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235
ts/api/handlers/embeddings.ts
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235
ts/api/handlers/embeddings.ts
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/**
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* Embeddings Handler
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*
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* Handles /v1/embeddings endpoint.
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*/
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import * as http from 'node:http';
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import type {
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IEmbeddingsRequest,
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IEmbeddingsResponse,
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IEmbeddingData,
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IApiError,
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} from '../../interfaces/api.ts';
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import { logger } from '../../logger.ts';
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import { ContainerManager } from '../../containers/container-manager.ts';
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/**
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* Handler for embeddings requests
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*/
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export class EmbeddingsHandler {
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private containerManager: ContainerManager;
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constructor(containerManager: ContainerManager) {
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this.containerManager = containerManager;
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}
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/**
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* Handle POST /v1/embeddings
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*/
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public async handleEmbeddings(
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res: http.ServerResponse,
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body: IEmbeddingsRequest,
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): Promise<void> {
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const modelName = body.model;
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logger.dim(`Embeddings request for model: ${modelName}`);
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try {
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// Find container with the embedding model
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const container = await this.containerManager.findContainerForModel(modelName);
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if (!container) {
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this.sendError(res, 404, `Embedding model "${modelName}" not found`, 'model_not_found');
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return;
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}
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// Generate embeddings
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const response = await this.generateEmbeddings(container, body);
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res.writeHead(200, { 'Content-Type': 'application/json' });
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res.end(JSON.stringify(response));
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} catch (error) {
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const message = error instanceof Error ? error.message : String(error);
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logger.error(`Embeddings error: ${message}`);
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this.sendError(res, 500, `Embeddings generation failed: ${message}`, 'server_error');
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}
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}
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/**
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* Generate embeddings from container
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*/
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private async generateEmbeddings(
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container: import('../../containers/base-container.ts').BaseContainer,
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request: IEmbeddingsRequest,
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): Promise<IEmbeddingsResponse> {
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const inputs = Array.isArray(request.input) ? request.input : [request.input];
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const embeddings: IEmbeddingData[] = [];
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let totalTokens = 0;
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// Generate embeddings for each input
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for (let i = 0; i < inputs.length; i++) {
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const input = inputs[i];
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const embedding = await this.getEmbeddingFromContainer(container, request.model, input);
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embeddings.push({
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object: 'embedding',
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embedding: embedding.vector,
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index: i,
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});
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totalTokens += embedding.tokenCount;
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}
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return {
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object: 'list',
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data: embeddings,
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model: request.model,
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usage: {
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prompt_tokens: totalTokens,
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total_tokens: totalTokens,
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},
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};
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}
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/**
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* Get embedding from container (container-specific implementation)
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*/
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private async getEmbeddingFromContainer(
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container: import('../../containers/base-container.ts').BaseContainer,
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model: string,
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input: string,
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): Promise<{ vector: number[]; tokenCount: number }> {
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const endpoint = container.getEndpoint();
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const containerType = container.type;
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// Route to container-specific embedding endpoint
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if (containerType === 'ollama') {
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return this.getOllamaEmbedding(endpoint, model, input);
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} else if (containerType === 'vllm') {
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return this.getVllmEmbedding(endpoint, model, input);
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} else if (containerType === 'tgi') {
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return this.getTgiEmbedding(endpoint, model, input);
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}
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throw new Error(`Container type ${containerType} does not support embeddings`);
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}
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/**
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* Get embedding from Ollama
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*/
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private async getOllamaEmbedding(
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endpoint: string,
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model: string,
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input: string,
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): Promise<{ vector: number[]; tokenCount: number }> {
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const response = await fetch(`${endpoint}/api/embeddings`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model,
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prompt: input,
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}),
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});
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if (!response.ok) {
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const errorText = await response.text();
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throw new Error(`Ollama embedding error: ${errorText}`);
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}
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const result = await response.json() as { embedding: number[] };
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// Estimate token count (rough approximation: ~4 chars per token)
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const tokenCount = Math.ceil(input.length / 4);
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return {
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vector: result.embedding,
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tokenCount,
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};
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}
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/**
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* Get embedding from vLLM (OpenAI-compatible)
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*/
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private async getVllmEmbedding(
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endpoint: string,
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model: string,
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input: string,
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): Promise<{ vector: number[]; tokenCount: number }> {
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const response = await fetch(`${endpoint}/v1/embeddings`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model,
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input,
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}),
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});
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if (!response.ok) {
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const errorText = await response.text();
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throw new Error(`vLLM embedding error: ${errorText}`);
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}
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const result = await response.json() as IEmbeddingsResponse;
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return {
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vector: result.data[0].embedding,
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tokenCount: result.usage.total_tokens,
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};
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}
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/**
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* Get embedding from TGI
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*/
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private async getTgiEmbedding(
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endpoint: string,
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_model: string,
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input: string,
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): Promise<{ vector: number[]; tokenCount: number }> {
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// TGI uses /embed endpoint
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const response = await fetch(`${endpoint}/embed`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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inputs: input,
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}),
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});
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if (!response.ok) {
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const errorText = await response.text();
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throw new Error(`TGI embedding error: ${errorText}`);
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}
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const result = await response.json() as number[][];
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// Estimate token count
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const tokenCount = Math.ceil(input.length / 4);
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return {
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vector: result[0],
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tokenCount,
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};
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}
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/**
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* Send error response
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*/
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private sendError(
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res: http.ServerResponse,
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statusCode: number,
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message: string,
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type: string,
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param?: string,
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): void {
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const error: IApiError = {
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error: {
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message,
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type,
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param,
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code: null,
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},
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};
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res.writeHead(statusCode, { 'Content-Type': 'application/json' });
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res.end(JSON.stringify(error));
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}
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}
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