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EmbeddingUtils.js
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162 lines (134 loc) · 6.75 KB
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// EmbeddingUtils.js
const { get_encoding } = require("@dqbd/tiktoken");
const encoding = get_encoding("cl100k_base");
// 配置
const embeddingMaxToken = parseInt(process.env.WhitelistEmbeddingModelMaxToken, 10) || 8000;
const safeMaxTokens = Math.floor(embeddingMaxToken * 0.85);
const MAX_BATCH_ITEMS = 100; // Gemini/OpenAI 限制
const DEFAULT_CONCURRENCY = parseInt(process.env.TAG_VECTORIZE_CONCURRENCY) || 5; // 🌟 读取并发配置
/**
* 内部函数:发送单个批次
*/
async function _sendBatch(batchTexts, config, batchNumber) {
const { default: fetch } = await import('node-fetch');
const retryAttempts = 3;
const baseDelay = 1000;
for (let attempt = 1; attempt <= retryAttempts; attempt++) {
try {
const requestUrl = `${config.apiUrl}/v1/embeddings`;
const requestBody = { model: config.model, input: batchTexts };
const requestHeaders = { 'Content-Type': 'application/json', 'Authorization': `Bearer ${config.apiKey}` };
const response = await fetch(requestUrl, {
method: 'POST',
headers: requestHeaders,
body: JSON.stringify(requestBody)
});
const responseBodyText = await response.text();
if (!response.ok) {
if (response.status === 429) {
// 429 限流时,增加等待时间
const waitTime = 5000 * attempt;
console.warn(`[Embedding] Batch ${batchNumber} rate limited (429). Retrying in ${waitTime/1000}s...`);
await new Promise(r => setTimeout(r, waitTime));
continue;
}
throw new Error(`API Error ${response.status}: ${responseBodyText.substring(0, 500)}`);
}
let data;
try {
data = JSON.parse(responseBodyText);
} catch (parseError) {
console.error(`[Embedding] JSON Parse Error for Batch ${batchNumber}:`);
console.error(`Response (first 500 chars): ${responseBodyText.substring(0, 500)}`);
throw new Error(`Failed to parse API response as JSON: ${parseError.message}`);
}
// 增强的响应结构验证和详细错误信息
if (!data) {
throw new Error(`API returned empty/null response`);
}
// 检查是否是错误响应
if (data.error) {
const errorMsg = data.error.message || JSON.stringify(data.error);
const errorCode = data.error.code || response.status;
console.error(`[Embedding] API Error for Batch ${batchNumber}:`);
console.error(` Error Code: ${errorCode}`);
console.error(` Error Message: ${errorMsg}`);
console.error(` Hint: Check if embedding model "${config.model}" is available on your API server`);
throw new Error(`API Error ${errorCode}: ${errorMsg}`);
}
if (!data.data) {
console.error(`[Embedding] Missing 'data' field in response for Batch ${batchNumber}`);
console.error(`Response keys: ${Object.keys(data).join(', ')}`);
console.error(`Response preview: ${JSON.stringify(data).substring(0, 500)}`);
throw new Error(`Invalid API response structure: missing 'data' field`);
}
if (!Array.isArray(data.data)) {
console.error(`[Embedding] 'data' field is not an array for Batch ${batchNumber}`);
console.error(`data type: ${typeof data.data}`);
console.error(`data value: ${JSON.stringify(data.data).substring(0, 200)}`);
throw new Error(`Invalid API response structure: 'data' is not an array`);
}
if (data.data.length === 0) {
console.warn(`[Embedding] Warning: Batch ${batchNumber} returned empty embeddings array`);
}
// 简单的 Log,证明并发正在跑
// console.log(`[Embedding] ✅ Batch ${batchNumber} completed (${batchTexts.length} items).`);
return data.data.sort((a, b) => a.index - b.index).map(item => item.embedding);
} catch (e) {
console.warn(`[Embedding] Batch ${batchNumber}, Attempt ${attempt} failed: ${e.message}`);
if (attempt === retryAttempts) throw e;
await new Promise(r => setTimeout(r, baseDelay * Math.pow(2, attempt)));
}
}
}
/**
* 🚀 终极版:并发批量获取 Embeddings
*/
async function getEmbeddingsBatch(texts, config) {
if (!texts || texts.length === 0) return [];
// 1. ⚡️ 第一步:纯 CPU 操作,先把所有文本切分成 Batches
const batches = [];
let currentBatch = [];
let currentBatchTokens = 0;
for (const text of texts) {
const textTokens = encoding.encode(text).length;
if (textTokens > safeMaxTokens) continue; // Skip oversize
const isTokenFull = currentBatch.length > 0 && (currentBatchTokens + textTokens > safeMaxTokens);
const isItemFull = currentBatch.length >= MAX_BATCH_ITEMS;
if (isTokenFull || isItemFull) {
batches.push(currentBatch);
currentBatch = [text];
currentBatchTokens = textTokens;
} else {
currentBatch.push(text);
currentBatchTokens += textTokens;
}
}
if (currentBatch.length > 0) batches.push(currentBatch);
console.log(`[Embedding] Prepared ${batches.length} batches. Executing with concurrency: ${DEFAULT_CONCURRENCY}...`);
// 2. 🌊 第二步:并发执行器
const results = new Array(batches.length); // 预分配结果数组,保证顺序
let cursor = 0; // 当前处理到的批次索引
// 定义 Worker:只要队列里还有任务,就不断抢任务做
const worker = async (workerId) => {
while (true) {
// 🔒 获取任务索引 (原子操作模拟)
const batchIndex = cursor++;
if (batchIndex >= batches.length) break; // 没任务了,下班
const batchTexts = batches[batchIndex];
// 执行请求 (Batch ID 从 1 开始显示)
results[batchIndex] = await _sendBatch(batchTexts, config, batchIndex + 1);
}
};
// 启动 N 个 Worker
const workers = [];
for (let i = 0; i < DEFAULT_CONCURRENCY; i++) {
workers.push(worker(i));
}
// 等待所有 Worker 下班
await Promise.all(workers);
// 3. 📦 第三步:展平结果
// results 数组里可能包含 undefined (如果某个 batch 最终失败),filter 掉保平安
return results.filter(r => r).flat();
}
module.exports = { getEmbeddingsBatch };