RAG

npm i @ai_kit/rag

import { createRag, MemoryVectorStore, RagDocument } from "@ai_kit/rag";
import { openai } from "@ai-sdk/openai";

const rag = createRag({
  embedder: openai.embedding("text-embedding-3-small"),
  store: new MemoryVectorStore(),
  chunker: { size: 240, overlap: 40 },
});

const doc = RagDocument.fromText(
  "Paris est la capitale de la France. Lyon est connue pour sa gastronomie. Marseille est un grand port sur la Mediterranee."
);

await rag.ingest({ namespace: "demo", documents: [doc] });

const results = await rag.search({
  namespace: "demo",
  query: "Quelle ville est la capitale de la France ?",
  topK: 3,
});

console.log(results.map((result) => ({ score: result.score, text: result.chunk.text })));

await rag.ingest({
  namespace: "demo",
  documents: [RagDocument.fromText("Paris est la capitale de la France", { lang: "fr" })],
  metadata: { tenant: "fr" }, // merged into each chunk metadata
});

const results = await rag.search({
  namespace: "demo",
  query: "capitale",
  filter: { tenant: "fr" }, // works with MemoryVectorStore and PgVectorStore
});