A RAG chunk store
memista is a natural chunk store for RAG: each chunk carries its embedding, text, and metadata, and search returns all three ranked by similarity. One binary, two files, best under ~100k chunks.
Retrieval-augmented generation needs a store that holds chunk text, some metadata (source, offsets, tags), and a vector — and hands all three back at query time. That’s exactly memista’s chunk shape.
Why it fits
- A chunk is
{ embedding, text, metadata }. Insert them, andsearchreturns the ranked text and metadata directly — no second lookup. - Metadata is a string you control (typically JSON), stored and returned verbatim, so you can carry source ids, document offsets, or tags.
- Isolation is per
database_id, so one memista instance can back several corpora, each its own two files.
Where it stops
memista is experimental and tested under ~100k chunks. For a personal or per-project RAG corpus that’s ample; for a multi-tenant SaaS indexing millions of chunks, reach for a distributed store. Compare memista vs pgvector if your metadata already lives in Postgres.
Related: run a nearest-neighbour search, how it works.
Frequently Asked Questions
Does memista store the chunk text, or just the vector?
Both. The text and a metadata string live in SQLite; the vector lives in USearch. Search returns the text and metadata, not just an id.