Combine the existing FTS5/BM25 keyword search with vector similarity search to support natural language app queries. Scoring handles the fallback naturally.
Architecture
Query
│
├──► FTS5 (BM25) ── keyword/trigram matching
│
├──► sqlite-vec (cosine) ── semantic similarity
│
└──► Hybrid merge
│
score = α × norm(bm25) + (1 - α) × cosine_sim
│
▼
Ranked results
Components
Embedding model
all-MiniLM-L6-v2 — 384-dimensional vectors, ~80 MB.
Run via ONNX Runtime in-process or as a local HTTP embedding server.
Vector table (sqlite-vec)
CREATE VIRTUAL TABLE IF NOT EXISTS apps_vec USING vec0(
id TEXT PRIMARY KEY,
embedding float[384]
);Loaded as a SQLite extension alongside FTS5.
Embedding pipeline
On every KindApp (32267) insert, concatenate name + summary + content,
compute the embedding, and upsert into apps_vec.
A one-time migration job embeds all existing apps.
Hybrid query
WITH fts_results AS (
SELECT fts.id, bm25(apps_fts, 0, 20, 5, 1) AS score
FROM apps_fts fts
WHERE apps_fts MATCH ?
LIMIT 50
),
vec_results AS (
SELECT id, distance
FROM apps_vec
WHERE embedding MATCH ?
ORDER BY distance
LIMIT 50
)
SELECT COALESCE(f.id, v.id) AS id,
COALESCE(f.score, 0) * :alpha + COALESCE(1.0 - v.distance, 0) * :beta AS combined
FROM fts_results f
FULL OUTER JOIN vec_results v ON f.id = v.id
ORDER BY combined DESC
LIMIT ?;Existing tag filters (#f, #t, authors, date range) apply as additional
WHERE clauses on a final JOIN to the events table, same as today.
Tuning
α controls the BM25/vector balance. Start at 0.4 and tune empirically.
- Short keyword queries (e.g.
"signal") — BM25 dominates via exact match.
- Natural language queries (e.g.
"privacy focused messenger") — cosine
similarity dominates when exact words don't appear in metadata.
Combine the existing FTS5/BM25 keyword search with vector similarity search to support natural language app queries. Scoring handles the fallback naturally.
Architecture
Components
Embedding model
all-MiniLM-L6-v2— 384-dimensional vectors, ~80 MB.Run via ONNX Runtime in-process or as a local HTTP embedding server.
Vector table (sqlite-vec)
CREATE VIRTUAL TABLE IF NOT EXISTS apps_vec USING vec0( id TEXT PRIMARY KEY, embedding float[384] );Loaded as a SQLite extension alongside FTS5.
Embedding pipeline
On every KindApp (32267) insert, concatenate
name + summary + content,compute the embedding, and upsert into
apps_vec.A one-time migration job embeds all existing apps.
Hybrid query
Existing tag filters (
#f,#t, authors, date range) apply as additionalWHERE clauses on a final JOIN to the
eventstable, same as today.Tuning
αcontrols the BM25/vector balance. Start at 0.4 and tune empirically."signal") — BM25 dominates via exact match."privacy focused messenger") — cosinesimilarity dominates when exact words don't appear in metadata.