Search decoded the vector blob into a []float32 and JSON-unmarshalled the
meta map for every row, then sorted all N and threw away everything past
topK. Meta only ever matters for a survivor, and the sort answered a
question a bounded heap answers cheaper.
The scan still visits every row — that is what picks the winners. What it
no longer does is allocate for a row it is about to discard. dotBlob reads
the vector out of its stored bytes, so scoring costs nothing; a row is
copied and its meta unmarshalled only once it has entered the topK.
At 10000 rows and topK 10: 70.6ms to 26.8ms, 58MB to 17.5MB, 240k allocs
to 60k.
Recall is unchanged where it is measured. recall+onnx scores 22/32 with
recall@1 70.4% and recall@3 85.2%, identical to before.
TestMemoryStoreSearchMatchesNaive pins the ranking against the full-sort
implementation it replaced, and TestDotBlobMatchesDot pins bit-identical
scores, which the 0.008 gate margin demands.
One behaviour did move: ties. sort.Slice is not stable, so equal scores
were ordered arbitrarily; the heap now keeps the earliest. Under the real
embedder an exact tie is a duplicate vector and nothing moved. Under the
hash embedder the eval's floor uses, everything ties at 0 and that run's
recall@3 went 74.1% to 81.5% — a number that measures tie order, not
retrieval. recall@1 and false recall, the two the eval asserts, are
unchanged on both runs.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01YMNNEkYx1mZFtHNrFk7uqb
allMemVectorMetas (memory.go) replaces the identical query-then-scan
block ReembedAll and RepairFactVectors each had for reading id+meta
out of memory_vectors — same query, same json.Unmarshal, different
structs built from the result.
Two RowsAffected() errors were silently dropped with `_`, inconsistent
with every other call site in the same files: AcceptProposedRoutine
now wraps the error instead of treating it as zero rows, and MarkAcked
had it stranded behind a dead branch (both arms returned nil) removed
along with the swallowed error.
No behavior change; internal/store and internal/memory pass with
-race.
Revert voided the fact row and left the vector, so recall kept serving the
voided fact's utterance and the documented repair reported success on a box that
stayed broken. There was no way to repair a poisoned box at all.
DeletePrefix covers every vector for the key, earlier rows included: their values
are superseded, and a superseded value has no business claiming a turn. It is
best-effort — the audit trail is already committed, and a fact that is voided but
still recallable beats a void that failed.
Speaker profiles share the vector table with notes and facts. The doc
comment said reading them through Catalog is what keeps recall from
ranking a voiceprint. It is not. Catalog controls how speaker code reads
its own rows and says nothing about Search, which scanned every row.
What actually hid them was cosine returning 0 on a width mismatch, so a
192-dim ECAPA row scored 0 against a 384-dim query. Some x-vector
exports are 384-dim, and one of those would have surfaced speaker:kami
as a recall hit carrying the name of a person.
Both backends now skip the prefix in Search, and the prefix is one
constant in internal/memory so the store layer can filter on it without
importing internal/speaker.
Two more differences between the backends closed here. ByPrefix on the
in-memory store returned the stored metadata map by reference, so a
caller editing a returned Record edited the row, while the persistent
one unmarshals fresh. And the append to upsert change in Insert is a fix
in its own right, not only a speaker concern: any re-indexed id used to
leave a second stale copy searchable.
Found in review of #74.
Maven can now be told who someone is. She cannot yet tell who is speaking,
and this commit is careful to say so rather than pretend otherwise.
What works: profiles are enrolled from several deliberately recorded samples,
listed, and deleted. They live in the existing memory_vectors table under a
"speaker:" id prefix, so there is no migration; what that needed was a wider
interface than memory.Store, hence memory.Catalog with ByPrefix and Delete.
Delete is the load-bearing half — a voiceprint someone asked to be rid of has
to actually go, and a search-only store cannot do that. InMemoryStore.Insert
became an upsert by id to match what the persistent store already did.
What does not work, and why it is not faked: there is no speaker-embedding
model on this box. Sixteen ggufs in /mnt/hdd1/llms, all text; no ECAPA, no
x-vector, no titanet, no wespeaker, no .onnx anywhere under /mnt/hdd1. So
newSpeakerEmbedder returns nil, internal/speaker falls back to
speaker.Disabled, Identify answers ErrDisabled, and the daemon logs which
half is off at startup. The plan's "simple MFCC + GMM" floor is refused in
the package comment: MFCC cosine distance detects channel and loudness as
much as voice, and a biometric that is confidently wrong writes false claims
about named people into his memory. A bad floor is worse than none here.
Refused as well, and the reason is in enroll.go's doc comment: the plan asked
for unknown speakers to be enrolled on first interaction with a TTS "кто
это?". There is no request shape in the protocol that could express that.
Taking a biometric of whoever walks past the microphone does it to guests who
are not party to the exchange, and a synthesised question into a room is not
consent from whoever answers.
Authority: enrolment is AuthStepUp, because it is a deliberate sit-down act
that writes a biometric of a named person and never something done by voice
mid-conversation. Deletion is one rung lower at AuthWrite, deliberately
inverting the usual pattern — getting rid of a biometric must never be the
harder half. Listing is AuthRead and never returns the vectors themselves.
Off unless configured: no speaker block means the three methods answer
ErrUnknownMethod, so a default box has no wire path that takes a voiceprint.
make build and make test pass.
Vikunja #255
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TrVSBKe3RFDF4fGYKWYQnX
Two additive proactive/recall features.
Routines (internal/routine): a third proactive class beside reminders
(user-stated) and care rules (world-state) — operator-declared clockwork.
config.routines[] (cron + literal RU body + severity) fire through the
normal dispatcher on schedule. Bodies are literal, not LLM-phrased (can't
hallucinate); rule name routine:<name> keeps them out of the care
autotuner; a cold-start guard seeds on first sight so a restart never
replays a missed schedule. Pure routine.Due + config validation, unit-
tested; the tick driver holds the last-fired map and calls fireRoutines.
Persistent memory (internal/store/memory.go): store.MemoryStore backs the
memory.Store interface with the SAME encrypted sqlite db — survives
restarts and recall text inherits at-rest encryption (no plaintext
sidecar). float32-blob vectors, brute-force cosine (ANN is a later swap
behind the interface), upsert-by-id. The daemon wires st.VectorMemory()
into wireVoice; the in-memory impl stays the test/no-store floor. Closes
the "in-memory only, lost on restart" gap (PROGRESS #8).
Gate green: gofmt/vet clean, -race across routine/config/store/mavend.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01U2PNdwDj2Gt8YW294J7oSc
claude
kami