@agentskit/rag — for agents

#Install

npm install @agentskit/rag

#Primary exports

• createRAG({ embed, store, chunkSize, chunkOverlap, topK, threshold }) — ingest(docs) + retrieve(request) + search(query).
• chunkText({ chunkSize, chunkOverlap, split }) — lower-level splitter.
• createRerankedRetriever(base, { candidatePool, topK, rerank }) — pluggable reranker (BM25 default). See RAG reranking.
• createHybridRetriever(base, { vectorWeight, bm25Weight }) — vector + BM25 hybrid.
• bm25Score, bm25Rerank — standalone helpers.
• voyageReranker(config) — Voyage AI reranker.
• jinaReranker(config) — Jina AI reranker.

#Document loaders

• loadUrl, loadGitHubFile, loadGitHubTree, loadNotionPage, loadConfluencePage, loadGoogleDriveFile, loadPdf (BYO parser). See Doc loaders.
• Cloud storage: loadS3, loadGcs, loadDropbox, loadOneDrive.

#Minimal example

import { createRAG, loadGitHubTree } from '@agentskit/rag'
import { fileVectorMemory } from '@agentskit/memory'
import { openaiEmbedder } from '@agentskit/adapters'

const rag = createRAG({
  embed: openaiEmbedder({ apiKey }),
  store: fileVectorMemory({ path: './kb.json' }),
})

await rag.ingest(await loadGitHubTree('org', 'repo', { token }))
const hits = await rag.search('onboarding flow')

#Related

• @agentskit/memory — vector stores.
• @agentskit/adapters — embedders.

#Source

• npm: https://www.npmjs.com/package/@agentskit/rag
• repo: https://github.com/AgentsKit-io/agentskit/tree/main/packages/rag