MonkeyCode API开发指南：构建自定义AI编程应用

MonkeyCode API开发指南：将AI编程能力集成到你的应用中

引言

MonkeyCode不仅是一款强大的IDE插件和Web应用，更提供了完整的RESTful API和SDK，让开发者可以将AI编程能力深度集成到自己的产品中。得益于开源架构和私有化部署能力，MonkeyCode的API可以安全地运行在企业内网环境中。

MonkeyCode API概览

核心API端点

端点	方法	功能	认证要求
/api/v1/complete	POST	代码补全	Bearer Token
/api/v1/explain	POST	代码解释	Bearer Token
/api/v1/fix	POST	Bug修复建议	Bearer Token
/api/v1/refactor	POST	代码重构	Bearer Token
/api/v1/chat	POST	对话式编程助手	Bearer Token
/api/v1/health	GET	健康检查	无需认证
/api/v1/metrics	GET	性能指标	Admin Token

快速开始：第一个API调用

1. 获取API Token

# 通过CLI生成Token
monkeycode token create \
    --name "my-app-integration" \
    --scope "complete,explain,fix" \
    --expires "90d"

# 输出示例：
# Token: mc_sk_live_xxxxxxxxxxxxxxxx
# 保存好Token，仅显示一次！

2. 基础代码补全请求

import requests
import json

class MonkeyCodeClient:
    """MonkeyCode API客户端"""
    
    def __init__(self, base_url: str, api_token: str):
        self.base_url = base_url.rstrip('/')
        self.headers = {
            'Authorization': f'Bearer {api_token}',
            'Content-Type': 'application/json',
            'User-Agent': 'MyApp/1.0'
        }
    
    def complete(
        self,
        code: str,
        language: str = "python",
        cursor_position: int = None,
        max_suggestions: int = 5
    ) -> dict:
        """调用代码补全API"""
        
        payload = {
            "code": code,
            "language": language,
            "max_suggestions": max_suggestions,
            "options": {
                "temperature": 0.7,
                "max_tokens": 256
            }
        }
        
        if cursor_position is not None:
            payload["cursor_position"] = cursor_position
        
        response = requests.post(
            f"{self.base_url}/api/v1/complete",
            headers=self.headers,
            json=payload,
            timeout=30
        )
        
        response.raise_for_status()
        return response.json()
    
    def explain(self, code: str, language: str = "python") -> dict:
        """调用代码解释API"""
        response = requests.post(
            f"{self.base_url}/api/v1/explain",
            headers=self.headers,
            json={"code": code, "language": language},
            timeout=60
        )
        response.raise_for_status()
        return response.json()

# 使用示例
client = MonkeyCodeClient(
    base_url="https://monkeycode.internal:8080",
    api_token="mc_sk_live_your_token_here"
)

result = client.complete(
    code="def fibonacci(n):\n    if n <= 1:\n        return n\n    return ",
    language="python"
)

print(json.dumps(result, indent=2, ensure_ascii=False))

返回数据结构

{
  "id": "req_abc123",
  "model": "monkeycode-7b-v2.5",
  "latency_ms": 45,
  "suggestions": [
    {
      "text": "fibonacci(n-1) + fibonacci(n-2)",
      "display_text": "fibonacci(n-1) + fibonacci(n-2)",
      "confidence": 0.94,
      "type": "code_completion",
      "documentation": "递归计算斐波那契数列的第n项"
    },
    {
      "text": "fib(n-1) + fib(n-2)",
      "confidence": 0.72,
      "type": "code_completion"
    }
  ],
  "context_used": {
    "language": "python",
    "lines_before_cursor": 4,
    "lines_after_cursor": 0
  },
  "usage": {
    "prompt_tokens": 128,
    "completion_tokens": 32,
    "total_tokens": 160
  }
}

高级功能集成

流式补全（SSE）

// JavaScript流式补全示例
async function streamCompletion(code, language, onDelta) {
    const response = await fetch('https://monkeycode.internal:8080/api/v1/complete/stream', {
        method: 'POST',
        headers: {
            'Authorization': `Bearer ${TOKEN}`,
            'Content-Type': 'application/json'
        },
        body: JSON.stringify({ code, language })
    });
    
    const reader = response.body.getReader();
    const decoder = new TextDecoder();
    let buffer = '';
    
    while (true) {
        const { done, value } = await reader.read();
        if (done) break;
        
        buffer += decoder.decode(value, { stream: true });
        const lines = buffer.split('\n');
        buffer = lines.pop();
        
        for (const line of lines) {
            if (line.startsWith('data: ')) {
                const data = JSON.parse(line.slice(6));
                onDelta(data); // 处理每个增量token
            }
        }
    }
}

// 使用示例 - 实时显示补全结果
streamCompletion(code, 'typescript', (delta) => {
    if (delta.type === 'token') {
        appendToEditor(delta.text);
    } else if (delta.type === 'done') {
        showCompletionSuggestions(delta.suggestions);
    }
});

批量处理API

// Go语言批量补全示例
package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "io"
    "net/http"
    "time"
)

type BatchCompletionRequest struct {
    Items []CompletionItem `json:"items"`
    Options Options         `json:"options"`
}

type CompletionItem struct {
    ID             string `json:"id,omitempty"`
    Code           string `json:"code"`
    Language       string `json:"language"`
    CursorPosition int    `json:"cursor_position,omitempty"`
}

func BatchComplete(client *http.Client, items []CompletionItem) ([]BatchResult, error) {
    reqBody := BatchCompletionRequest{
        Items:   items,
        Options: Options{MaxConcurrent: 10},
    }
    
    data, _ := json.Marshal(reqBody)
    
    req, _ := http.NewRequest("POST", 
        "https://monkeycode.internal:8080/api/v1/batch-complete",
        bytes.NewReader(data))
    req.Header.Set("Authorization", "Bearer "+token)
    req.Header.Set("Content-Type", "application/json")
    
    resp, err := client.Do(req)
    if err != nil { return nil, err }
    defer resp.Body.Close()
    
    body, _ := io.ReadAll(resp.Body)
    var results []BatchResult
    json.Unmarshal(body, &results)
    return results, nil
}

SDK集成（多语言）

Python SDK

pip install monkeycode-sdk

from monkeycode import MonkeyCode

# 初始化客户端
mc = MonkeyCode(
    server_url="https://monkeycode.internal:8080",
    api_token="your-token",
    timeout=30
)

# 异步支持
async with mc.async_client() as amc:
    result = await amc.complete(
        code="def quicksort(arr):",
        language="python"
    )

Node.js SDK

npm install @monkeycode/sdk

const { MonkeyCode } = require('@monkeycode/sdk');

const mc = new MonkeyCode({
    baseUrl: 'https://monkeycode.internal:8080',
    token: process.env.MONKEYCODE_TOKEN
});

// TypeScript完整类型支持
interface CompletionOptions {
    language: string;
    maxTokens?: number;
    temperature?: number;
    stopSequences?: string[];
}

const result = await mc.complete({
    code: sourceCode,
    options: { language: 'typescript', temperature: 0.5 }
});

Java SDK

<!-- Maven依赖 -->
<dependency>
    <groupId>ai.monkeycode</groupId>
    <artifactId>monkeycode-java-sdk</artifactId>
    <version>2.5.0</version>
</dependency>

public class CodeAssistantService {
    private final MonkeyCodeClient client;
    
    public CodeAssistantService(String serverUrl, String token) {
        this.client = MonkeyCodeClient.builder()
            .baseUrl(serverUrl)
            .token(token)
            .connectTimeout(Duration.ofSeconds(10))
            .readTimeout(Duration.ofSeconds(30))
            .build();
    }
    
    public CompletionResult completeCode(String code, Language language) {
        CompletionRequest request = CompletionRequest.builder()
            .code(code)
            .language(language)
            .maxSuggestions(3)
            .build();
            
        return client.complete(request).join(); // 支持异步
    }
}

私有化部署下的API安全最佳实践

网络层安全

# Nginx反向代理配置 - API专用
upstream monkeycode_api {
    server 127.0.0.1:8080;
    keepalive 32;
}

server {
    listen 443 ssl http2;
    server_name api.monkeycode.internal;

    # TLS 1.3 only
    ssl_protocols TLSv1.3;
    ssl_certificate /etc/ssl/certs/api.crt;
    ssl_certificate_key /etc/ssl/private/api.key;

    location /api/ {
        proxy_pass http://monkeycode_api;
        proxy_http_version 1.1;
        
        # 安全头
        add_header X-Content-Type-Options nosniff;
        add_header X-Frame-Options DENY;
        add_header X-XSS-Protection "1; mode=block";
        
        # 速率限制
        limit_req zone=api burst=20 nodelay;
        limit_req_status 429;
        
        # 请求体大小限制
        client_max_body_size 2m;
    }

    # 限流定义
    limit_req_zone $binary_remote_addr zone=api:10m rate=100r/m;
}

应用层安全

class SecureAPIClient(MonkeyCodeClient):
    """安全的MonkeyCode API客户端"""
    
    def __init__(self, base_url, api_token, encryption_key=None):
        super().__init__(base_url, api_token)
        self.encryption_key = encryption_key
    
    def secure_complete(self, code, **kwargs):
        """加密传输敏感代码"""
        if self.encryption_key:
            # AES-GCM加密代码内容
            code = self.encrypt(code)
        
        result = self.complete(code, **kwargs)
        
        if self.encryption_key:
            # 解密响应
            for s in result.get('suggestions', []):
                s['text'] = self.decrypt(s['text'])
        
        return result
    
    def audit_log(self, request_id, action):
        """记录审计日志"""
        log_entry = {
            "timestamp": datetime.utcnow().isoformat(),
            "request_id": request_id,
            "action": action,
            "user": self.user_id,
            "ip": self.client_ip
        }
        # 发送到审计系统...

典型集成场景

场景一：在线IDE平台

用户浏览器 ──► IDE前端服务 ──► MonkeyCode API ──► 模型推理
                    │                  │
                    │              缓存层(Redis)
                    │              审计日志
                    ▼
              用户会话管理
              权限验证
              用量计费

场景二：内部工具链

# Git Hook + MonkeyCode 集成
pre-commit:
  script: |
    git diff --cached --name-only | grep '\.py$' | while read file; do
      python3 << EOF
      import requests
      code = open('$file').read()
      r = requests.post("$MONKEYCODE_URL/api/v1/lint", 
          json={"code": code, "file": "$file"},
          headers={"Authorization": "Bearer $MC_TOKEN"})
      issues = r.json().get('issues', [])
      if issues:
        print(f"⚠️ $file: {len(issues)} issues found")
        for i in issues[:3]:
          print(f"  Line {i['line']}: {i['message']}")
      EOF
    done

场景三：智能客服/知识库

class ProgrammingKnowledgeBot:
    """基于MonkeyCode的编程知识问答机器人"""
    
    def __init__(self):
        self.mc = MonkeyCodeClient(URL, TOKEN)
        self.cache = LRUCache(maxsize=10000)
    
    async def answer(self, question: str, context: str = "") -> str:
        cache_key = hashlib.md5((question+context).encode()).hexdigest()
        
        if cached := self.cache.get(cache_key):
            return cached
        
        prompt = f"# 问题\n{question}\n"
        if context:
            prompt += f"\n# 相关代码上下文\n{context}\n"
        prompt += "\n# 请用中文详细解答\n"
        
        result = self.mc.chat(prompt, system_role="expert_programmer")
        
        self.cache[cache_key] = result['response']
        return result['response']

性能优化建议

优化项	效果	实现方式
连接池复用	减少50%连接开销	HTTP Keep-Alive
请求批处理	吞吐提升3x	批量API端点
本地缓存	P99延迟降低70%	Redis缓存相似查询
异步调用	并发能力提升10x	WebSocket/SSE
CDN边缘节点	全球延迟<50ms	边缘部署MonkeyCode

总结

通过MonkeyCode的RESTful API和多语言SDK，你可以：

🔌 无缝集成 — 几行代码即可接入AI编程能力
🔒 私有部署 — API运行在企业内网，数据绝对安全
⚡ 高性能 — 本地部署带来超低延迟
🛠️ 完全可控 — 开源架构允许深度定制
💰 成本透明 — 无按调用计费，一次投入无限使用

🚀 立即开始将MonkeyCode AI能力集成到你的应用中吧！