Implements the optional vec backend from the #000039 doc, with the
"obvious" v1 tuning, and demonstrates it on a real corpus shard.
arborist/search/vec.py (new):
- VecBackend(SearchBackend) — ANN over chunk_vecs, UNGROUNDED hits
(same as FTS5; vec changes recall, never warrant — embeddings are
soft signal, never in the proof path).
- chunk_vecs vec0 virtual table + vec_meta — sibling tables, additive,
don't touch chunks/documents/the audit chain.
- embed_documents() — batched ingest; delete-then-insert per chunk_id
(vec0 doesn't honor INSERT-OR-REPLACE — re-inserting an existing PK
is a hard UNIQUE error), so re-runs are idempotent and content-
changed → re-embed works. Skips cold-evicted chunks (content NULL).
- Pluggable Embedder callable; default = fastembed bge-small-en-v1.5
(~130 MB ONNX, downloads on first use). load_vec_extension(conn)
toggles enable_load_extension + sqlite_vec.load.
- v1 hyperparams (VEC_BACKEND_VERSION = vec-v1-bge-small-en-v1.5-
384float32-cosine-flat): model bge-small-en-v1.5, dim 384, quant
float32 (int8/binary = the production storage knob per §3.1, not
wired in v1), metric cosine (bge outputs L2-normalized, so cosine
ranking ≡ L2 ranking), ANN flat (vec0 default), top_k 20. These
five fold into governance_policy_hash in a later phase (§6).
CLI (arborist/cli.py):
- — populate chunk_vecs
for --db; prints progress + timing.
- — semantic ANN search (errors with
an install/embed hint if [vec] missing or chunk_vecs empty).
- Both surfaced only when sqlite_vec imports (mirrors the [html] /
selectolax pattern).
pyproject.toml: [vec] optional extra (sqlite-vec>=0.1.9, fastembed>=0.4);
added to [dev]. Note: sentence-transformers is the heavier "official"
embedder path §5 names; fastembed is the lightweight ONNX one.
tests/test_search_vec.py (7 tests, skip-if-no-[vec]): deterministic
stub embedder (hash → unit vector) so the suite exercises the
sqlite-vec plumbing — ext load, schema, ingest, KNN, JOIN, Hit shape,
limit, idempotent re-embed, --limit cap, empty/unpopulated — without
the heavy fastembed model. Semantic quality is demonstrated on a
shard, not unit-tested.
Demonstrated on ~/.arborist/shards/crawl_appliedcombinatorics_org.db:
168 chunks embedded in ~37 s (mostly model load); semantic queries
return topically-correct hits — "how many ways to choose k things
from n" → top hit "AC Combinations", "binomial coefficient counting"
→ "AC Introduction" (integer-solution counting) + "AC Combinatorial
Proofs". None of the query tokens need stem-match the chunk — the
semantic-allusion-gap closure the ticket promised. chain-check on
that shard reports 0 after embedding (chunk_vecs is a sibling table).
#000039 status flipped to "in progress · Phase 1 landed"; Phase 2
(RRF hybrid fusion in query.py) gated on a ≥5pp recall-lift
measurement with no STRICT-rate regression (§8).
(Unrelated: tests/test_weights.py::test_as_dict_returns_all_eleven_fields
fails in the working tree — that's a parallel-clone in-flight change
to arborist/substrate/weights.py + its test, not touched here.)
russell@unturf.com
38d9116c88