⁢⁢⁤⁤⁡⁡⁡⁣‬⁣‬‬⁡‬⁣⁤⁣⁢⁣⁡⁢⁣⁣⁣通过CZLOapi，部署ChatGPT到飞书(Docker) 本项目基于CZLOapi 国内 OPENAI 代理服务，无须科学上网。Docker飞书开发平台Github 开源项目修改而来的 Docker 镜像(原项目：https://github.com/ConnectAI-E/Feishu-OpenAI）支持的模型gpt-3.5-turbo-0613gpt-3.5-turbo-0613-16kgpt-4-0613(需充值 $5.0)交流群陈武 邀请你加入飞书群，快点击 https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=8bfv9781-d031-4504-b831-ae5307a44a8b 加入吧！演示​​一、CZLOapi-获取 key网址：https://oapi.czl.net注册与登录注册送约 500 汉字的额度，因为太少，所以显示余额为 0，实际是可以使用的​​‍获取 KEY,把生成的 key 粘贴出来保存例子：sk-BdH2ECx9UeUmqI2j86B9E6C​​‍二、飞书开发平台新建应用网址：https://open.feishu.cn/app/新建应用，保存 APPID 和 Secret例子：cli_a41fe20b2539d00b、0kMAQp1c0ydxrBwkQHT7OgGMeHzXuuwr​​‍前往 应用功能-机器人​, 创建机器人​​获取 Encrypt Key​ 和 Verification Token​例子：44xyLjvx1s94sfTCddS8xf4Iif2UrTwZDC4rdwPrXTjc3Je12dt75cmk3mTyoX1G​​三、部署 Docker 应用获取 docker 镜像​​docker pull woodchen/czlchat-feishu:<版本> 3.5-0613:gpt-3.5-turbo-0613 3.5-0613-16k:gpt-3.5-turbo-0613-16k 4.0-0613:gpt-4-0613部署容器环境变量BOT_NAME:上面添加的应用名称，在示例里是 testgptAPP_ID:上面第二步第 1 小步获取到的APP_SECRET:上面第二步第 1 小步获取到的OPENAI_KEY:上面第一步第 2 小步获取到的APP_ENCRYPT_KEY：上面第二步第 3 小步获取到的APP_VERIFICATION_TOKEN：上面第二步第 3 小步获取到的端口容器内端口为 9000，外部端口可自定义命令部署docker run -d \ -e BOT_NAME=<BOT_NAME> \ -e APP_ID=<APP_ID> \ -e APP_SECRET=<APP_SECRET> \ -e OPENAI_KEY=<OPENAI_KEY> \ -e APP_ENCRYPT_KEY=<APP_ENCRYPT_KEY> \ -e APP_VERIFICATION_TOKEN=<APP_VERIFICATION_TOKEN> \ -p <外部端口>:9000 \ woodchen/czlchat-feishu:<版本>可视化示例图​​​​反代以供访问原理都相同​​​​测试访问访问地址为：https://访问地址/ping访问显示应该为这样，如不对，请查看 docker 日志​​​​四、飞书开发平台添加调用地址并发布版本添加事件订阅地址和卡片消息地址事件订阅格式为：https://访问地址/webhook/event卡片消息地址为：https://访问地址/webhook/card如图所示地址，只是因为视频为测试地址，所以保存不成功​​​​添加权限和能力在事件订阅板块，搜索三个词 机器人进群​、 接收消息​、 消息已读​, 把他们后面所有的权限全部勾选。进入权限管理界面，搜索 图片​, 勾选 获取与上传图片或文件资源​。 最终会添加下列回调事件:im:resource(获取与上传图片或文件资源)im:messageim:message.group_at_msg(获取群组中所有消息)im:message.group_at_msg:readonly(接收群聊中 @ 机器人消息事件)im:message.p2p_msg(获取用户发给机器人的单聊消息)im:message.p2p_msg:readonly(读取用户发给机器人的单聊消息)im:message:send_as_bot(获取用户在群组中 @ 机器人的消息)im:chat:readonly(获取群组信息)im:chat(获取与更新群组信息)​​​​创建版本并发布、审核应用我这里开启了免审，不然一般会弹出一个审核按钮 进入管理后台审核​，点击进去审核通过就可以了​​​​五、截止这里，已经可以使用了在机器人消息上点回复即可连续对话​​​​查看余额，此处为 CZLOapi 的余额​​​​清除上下文 /clear ​和更多功能​​​​补充：OPENAI 的模型说明​​​​​​​​提交新模型需求，或希望前端可以修改模型我也希望可以支持前端修改模型，由于能力问题无法实现，本项目源码在 https://github.com/woodchen-ink/CZLChat-Feishu，有条件可以去提交 pr。GPT-4 模型说明GPT-4 模型需要充值达 $5.0 才可以使用图片生成说明本系统只支持对话，不支持图片生成流式输出和多话题对话建议使用下方 CZLChat 或者自行部署开源项目ChatGPT Next Web项目主页：https://github.com/Yidadaa/ChatGPT-Next-Webdocker run --name chat-next-web -d -p 3001:3000 -e CODE="页面访问密码" -e OPENAI_API_KEY="czloapi获取的key" -e BASE_URL="https://oapi.czl.net" yidadaa/chatgpt-next-web 注意修改 端口号​、OPENAI_API_KEY​。ChatGPT Web项目主页：https://github.com/Chanzhaoyu/chatgpt-webdocker run --name chatgpt-web -d -p 3002:3002 -e OPENAI_API_BASE_URL="https://oapi.czl.net" -e OPENAI_API_KEY="czloapi获取的key" chenzhaoyu94/chatgpt-web注意修改 端口号 ​和 OPENAI_API_KEY​。广告如果只需要聊天、思维导图和基础功能，可以考虑 CZLChat，支持网页端，MAC 和 windows，界面如下​​​​

CZLOapi-OPENAI反向代理接口，中转请求，价格便宜线路稳定 网址：https://oapi.czl.net产品介绍概述我们的系统为您提供全面的 AI接口代理 服务，以支持您的软件和应用程序。通过我们的系统，您可以轻松生成令牌并充值，以便在您的项目中无缝集成 AI 的强大功能。调用地址https://oapi.czl.net 全部地址：https://oapi.czl.net/v1/chat/completions特点全面兼容：我们的系统支持所有调用 AI 的软件和应用。无论您是在开发聊天机器人、自然语言处理模型还是其他 AI 功能，我们都能为您提供全面的支持。强大的功能：我们的代理服务支持流式输出和连续对话等高级功能。您可以实现更加智能和自然的交互体验，让您的用户获得更加精准和有趣的回复。稳定高效：我们的系统经过精心优化，确保稳定有效的使用体验。无论您在国内还是国外，我们都提供快速可靠的服务，让您无需担忧网络延迟或不稳定的问题。安全可靠：我们重视用户隐私和数据安全，采取严格的安全措施来保护您的信息和交互数据。您可以放心使用我们的服务，专注于开发和创新。简单易用：我们的系统提供简洁明了的界面和易于操作的流程，让您快速上手并集成 AI 的功能。无论您是开发者还是非技术人员，都能轻松使用我们的服务。模型全面：除了Oapi4-32K，其他所有模型都支持，例如：3.5-turbo,3.5-turbo-16k,4等。充值达$5即可联系右下角在线客服开通Oapi-4权限。‍价格和套餐定价VersionModelPriceOapi-3.5-turbo4K context$0.003 / 1K tokens 16K context$0.006 / 1K tokensOapi-48K context$0.09 / 1K tokens套餐充值金额原价(￥)优惠价(￥)折合汇率$0.50¥3.50￥3.501:7$5.00¥35.00￥32.001:6.4$10.00¥70.00￥60.001:6$50.00¥350.00￥280.001:5.6我们的收费制度为预付费，充值后可以随时使用。注意：以上价格仅供参考，详细定价可能会根据市场情况做出调整。请在购买时以我们网站上的最新价格为准。感谢您对我们的支持！‍

怎么批量查询OPENAI余额、到期时间、GPT-4、是否绑卡？获取SESS ID 在线访问地址：https://o-b.pages.dev7月22日更新后，需登录一次账号，F12查看sess码，使用sess码进行查询7月22日更新后，已删除key校验规则，需使用sess码进行查询。使用key只能查询总额度、绑卡、GPT4和组织ID。SESS ID获取方法我在Github上开源了这个项目仓库地址为：https://github.com/woodchen-ink/openai-billing-query批量可视化查询openai(chatgpt)余额，支持显示总量，已使用，剩余量，已用比例，到期时间，GPT-4，是否绑卡支持自定义反代接口在第361行添加自己的接口代码<option value="反代网址">【自定义名称】自定义名称</option>反代代码示例​​

How to use Azure's OPENAI interface? Resolve with Node.js You can use this project to deploy a proxy with Node.js (Node>=16) to call Azure's OpenAI interface in the same way as you would call OpenAI, thereby reducing the deployment learning cost.Github project address: https://github.com/woodchen-ink/azure-to-openai-adapterIf you can access it, just visit Github directly; if not, follow the steps in this tutorial.Environment requirements: node>=16Create a new folder and create the following files insideapp.jsimport express from 'express'; import cors from 'cors'; import fetch from 'node-fetch'; const app = express(); const port = 3000; // 这里修改为你想用的端口号 // Please modify the port number to the one you want to use. app.use(cors()); app.use(express.json()); const resourceName = "test-czl"; // 这里是你azure账号的OPENAI资源名称 // Here is the name of your OPENAI resource on your Azure account. const mapper = { "gpt-3.5-turbo": "test0", //这个的右边是你部署的3.5模型的名称 // On the right side of this is the name of the deployed 3.5 model. "gpt-4": "test1", //这个的右边是你部署的4模型的名称 //On the right side of this is the name of the deployed 4 model. }; const apiVersion = "2023-03-15-preview"; app.options("*", cors()); app.post("/v1/chat/completions", async (req, res) => { await handleRequest(req, res, "chat/completions"); }); app.post("/v1/completions", async (req, res) => { await handleRequest(req, res, "completions"); }); app.get("/v1/models", async (req, res) => { await handleModels(req, res); }); async function handleRequest(req, res, path) { const body = req.body; const modelName = body?.model; const deployName = mapper[modelName] || ""; if (deployName === "") { return res.status(403).send("Missing model mapper"); } const fetchAPI = `https://${resourceName}.openai.azure.com/openai/deployments/${deployName}/${path}?api-version=${apiVersion}`; const authKey = req.headers.authorization; if (!authKey) { return res.status(403).send("Not allowed"); } const payload = { method: "POST", headers: { "Content-Type": "application/json", "api-key": authKey.replace("Bearer ", ""), }, body: typeof body === "object" ? 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"node": ">= 0.8" } }, "node_modules/utils-merge": { "version": "1.0.1", "resolved": "https://registry.npmmirror.com/utils-merge/-/utils-merge-1.0.1.tgz", "integrity": "sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA==", "engines": { "node": ">= 0.4.0" } }, "node_modules/vary": { "version": "1.1.2", "resolved": "https://registry.npmmirror.com/vary/-/vary-1.1.2.tgz", "integrity": "sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg==", "engines": { "node": ">= 0.8" } }, "node_modules/web-streams-polyfill": { "version": "3.2.1", "resolved": "https://registry.npmmirror.com/web-streams-polyfill/-/web-streams-polyfill-3.2.1.tgz", "integrity": "sha512-e0MO3wdXWKrLbL0DgGnUV7WHVuw9OUvL4hjgnPkIeEvESk74gAITi5G606JtZPp39cd8HA9VQzCIvA49LpPN5Q==", "engines": { "node": ">= 8" } } } }Run the following command to install dependencies npm install express cors node-fetchRun the following command to start the service (for production environments, it is recommended to use tools like pm2 to run in the background)node app.jsOnce deployed, you can use the Azure interface by calling it in the same way as you would call the OpenAI interfaceThe interface address is: http://localhost:3000/v1/chat/completionsEnter the Azure key for the "key" field

Azure的OPENAI接口怎么用？用Node.js来解决 可以通过本项目，使用node.js(node>=16)部署代理，以调用openai的方式，调用azure的openai接口，从而降低部署学习成本。Github项目地址：https://github.com/woodchen-ink/azure-to-openai-adapter如果可以访问，直接访问github就行，如果不能访问，按照本教程一步步来。环境需求：node>=16新建一个文件夹，里面新建以下文件app.jsimport express from 'express'; import cors from 'cors'; import fetch from 'node-fetch'; const app = express(); const port = 3000; // 这里修改为你想用的端口号 // Please modify the port number to the one you want to use. app.use(cors()); app.use(express.json()); const resourceName = "test-czl"; // 这里是你azure账号的OPENAI资源名称 // Here is the name of your OPENAI resource on your Azure account. const mapper = { "gpt-3.5-turbo": "test0", //这个的右边是你部署的3.5模型的名称 // On the right side of this is the name of the deployed 3.5 model. "gpt-4": "test1", //这个的右边是你部署的4模型的名称 //On the right side of this is the name of the deployed 4 model. }; const apiVersion = "2023-03-15-preview"; app.options("*", cors()); app.post("/v1/chat/completions", async (req, res) => { await handleRequest(req, res, "chat/completions"); }); app.post("/v1/completions", async (req, res) => { await handleRequest(req, res, "completions"); }); app.get("/v1/models", async (req, res) => { await handleModels(req, res); }); async function handleRequest(req, res, path) { const body = req.body; const modelName = body?.model; const deployName = mapper[modelName] || ""; if (deployName === "") { return res.status(403).send("Missing model mapper"); } const fetchAPI = `https://${resourceName}.openai.azure.com/openai/deployments/${deployName}/${path}?api-version=${apiVersion}`; const authKey = req.headers.authorization; if (!authKey) { return res.status(403).send("Not allowed"); } const payload = { method: "POST", headers: { "Content-Type": "application/json", "api-key": authKey.replace("Bearer ", ""), }, body: typeof body === "object" ? JSON.stringify(body) : "{}", }; try { const response = await fetch(fetchAPI, payload); const jsonResponse = await response.json(); res.json(jsonResponse); } catch (error) { res.status(500).send(error.message); } } async function handleModels(req, res) { const data = { object: "list", data: [], }; for (let key in mapper) { data.data.push({ id: key, object: "model", created: 1677610602, owned_by: "openai", }); } res.json(data); } app.listen(port, () => { console.log(`Server is listening at http://localhost:${port}`); });package.json{ "dependencies": { "cors": "^2.8.5", "express": "^4.18.2", "node-fetch": "^3.3.1" }, "type": "module" }package-lock.json{ "name": "azure-openai-proxy", "lockfileVersion": 3, "requires": true, "packages": { "": { "dependencies": { "cors": "^2.8.5", "express": "^4.18.2", "node-fetch": "^3.3.1" } }, "node_modules/accepts": { "version": "1.3.8", "resolved": "https://registry.npmmirror.com/accepts/-/accepts-1.3.8.tgz", "integrity": 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app.js这就部署好了，按照调用openai的方式调用azure接口就可以使用了接口地址为：http://localhost:3000/v1/chat/completionskey输入azure的密钥

获得了Azure的OPENAI资格，但不知道怎么调用？用Cloudflare或docker解决 新发布一个通过Node.js来实现代理的开源代码：https://github.com/woodchen-ink/azure-to-openai-adapter可以通过Github上的这一个开源项目进行转换，转换后使用正常调用openai接口的开源项目即可以正常使用项目地址：https://github.com/haibbo/cf-openai-azure-proxy项目也支持docker部署，可以自行到github上查看，这里只介绍了cloudflare的方法。到Cloudflare新建一个worker服务，快速部署后编辑代码// The name of your Azure OpenAI Resource. const resourceName='test0' // 这里是你azure账号的OPENAI资源名称，不知道看下面截图 // The deployment name you chose when you deployed the model. const mapper = { 'gpt-3.5-turbo': 'test0', //这个的右边是你部署的3.5模型的名称，不知道看下面截图 'gpt-4': 'test1' //这个的右边是你部署的4模型的名称，不知道看下面截图 }; const apiVersion="2023-03-15-preview" addEventListener("fetch", (event) => { event.respondWith(handleRequest(event.request)); }); async function handleRequest(request) { if (request.method === 'OPTIONS') { return handleOPTIONS(request) } const url = new URL(request.url); if (url.pathname === '/v1/chat/completions') { var path="chat/completions" } else if (url.pathname === '/v1/completions') { var path="completions" } else if (url.pathname === '/v1/models') { return handleModels(request) } else { return new Response('404 Not Found', { status: 404 }) } let body; if (request.method === 'POST') { body = await request.json(); } const modelName = body?.model; const deployName = mapper[modelName] || '' if (deployName === '') { return new Response('Missing model mapper', { status: 403 }); } const fetchAPI = `https://${resourceName}.openai.azure.com/openai/deployments/${deployName}/${path}?api-version=${apiVersion}` const authKey = request.headers.get('Authorization'); if (!authKey) { return new Response("Not allowed", { status: 403 }); } const payload = { method: request.method, headers: { "Content-Type": "application/json", "api-key": authKey.replace('Bearer ', ''), }, body: typeof body === 'object' ? JSON.stringify(body) : '{}', }; let response = await fetch(fetchAPI, payload); response = new Response(response.body, response); response.headers.set("Access-Control-Allow-Origin", "*"); if (body?.stream != true){ return response } let { readable, writable } = new TransformStream() stream(response.body, writable); return new Response(readable, response); } function sleep(ms) { return new Promise(resolve => setTimeout(resolve, ms)); } // support printer mode and add newline async function stream(readable, writable) { const reader = readable.getReader(); const writer = writable.getWriter(); // const decoder = new TextDecoder(); const encoder = new TextEncoder(); const decoder = new TextDecoder(); // let decodedValue = decoder.decode(value); const newline = "\n"; const delimiter = "\n\n" const encodedNewline = encoder.encode(newline); let buffer = ""; while (true) { let { value, done } = await reader.read(); if (done) { break; } buffer += decoder.decode(value, { stream: true }); // stream: true is important here,fix the bug of incomplete line let lines = buffer.split(delimiter); // Loop through all but the last line, which may be incomplete. for (let i = 0; i < lines.length - 1; i++) { await writer.write(encoder.encode(lines[i] + delimiter)); await sleep(30); } buffer = lines[lines.length - 1]; } if (buffer) { await writer.write(encoder.encode(buffer)); } await writer.write(encodedNewline) await writer.close(); } async function handleModels(request) { const data = { "object": "list", "data": [] }; for (let key in mapper) { data.data.push({ "id": key, "object": "model", "created": 1677610602, "owned_by": "openai", "permission": [{ "id": "modelperm-M56FXnG1AsIr3SXq8BYPvXJA", "object": "model_permission", "created": 1679602088, "allow_create_engine": false, "allow_sampling": true, "allow_logprobs": true, "allow_search_indices": false, "allow_view": true, "allow_fine_tuning": false, "organization": "*", "group": null, "is_blocking": false }], "root": key, "parent": null }); } const json = JSON.stringify(data, null, 2); return new Response(json, { headers: { 'Content-Type': 'application/json' }, }); } async function handleOPTIONS(request) { return new Response(null, { headers: { 'Access-Control-Allow-Origin': '*', 'Access-Control-Allow-Methods': '*', 'Access-Control-Allow-Headers': '*' } }) }填写代码后部署服务，然后使用一般的开源项目就可以调用，代理地址填cloudflare的worker访问地址，key是azure里的密钥如果要简单测试，可以试一下我的单页面开源项目