AI Briefing
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HyperspaceDB v3.1.0 Released

·2026.06.23 20:12

Key point

HyperspaceDB, a Spatial AI engine that maximizes memory efficiency by introducing Matryoshka learning and Lorentz geometry, has been released.

Details

HyperspaceDB v3.1.0 has transitioned into a Spatial AI Engine to solve the memory overhead problem of existing vector DBs.

The key technical features are as follows:

  • Schema-Driven MRL (Matryoshka Representation Learning): Instead of loading the entire vector into memory, only a lightweight navigation core (e.g., 129 dimensions) is kept in RAM, while the rest of the data is streamed from NVMe SSD. In tests, based on 100,000 vectors, it uses only about 72MB of RAM, far less than Chroma (>3,000MB) and Milvus (~1,700MB).
  • 801D Hybrid Vectors: Supports hybrid vectors combining Lorentz hyperboloid (33 dimensions) and Euclidean (768 dimensions) for modeling hierarchical structures (legal, medical, etc.).
  • Sidecar Document Storage: Stores original text directly within the index without a separate DB, minimizing filtering overhead through Zstd compression and Typed Metadata.
  • Rust-Based Performance: Delivers stable performance even under a load of 1,000 concurrent clients through a lock-free HNSW structure.
rustragvector-databasematryoshka-embeddingsspatial-aihyperspacedb
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