feat: implement QuestionBank CRUD with pagination and template query
- Add pagination support to findAll (page, limit query params) - Add findByTemplateId method to service - Add GET /by-template/:templateId endpoint to controller - Service already includes CRUD for QuestionBank and QuestionBankItem
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Developer
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import { Injectable, Logger } from '@nestjs/common';
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import { ConfigService } from '@nestjs/config';
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import { ModelConfigService } from '../model-config/model-config.service';
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import { I18nService } from '../i18n/i18n.service';
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export interface EmbeddingResponse {
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data: Array<{
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embedding: number[];
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index: number;
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}>;
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model: string;
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usage: {
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prompt_tokens: number;
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total_tokens: number;
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};
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}
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@Injectable()
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export class EmbeddingService {
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private readonly logger = new Logger(EmbeddingService.name);
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private readonly defaultDimensions: number;
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constructor(
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private modelConfigService: ModelConfigService,
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private configService: ConfigService,
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private i18nService: I18nService,
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) {
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this.defaultDimensions = parseInt(
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this.configService.get<string>('DEFAULT_VECTOR_DIMENSIONS', '2560'),
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);
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this.logger.log(
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`Default vector dimensions set to ${this.defaultDimensions}`,
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);
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}
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async getEmbeddings(
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texts: string[],
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embeddingModelConfigId: string,
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): Promise<number[][]> {
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this.logger.log(`Generating embeddings for ${texts.length} texts`);
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const modelConfig = await this.modelConfigService.findOne(
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embeddingModelConfigId,
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);
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if (!modelConfig || modelConfig.type !== 'embedding') {
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throw new Error(
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this.i18nService.formatMessage('embeddingModelNotFound', {
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id: embeddingModelConfigId,
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}),
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);
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}
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if (modelConfig.isEnabled === false) {
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throw new Error(
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`Model ${modelConfig.name} is disabled and cannot generate embeddings`,
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);
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}
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if (!modelConfig.baseUrl) {
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throw new Error(
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`Model ${modelConfig.name} does not have baseUrl configured`,
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);
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}
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// Determine max batch size based on model name
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const maxBatchSize = this.getMaxBatchSizeForModel(
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modelConfig.modelId,
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modelConfig.maxBatchSize,
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);
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// Split processing if batch size exceeds limit
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if (texts.length > maxBatchSize) {
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this.logger.log(
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`Splitting ${texts.length} texts into batches (model batch limit: ${maxBatchSize})`,
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);
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const allEmbeddings: number[][] = [];
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for (let i = 0; i < texts.length; i += maxBatchSize) {
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const batch = texts.slice(i, i + maxBatchSize);
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const batchEmbeddings = await this.getEmbeddingsForBatch(
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batch,
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modelConfig,
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maxBatchSize,
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);
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allEmbeddings.push(...batchEmbeddings);
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// Wait briefly to avoid API rate limiting
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if (i + maxBatchSize < texts.length) {
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await new Promise((resolve) => setTimeout(resolve, 100)); // Wait 100ms
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}
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}
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return allEmbeddings;
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} else {
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// Normal processing (within batch size)
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return await this.getEmbeddingsForBatch(
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texts,
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modelConfig,
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maxBatchSize,
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);
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}
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}
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/**
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* Determine max batch size based on model ID
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*/
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private getMaxBatchSizeForModel(
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modelId: string,
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configuredMaxBatchSize?: number,
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): number {
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// Model-specific batch size limits
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if (
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modelId.includes('text-embedding-004') ||
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modelId.includes('text-embedding-v4') ||
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modelId.includes('text-embedding-ada-002')
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) {
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return Math.min(10, configuredMaxBatchSize || 100); // Google limit: 10
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} else if (
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modelId.includes('text-embedding-3') ||
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modelId.includes('text-embedding-003')
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) {
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return Math.min(2048, configuredMaxBatchSize || 2048); // OpenAI v3 limit: 2048
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} else {
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// Default: smaller of configured max or 100
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return Math.min(configuredMaxBatchSize || 100, 100);
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}
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}
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/**
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* Process single batch embedding
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*/
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private async getEmbeddingsForBatch(
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texts: string[],
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modelConfig: any,
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maxBatchSize: number,
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): Promise<number[][]> {
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const apiUrl = modelConfig.baseUrl.endsWith('/embeddings')
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? modelConfig.baseUrl
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: `${modelConfig.baseUrl}/embeddings`;
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let lastError;
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const MAX_RETRIES = 3;
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for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {
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try {
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const controller = new AbortController();
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const timeoutId = setTimeout(() => {
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controller.abort();
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this.logger.error(`Embedding API timeout after 60s: ${apiUrl}`);
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}, 60000); // 60s timeout
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this.logger.log(
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`[Model call] Type: Embedding, Model: ${modelConfig.name} (${modelConfig.modelId}), Text count: ${texts.length}`,
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);
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this.logger.log(
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`Calling embedding API (attempt ${attempt}/${MAX_RETRIES}): ${apiUrl}`,
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);
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let response;
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try {
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response = await fetch(apiUrl, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${modelConfig.apiKey}`,
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},
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body: JSON.stringify({
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encoding_format: 'float',
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input: texts,
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model: modelConfig.modelId,
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}),
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signal: controller.signal,
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});
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} finally {
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clearTimeout(timeoutId);
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}
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if (!response.ok) {
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const errorText = await response.text();
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// Detect batch size limit error
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if (
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errorText.includes('batch size is invalid') ||
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errorText.includes('batch_size') ||
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errorText.includes('invalid') ||
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errorText.includes('larger than')
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) {
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this.logger.warn(
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`Batch size limit error detected. Splitting batch in half and retrying: ${maxBatchSize} -> ${Math.floor(maxBatchSize / 2)}`,
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);
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// Split batch into smaller units and retry
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if (texts.length > 1) {
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const midPoint = Math.floor(texts.length / 2);
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const firstHalf = texts.slice(0, midPoint);
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const secondHalf = texts.slice(midPoint);
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const firstResult = await this.getEmbeddingsForBatch(
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firstHalf,
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modelConfig,
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Math.floor(maxBatchSize / 2),
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);
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const secondResult = await this.getEmbeddingsForBatch(
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secondHalf,
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modelConfig,
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Math.floor(maxBatchSize / 2),
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);
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return [...firstResult, ...secondResult];
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}
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}
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// Detect context length excess error
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if (
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errorText.includes('context length') ||
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errorText.includes('exceeds')
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) {
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const avgLength =
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texts.reduce((s, t) => s + t.length, 0) / texts.length;
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const totalLength = texts.reduce((s, t) => s + t.length, 0);
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this.logger.error(
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`Text length exceeds limit: ${texts.length} texts, ` +
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`total ${totalLength} characters, average ${Math.round(avgLength)} characters, ` +
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`model limit: ${modelConfig.maxInputTokens || 8192} tokens`,
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);
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throw new Error(
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`Text length exceeds model limit. ` +
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`Current: ${texts.length} texts with total ${totalLength} characters, ` +
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`model limit: ${modelConfig.maxInputTokens || 8192} tokens. ` +
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`Advice: Reduce chunk size or batch size`,
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);
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}
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// Retry on 429 (Too Many Requests) or 5xx (Server Error)
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if (response.status === 429 || response.status >= 500) {
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this.logger.warn(
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`Temporary error from embedding API (${response.status}): ${errorText}`,
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);
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throw new Error(`API Error ${response.status}: ${errorText}`);
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}
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this.logger.error(`Embedding API error details: ${errorText}`);
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this.logger.error(
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`Request parameters: model=${modelConfig.modelId}, inputLength=${texts[0]?.length}`,
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);
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throw new Error(
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`Embedding API call failed: ${response.statusText} - ${errorText}`,
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);
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}
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const data: EmbeddingResponse = await response.json();
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const embeddings = data.data.map((item) => item.embedding);
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// Get dimensions from actual response
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const actualDimensions =
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embeddings[0]?.length || this.defaultDimensions;
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this.logger.log(
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`Got ${embeddings.length} embedding vectors from ${modelConfig.name}. Dimensions: ${actualDimensions}`,
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);
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return embeddings;
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} catch (error) {
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lastError = error;
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// If not the last attempt and error appears temporary (or for robustness on all), retry after waiting
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if (attempt < MAX_RETRIES) {
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const delay = Math.pow(2, attempt - 1) * 1000; // 1s, 2s, 4s
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this.logger.warn(
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`Embedding request failed. Retrying after ${delay}ms: ${error.message}`,
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);
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await new Promise((resolve) => setTimeout(resolve, delay));
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continue;
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}
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}
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}
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throw lastError;
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}
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private getEstimatedDimensions(modelId: string): number {
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// Use default dimensions from environment variable
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return this.defaultDimensions;
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}
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}
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