API 能力 OpenAI 官方库使用使用官方 OpenAI SDK 无缝接入 API易服务

在 OpenAI 官方库使用 API易完全兼容 OpenAI API 格式，您可以直接使用官方 OpenAI SDK，只需简单修改配置即可无缝切换到 API易服务。

支持的官方 SDK API易支持所有官方 OpenAI SDK： Python (openai) Node.js (openai) .NET (OpenAI) Go (go-openai) Java (第三方) PHP (第三方) Ruby (第三方)

Python SDK

安装

Copy pip install openai

基础配置

Copy from openai import OpenAI

配置 API易服务

client = OpenAI( api_key="YOUR_API_KEY", # 您的 API易密钥 base_url="https://api.apiyi.com/v1" # API易端点 )

使用方式与官方完全相同

response = client.chat.completions.create( model="gpt-3.5-turbo", messages=[ {"role": "user", "content": "Hello!"} ] )

print(response.choices[0].message.content)

环境变量配置

Copy import os from openai import OpenAI

设置环境变量

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY" os.environ["OPENAI_BASE_URL"] = "https://api.apiyi.com/v1"

使用默认配置

client = OpenAI()

response = client.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": "Explain quantum computing"}] )

异步使用

Copy import asyncio from openai import AsyncOpenAI

async def main(): client = AsyncOpenAI( api_key="YOUR_API_KEY", base_url="https://api.apiyi.com/v1" )

response = await client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

asyncio.run(main())

流式输出

Copy from openai import OpenAI

client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apiyi.com/v1" )

stream = client.chat.completions.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Write a short story"}], stream=True )

for chunk in stream: if chunk.choices[0].delta.content is not None: print(chunk.choices[0].delta.content, end="")

Node.js SDK

安装

Copy npm install openai

基础配置

Copy import OpenAI from 'openai';

const openai = new OpenAI({ apiKey: 'YOUR_API_KEY', baseURL: 'https://api.apiyi.com/v1' });

const response = await openai.chat.completions.create({ model: 'gpt-3.5-turbo', messages: [{ role: 'user', content: 'Hello!' }] });

console.log(response.choices[0].message.content);

环境变量配置

Copy // 设置环境变量 process.env.OPENAI_API_KEY = 'YOUR_API_KEY'; process.env.OPENAI_BASE_URL = 'https://api.apiyi.com/v1';

import OpenAI from 'openai';

// 使用默认配置 const openai = new OpenAI();

const response = await openai.chat.completions.create({ model: 'gpt-4', messages: [{ role: 'user', content: 'Explain AI to a 5-year-old' }] });

流式输出

Copy const stream = await openai.chat.completions.create({ model: 'gpt-3.5-turbo', messages: [{ role: 'user', content: 'Tell me a joke' }], stream: true });

for await (const chunk of stream) { if (chunk.choices[0]?.delta?.content) { process.stdout.write(chunk.choices[0].delta.content); } }

TypeScript 支持

Copy import OpenAI from 'openai'; import type { ChatCompletionCreateParamsNonStreaming } from 'openai/resources/chat/completions';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY!, baseURL: 'https://api.apiyi.com/v1' });

const params: ChatCompletionCreateParamsNonStreaming = { model: 'gpt-3.5-turbo', messages: [{ role: 'user', content: 'Hello TypeScript!' }], temperature: 0.7 };

const response = await openai.chat.completions.create(params);

.NET SDK

安装

Copy dotnet add package OpenAI

基础配置

Copy using OpenAI; using OpenAI.Chat;

var client = new OpenAIClient("YOUR_API_KEY", new OpenAIClientOptions { Endpoint = new Uri("https://api.apiyi.com/v1") });

var chatClient = client.GetChatClient("gpt-3.5-turbo");

var response = await chatClient.CompleteChatAsync("Hello!");

Console.WriteLine(response.Value.Content[0].Text);

流式输出

Copy await foreach (var update in chatClient.CompleteChatStreamingAsync("Tell me a story")) { if (update.ContentUpdate.Count > 0) { Console.Write(update.ContentUpdate[0].Text); } }

Go SDK

安装

Copy go get github.com/sashabaranov/go-openai

基础配置

Copy package main

import ( "context" "fmt" "github.com/sashabaranov/go-openai" )

func main() { config := openai.DefaultConfig("YOUR_API_KEY") config.BaseURL = "https://api.apiyi.com/v1"

client := openai.NewClientWithConfig(config)

resp, err := client.CreateChatCompletion(
    context.Background(),
    openai.ChatCompletionRequest{
        Model: openai.GPT3Dot5Turbo,
        Messages: []openai.ChatCompletionMessage{
            {
                Role:    openai.ChatMessageRoleUser,
                Content: "Hello!",
            },
        },
    },
)

if err != nil {
    fmt.Printf("Error: %v\n", err)
    return
}

fmt.Println(resp.Choices[0].Message.Content)

}

流式输出

Copy stream, err := client.CreateChatCompletionStream( context.Background(), openai.ChatCompletionRequest{ Model: openai.GPT3Dot5Turbo, Messages: []openai.ChatCompletionMessage{ { Role: openai.ChatMessageRoleUser, Content: "Write a haiku", }, }, Stream: true, }, )

if err != nil { fmt.Printf("Error: %v\n", err) return } defer stream.Close()

for { response, err := stream.Recv() if errors.Is(err, io.EOF) { break }

if err != nil {
    fmt.Printf("Error: %v\n", err)
    return
}

fmt.Print(response.Choices[0].Delta.Content)

}

Java SDK

依赖配置

Copy

基础使用

Copy import com.theokanning.openai.OpenAiService; import com.theokanning.openai.completion.chat.*;

import java.time.Duration; import java.util.List;

public class APIYiExample { public static void main(String[] args) { OpenAiService service = new OpenAiService( "YOUR_API_KEY", Duration.ofSeconds(60), "https://api.apiyi.com/v1/" );

    ChatCompletionRequest request = ChatCompletionRequest.builder()
        .model("gpt-3.5-turbo")
        .messages(List.of(
            new ChatMessage(ChatMessageRole.USER, "Hello!")
        ))
        .build();

    ChatCompletionResult result = service.createChatCompletion(request);
    System.out.println(result.getChoices().get(0).getMessage().getContent());
}

}

模型切换

使用不同模型

Copy

GPT 模型

response = client.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": "Hello"}] )

Claude 模型

response = client.chat.completions.create( model="claude-3-opus-20240229", messages=[{"role": "user", "content": "Hello"}] )

Gemini 模型

response = client.chat.completions.create( model="gemini-pro", messages=[{"role": "user", "content": "Hello"}] )

动态模型选择

Copy def chat_with_model(message: str, model: str = "gpt-3.5-turbo"): """支持动态切换模型的聊天函数""" response = client.chat.completions.create( model=model, messages=[{"role": "user", "content": message}] ) return response.choices[0].message.content

使用不同模型

print(chat_with_model("解释量子计算", "gpt-4")) print(chat_with_model("解释量子计算", "claude-3-opus-20240229")) print(chat_with_model("解释量子计算", "gemini-pro"))

高级功能

Function Calling

Copy tools = [ { "type": "function", "function": { "name": "get_weather", "description": "获取指定城市的天气信息", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "城市名称" } }, "required": ["location"] } } } ]

response = client.chat.completions.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "北京天气怎么样？"}], tools=tools, tool_choice="auto" )

if response.choices[0].message.tool_calls: print("AI 想要调用函数：", response.choices[0].message.tool_calls[0].function.name)

图像输入

Copy response = client.chat.completions.create( model="gpt-4o", messages=[ { "role": "user", "content": [ {"type": "text", "text": "这张图片里有什么？"}, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg" } } ] } ] )

嵌入向量

Copy response = client.embeddings.create( model="text-embedding-3-small", input="要嵌入的文本内容" )

embedding = response.data[0].embedding print(f"向量维度：{len(embedding)}")

错误处理

基础错误处理

Copy from openai import OpenAI, OpenAIError

try: response = client.chat.completions.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello"}] ) except OpenAIError as e: print(f"API 错误：{e}")

详细错误处理

Copy from openai import ( OpenAI, APIError, APIConnectionError, RateLimitError, InternalServerError )

try: response = client.chat.completions.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello"}] ) except RateLimitError: print("请求频率超限，请稍后重试") except APIConnectionError: print("网络连接错误") except InternalServerError: print("服务器内部错误") except APIError as e: print(f"API 错误：{e}")

最佳实践

配置管理

Copy import os from openai import OpenAI

class APIYiClient: def init(self): self.client = OpenAI( api_key=os.getenv("APIYI_API_KEY"), base_url=os.getenv("APIYI_BASE_URL", "https://api.apiyi.com/v1") )

def chat(self, message: str, model: str = "gpt-3.5-turbo"):
    return self.client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": message}]
    )

重试机制

Copy import time import random from openai import OpenAI, RateLimitError

def chat_with_retry(client, messages, max_retries=3): for attempt in range(max_retries): try: return client.chat.completions.create( model="gpt-3.5-turbo", messages=messages ) except RateLimitError: if attempt < max_retries - 1: wait_time = (2 ** attempt) + random.uniform(0, 1) time.sleep(wait_time) else: raise

成本控制

Copy def controlled_chat(message: str, max_tokens: int = 150): """控制输出长度以管理成本""" response = client.chat.completions.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": message}], max_tokens=max_tokens, temperature=0.7 ) return response

迁移指南

从 OpenAI 迁移如果您已经在使用 OpenAI 官方服务，迁移到 API易非常简单：更改 base_url

Copy

原来的配置

client = OpenAI(api_key="sk-...")

改为 API易

client = OpenAI( api_key="YOUR_APIYI_KEY", base_url="https://api.apiyi.com/v1" ) 更新环境变量

Copy

原来

export OPENAI_API_KEY="sk-..."

改为

export OPENAI_API_KEY="YOUR_APIYI_KEY" export OPENAI_BASE_URL="https://api.apiyi.com/v1" 代码无需修改所有其他代码保持不变，包括：方法调用参数格式响应处理

多服务兼容

Copy class MultiProviderClient: def init(self): self.apiyi_client = OpenAI( api_key="APIYI_KEY", base_url="https://api.apiyi.com/v1" ) self.openai_client = OpenAI(api_key="OPENAI_KEY")

def chat(self, message: str, provider: str = "apiyi"):
    client = self.apiyi_client if provider == "apiyi" else self.openai_client
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": message}]
    )

配置 API易 服务 ​

使用方式与官方完全相同 ​

设置环境变量 ​

使用默认配置 ​

GPT 模型 ​

Claude 模型 ​

Gemini 模型 ​

使用不同模型 ​

原来的配置 ​

改为 API易 ​

原来 ​

改为 ​

配置 API易服务

使用方式与官方完全相同

设置环境变量

使用默认配置

GPT 模型

Claude 模型

Gemini 模型

使用不同模型

原来的配置

改为 API易

原来

改为