CORRELATION_REVIEW_0907b.md measured that radio housekeeping (unit
check-ins, roll call, 10-8/10-98 clearings) is being promoted to
incidents. Every case reads corr_llm_action=orphan, corr_rules_action=new,
corr_consensus=tiebreak -> new: the cheap LLM correctly reads "not an
incident", the rules engine says `new` only because there is no incident to
link to, and the smart tiebreaker then sides with rules ~21/21. Reframing
the tiebreaker prompt (#116) did nothing. The fix is a consensus-logic gate,
not another prompt.
Fix 1 (routers/upload.py) - LLM-orphan gate in _correlate_with_consensus:
when the cheap LLM says `orphan` and the rules engine says `new` with NO
positive event signal, resolve to `orphan` and skip the tiebreak call
entirely. "No positive signal" = the rules corr_debug carries neither a
positive corr_path (unit-continuity / location / fast/disambig / fast/single)
nor a positive corr_fit_signal (unit_overlap / location_proximity). When it
does carry one, the existing escalation-to-tiebreak is kept so a genuine
event the LLM misreads as orphan still gets the second look. The resolved
outcome records corr_consensus="llm_orphan_gate" (greppable, distinct from
"tiebreak") and keeps corr_llm_reasoning / corr_rules_action /
corr_llm_action populated.
Fix 2 (incident_correlator.py) - tighten corr_path=location: the location
path linked on a bare sub-location_proximity_km (0.5 km) distance with no
unit or content check, which stitched a vehicle lockout to a station-restroom
slip and merged two different churches an hour apart. A location link now
requires unit overlap with the candidate OR a distance under a tighter bar
(_LOCATION_TIGHT_PROXIMITY_KM = 0.2 km). Pursuit incidents keep their
movement-speed-validated wide radius. A surviving location link now also
writes corr_fit_signal (unit_overlap | location_proximity), consistent with
Fix 1's positive-signal set.
Tests: new tests/test_consensus_gate.py (13 cases) - the gate resolves to
orphan without calling tiebreak on a no-signal disagreement; a unit_overlap /
location_proximity / unit-continuity / fast-disambig rules signal still
escalates; llm=link vs rules=new still escalates; the location path drops a
shared-area candidate with neither unit overlap nor tight proximity, links on
unit overlap, and links on tight proximity alone. Full c2-core suite
309 -> 322 passing.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Tbknwttzou4s46PAykmtix
The last leg of the #80/#95 scene-context leak. llm_correlator._call_block
read call_doc's whole-call transcript for every scene, so on a multi-scene
call every scene's cheap-tier and tiebreaker decision was made against text
that also contained the other scenes.
- intelligence.py: each processed[] scene now carries its own "transcript" —
transcript_corrected, else this scene's segments joined, else (single scene)
the whole transcript.
- _build_context / preview_correlation / correlate_call: take a `transcript`
param; _build_context resolves ctx["scene_transcript"] from it, falling
back to the call doc (sweep, single-scene, tests) — the fallback is kept
here, unlike embedding/severity, because a scene always has real text.
- upload.py: both scene loops pass scene["transcript"].
- llm_correlator._call_block: reads ctx["scene_transcript"] (call-doc
fallback retained for test-built ctx).
- recorrelation_sweep: passes the call doc's text explicitly.
- +1 regression test. Full c2-core suite green (296 passed, sandboxed venv).
NOT for merge until the running correlation measurement window closes and its
dump is analysed — deploying a correlator change mid-window would mix old and
new behaviour in the sample.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
intelligence.py writes only the primary scene's embedding and severity to
calls/{id}. _build_context read them back off the call doc, so every
non-primary scene of a multi-scene call was correlated against scene 1's
semantic vector and severity rung: a scene about a different event scored
on the embedding path against the wrong incident, and could inherit a
minor/moderate/major severity it never had, clearing the creation gate on
borrowed weight. Same defect and same fix as the #87 coords leak.
- _build_context / preview_correlation / correlate_call: take embedding and
severity as params; drop the call_doc.get() fallbacks. A scene that
passes none has none, and is judged thin on its own signal.
- upload.py: both scene loops pass scene["embedding"] / scene["severity"];
_correlate_with_consensus forwards them. The no-scene unclassified branch
passes neither (correct: no scene, judged thin).
- recorrelation_sweep: passes the call doc's stored values explicitly
(whole-call re-link, link-only, so a borrowed severity cannot create).
- intelligence.py: SCENE DETECTION prompt tightened toward one scene
(server-26#5, partial) - MULTIPLE only for genuinely separate events,
"when unsure, one scene", plus a not-a-new-scene list.
- test_incident_identity.py: +2 regression tests mirroring the #87 test.
Full c2-core suite green (295 passed). #5 prompt change is unmeasured -
needs a scoped correlation-only window. Known remaining legs, tracked
separately: llm_correlator._call_block still reads the whole-call
transcript per scene; content-divergence veto skips on a None embedding.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
config/ai_features was not the switch it was documented to be. Three paths
spent money with it off, and one path read it wrong, so per-system opt-outs
did not opt anything out.
- Correlation in the ingest pipeline tested the raw global flag instead of the
per-system resolution. With a system opted out, extraction was skipped but
the no-scenes fallback still correlated the call with empty tags, taking the
thin/recency path and attaching it to whatever incident was most recent on
that system. The opt-out did not disable correlation, it disabled good
correlation and left the worst kind running. (#75)
- Transcript correction ran on every transcribed call gated only by an env var,
spending Gemini tokens and a Places lookup per proposed location. An
"STT-only" window was never STT-only and its cost could not be attributed.
Now behind transcript_correction_enabled. (#76)
- _run_extraction_pipeline and the vocabulary learner, both reachable from
PATCH /calls/{id}/transcript, checked no flags at all. (#76, #81)
The flag resolver now lives in feature_flags.resolve_flags() rather than as a
local helper in upload.py. Three copies of that logic is how #75 happened.
PATCH /calls/{id}/transcript now refuses with 409 when correlation is off.
That route wipes tags, severity, location, units, embedding and unlinks the
call from every incident before queueing re-extraction. Gating extraction
alone would have made it destructive-only in the standing flags-off
configuration: the call left blank and orphaned forever, with the route still
answering 200. The wipe and the rebuild are one transaction in intent, so it
refuses before the first write.
Also: the summarizer's stale-incident sweep is no longer behind
summaries_enabled. It is pure Firestore with no model call in it, and gating
it meant nothing auto-resolved while AI was off - so every incident stayed
active forever and the candidate set every correlation reads kept growing.
transcript_correction_enabled is documented as NOT a pure cost lever. The
corrector is also the noise gate that sets not_speech; with it off, recogniser
noise reaches extraction as a real transcript, comes back thin, and
auto-attaches. Never open an evaluation window with correction off and
correlation on.
14 tests added covering flag precedence, both pipeline paths, the 409, the
correction gate and the summarizer no-op. Suite: 264 passed.
Refs #75, #76, #81, #45.
Correction existed, but as a line in intelligence.py's EXTRACTION_PROMPT --
which put it in the wrong place twice over. The same model call that extracted
units, location and severity emitted the correction afterwards, so extraction
reasoned over text already known to be wrong; and it sat behind
correlation_enabled, so during a cost-controlled STT-only window nothing was
ever corrected at all. That is the normal state during development.
internal/transcript_correction.py is now its own pass, between the degenerate
filter and the Firestore write. It receives an already-produced transcript plus
a reference list, so unlike a Whisper prompt it has no series to extend -- the
distinction that keeps vocabulary out of the recogniser's prompt, where an
enumerated ten-code list once made it hallucinate ten-code runs.
Reference data is merged from the talkgroup and the system, TALKGROUP FIRST. A
system spanning several counties can have a talkgroup covering one
municipality, and that municipality's streets must not be buried under a
county-wide list. A single-municipality system is the degenerate case: populate
the system level and every talkgroup inherits it. Area context is now SET --
municipality, county, roads, landmarks, on both scopes -- rather than guessed
from talkgroup names, which is what vocabulary_learner did and which is close
to useless across multiple counties.
Segments are corrected too, not just the joined text. extract_scenes builds its
prompt from numbered segments whenever there is more than one, so a correction
that only fixed the transcript would have been discarded on exactly the
multi-transmission calls carrying the most content. Alignment is enforced: an
array of the wrong length or type is dropped whole, because scenes map back to
transmissions by index and a shifted array would misattribute audio silently.
Whisper is also retried once on degenerate output. Call e49ea32c produced a
56-word ten-code counting run on one attempt and ordinary speech on the next --
same clip, same temperature=0 -- so a hallucination is a coin-flip, and
discarding on the first bad roll threw away a recoverable transcript.
Two things found on the way:
PUT /systems/{id} wiped ten_codes on every save. The systems form sends only
{name, type, config}, and model_dump() wrote every omitted field as its default
over the top. Now exclude_unset. area_context would have been the next victim,
which is why it gets its own route alongside ten-codes rather than a field on
that payload.
Closes server-26#36.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
84 of the 100 incidents in the 2026-08-23 dump were titled "Ems — TGID 9048"
or "Other — TGID 9600" -- the fallback, not a description. The title is the
incident's name everywhere it appears: list rows, map pins, Discord alerts.
_create_incident builds it from a content tag and a talkgroup label, and the
label was collapsing to "TGID {id}" because talkgroup_name arrived as None.
It is a plain form field on /upload, forwarded untouched into correlation, and
the node only sends it when OP25 had the name in its loaded tags file -- which
is exactly the case C2 can cover from its own systems collection, where all 125
talkgroup definitions live.
The lookup already existed, on the other path: mqtt_handler resolved it from
the system config on call_start. So the call document held the right name while
the pipeline that titles the incident ignored it. That asymmetry is the bug.
internal/talkgroups.py is now the one implementation -- caller's hint, then the
call document, then the system config -- and both paths use it.
_run_intelligence_pipeline resolves once at the funnel /upload and
/calls/{id}/reprocess share, so the dispatch-channel test, scene extraction and
the title all see a real name. When the call document was the thing missing it,
the resolved name is written back, so the archive and the orphan panel stop
showing a bare TGID too.
Also gives fast/thin a corr_fit_signal. It is 63% of all links and was the only
path writing none, so corr_fit_signal was absent on 295 of 309 calls and the
admin debug view's distribution panel read empty -- looking broken when it was
faithfully reporting that the dominant path records nothing. It now says
thin_recency, which is what actually decided it.
Closes server-26#34. Refs server-26#35 -- the tier's 3.5% invocation rate is a
cost/benefit question, not a bug, and stays open.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
/upload compared the per-node API key with a plain !=, which short-circuits on
the first differing byte and so leaks a little information about how much of a
guess was correct.
The reason to fix it is less the timing channel itself -- an HTTP round trip is
noisy -- than the inconsistency: enrollment.py and dynsec.py both went out of
their way to use secrets.compare_digest for the same class of credential, so the
codebase contradicted itself on whether this mattered. Now it does not.
Also coalesces a missing api_key field to "" so compare_digest is never handed
None, which would raise TypeError and turn a malformed node_keys document into a
500 instead of a 401.
Closeslogan/server-26#12
#17: severity was written once at _create_incident and never touched again,
so an incident that opened routine and escalated to a working fire stayed
routine forever. _update_incident now merges call_severity into the incident
via _max_severity() on every link.
Severity is monotonic: it only ever rises, never falls. An incident briefly
assessed "major" genuinely was major at that moment; a later, calmer-sounding
call is evidence the situation is winding down, not that the earlier read was
wrong. status/resolved_at exist to retire an incident — severity should stay
as the high-water mark so the worst-first rail, "Major only" filter, and map
colouring never bury a call that was genuinely major. See _max_severity's
docstring in incident_correlator.py for the full argument.
#18: none of the resolution sites wrote resolved_at, so an incident's
lifespan couldn't be reconstructed for the history-scrub feature. Added
resolved_at alongside status="resolved" at all six sites that flip it:
- incident_correlator.py _update_incident (signal-based: units all cleared)
- incident_correlator.py maybe_resolve_parent (master auto-resolve)
- summarizer.py _stale_sweep (90-minute auto-resolve)
- upload.py, both scene-resolution loops (single- and multi-scene)
- calls.py reprocess/correction path
(_update_incident's signal-resolve and maybe_resolve_parent's master-resolve
weren't named in the issue's four call sites, but they set status the same
way and were missing resolved_at too.)
No backfill: existing resolved incidents keep resolved_at = null, which
means "resolved before this field existed," not "never resolved." Backfilling
from updated_at would be a guess dressed up as data.
Tests: added to tests/test_correlator_gate.py, which needs no Firestore for
the pure _max_severity cases and patches fstore for the _update_incident/
maybe_resolve_parent writes. Covers the escalation case (routine -> major),
the no-downgrade case, and resolved_at on both the signal-resolve and
master-resolve paths. 52/52 passing in that file; 83 passed / 10
pre-existing failures for drb-c2-core overall (baseline was 69/10 — the
+14 is exactly the new tests, no regressions).
Fixes#17, #18.
The previous commit shipped Firestore rules that reference an org_id claim
nothing issues yet, and an org_id filter nothing writes yet - this is the
commit that makes both real. Backend half of SAAS_PLAN.md B2/B2b/B2c.
Data model: organizations/{org_id} and org_members/{uid} are new
collections (models.py OrganizationRecord/OrgMember). org_id is now an
Optional field on NodeRecord, SystemRecord, CallRecord, IncidentRecord,
AlertRule, and AlertEvent - optional because every existing document
predates it; scripts/backfill_org_id.py (written, not run - it touches
production Firestore and Firebase Auth claims) is what closes that gap
later. plan_id/subscription_status/stripe_* on OrganizationRecord are
deliberately None: no billing or pricing model has been decided, so this is
a seam, not a promise. app/internal/tenancy.py holds FOUNDING_ORG_ID, the
org every pre-tenancy document and every legacy enrollment path resolves
into.
Where org_id comes from, end to end: a customer's node enrolls with a
per-org token (new enrollment_tokens/{token_hash} collection, minted via
POST /org/enrollment-tokens - new routers/org.py) instead of the old
fleet-wide ENROLLMENT_TOKEN, which still works as a fallback that resolves
to FOUNDING_ORG_ID so an already-deployed node's .env doesn't start failing
today. The node's org_id then flows onto every call it produces
(mqtt_handler.py's call_start/call_end, upload.py's /upload handler all
resolve it from the node doc), and onto every incident correlated from
those calls (incident_correlator.py's _create_incident/_create_master_incident).
That last one is the part that isn't just a read filter: _build_context's
`all_active = collection_list("incidents", status="active")` fed every
correlation candidate - fast-path talkgroup match, unit-continuity,
disambiguation - from the entire incidents collection, unscoped. Without
scoping it to the call's own org_id, a call from org A could link into an
incident org B already owns, which is a cross-tenant data merge at
correlation time, not just an over-broad read. Same shape of bug in
alerter.py: rule matching pulled every enabled alert_rule regardless of
org, so org A's keyword rule could fire (and POST org A's Discord webhook)
on org B's radio traffic. Both now resolve org_id from the call doc itself
rather than threading a new parameter through every caller.
Every list/get route gained org scoping via a new resolve_caller_org_id()
helper in internal/auth.py, which handles the three credential shapes those
routes accept (service key, node api_key, Firebase user) uniformly and
returns None (unrestricted) for the service key and platform admins -
preserving today's single-org behaviour exactly while closing the leak for
everyone else: GET /nodes, /systems, /calls, /incidents, /alerts,
/alert-rules. Write routes for nodes/systems (approve, create, delete, etc.)
deliberately stay platform-admin-only for now rather than being loosened to
org-owner/operator - that's a real gap called out in SAAS_PLAN.md 2.4's
"should be" column, but it's a separate authorization redesign the 12-item
build order doesn't actually enumerate, and doing it half-considered here
risked being exactly the "half-applied filter is worse than none" failure
mode the plan warns about. Today's founding org keeps working unchanged;
loosening node/system management to org owners is follow-up work, flagged
rather than guessed at.
Also closed the four spend/access-attack routes SAAS_PLAN.md B2c called out
by file and line: POST /calls/{id}/reprocess is now admin-only (was any
signed-in viewer looping the Whisper+Gemini pipeline for free - DEFERRED.md
had this as a live, independent-of-SaaS exploit) plus a per-call rate
limiter as a second guard; POST /alerts/{id}/acknowledge now checks the
alert's org_id; GET /admin/features moved from require_firebase_token to
require_admin_token; and trips.py's four unauthenticated mutation routes
(create_trip, update_trip_tags, create_event, update_event) are now
restricted to the founding org (or the bot's service key, or a platform
admin) - trips has no org_id of its own and isn't getting one, since
[[trips-feature-intentional]] says it's an internal utility riding along on
this stack, not a tenant-scoped product surface.
New public-but-scoped seam: POST /auth/signup (routers/links.py, alongside
the existing /auth/link* routes) provisions an organizations doc and an
owner org_members doc for a just-created Firebase user, then sets their
org_id/org_role claims - idempotent, so a double-submit doesn't create two
orgs. This is the only route that turns "has a Firebase account" into "can
read anything," which is what the frontend AuthProvider no-claim guard
(next commit) is built around.
Also new: GET/PATCH /org for the organization profile (closes the disabled
"Save changes" button noted in DEFERRED.md - there was no organizations
concept to save into before this), and POST /waitlist (public, source-IP
rate-limited, not coupled to any plan or tier - the commercial model is
still an open decision per SAAS_PLAN.md section 6).
Verified: all touched files py_compile clean; c2-core pytest is 69
passed / 10 failed, matching the documented pre-existing baseline exactly
(DEFERRED.md - mqtt_handler/node_sweeper test-vs-code drift, unrelated to
this change) - no new failures. flake8 --max-line-length=120 shows no new
violations in any touched file (checked each new E501/E221/E30x against
`git diff` to confirm it predates this commit); c2-core has no CI lint gate
regardless (CLAUDE.md - flake8 only runs in Client CI).
No new environment variables. Firestore composite indexes for the queries
this introduces were already shipped in the previous commit
(infra/firestore/firestore.indexes.json).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two independent sources of garbage in the AI pipeline, both visible in the
2026-08-16 correlation dump.
1. Hallucinated transcripts. The Whisper prompt opened with an enumerated run
of ten-codes: 10-4, 10-23, 10-20, 10-97 and so on. Whisper treats prompt
text as preceding transcript, so on noisy or silent audio it continued the
series, emitting transcripts that count upward from 10-4 to 10-99. The
existing no_speech_prob filter could not catch these: the model is highly
confident in text it invented by continuing a pattern.
The prompt no longer contains a series to extend, and _is_degenerate()
rejects the three shapes this failure takes: ascending ten-code runs, one
phrase looping, and near-identical segments across a whole recording.
Verified against 13 transcripts from production: all four known
hallucinations rejected, all nine real ones kept, including terse traffic
containing legitimate codes.
2. Duplicate recordings. node-002 and node-PI-2 both cover TG 9048 and both
uploaded the same transmissions, ~1.1s apart. Nine pairs appeared in one
dump. Each was transcribed, billed and correlated twice, and the resulting
incident listed two units where there was one.
Canonical selection is by earliest started_at, tie-broken on call_id, NOT
by upload order: upload order varies with encode time and network latency,
so it would make the authoritative recording non-deterministic. Call
documents are created from MQTT call_start before uploads arrive, so both
nodes independently reach the same verdict. The loser keeps its audio (it
may be the cleaner capture) but is excluded from STT, correlation, the
re-correlation sweep and the orphan debug view.
Also fixes _sync_transcribe returning a bare None when OPENAI_API_KEY is
missing, where the caller unpacks two values. A missing key surfaced as a
misleading "Transcription failed" instead of the real warning.
Adds tests/test_dedup.py (15 cases). dedup.py reaches Firestore through an
injected callable so it stays importable without firebase-admin present.
upload_audio() could only sign a URL when GCP_CREDENTIALS_PATH pointed at a
service-account key file. The deployed VM runs on Application Default
Credentials with no key file, so every upload silently took the fallback
branch and returned a bare gs:// URI. That broke two things at once:
* Browsers cannot fetch a gs:// URI, so no recording was ever playable.
* _public_url_to_gcs_uri() only matched https://storage.googleapis.com/ and
returned None for it, so `if gcs_uri:` in the upload path was always false
and transcription never ran. Nothing was logged, which is why this looked
like an OpenAI credits problem rather than a storage one.
The fallback also interpolated the client-supplied filename instead of the
call_id-derived safe name, so the URI did not even name the object written.
Calls now store only the canonical gs:// location. A short-lived playback link
is minted per read as an HMAC over (call_id, expiry) keyed by SERVICE_KEY, and
audio is served from the private bucket by the new /media route. An <audio src>
cannot carry an Authorization header, so the link has to be the credential;
that router is therefore public with the check done inline, as enrollment.py
already does. Signing GCS URLs from the VM would have needed a
serviceAccountTokenCreator grant on its own service account — this avoids the
IAM change entirely and keeps the bucket private.
gcs_uri_for_call() reconstructs the object name from call_id, so recordings
made before this fix are reachable again without a data migration.
Frontend rows come straight from Firestore via onSnapshot and never see a
server-minted field, so CallRow fetches the link lazily on expand.
Also removes the last long-lived (1 year) signed URL and the log line that
printed it.
auth.py
secrets.compare_digest replaces == for service key comparison (timing-safe)
Added require_service_key — bot-only endpoints (trip/event join/leave)
Added require_service_key_or_admin — node commands/config (bot via service key OR dashboard admin via Firebase)
Added _RateLimiter with three shared instances: trip_chat_limiter (20/5min per user), summarize_limiter (5/10min per incident), bootstrap_limiter (2/hr per system)
nodes.py
send_command and assign_system now require require_service_key_or_admin — the Discord bot can still call them via service key, but regular Firebase users are blocked
tokens.py
add_token, flush_tokens, set_preferred_system, delete_token all require require_admin_token
Token masking changed from token[:10] + "…" + token[-4:] to "•••" + token[-4:]
systems.py
All write endpoints (create, update, delete, ai-flags, ten-codes, vocabulary writes, bootstrap) now require require_admin_token
bootstrap_vocabulary also calls bootstrap_limiter.check(system_id)
incidents.py
POST /incidents/summarize (bulk) now requires require_admin_token
POST /incidents/{id}/summarize now calls summarize_limiter.check(incident_id)
trips.py
join_trip, leave_trip, join_event, leave_event require require_service_key — only the Discord bot can set Discord attendee identity
delete_trip, delete_event require require_service_key_or_admin
trip_chat rate-limited per caller UID, history stripped to user/assistant roles only, user message truncated to 2000 chars, Maps query strings capped at 200 chars
upload.py
Rejects files larger than settings.upload_max_bytes (default 100MB) with 413
storage.py
_safe_audio_filename() derives GCS object name from call_id + allowlisted extension, completely ignoring the client-supplied filename
config.py
Added upload_max_bytes: int = 100 * 1024 * 1024
Both Dockerfiles — python:3.14-slim → python:3.12-slim
Refactor incident_correlator.py to a decision/commit split (preview_correlation
/ apply_correlation) so the rules engine and LLM can both produce decisions before
anything is written to Firestore.
Add llm_correlator.py: cheap Gemini Flash first-pass + Gemini Pro tiebreaker.
Wire _correlate_with_consensus in upload.py — rules-only fallback when key is
absent or call is thin; agreed/tiebreak consensus written to corr_debug.
- Geocoding: reject GEOMETRIC_CENTER/APPROXIMATE results — vague location strings
(regions, city centroids) were resolving to node-area coords and creating false
proximity matches that merged unrelated incidents
- Thin path: on dispatch channels with multiple active incidents, skip attachment
rather than guessing — "10-4" with 3 active incidents is genuinely ambiguous
- Short transcripts (≤5 words) now write skip_reason="transcript_too_short" to
the call doc, matching garbage transcript behavior
- upload.py no-scenes fallback now checks skip_reason before running correlation —
flagged calls (garbage, too short) no longer attach via thin path
- Update Server README to reflect current project purpose, goals, and pipeline
- Cap unit-continuity path at 20 min idle (unit_continuity_max_idle_minutes)
- Block time_fallback and unit-continuity matching on reassignment calls
- Expand reassignment detection to cover unit-initiated self-reassignment
- Skip GPT extraction entirely for transcripts ≤5 words (prevents hallucinated tags/units)
- Reduce geocode_max_km from 75 to 40 to reject far-out-of-area results
- Include county in geocoding query for tighter jurisdiction anchoring
Correlator
- Raise fast-path idle gate 30 → 90 min (tg_fast_path_idle_minutes)
- Fix disambiguate always-commits bug: run _call_fits_incident on winner
before committing; fall through to new-incident creation if it fails
- Add unit-continuity path (path 1.5): matches all_active by shared unit
IDs with a reassignment guard, bridges calls past the idle gate
- Add tag-based incident_type inference (_TAG_TYPE_HINTS) as GPT fallback,
rescuing tagged calls that would have been dropped (616 observed orphans)
- Add master/child incident model: _create_master_incident, _demote_to_child,
_add_child_to_master; new incidents stamped incident_type="master"
- Add cross-system parent detection (_find_cross_system_parent): two-signal
scoring (road overlap=0.4, embedding≥0.78=0.3, proximity=0.3, threshold=0.5)
wired into create-if-new path; creates master shell on first cross-system match
- Add maybe_resolve_parent: auto-resolves master when all children close;
called from upload pipeline (LLM closure) and summarizer stale sweep
- Add signal-based auto-resolve via units_active/units_cleared tracking:
GPT now extracts cleared_units per scene; _update_incident moves units
between active/cleared lists and resolves the incident when active empties;
stored on call doc for re-correlation sweep reuse
- Add _create_incident initialization of units_active/units_cleared fields
Re-correlation sweep
- Add corr_sweep_count + MAX_SWEEP_ATTEMPTS=3: orphans get 3 attempts
then are tombstoned as corr_path="unlinked", ending the re-sweep loop
(previously hammering each orphan 29-31 times per shift)
Intelligence extraction
- Add cleared_units to GPT prompt schema and rules
- Extract and propagate cleared_units per scene; merge across scenes;
store on call doc for re-correlation sweep
Token management
- Fix token release bug: remove release_token call on discord_connected=False
in MQTT checkin (transient Discord drops were orphaning bots mid-shift)
- Add PUT /tokens/{id}/prefer/{system_id} endpoint: lock a bot token to a
system; pass _none as system_id to clear; stored bidirectionally on both
token and system documents
- discord_join handler resolves preferred_token_id from system doc and passes
system_name in MQTT payload
### 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.
- *`correlate_call`* — added units and vehicles optional params; when provided (per-scene from intelligence extraction), they take priority over the merged call-document values, preventing multi-scene unit contamination
- *Cross-TGID correlation path (2.5)* — *new path between location and slow paths*: when a call shares 2+ unit IDs with a recent same-system, same-type incident AND embedding similarity ≥ 0.85, it links them — catches multi-talkgroup pursuits like the bicycle search that split across dispatch/tactical/geographic channels
# `app/internal/intelligence.py`
- *`reassignment` field* — added to the GPT-4o-mini prompt schema and rules; `true` when dispatch is actively pulling a unit to a new, different call (not a status update or en route acknowledgement); returned in every processed scene dict
- *Tag location rule* — added explicit instruction to the prompt: tags must describe what happened, not where; place names, road names, and talkgroup names are explicitly forbidden as tags
# `app/routers/upload.py`
- Both scene correlation call sites (`_run_extraction_pipeline` and `_run_intelligence_pipeline`) now pass `units=corr_units` where `corr_units = [] if scene.get("reassignment") else scene.get("units") `— suppresses unit overlap matching when a unit is being reassigned to a new call, preventing chaining into their previous incident
- Both sites also pass `vehicles=scene.get("vehicles")` (per-scene vehicles, from the multi-scene units fix)
# `app/config.py`
- `embedding_cross_tg_threshold: float = 0.85` — threshold for the new cross-TGID path
incident_correlator.py — full rewrite: always runs on every call, fetches all active incidents cross-type, fast path collects all talkgroup matches and disambiguates by unit/vehicle overlap → location proximity → embedding, new location proximity path, slow path requires location corroboration, "Auto:" stripped from titles, "auto-generated" tag added, units/vehicles now accumulated on update
intelligence.py — resolved field in GPT schema, returned as 5th value
upload.py — both pipelines unpack 5-tuple, always call correlate, auto-resolve on resolved=True
summarizer.py — stale sweep runs each tick, resolves incidents idle for 90+ minutes
config.py — correlation_window_hours=2, embedding_similarity_threshold=0.93, location_proximity_km=0.5, incident_auto_resolve_minutes=90
Upload 404 warning doc_set(merge=True) in upload.py — creates doc if missing
MQTT call_end 404 error doc_set(merge=True) in mqtt_handler.py — same root cause
Transcription 404 (saving transcript to nonexistent doc) doc_set(merge=True) in transcription.py
Transcription ADC credentials error Explicit service_account.Credentials from gcp-key.json in _sync_transcribe — same pattern as storage.py
Logan CusanoandClaude Sonnet 5