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Create app.js
Browse files
app.js
ADDED
@@ -0,0 +1,191 @@
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const express = require("express");
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const app = express();
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const port = 3000;
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app.use(express.json());
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app.get("//models", async (req, res) => {
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try {
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res.status(200).json({ object: "list", data: createFakeModelsList() });
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} catch {}
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});
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app.post("//chat/completions", async (clientRequest, clientResponse) => {
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try {
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const {
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frequency_penalty,
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presence_penalty,
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max_tokens,
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stop,
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temperature,
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top_p,
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} = clientRequest.body;
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const apiRequestBody = {
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model: "gpt-4",
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prompt: convertChatMLPrompt(clientRequest.body.messages),
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frequency_penalty,
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presence_penalty,
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max_tokens,
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stop,
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temperature,
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top_p,
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};
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const apiResponse = await fetch(process.env.API_URL, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify(apiRequestBody),
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});
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if (clientRequest.body.stream) {
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handleResponseAsStream(clientResponse, apiResponse);
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} else {
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handleResponseAsNonStreamable(clientResponse, apiResponse);
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}
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} catch {}
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});
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app.listen(port, () => {
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console.log(`Example app listening on port ${port}`);
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});
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async function handleResponseAsNonStreamable(clientResponse, apiResponse) {
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const apiText = await apiResponse.text();
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const clientMessage = createClientMessage(apiText);
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clientResponse.send(JSON.stringify(clientMessage));
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}
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async function handleResponseAsStream(clientResponse, apiResponse) {
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const reader = apiResponse.body.getReader();
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const nextDecoder = new TextDecoder();
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clientResponse.write("data: " + JSON.stringify(createBeginChunk()) + "\n\n");
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new ReadableStream({
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start(controller) {
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return pump();
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function pump() {
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return reader.read().then(({ done, value }) => {
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const textData = nextDecoder.decode(value);
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clientResponse.write(
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"data: " + JSON.stringify(createMessageChunk(textData)) + "\n\n"
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);
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// When no more data needs to be consumed, close the stream
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if (done) {
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clientResponse.write(
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"data: " + JSON.stringify(createEndChunk()) + "\n\n"
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);
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clientResponse.end();
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controller.close();
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return;
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}
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// Enqueue the next data chunk into our target stream
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controller.enqueue(value);
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return pump();
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});
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}
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},
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});
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}
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function getCurrentDate() {
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return Math.floor(new Date().getTime());
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}
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function convertChatMLPrompt(messages) {
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const messageStrings = [];
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messages.forEach((m) => {
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if (m.role === "system" && m.name === undefined) {
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messageStrings.push("System: " + m.content);
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} else if (m.role === "system" && m.name !== undefined) {
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messageStrings.push(m.name + ": " + m.content);
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} else {
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messageStrings.push(m.role + ": " + m.content);
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}
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});
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return messageStrings.join("\n") + "\nassistant:";
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}
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const createClientMessage = (text) => ({
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id: "chatcmpl-123",
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object: "chat.completion",
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created: getCurrentDate(),
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model: "gpt-4",
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choices: [
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{
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index: 0,
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message: { role: "assistant", content: text },
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logprobs: null,
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finish_reason: "stop",
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},
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],
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});
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const createBeginChunk = () => ({
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id: "chatcmpl-123",
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object: "chat.completion.chunk",
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created: getCurrentDate(),
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model: "gpt-4",
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system_fingerprint: "",
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choices: [
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{
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index: 0,
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delta: { role: "assistant", content: "" },
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logprobs: null,
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finish_reason: null,
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},
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],
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});
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const createMessageChunk = (text) => ({
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id: "chatcmpl-123",
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object: "chat.completion.chunk",
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created: getCurrentDate(),
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model: "gpt-4",
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system_fingerprint: "",
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choices: [
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{
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index: 0,
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delta: { content: text },
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logprobs: null,
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finish_reason: null,
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},
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],
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});
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const createEndChunk = () => ({
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id: "chatcmpl-123",
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object: "chat.completion.chunk",
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created: getCurrentDate(),
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model: "gpt-4",
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system_fingerprint: "",
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choices: [{ index: 0, delta: {}, logprobs: null, finish_reason: "stop" }],
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});
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function createFakeModelsList() {
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return [
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{
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id: "gpt-4",
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object: "model",
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created: getCurrentDate(),
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owned_by: "openai",
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permission: [
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{
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id: "modelperm-gpt-4",
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object: "model_permission",
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created: getCurrentDate(),
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182 |
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organization: "*",
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183 |
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group: null,
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is_blocking: false,
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},
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],
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root: "gpt-4",
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188 |
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parent: null,
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},
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190 |
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];
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}
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