AI & ML platforms
Retrieval, online features, and semantic search over live data — on one engine that stores vectors next to the rows they describe.
The problem
Why this is hard today
AI platforms accrete stores: a vector database for embeddings, a feature store for online features, a warehouse for training data, and the system of record underneath. Every boundary is a sync job, and every sync job is a freshness gap the model pays for at inference time.
The gaps compound: retrieval answers from a stale corpus, features are read from a snapshot that predates the current request, and semantic search indexes lag the data they describe.
A single engine with first-class vector(N) columns, HNSW ANN in SQL, keyed online features with read-your-writes, and continuous transforms to keep it all fresh removes the sync layer instead of scheduling it.
Where NYXDB fits
Use-case journeys
RAG retrieval infrastructure
Store embeddings as vector(N), run ANN search that rewrites to VectorTopK, and keep context fresh with transforms.
AI & vector searchOnline feature serving
Keyed tables serve online features with read-your-writes and exact counts — no separate feature store.
Fraud & riskSemantic search over live data
Index embeddings that stay fresh as the underlying data changes, driven by continuous transforms.
AI & vector searchArchitecture
How NYXDB fits AI & ML platforms
Embeddings, source rows, and online features live in one engine; HNSW indexes power ANN search; transforms keep retrieval and feature tables fresh from the stream.
- 01
Embed
vector(N) columns store embeddings next to source data.
- 02
Index
Per-part HNSW (usearch) built at flush; cosine default.
- 03
Serve
VectorTopK ANN search and keyed online features in one SQL surface.
- 04
Refresh
Transforms materialize fresh embeddings and features from the stream.
Real SQL
Representative query
SELECT id, cosine_distance(emb, '[1,0,0,0,0,0,0,0]')FROM emb_tORDER BY cosine_distance(emb, '[1,0,0,0,0,0,0,0]')LIMIT 3;Every statement follows the engine’s own test SQL shapes. See the SQL reference for full syntax.
Capabilities
What you get
First-class vectors
vector(N) columns next to source rows.
HNSW ANN
Per-part index; ORDER BY distance → VectorTopK.
Online features
Keyed tables, read-your-writes.
Always fresh
Transforms keep retrieval + features live.
Proof
Measured where it counts
vector(N) kernel speedup
PR #370 / #131 (kernel)
ANN rewrite (AnnRewriteRule)
ADR-064
query latency class
placeholder — not yet substantiated
▲Figures marked TODO-verify are placeholders pending a published, reproducible benchmark; substantiated numbers cite their source.
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