LatticeDB

Embedded property graph with native vector, full-text, and durable event primitives.

LatticeDB is a single-file local database for connected, semantic, and textual data. It lets applications traverse relationships, run HNSW vector similarity, run BM25 full-text search, and consume durable graph/application events from the same file.

  • One file. Your entire database is a single portable file. No server, no configuration.
  • One query layer. Graph traversal, HNSW vector similarity, and BM25 full-text — in one query.
  • One event log. Durable named streams and the built-in graph changefeed share the same transaction/WAL path as graph writes.
  • Sub-millisecond. 0.13 us node lookups. 0.83 ms vector search at 1M vectors with 100% recall.
-- Find chunks similar to a query, traverse to their document, then to the author
MATCH (chunk:Chunk)-[:PART_OF]->(doc:Document)-[:AUTHORED_BY]->(author:Person)
WHERE chunk.embedding <=> $query_vector < 0.3
  AND doc.content @@ "neural networks"
RETURN doc.title, chunk.text, author.name
ORDER BY chunk.embedding <=> $query_vector
LIMIT 10

Features

Graph

  • Nodes and edges with labels and arbitrary properties
  • Multi-hop traversal, variable-length paths (*1..3)
  • ACID transactions with snapshot isolation
  • MERGE, WITH, UNWIND, aggregations (count, sum, avg, min, max, collect)

Vector Search

  • HNSW approximate nearest neighbor with configurable M, ef
  • Built-in hash embeddings or HTTP client for Ollama/OpenAI
  • Batch insert for bulk loading

Full-Text Search

  • BM25-ranked inverted index with tokenization and stemming
  • Fuzzy search with configurable Levenshtein distance

Cypher Query Language

  • MATCH, WHERE, RETURN, CREATE, DELETE, SET, REMOVE
  • ORDER BY, LIMIT, SKIP, DETACH DELETE
  • Vector distance operator: <=>
  • Full-text search operator: @@
  • Parameters: $name

Operations

  • Single-file storage with write-ahead log for crash recovery
  • Durable streams and semantic graph changefeeds
  • Zero configuration — open a file and start working
  • Clean C API; Python, TypeScript, and Go bindings wrap it

Use Cases

  • RAG Systems — Vector search finds relevant chunks, graph traversal gathers context
  • Knowledge Graphs — Linked notes and documents with semantic search
  • AI Agents — Persistent memory with relationship awareness
  • Local Development — Lightweight alternative to Neo4j or Weaviate for prototyping

Getting Started

Head to the Installation page to install LatticeDB, then follow the Quick Start guide to build your first knowledge graph.