metrics} />
            </div>
        </div>
    )
}

# 语音指令处理示例
@voice_command_handler
def handle_voice_command(command: str):
    # 语音识别后的处理
    if "create component" in command.lower():
        component_name = extract_entity(command, "component")
        props = extract_props(command)
        
        # 调用Craft生成组件
        return craft.generate(
            f"Create a React component named {component_name} "
            f"with these props: {', '.join(props)}"
        )
    
    elif "fix error" in command.lower():
        error = extract_error(command)
        return craft.debug(error, context=current_file)

// 多人协作时的冲突解决策略
class CollaborationSession {
    private changes: ChangeSet[];
    private participants: Member[];
    
    async mergeChanges(change: Change): Promise<Resolution> {
        // 自动合并无冲突变更
        if (this.isSafeMerge(change)) {
            this.applyChange(change);
            return { status: 'auto-merged' };
        }
        
        // 复杂冲突的解决流程
        const resolution = await this.resolveConflict(change);
        if (resolution.strategy === 'craft-suggest') {
            const suggestion = await craft.suggestMerge(
                this.currentCode,
                change.diff
            );
            return {
                status: 'suggested',
                solution: suggestion
            };
        }
        
        // ...其他处理逻辑
    }
}

# team-knowledge-base.yaml
shared_patterns:
  - name: "API Error Handling"
    examples:
      - language: javascript
        code: |
          async function fetchWithRetry(url, retries = 3) {
            try {
              const response = await fetch(url);
              if (!response.ok) throw new Error(response.statusText);
              return await response.json();
            } catch (error) {
              if (retries <= 0) throw error;
              await new Promise(res => setTimeout(res, 1000));
              return fetchWithRetry(url, retries - 1);
            }
          }
    best_practices:
      - "Always include retry logic for transient errors"
      - "Exponential backoff is preferred for production"

// 为嵌入式设备优化的代码生成
#[cfg(target_os = "embedded")]
fn generate_embedded_code(spec: &str) -> Result<Vec<u8>, CraftError> {
    // 启用精简模型
    let model = load_model("craft-lite-v1.2")?;
    
    // 内存约束设置
    let mut alloc = LimitedAllocator::new(1024 * 512); // 512KB
    
    // 生成优化后的C代码
    let output = model.generate(
        spec,
        CodegenOptions {
            optimization_level: 3,
            target: Target::C99,
            allocator: &mut alloc,
            // 禁用高级特性
            features: Features::empty()
                .without("dynamic_allocation")
                .without("recursion"),
        }
    )?;
    
    Ok(output)
}

# 离线包安装命令
codebuddy craft install-offline \
  --model craft-core-1.3-offline \
  --dependencies "python>=3.8,<4.0" \
  --storage-limit 5GB \
  --platform linux-arm64

# 离线使用示例
codebuddy craft generate --offline \
  --prompt "Create a REST API endpoint in Go that processes IoT sensor data"

public class ContentFilter {
    private static final Set<String> BLACKLISTED_TERMS = loadBlacklist();
    private final EthicalGuidelines guidelines;
    
    public FilterResult checkInput(String input) {
        // 检查有害内容
        EthicalCheckResult ethicalCheck = guidelines.check(input);
        if (!ethicalCheck.isApproved()) {
            return FilterResult.rejected(ethicalCheck.reasons());
        }
        
        // 检查技术可行性
        TechnicalFeasibility techCheck = TechnicalValidator.validate(input);
        if (!techCheck.isFeasible()) {
            return FilterResult.rejected(techCheck.constraints());
        }
        
        return FilterResult.approved();
    }
    
    // 自动生成的免责声明
    public String getDisclaimer() {
        return """
        Generated code should be reviewed by qualified engineers before 
        deployment. The AI is not responsible for any damages caused by 
        the use of this code in production systems.
        """;
    }
}

{
  "generation_report": {
    "input_analysis": {
      "detected_intent": "create REST API for user management",
      "identified_components": ["CRUD operations", "JWT authentication"]
    },
    "decision_path": [
      {
        "step": "framework_selection",
        "options": ["Express", "FastAPI", "Spring Boot"],
        "selected": "FastAPI",
        "reason": "Matches Python preference in context"
      },
      {
        "step": "authentication_strategy",
        "options": ["JWT", "Session", "OAuth"],
        "selected": "JWT",
        "reason": "Best fit for stateless API described"
      }
    ],
    "potential_issues": [
      {
        "type": "security",
        "concern": "No rate limiting specified",
        "mitigation": "Added Redis-based rate limiting"
      }
    ]
  }
}

class SelfImprovingAgent:
    def __init__(self, base_model):
        self.model = base_model
        self.feedback_loop = FeedbackAnalyzer()
        self.training_scheduler = TrainingScheduler()
    
    def process_feedback(self, user_feedback, code_quality_metrics):
        # 分析反馈并生成训练数据
        training_data = self.feedback_loop.analyze(
            user_feedback, 
            code_quality_metrics
        )
        
        # 触发增量训练
        if self.training_scheduler.should_retrain(training_data):
            self.model = self.model.fine_tune(training_data)
            
            # 生成改进报告
            report = self.generate_improvement_report()
            return {
                "status": "model_updated",
                "improvements": report
            }
        
        return {"status": "feedback_recorded"}

% 符号规则与神经网络的结合示例
safe_file_operation(Operation) :-
    neural_check(Operation, safety_score),
    safety_score > 0.8,
    symbolic_verify(Operation, constraints).

symbolic_verify(delete_file(Path), constraints) :-
    not(protected_location(Path)),
    has_permission(Path).

neural_check(Operation, Score) :-
    craft_neural_model(Operation, [safety:Score|_]).

% 生成的防护代码
delete_file(Path) :-
    safe_file_operation(delete_file(Path)) ->
        fs_delete(Path);
        throw('Unsafe operation blocked').