Multimodal database for SQL, graph, and vector search

AionDB is a multimodal (multi-model) database in Rust. Relational data, graph relationships, and vector embeddings live in one engine.

The point is to stop copying the same application data into a SQL database, a graph database, and a vector database whenever a query needs all three.

Why a multimodal database?

Many applications need several data shapes at once:

tables for users, documents, tickets, products, events, business state;
graph edges for connections between those records;
vectors for semantic search, recommendations, RAG, AI assistants.

AionDB exposes the three through one PostgreSQL-compatible engine. Tables remain the source of truth. Graph and vector queries operate over the same rows.

Hybrid queries

AionDB targets queries that combine SQL filtering, graph traversal, and vector similarity:

MATCH (u:User)-[:WROTE]->(d:Document)-[:CITES]->(ref:Document)
WHERE d.kind = 'runbook'
RETURN d.title,
       ref.title,
       l2_distance(d.embedding, '[0.1,0.8,0.2]') AS distance
ORDER BY distance ASC
LIMIT 5;

Useful for knowledge bases, product catalogs, support tooling, private copilots, and any application that needs structured business context alongside semantic retrieval.

PostgreSQL-compatible tooling

AionDB speaks the PostgreSQL wire protocol so psql, pgAdmin, migrations, ORMs, and standard drivers keep working when the features they touch are supported.

Project status

AionDB is alpha software. It does not replace PostgreSQL in production today. The narrower claim: it gives teams an engine for evaluating a multimodal database that combines SQL, graph, and vector search without running three separate services next to each other.

Start with the documentation and the reproducible benchmarks.