Colección de interfaces libres de inversión de macromodelos de diálogo a API

免费登录 GPT3.5 转 API

https://github.com/missuo/FreeGPT35

https://github.com/aurora-develop/aurora

https://github.com/Dalufishe/freegptjs

https://github.com/PawanOsman/ChatGPT

https://github.com/nashsu/FreeAskInternet

https://github.com/aurorax-neo/free-gpt3.5-2api

https://github.com/aurora-develop/free-gpt3.5-2api

https://github.com/LanQian528/chat2api

https://github.com/k0baya/FreeGPT35-Glitch

https://github.com/cliouo/FreeGPT35-Vercel

https://github.com/hominsu/freegpt35

https://github.com/xsigoking/chatgpt-free-api

https://github.com/skzhengkai/free-chatgpt-api

https://github.com/aurora-develop/aurora-glitch （使用glitch资源）

https://github.com/fatwang2/coze2openai（COZE转API，GPT4）

国产模型逆向

búsqueda en profundidad（DeepSeek）接口转API deepseek-free-api

Moonshot AI（Kimi.ai）接口转API kimi-free-api

阶跃星辰 (跃问StepChat) 接口转API step-free-api

阿里通义 (Qwen) 接口转API qwen-free-api

ZhipuAI (discurso intelectualmente estimulante) 接口转API glm-free-api

秘塔AI (metaso) 接口转API metaso-free-api

字节跳动（豆包）接口转API doubao-free-api

字节跳动（即梦AI）接口转API jimeng-free-api

讯飞星火（Spark）接口转API spark-free-api

MiniMax（海螺AI）接口转API hailuo-free-api

聆心智能 (Emohaa) 接口转API emohaa-free-api

带有聊天界面的免登录项目

https://github.com/Mylinde/FreeGPT

cloudflare 的work代码，绑个自己域名玩：

addEventListener("fetch", event => {
event.respondWith(handleRequest(event.request))
})

async function handleRequest(request) {
// 确保请求是 POST 请求，并且路径正确
if (request.method === "POST" && new URL(request.url).pathname === "/v1/chat/completions") {
const url = 'https://multillm.ai-pro.org/api/openai-completion'; // 目标 API 地址
const headers = new Headers(request.headers);

// 添加或修改需要的 headers
headers.set('Content-Type', 'application/json');

// 获取请求的 body 并解析 JSON
const requestBody = await request.json();
const stream = requestBody.stream; // 获取 stream 参数

// 构造新的请求
const newRequest = new Request(url, {
method: 'POST',
headers: headers,
body: JSON.stringify(requestBody) // 使用修改后的 body
});

try {
// 向目标 API 发送请求
const response = await fetch(newRequest);

// 根据 stream 参数确定响应类型
if (stream) {
// 处理流式响应
const { readable, writable } = new TransformStream();
response.body.pipeTo(writable);
return new Response(readable, {
headers: response.headers
});
} else {
// 正常返回响应
return new Response(response.body, {
status: response.status,
headers: response.headers
});
}
} catch (e) {
// 如果请求失败，返回错误信息
return new Response(JSON.stringify({ error: 'Unable to reach the backend API' }), { status: 502 });
}
} else {
// 如果请求方法不是 POST 或路径不正确，返回错误
return new Response('Not found', { status: 404 });
}
}

POST示例：

curl --location 'https://ai-pro-free.aivvm.com/v1/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-4-turbo",
"messages": [
{
"role": "user", "content": "鲁迅为什么打周树人"
}],
"stream": true
}'

加个伪流式代码（输出会更慢）：

addEventListener("fetch", event => {
event.respondWith(handleRequest(event.request))
})

async function handleRequest(request) {
if (request.method === "OPTIONS") {
return new Response(null, {
headers: {
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Headers": '*'
}, status: 204
})
}
// 确保请求是 POST 请求，并且路径正确
if (request.method === "POST" && new URL(request.url).pathname === "/v1/chat/completions") {
const url = 'https://multillm.ai-pro.org/api/openai-completion'; // 目标 API 地址
const headers = new Headers(request.headers);

// 添加或修改需要的 headers
headers.set('Content-Type', 'application/json');

// 获取请求的 body 并解析 JSON
const requestBody = await request.json();
const stream = requestBody.stream; // 获取 stream 参数

// 构造新的请求
const newRequest = new Request(url, {
method: 'POST',
headers: headers,
body: JSON.stringify(requestBody) // 使用修改后的 body
});

try {
// 向目标 API 发送请求
const response = await fetch(newRequest);

// 根据 stream 参数确定响应类型
if (stream) {
const originalJson = await response.json(); // 一次性读取完整数据
// 创建一个可读流
const readableStream = new ReadableStream({
start(controller) {
// 发送开始数据
const startData = createDataChunk(originalJson, "start");
controller.enqueue(new TextEncoder().encode('data: ' + JSON.stringify(startData) + '\n\n'));

// 假设根据 originalJson 处理并发送多个数据块
// 例如，模拟分批次发送数据
const content = originalJson.choices[0].message.content; // 假设这是要发送的内容
const newData = createDataChunk(originalJson, "data", content);
controller.enqueue(new TextEncoder().encode('data: ' + JSON.stringify(newData) + '\n\n'));

// 发送结束数据
const endData = createDataChunk(originalJson, "end");
controller.enqueue(new TextEncoder().encode('data: ' + JSON.stringify(endData) + '\n\n'));

controller.enqueue(new TextEncoder().encode('data: [DONE]'));
// 标记流的结束
controller.close();
}
});
return new Response(readableStream, {
headers: {
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Headers": '*',
'Content-Type': 'text/event-stream',
'Cache-Control': 'no-cache',
'Connection': 'keep-alive'
}
});
} else {
// 正常返回响应
return new Response(response.body, {
status: response.status,
headers: response.headers
});
}
} catch (e) {
// 如果请求失败，返回错误信息
return new Response(JSON.stringify({ error: 'Unable to reach the backend API' }), { status: 502 });
}
} else {
// 如果请求方法不是 POST 或路径不正确，返回错误
return new Response('Not found', { status: 404 });
}
}

// 根据类型创建不同的数据块
function createDataChunk(json, type, content = {}) {
switch (type) {
case "start":
devolver {
id: json.id,
object: "chat.completion.chunk",
created: json.created,
model: json.model,
choices: [{ delta: {}, index: 0, finish_reason: null }]};
case "data":
devolver {
id: json.id,
object: "chat.completion.chunk",
created: json.created,
model: json.model,
choices: [{ delta: { content }, index: 0, finish_reason: null }]};
case "end":
devolver {
id: json.id,
object: "chat.completion.chunk",
created: json.created,
model: json.model,
choices: [{ delta: {}, index: 0, finish_reason: 'stop' }]};
default:
return {};
}
}