No AI client set a timeout: OpenAI's SDK waits 600s per attempt and retries
twice (timeouts included); google-genai waits forever. A hung provider call
had no upper bound, so recorrelation_sweep's 15-minute pipeline hold-back was
a guess against an unbounded tail.
- config: stt_timeout_seconds (120), openai_timeout_seconds (60),
openai_max_retries (1), gemini_timeout_seconds (60).
- app/internal/ai_clients.py builds every OpenAI/AsyncOpenAI client with them
(transcription, extraction, embeddings, summarizer, vocabulary learner,
trip chat). gemini.py's shared client gets HttpOptions(timeout=ms).
- A timeout raises like any other provider error, so existing fallbacks are
unchanged: rules-only correlation, rules decision on tiebreak, raw
transcript on correction, ai_health "transient".
- MIN_MINUTES_SINCE_PIPELINE_START is now derived from those timeouts
(pipeline_worst_case_seconds, 3-scene budget + 2 min non-AI slack), floor
15: 21 min with the defaults. Loop warns if it reaches the scan window.
- tests/test_ai_timeouts.py: client limits, a real hung-socket request that
gives up in <5s, timeout fallback per tier, the derived bound, and a guard
against any new OpenAI( outside ai_clients.py.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
#36 — the correction pass shipped in 58efdbd was right, its reference-data
shape was not. One shape now, at both scopes, every field nullable:
area_context: { municipality?, county?, state?,
center?, radius_km?, resolved_from?, resolved_at?,
local_knowledge?: [{term, meaning}] }
`state` closes the ambiguity that made "Ossining" a national guess.
`local_knowledge` replaces roads[]/landmarks[], which could not hold
intersections, schools or nicknames and carried no meanings — `11-X-ray` is
useless alone, `11-X-ray — MTA PD patrol unit` is what a corrector can act on.
Pre-#36 roads[]/landmarks[] are read forward as bare terms so nothing an
operator already entered is lost.
Nullability is the mechanism: which scope gets filled is the operator's
declaration of how homogeneous the system is. One town — fill it once at system
level. Statewide — leave it blank and fill each talkgroup.
The backend owns the derived anchor. PUT /systems/{id} merges config.talkgroups[]
against what is stored instead of writing the client's blob verbatim, which
would have erased the anchor and the pending queue — the same defect as the
ten_codes wipe.
#37 — Maps as a verifier, not as prompt stuffing. The corrector emits its
location nouns; each is geocoded against the talkgroup's anchor, and on a miss
we look for a sound-alike that does resolve there, correct to it, and propose
{term, meaning} to that talkgroup. Cost scales with location nouns, not calls.
No anchor means SKIP. An area too wide to discriminate stores no anchor at all,
because a statewide radius would confirm anything inside it — verification that
passes everything is worse than none, since it reads as a check in the data.
Also re-anchors _geocode_location, which rejected results >40km from the NODE
(server-26#6). An antenna is not a jurisdiction; distance-from-node was always
a stand-in for the anchor and is now only the fallback.
The induction loop proposes at talkgroup level and never promotes. Blast
radius: a wrong term on a channel misleads that channel, the same term
system-wide misleads one 400km away on a statewide system.
38 new tests; 240 pass. Frontend typechecks clean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
When the induction loop proposes a new vocabulary term, it now records
which sampled call(s) most likely produced the suggestion. Admins see
a collapsible "▶ source" player under each pending term showing the
audio clip and transcript, so they can hear what was actually said
before approving or dismissing.
- vocabulary_learner: track sampled call docs, attach source_call_ids
to each pending term via word-overlap search with fallback
- types: VocabularyPendingTerm.source_call_ids?: string[]
- c2api: add getCall(id) using existing GET /calls/{call_id} endpoint
- VocabularyPanel: SourceCallPlayer component — lazy-loads call on
first expand, shows audio controls + transcript snippet
The loop slept 24h before its first pass, so suggestions would never
appear unless the server was up for a full day. Move the sleep to the
end so the first induction pass runs ~30s after startup.
### Firestore read reductions
**1. `doc_get_cached()` in `firestore.py` — new 5-min TTL cache**
One place, benefits everything. System and node config documents almost never change during a monitoring session.
**2. System doc: 4 reads → 1 per call**
| Before | After |
|---|---|
| `upload.py` — `doc_get("systems")` for ai_flags | `doc_get_cached` |
| `transcription.py` — `get_vocabulary()` → `doc_get("systems")` | cache hit |
| `intelligence.py` — `get_vocabulary()` → `doc_get("systems")` | cache hit |
| `intelligence.py` — `doc_get("systems")` again for ten_codes | eliminated (reads same cached doc) |
**3. Node doc: cached in `_on_call_start` and `intelligence.py`**
The node is read every call event to get `assigned_system_id` and lat/lon for geocoding. Both now use the cache — node assignments and positions essentially never change at runtime.
**4. Node sweeper: 30s → 90s interval**
The sweeper was doing a full node collection scan 3× more often than necessary — the offline threshold is already 90s. Cuts sweeper reads by 66%.
**5. Vocabulary induction: scans all-time calls → last 7 days**
Previously fetched every ended call for a system (could be thousands). Now scoped to the last 7 days.
> **Note:** The vocabulary induction query `(system_id == X, ended_at >= cutoff)` needs a Firestore
> composite index on `(system_id ASC, ended_at ASC)`. When the induction loop first fires it will log
> an error with a Firebase Console link to create it in one click.
Logan CusanoandClaude Opus 5.5