Full-Text Search

LatticeDB includes a BM25-scored inverted index for full-text search. This guide covers indexing, searching, and fuzzy matching.

How It Works

LatticeDB's full-text search uses:

Tokenization — text is split into terms
Stemming — terms are reduced to their root form
Inverted index — maps terms to the nodes containing them
BM25 scoring — ranks results by relevance considering term frequency, document frequency, and document length

Indexing Text

Index text content on a node within a write transaction:

with db.write() as txn:
    node = txn.create_node(labels=["Document"], properties={"title": "My Doc"})
    txn.fts_index(node.id, "The quick brown fox jumps over the lazy dog")
    txn.commit()

await db.write(async (txn) => {
  const node = await txn.createNode({
    labels: ["Document"],
    properties: { title: "My Doc" },
  });
  await txn.ftsIndex(node.id, "The quick brown fox jumps over the lazy dog");
});

Searching

Programmatic API

results = db.fts_search("quick fox", limit=10)
for r in results:
    print(f"Node {r.node_id}: score={r.score:.4f}")

const results = await db.ftsSearch("quick fox", { limit: 10 });
for (const r of results) {
  console.log(`Node ${r.nodeId}: score=${r.score.toFixed(4)}`);
}

Cypher

MATCH (d:Document)
WHERE d.content @@ "quick fox"
RETURN d.title

Fuzzy Search

Fuzzy search tolerates typos using Levenshtein edit distance:

# Finds "machine learning" despite typos
results = db.fts_search_fuzzy("machin lerning", limit=10)

Controlling Sensitivity

results = db.fts_search_fuzzy(
    "machne",
    limit=10,
    max_distance=2,      # Max edit distance (default: 2)
    min_term_length=4,   # Min term length for fuzzy matching (default: 4)
)

const results = await db.ftsSearchFuzzy("machne", {
  limit: 10,
  maxDistance: 2,
  minTermLength: 4,
});

max_distance — maximum Levenshtein edit distance. Higher values find more matches but may include irrelevant results.
min_term_length — minimum term length to apply fuzzy matching. Short terms (like "a", "the") are matched exactly.

Combining with Vector Search

Use both search modes in a single Cypher query for hybrid retrieval:

MATCH (chunk:Chunk)
WHERE chunk.embedding <=> $query < 0.5
  AND chunk.text @@ "neural networks"
RETURN chunk.text
ORDER BY chunk.embedding <=> $query
LIMIT 10

Performance

Full-text search in LatticeDB is fast:

Operation	Latency
FTS search (100 docs)	19 us

This is ~300x faster than SQLite FTS5 and competitive with Tantivy, a dedicated Rust search library. See Benchmarks for details.

LatticeDB Documentation