// HACKER NEWS — CYBERSECURITY
Show HN: LatticeDB – Like SQLite but for graph databases
Embedded property-graph database with native vector and full-text indexing.
LatticeDB is a single-file local database for connected, semantic, and textual data. It lets you traverse relationships, run vector similarity search, and do BM25 full-text search over the same dataset in one engine and one query layer. It is designed for relationship-heavy workloads on a single machine, with zero-config operation and an embedded single-writer model.
LatticeDB is an embedded, single-file graph database that lets local applications query the same data by relationship, semantics, and text, then consume durable graph and application events from the same file. Workloads like Graph RAG, agent memory, and local knowledge tools are examples built on those primitives, not the definition of the engine.
Published wheels are expected to bundle liblattice on supported platforms. Source installs can also bundle a staged native library during wheel builds with LATTICE_BUNDLE_LIB_DIR=/path/to/lib.
Published package tarballs are expected to bundle liblattice on supported platforms. Source checkouts can stage the native library into the package with LATTICE_BUNDLE_LIB_DIR=/path/to/lib npm run bundle:native.
See bindings/go/README.md for the current cgo workflow. The default consumer path uses installed pkg-config metadata; in-repo development can use -tags repolocal against zig-out/lib.
There is also a runnable graph/vector/text retrieval example in examples/go.
Recent binding-surface cleanups moved embedding helpers into dedicated modules and subpackages. See docs/client_api_migration.md for the preferred imports and current compatibility aliases.
A complete example: create a small knowledge graph with documents and authors, store embeddings, index text, then query across all three search modes.
Benchmarked on Apple M1, single-threaded, with auto-scaled buffer pool. Run zig build benchmark to reproduce.
For the repeated-term FTS indexing workload that previously exposed quadratic append behavior, run zig build fts-benchmark.
128-dimensional cosine vectors, M=16, ef_construction=200, ef_search=64, k=10. Run zig build vector-benchmark to reproduce.