Vectors, AI & ML
AetheriusDB serves vector search and AI workloads from the same engine that runs your SQL — no separate vector store, no model-serving sidecar to keep in sync. This section covers the stable foundation (native vector columns and kNN search) and the AI features layered on top, each with an honest availability badge.
Why it matters
Section titled “Why it matters”Most teams run their vectors in a second system — a dedicated vector store beside the transactional database — and pay for it in sync jobs, double storage, and queries that can’t combine a filter with a similarity search. AetheriusDB stores embeddings as native columns and searches them in the same SQL, so a metadata filter and a nearest-neighbour ranking are one query, over one copy of the data.
- One store, no sync — embeddings live next to the rows they describe; there’s no separate vector database to keep in step.
- Filter and search together — pre-filter candidates with ordinary SQL predicates, then rank by vector distance, in a single statement.
- No model-serving sidecar — the AI features run in-process against your data.
How fast it is
Section titled “How fast it is”- Indexed kNN, not a full scan. HNSW / IVFFLAT indexes answer k-nearest-neighbour queries quickly, and the SQL pre-filter shrinks the candidate set before the distance math runs.
- Inline distances in plain SQL. Cosine / L2 ranking over a native
TENSOR(N)column runs in the query engine — no export to an external service and back.
Start here
Section titled “Start here”The stable foundation is two features you can use today:
- Tensor & Vector Types — declare native
fixed-dimension
TENSOR(N)columns, store embeddings inline, and rank rows by cosine or L2 distance in plain SQL. - Vector Search (kNN) — build HNSW or IVFFLAT indexes and run fast k-nearest-neighbour queries that pre-filter with ordinary SQL predicates.
The full section
Section titled “The full section”| Page | Availability | What it covers |
|---|---|---|
| Tensor & Vector Types | Stable | Native TENSOR(N) columns and distance functions. |
| Vector Search (kNN) | Stable | HNSW / IVFFLAT indexes and kNN queries. |
| Semantic NLP & Text Encoding | Beta | How text columns are encoded for faster joins and scans. |
| AI Quality Constraints | Beta | AI_CHECK anomaly detection at ingest with a dead-letter table. |
| AI Legacy Transmuter | Roadmap | Migrate a legacy schema into a queryable staging branch. |
| Multimodal Fabric | Roadmap | Auto-extract embeddings, transcripts, and entities from media. |
| Cognitive Kernel | Roadmap | Ask questions in natural language, in-process. |