openai.ts•5.29 kB
import { OpenAI } from "openai"
import { ApiHandlerOptions } from "../../shared/api"
import { IEmbedder, EmbeddingResponse, EmbedderInfo } from "../interfaces/embedder"
import {
MAX_BATCH_TOKENS,
MAX_ITEM_TOKENS,
MAX_BATCH_RETRIES as MAX_RETRIES,
INITIAL_RETRY_DELAY_MS as INITIAL_DELAY_MS,
} from "../constants"
import { fetch, ProxyAgent } from "undici"
/**
* OpenAI implementation of the embedder interface with batching and rate limiting
*/
export class OpenAiEmbedder implements IEmbedder {
private embeddingsClient: OpenAI
private readonly defaultModelId: string
/**
* Creates a new OpenAI embedder
* @param options API handler options
*/
constructor(options: ApiHandlerOptions & { openAiEmbeddingModelId?: string }) {
const apiKey = options.openAiNativeApiKey ?? "not-provided"
// 检查环境变量中的代理设置
const httpsProxy = process.env['HTTPS_PROXY'] || process.env['https_proxy']
const httpProxy = process.env['HTTP_PROXY'] || process.env['http_proxy']
// OpenAI API 使用 HTTPS，所以优先使用 HTTPS 代理
const proxyUrl = httpsProxy || httpProxy
let dispatcher: any = undefined
if (proxyUrl) {
try {
dispatcher = new ProxyAgent(proxyUrl)
console.log('✓ OpenAI using undici ProxyAgent:', proxyUrl)
} catch (error) {
console.error('✗ Failed to create undici ProxyAgent for OpenAI:', error)
}
} else {
// console.log('ℹ No proxy configured for OpenAI')
}
const clientConfig: any = {
apiKey,
dangerouslyAllowBrowser: true,
}
if (dispatcher) {
clientConfig.fetch = (url: string, init?: any) => {
return fetch(url, {
...init,
dispatcher
})
}
console.log('📝 调试: OpenAI客户端将使用 undici ProxyAgent 代理')
} else {
clientConfig.fetch = fetch
// console.log('📝 调试: OpenAI客户端不使用代理 (undici)')
}
this.embeddingsClient = new OpenAI(clientConfig)
this.defaultModelId = options.openAiEmbeddingModelId || "text-embedding-3-small"
}
/**
* Creates embeddings for the given texts with batching and rate limiting
* @param texts Array of text strings to embed
* @param model Optional model identifier
* @returns Promise resolving to embedding response
*/
async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
const modelToUse = model || this.defaultModelId
const allEmbeddings: number[][] = []
const usage = { promptTokens: 0, totalTokens: 0 }
const remainingTexts = [...texts]
while (remainingTexts.length > 0) {
const currentBatch: string[] = []
let currentBatchTokens = 0
const processedIndices: number[] = []
for (let i = 0; i < remainingTexts.length; i++) {
const text = remainingTexts[i]
const itemTokens = Math.ceil(text.length / 4)
if (itemTokens > MAX_ITEM_TOKENS) {
console.warn(
`Text at index ${i} exceeds maximum token limit (${itemTokens} > ${MAX_ITEM_TOKENS}). Skipping.`,
)
processedIndices.push(i)
continue
}
if (currentBatchTokens + itemTokens <= MAX_BATCH_TOKENS) {
currentBatch.push(text)
currentBatchTokens += itemTokens
processedIndices.push(i)
} else {
break
}
}
// Remove processed items from remainingTexts (in reverse order to maintain correct indices)
for (let i = processedIndices.length - 1; i >= 0; i--) {
remainingTexts.splice(processedIndices[i], 1)
}
if (currentBatch.length > 0) {
try {
const batchResult = await this._embedBatchWithRetries(currentBatch, modelToUse)
allEmbeddings.push(...batchResult.embeddings)
usage.promptTokens += batchResult.usage.promptTokens
usage.totalTokens += batchResult.usage.totalTokens
} catch (error) {
console.error("Failed to process batch:", error)
throw new Error("Failed to create embeddings: batch processing error")
}
}
}
return { embeddings: allEmbeddings, usage }
}
/**
* Helper method to handle batch embedding with retries and exponential backoff
* @param batchTexts Array of texts to embed in this batch
* @param model Model identifier to use
* @returns Promise resolving to embeddings and usage statistics
*/
private async _embedBatchWithRetries(
batchTexts: string[],
model: string,
): Promise<{ embeddings: number[][]; usage: { promptTokens: number; totalTokens: number } }> {
for (let attempts = 0; attempts < MAX_RETRIES; attempts++) {
try {
const response = await this.embeddingsClient.embeddings.create({
input: batchTexts,
model: model,
})
return {
embeddings: response.data.map((item) => item.embedding),
usage: {
promptTokens: response.usage?.prompt_tokens || 0,
totalTokens: response.usage?.total_tokens || 0,
},
}
} catch (error: any) {
const isRateLimitError = error?.status === 429
const hasMoreAttempts = attempts < MAX_RETRIES - 1
if (isRateLimitError && hasMoreAttempts) {
const delayMs = INITIAL_DELAY_MS * Math.pow(2, attempts)
await new Promise((resolve) => setTimeout(resolve, delayMs))
continue
}
throw error
}
}
throw new Error(`Failed to create embeddings after ${MAX_RETRIES} attempts`)
}
get embedderInfo(): EmbedderInfo {
return {
name: "openai",
}
}
}