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Development GuideUpdated 2025-02-09

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API Integration Guide

学习如何将大模型能力集成到您的应用程序中

API集成SDKRESTful开发

API 集成开发指南

概述

本文档介绍如何通过 API 将大语言模型能力集成到您的应用程序中，支持多种编程语言和框架。

快速开始

获取 API 密钥

  1. 登录控制台: https://console.m9ai.work
  2. 进入「API 管理」页面
  3. 点击「创建密钥」，复制生成的 API Key

基础请求示例

cURL

bash
1curl -X POST "https://api.m9ai.work/v1/chat/completions" \
2  -H "Authorization: Bearer YOUR_API_KEY" \
3  -H "Content-Type: application/json" \
4  -d '{
5    "model": "gpt-4",
6    "messages": [
7      {"role": "user", "content": "你好！"}
8    ]
9  }'

Python

python
1from openai import OpenAI
2
3client = OpenAI(
4    api_key="YOUR_API_KEY",
5    base_url="https://api.m9ai.work/v1"
6)
7
8response = client.chat.completions.create(
9    model="gpt-4",
10    messages=[
11        {"role": "user", "content": "你好！"}
12    ]
13)
14
15print(response.choices[0].message.content)

JavaScript

javascript
1import OpenAI from 'openai';
2
3const client = new OpenAI({
4  apiKey: 'YOUR_API_KEY',
5  baseURL: 'https://api.m9ai.work/v1',
6});
7
8async function chat() {
9  const response = await client.chat.completions.create({
10    model: 'gpt-4',
11    messages: [{ role: 'user', content: '你好！' }],
12  });
13  
14  console.log(response.choices[0].message.content);
15}
16
17chat();

API 参考

对话补全

Endpoint: POST /v1/chat/completions

请求参数

参数类型必需描述
modelstring是模型名称: gpt-4, gpt-3.5-turbo
messagesarray是消息列表
temperaturefloat否随机性 (0-2), 默认 0.7
max_tokensinteger否最大生成 token 数
streamboolean否是否流式输出

响应格式

json
1{
2  "id": "chatcmpl-xxx",
3  "object": "chat.completion",
4  "created": 1677652288,
5  "model": "gpt-4",
6  "choices": [{
7    "index": 0,
8    "message": {
9      "role": "assistant",
10      "content": "你好！有什么可以帮助你的吗？"
11    },
12    "finish_reason": "stop"
13  }],
14  "usage": {
15    "prompt_tokens": 9,
16    "completion_tokens": 12,
17    "total_tokens": 21
18  }
19}

文本嵌入

Endpoint: POST /v1/embeddings

python
1response = client.embeddings.create(
2    model="text-embedding-ada-002",
3    input="要嵌入的文本"
4)
5
6embedding = response.data[0].embedding

流式输出

python
1response = client.chat.completions.create(
2    model="gpt-4",
3    messages=[{"role": "user", "content": "讲个故事"}],
4    stream=True
5)
6
7for chunk in response:
8    if chunk.choices[0].delta.content:
9        print(chunk.choices[0].delta.content, end="")

高级用法

函数调用

python
1def get_weather(location, unit="celsius"):
2    # 实际天气查询逻辑
3    return {"temperature": 25, "condition": "sunny"}
4
5tools = [{
6    "type": "function",
7    "function": {
8        "name": "get_weather",
9        "description": "获取指定位置的天气信息",
10        "parameters": {
11            "type": "object",
12            "properties": {
13                "location": {
14                    "type": "string",
15                    "description": "城市名称，如北京、上海"
16                },
17                "unit": {
18                    "type": "string",
19                    "enum": ["celsius", "fahrenheit"],
20                    "description": "温度单位"
21                }
22            },
23            "required": ["location"]
24        }
25    }
26}]
27
28response = client.chat.completions.create(
29    model="gpt-4",
30    messages=[{"role": "user", "content": "北京今天天气怎么样？"}],
31    tools=tools,
32    tool_choice="auto"
33)
34
35# 处理函数调用
36if response.choices[0].finish_reason == "tool_calls":
37    tool_call = response.choices[0].message.tool_calls[0]
38    function_name = tool_call.function.name
39    arguments = json.loads(tool_call.function.arguments)
40    
41    if function_name == "get_weather":
42        result = get_weather(**arguments)
43        print(result)

错误处理

python
1from openai import APIError, RateLimitError, AuthenticationError
2
3try:
4    response = client.chat.completions.create(...)
5except AuthenticationError:
6    print("API 密钥无效，请检查配置")
7except RateLimitError:
8    print("请求过于频繁，请稍后重试")
9except APIError as e:
10    print(f"API 错误: {e.message}")

最佳实践

1. 连接池配置

python
1import httpx
2
3# 自定义 HTTP 客户端
4http_client = httpx.Client(
5    limits=httpx.Limits(
6        max_connections=100,
7        max_keepalive_connections=20
8    ),
9    timeout=httpx.Timeout(60.0)
10)
11
12client = OpenAI(
13    api_key="YOUR_API_KEY",
14    base_url="https://api.m9ai.work/v1",
15    http_client=http_client
16)

2. 重试策略

python
1from tenacity import retry, stop_after_attempt, wait_exponential
2
3@retry(
4    stop=stop_after_attempt(3),
5    wait=wait_exponential(multiplier=1, min=4, max=10)
6)
7def chat_with_retry(messages):
8    return client.chat.completions.create(
9        model="gpt-4",
10        messages=messages
11    )

3. 请求超时设置

python
1response = client.chat.completions.create(
2    model="gpt-4",
3    messages=messages,
4    timeout=30.0  # 30秒超时
5)

SDK 下载

  • Python: pip install openai
  • JavaScript: npm install openai
  • Go: go get github.com/sashabaranov/go-openai
  • Java: Maven/Gradle 依赖见文档

更多资源

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