inference-server/dist/api/openai/handlers/embeddings.js

Libraries and server to build AI applications. Adapters to various native bindings allowing local inference. Integrate it with your application, or use as a microservice.

import { parseJSONRequestBody } from '../../../api/parseJSONRequestBody.js';
import { omitEmptyValues } from '../../../lib/util.js';
export function createEmbeddingsHandler(inferenceServer) {
    return async (req, res) => {
        let args;
        try {
            const body = await parseJSONRequestBody(req);
            args = body;
        }
        catch (e) {
            console.error(e);
            res.writeHead(400, { 'Content-Type': 'application/json' });
            res.end(JSON.stringify({ error: 'Invalid request' }));
            return;
        }
        // TODO ajv schema validation?
        if (!args.model || !args.input) {
            res.writeHead(400, { 'Content-Type': 'application/json' });
            res.end(JSON.stringify({ error: 'Invalid request' }));
            return;
        }
        if (!inferenceServer.modelExists(args.model)) {
            res.writeHead(400, { 'Content-Type': 'application/json' });
            res.end(JSON.stringify({ error: 'Invalid model' }));
            return;
        }
        const controller = new AbortController();
        req.on('close', () => {
            console.debug('Client closed connection');
            controller.abort();
        });
        req.on('end', () => {
            console.debug('Client ended connection');
            controller.abort();
        });
        try {
            let input = args.input;
            if (typeof input !== 'string') {
                throw new Error('Input must be a string');
            }
            const embeddingsReq = omitEmptyValues({
                model: args.model,
                input: args.input,
            });
            const { instance, release } = await inferenceServer.requestInstance(embeddingsReq, controller.signal);
            const task = instance.processEmbeddingTask(embeddingsReq);
            const result = await task.result;
            release();
            const response = {
                model: instance.modelId,
                object: 'list',
                data: result.embeddings.map((embedding, index) => ({
                    embedding: Array.from(embedding),
                    index,
                    object: 'embedding',
                })),
                usage: {
                    prompt_tokens: result.inputTokens,
                    total_tokens: result.inputTokens,
                },
            };
            res.writeHead(200, { 'Content-Type': 'application/json' });
            res.end(JSON.stringify(response, null, 2));
        }
        catch (err) {
            console.error(err);
            res.writeHead(500, { 'Content-Type': 'application/json' });
            res.end(JSON.stringify({ error: 'Internal server error' }));
        }
    };
}
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