Data layerRAG
createRAG
One-liner RAG pipeline — chunk, embed, store, retrieve, search.
import { createRAG } from '@agentskit/rag'
import { openaiEmbedder } from '@agentskit/adapters'
import { fileVectorMemory } from '@agentskit/memory'
const rag = createRAG({
embed: openaiEmbedder({ apiKey: process.env.OPENAI_API_KEY! }),
store: fileVectorMemory({ path: '.agentskit/vec.json', dim: 1536 }),
})
await rag.ingest([{ id: 'doc-1', text: longDoc }])
const hits = await rag.retrieve('How does token budgeting work?')API
| Method | Purpose |
|---|---|
ingest(docs, opts?) | chunk + embed + store |
retrieve(query, opts?) | vector top-k |
search(query, opts?) | alias for retrieve with filters |
delete(ids) | remove docs |
Options
chunkSize/chunkOverlap/split— passed to chunking.topK— default retrieve count.