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 { getModelQueryPrefix } from "../../shared/embeddingModels"
import { withValidationErrorHandling, formatEmbeddingError, HttpError } from "../shared/validation-helpers"
import { handleOpenAIError } from "../shared/openai-error-handler"
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
private readonly _optimalBatchSize: number
/**
* Creates a new OpenAI embedder
* @param options API handler options
*/
constructor(options: ApiHandlerOptions & { openAiEmbeddingModelId?: string }) {
const apiKey = options.openAiNativeApiKey ?? "not-provided"
// Initialize optimal batch size for OpenAI (can be customized via options)
this._optimalBatchSize = options['openaiBatchSize'] || 60
// Wrap OpenAI client creation to handle invalid API key characters
try {
// 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 Embedding using undici ProxyAgent:', proxyUrl)
} catch (error) {
console.error('✗ Failed to create undici ProxyAgent for OpenAI Embedding:', error)
}
}
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
}
this.embeddingsClient = new OpenAI(clientConfig)
} catch (error) {
// Use the error handler to transform ByteString conversion errors
throw handleOpenAIError(error, "OpenAI")
}
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
// Apply model-specific query prefix if required
const queryPrefix = getModelQueryPrefix("openai", modelToUse)
const processedTexts = queryPrefix
? texts.map((text, index) => {
// Prevent double-prefixing
if (text.startsWith(queryPrefix)) {
return text
}
const prefixedText = `${queryPrefix}${text}`
const estimatedTokens = Math.ceil(prefixedText.length / 4)
if (estimatedTokens > MAX_ITEM_TOKENS) {
console.warn(
`Text at index ${index} with prefix exceeds token limit (${estimatedTokens} > ${MAX_ITEM_TOKENS}). Using original text.`,
)
// Return original text if adding prefix would exceed limit
return text
}
return prefixedText
})
: texts
const allEmbeddings: number[][] = []
const usage = { promptTokens: 0, totalTokens: 0 }
const remainingTexts = [...processedTexts]
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 ${i} exceeds token limit (${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) {
const batchResult = await this._embedBatchWithRetries(currentBatch, modelToUse)
allEmbeddings.push(...batchResult.embeddings)
usage.promptTokens += batchResult.usage.promptTokens
usage.totalTokens += batchResult.usage.totalTokens
}
}
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,
// OpenAI package (as of v4.78.1) has a parsing issue that truncates embedding dimensions to 256
// when processing numeric arrays, which breaks compatibility with models using larger dimensions.
// By requesting base64 encoding, we bypass the package's parser and handle decoding ourselves.
encoding_format: "base64",
})
// Convert base64 embeddings to float32 arrays
const processedEmbeddings = response.data.map((item) => {
if (typeof item.embedding === "string") {
const buffer = Buffer.from(item.embedding, "base64")
const float32Array = new Float32Array(buffer.buffer, buffer.byteOffset, buffer.byteLength / 4)
return Array.from(float32Array)
}
return item.embedding
})
return {
embeddings: processedEmbeddings,
usage: {
promptTokens: response.usage?.prompt_tokens || 0,
totalTokens: response.usage?.total_tokens || 0,
},
}
} catch (error: any) {
// TelemetryService calls removed as per requirements
const hasMoreAttempts = attempts < MAX_RETRIES - 1
// Check if it's a rate limit error
const httpError = error as HttpError
if (httpError?.status === 429 && hasMoreAttempts) {
const delayMs = INITIAL_DELAY_MS * Math.pow(2, attempts)
console.warn(
`Rate limit hit. Retrying in ${delayMs}ms (attempt ${attempts + 1}/${MAX_RETRIES})`,
)
await new Promise((resolve) => setTimeout(resolve, delayMs))
continue
}
// Log the error for debugging
console.error(`OpenAI embedder error (attempt ${attempts + 1}/${MAX_RETRIES}):`, error)
// Format and throw the error
throw formatEmbeddingError(error, MAX_RETRIES)
}
}
throw new Error(`Failed to generate embeddings after ${MAX_RETRIES} attempts`)
}
/**
* Validates the OpenAI embedder configuration by attempting a minimal embedding request
* @returns Promise resolving to validation result with success status and optional error message
*/
async validateConfiguration(): Promise<{ valid: boolean; error?: string }> {
return withValidationErrorHandling(async () => {
try {
// Test with a minimal embedding request
const response = await this.embeddingsClient.embeddings.create({
input: ["test"],
model: this.defaultModelId,
encoding_format: "base64",
})
// Check if we got a valid response
if (!response.data || response.data.length === 0) {
return {
valid: false,
error: "Invalid response format from OpenAI API",
}
}
return { valid: true }
} catch (error) {
// TelemetryService calls removed as per requirements
throw error
}
}, "openai")
}
get embedderInfo(): EmbedderInfo {
return {
name: "openai",
}
}
/**
* Gets the optimal batch size for this OpenAI embedder
*/
get optimalBatchSize(): number {
return this._optimalBatchSize
}
}
