ThunderAI/api_webchat/model-worker.js

86 lines
3.5 KiB
JavaScript

const MOCK_TOKENS = ['Good', ' morning', ' Mr', ' Plop', 'py', ',', 'and', ' I', ' said', '\n', '"', 'Good', ' morn', 'ing', ' Mrs',' Plop', 'py', ,'"', '\n', 'Oh', ' how', ' the', ' win', 'ter', ' even', 'ings', ' must', ' just', ' fly'];
const API_URL = "https://api.openai.com/v1/chat/completions";
let openaiApiKey;
let conversationHistory = [];
let assistantResponseAccumulator = '';
function mockDelay(ms) {
return new Promise(resolve => setTimeout(resolve, ms));
}
async function processMockTokens() {
for (const token of MOCK_TOKENS) {
await mockDelay(Math.random() * 50 + 50); // Random delay between 100ms and 150ms
postMessage({ type: 'newToken', payload: { token } });
}
postMessage({ type: 'tokensDone' });
}
self.onmessage = async function(event) {
if (event.data.type === 'init') {
openaiApiKey = event.data.openaiApiKey;
} else if (event.data.type === 'chatMessage') {
// MOCK
if( openaiApiKey === null ) {
// Simulate sending the message to an HTTP endpoint
await mockDelay(1000); // Wait for 1 second
// Notify that the chat message was sent
postMessage({ type: 'messageSent' });
// Start processing tokens
await processMockTokens();
// OPENAI
} else {
conversationHistory.push({ role: 'user', content: event.data.message });
// https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo
// 4096 output tokens
// 128,000 input tokens
const response = await fetch(API_URL, {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${openaiApiKey}`,
},
body: JSON.stringify({
model: "gpt-4-1106-preview",
messages: conversationHistory,
stream: true,
}),
});
postMessage({ type: 'messageSent' });
const reader = response.body.getReader();
const decoder = new TextDecoder("utf-8");
while (true) {
const { done, value } = await reader.read();
if (done) {
conversationHistory.push({ role: 'assistant', content: assistantResponseAccumulator });
assistantResponseAccumulator = '';
postMessage({ type: 'tokensDone' });
break;
}
// lots of low-level OpenAI response parsing stuff
const chunk = decoder.decode(value);
const lines = chunk.split("\n");
const parsedLines = lines
.map((line) => line.replace(/^data: /, "").trim()) // Remove the "data: " prefix
.filter((line) => line !== "" && line !== "[DONE]") // Remove empty lines and "[DONE]"
.map((line) => JSON.parse(line)); // Parse the JSON string
for (const parsedLine of parsedLines) {
const { choices } = parsedLine;
const { delta } = choices[0];
const { content } = delta;
// Update the UI with the new content
if (content) {
assistantResponseAccumulator += content;
postMessage({ type: 'newToken', payload: { token: content } });
}
}
}
}
}
};