RAG Q&A endpoint

Build a minimal retrieval-augmented Q&A flow with @tyravel/rag and @tyravel/vector.

Scaffold

tyravel new knowledge-base --ai
npm install
tyravel vector:install

The --ai flag adds vector config, embed jobs, models, and example routes.

Ingest documents

import { ingestFile } from '@tyravel/rag';

await ingestFile('storage/docs/handbook.pdf', {
  source: 'handbook',
  chunkSize: 800,
});

Embed chunks:

tyravel vector:embed

Ask endpoint

import { Route } from '@tyravel/core';
import { Response } from '@tyravel/http';
import { retrieveAndAnswer } from '@tyravel/rag';

Route.post('/api/ask', async (request) => {
  const { question } = await request.json();
  const answer = await retrieveAndAnswer(question, { topK: 5 });
  return Response.json(answer);
});

Use your preferred LLM SDK in the app layer — Tyravel handles storage, retrieval, and prompt templates.

Example app

See examples/rag for ingest → embed → ask → stream with GraphQL read API.