Author SHA1 Message Date
logan 731b54bed9 Merge pull request 'clearance: act on drb-correlation-review of f0a88d4' (#175) from fix/clearance-review into main
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2026-09-26 17:26:41 -04:00
Logan CusanoandClaude Opus 5.5 3c642e2946 clearance: act on drb-correlation-review of f0a88d4
Review of the first clearance fix found it pushes toward early resolve:
- parser cleared units on questions ("45-9, are you clear?"), negations
  ("not clear yet"), orders ("clear the scene", "clear to transport"),
  places/times ("Room 2 clear", "1400 hours, clear") and split "45 9" into
  unit 45. Now rejects "?", not/is/are/you, anything after the status word
  but sign-offs, place/time words; joins "45 9" -> "45-9".
- a clear from a unit never active on an incident was recorded in
  units_cleared and could pass the all-clear gate. Only units actually
  active there can clear there now.
- clearance-only calls skip the LLM tier (same as thin calls): only the
  rules engine's unit match can say which incident a 10-8 belongs to.
- replay incident view carries srcaddr/srcaddrs for the radio-ID clearance
  investigation.

Replay 09-22 10:00-12:00 ET with f0a88d4: real clears 0 -> 2 (both LLM
closure), unit clears still 0 — the parsed clears are right but those
units were never recorded as assigned (Whisper mangles unit IDs at
dispatch), which this commit does not fix.

c2-core: 465 pass.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-09-26 17:26:38 -04:00
logan f0a88d401c Merge pull request 'correlator/intelligence: let 10-8s actually close incidents' (#174) from fix/clearance-signal into main
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2026-09-26 16:49:33 -04:00
Logan CusanoandClaude Opus 5.5 8eac32caf5 correlator/intelligence: let 10-8s actually close incidents
Replay of 09-22 10:00-12:00 ET (server-26#170): 0 of 19 incidents resolved
on a clear, 19 on the idle timer, although 25 transmissions said 10-8/clear.
Three independent breaks:

1. Short clears never reached extraction. "45-9, I'm clear." is <=5 words,
   so extract_scenes skipped it before GPT and cleared_units stayed empty.
   A rule parser now names the unit when it precedes the status word
   (never guesses: "10-8, 10-8." / "CMT clear." clear nobody) and returns a
   minimal scene that links by unit overlap but cannot open an incident.
2. Clearance compared unit strings exactly, so "11-Adam" clearing never
   removed "11 Adam". Now by _normalize_unit key.
3. units_active collected "Desk", "Central", "Division", "unknown", plate
   phonetics — none of which ever clear, so all-clear could never pass.
   Only units carrying a number (and not a ten-code) are tracked now; the
   rest stay in `units` for matching.

c2-core: 463 pass.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-09-26 16:49:30 -04:00
logan 6e82ee8579 Merge pull request 'correlation: smart tiebreak model gemini-2.5-pro is gone; use gemini-3.8-flash' (#173) from fix/smart-model into main
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2026-09-26 16:07:56 -04:00
Logan CusanoandClaude Opus 5.5 cdc61dcc9d correlation: smart tiebreak model gemini-2.5-pro is gone; use gemini-3.8-flash
The first replay (server-26#170) aborted on 5x 'model is unavailable'
from the tiebreak tier. Google's model list shows 2.5 closed to new
projects and no stable Pro model; 3.8-flash is the newest stable.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-09-26 16:07:53 -04:00
logan 032e9bd653 Merge pull request 'Replay: fail fast on a dead AI account; extraction reports to ai_health' (#172) from fix/replay-visibility into main
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2026-09-26 15:39:04 -04:00
logan 8dadbdd977 Merge pull request 'Admin Replay: re-run the pipeline over past calls in a sandbox' (#171) from feat/replay into main
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2026-09-26 15:24:37 -04:00
7 changed files with 206 additions and 10 deletions
+4 -1
View File
@@ -45,7 +45,10 @@ class Settings(BaseSettings):
# while correlation behaviour was being tuned against rules-only output.
# Verify against https://ai.google.dev/gemini-api/docs/models before changing.
corr_cheap_model: str = "gemini-3.6-flash" # was gemini-2.0-flash (shut down)
corr_smart_model: str = "gemini-2.5-pro" # was gemini-1.5-pro (shut down)
# gemini-2.5-pro was closed to new projects by 2026-09 (every tiebreak 404'd
# in the first replay run, server-26#170); Google lists no stable Pro model,
# so the smart tier is the newest stable Flash instead.
corr_smart_model: str = "gemini-3.8-flash" # was gemini-2.5-pro, gemini-1.5-pro
# Transcript correction (server-26#36). Runs inside transcription, once per
# transcribed call above MIN_WORDS_FOR_CORRECTION, so it is priced like STT
# rather than like the correlation tier — cheap model on purpose.
@@ -224,6 +224,28 @@ def _normalize_unit(unit: str) -> str:
return key or unit.strip().lower()
def _is_trackable_unit(unit: str) -> bool:
"""
Whether a unit is concrete enough to hold an incident open until it clears.
Extraction lists everything that sounds like a unit — "Desk", "Central",
"Division", "sergeant", "unknown", and plate phonetics ("John Henry
Zebra"). None of those ever transmit a 10-8, so while they sat in
units_active the all-clear gate below could never pass: in the first
replay (server-26#170, 09-22 10:00-12:00) 0 of 19 incidents resolved on
a clear and every one had such a name in units_active. A real radio unit
ID carries a number ("45-9", "11-Adam 2", "Whitestone 1", "E-14"), so
only those gate resolution. The others are still kept in `units` and
still match for correlation.
"""
if _TEN_CODE_RE.match((unit or "").strip()):
return False # "10-8" read back as a unit ID is the status, not a unit
return any(ch.isdigit() for ch in unit or "")
_TEN_CODE_RE = re.compile(r"^10[\s-]?\d{1,2}$")
def _unit_keys(units: Optional[list[str]]) -> set[str]:
"""Comparison keys for a unit list, empties dropped."""
return {k for k in (_normalize_unit(u) for u in (units or [])) if k}
@@ -1910,11 +1932,19 @@ def _apply_unit_clearance(inc: dict, cleared: list[str]) -> tuple[list[str], lis
"""
units_active = list(inc.get("units_active") or [])
units_cleared = list(inc.get("units_cleared") or [])
for u in cleared:
if u in units_active:
units_active.remove(u)
if u not in units_cleared:
# Compared by normalised key: the unit that cleared as "11-Adam" is the
# one that went active as "11 Adam", and exact equality left it active.
# Only a unit that was actually active here can clear here — a clear from
# a unit never on this incident says nothing about whether it is over, and
# counting it let one stray 10-8 close an incident still being worked.
cleared_keys = _unit_keys(cleared)
releasing = [u for u in units_active if _normalize_unit(u) in cleared_keys]
units_active = [u for u in units_active if _normalize_unit(u) not in cleared_keys]
known_cleared = _unit_keys(units_cleared)
for u in releasing:
if _normalize_unit(u) not in known_cleared:
units_cleared.append(u)
known_cleared.add(_normalize_unit(u))
auto_resolved = bool(units_cleared) and not units_active
return units_active, units_cleared, auto_resolved
@@ -2009,9 +2039,11 @@ async def _update_incident(
# units_active = units currently on scene; units_cleared = units back in service
units_active = list(inc.get("units_active") or [])
units_cleared = list(inc.get("units_cleared") or [])
tracked = _unit_keys(units_active) | _unit_keys(units_cleared)
for u in call_units:
if u not in units_cleared and u not in units_active:
if _is_trackable_unit(u) and _normalize_unit(u) not in tracked:
units_active.append(u)
tracked.add(_normalize_unit(u))
inc_with_active_update = {**inc, "units_active": units_active, "units_cleared": units_cleared}
units_active, units_cleared, _ = _apply_unit_clearance(inc_with_active_update, cleared_units or [])
@@ -2143,7 +2175,7 @@ async def _create_incident(
"system_ids": [system_id] if system_id else [],
"tags": tags + ["auto-generated"],
"units": call_units,
"units_active": list(call_units),
"units_active": [u for u in call_units if _is_trackable_unit(u)],
"units_cleared": [],
"vehicles": call_vehicles,
"srcaddrs": [call_srcaddr] if call_srcaddr else [],
+76 -2
View File
@@ -247,18 +247,26 @@ async def extract_scenes(
f"Intelligence: call {call_id} — transcript too short for extraction "
f"({len(transcript.split())} words), skipping"
)
cleared_unit = _short_clearance_unit(transcript)
try:
# Severity is still recorded: a five-word acknowledgement is genuinely
# routine traffic, and downstream code treats a missing severity as
# "not yet processed" rather than "nothing happened".
await fstore.doc_set("calls", call_id, {
updates = {
"skip_reason": "transcript_too_short",
"severity": "routine",
"chatter_classifier_verdict": chatter_is_chatter,
"chatter_classifier_reason": chatter_reason,
})
}
if cleared_unit:
updates["units"] = [cleared_unit]
updates["cleared_units"] = [cleared_unit]
await fstore.doc_set("calls", call_id, updates)
except Exception:
pass
if cleared_unit:
logger.info(f"Intelligence: call {call_id} — short clearance from {cleared_unit!r}")
return [_clearance_scene(transcript, cleared_unit)]
return []
try:
@@ -469,6 +477,72 @@ async def extract_scenes(
return processed
# "45-9, I'm clear." / "Vehicle 1, clear." / "Car 12 10-8" — a unit reporting
# itself back in service is the one signal that ends an incident, and it is
# almost always five words or fewer, which is exactly the population the
# too-short skip above keeps away from GPT. In the first replay
# (server-26#170, 09-22 10:00-12:00 ET) 25 transmissions said 10-8/clear and
# 2 reached cleared_units. Rule-based on purpose: no model call, and only a
# unit named BEFORE the status word counts, so "10-8, 10-8." or "CMT clear."
# (no number) clears nobody rather than guessing.
_CLEAR_WORD_RE = re.compile(
r"\b(clear|10-?8|10-?98|back in service|in service|available)\b", re.IGNORECASE
)
_TEN_CODE_TOKEN_RE = re.compile(r"^10-?\d{1,2}$")
_UNIT_PREFIX_WORDS = {"unit", "car", "vehicle", "engine", "ladder", "medic", "rescue", "post", "truck", "squad"}
# A number after one of these is a place or a time, not a radio unit.
_NOT_UNIT_PREFIX_WORDS = {"room", "route", "rt", "exit", "pole", "apartment", "apt", "floor",
"building", "hours", "hour", "block", "lane", "highway", "interstate"}
# The status word has to END the transmission: "clear the scene", "clear to
# transport", "available for" are orders or plans, not a unit back in service.
_TRAILING_OK = {"10-4", "thanks", "thank", "you", "k", "over", "now", "again", "from", "headquarters", "hq",
"central", "dispatch"}
def _short_clearance_unit(transcript: str) -> Optional[str]:
text = (transcript or "").strip()
if not text or "?" in text:
return None # "45-9, are you clear?" asks; it doesn't report
m = _CLEAR_WORD_RE.search(text)
if not m:
return None
before = [t.strip(".,;:!") for t in text[: m.start()].split()]
before = [t for t in before if t]
after = [t.strip(".,;:!").lower() for t in text[m.end():].split()]
if any(t and t not in _TRAILING_OK for t in after):
return None
if any(t.lower() in {"not", "is", "are", "negative", "you"} for t in before):
return None # "not clear yet", "Is 45-9 clear", "you clear"
for i, tok in enumerate(before[:4]):
if not any(ch.isdigit() for ch in tok) or _TEN_CODE_TOKEN_RE.match(tok):
continue
prev = before[i - 1].lower() if i else ""
if prev in _NOT_UNIT_PREFIX_WORDS:
return None
nxt = before[i + 1] if i + 1 < len(before) else ""
if nxt.lower() in _NOT_UNIT_PREFIX_WORDS:
return None # "1400 hours, clear"
if nxt.isdigit():
tok = f"{tok}-{nxt}" # "45 9 clear" is unit 45-9, not unit 45
elif nxt.isalpha() and nxt[0].isupper() and nxt.lower() not in {"i'm", "im", "we're", "copy"}:
tok = f"{tok} {nxt}" # "11 Adam, clear"
if prev in _UNIT_PREFIX_WORDS:
return f"{before[i - 1]} {tok}"
return tok
return None
def _clearance_scene(transcript: str, unit: str) -> dict:
"""A minimal scene for a rule-parsed clearance: the unit, and nothing that
could make the incident-creation gate open a new incident for it."""
return {
"tags": [], "incident_type": None, "location": None, "location_coords": None,
"resolved": False, "severity": "routine", "vehicles": [], "units": [unit],
"cleared_units": [unit], "reassignment": False, "transcript": transcript,
"transcript_corrected": None, "segment_indices": [], "embedding": None,
}
def _geo_dist_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
"""Haversine distance in km between two lat/lon points."""
R = 6371.0
@@ -275,6 +275,12 @@ async def decide(call_id: str, ctx: dict) -> Optional[dict]:
if ctx["is_thin_call"]:
return None # thin calls have no transcript/units/coords to reason about
if _is_clearance_only(ctx):
# "45-9, I'm clear." carries one fact: which unit is done. Only the
# rules engine's unit match can say which incident that is; an LLM
# link here would apply the clear to whatever incident it picked.
return None
if not ctx["recent"]:
return None # no incidents to correlate against — rules handles new-only
@@ -295,6 +301,14 @@ async def decide(call_id: str, ctx: dict) -> Optional[dict]:
return None
def _is_clearance_only(ctx: dict) -> bool:
units = ctx.get("call_units") or []
cleared = ctx.get("call_cleared") or []
return bool(cleared) and set(units) <= set(cleared) and not (
ctx.get("tags") or ctx.get("location") or ctx.get("call_vehicles") or ctx.get("incident_type")
)
_dead_models: set[str] = set()
+2
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@@ -140,6 +140,7 @@ def _call_row(c: dict) -> dict:
"cleared_units": c.get("cleared_units"),
"location": c.get("location"),
"skip_reason": c.get("skip_reason"),
"srcaddr": c.get("srcaddr"),
"corr_path": [p for p in paths if p] or ([c["corr_path"]] if c.get("corr_path") else []),
"incident_ids": c.get("incident_ids") or [],
}
@@ -174,6 +175,7 @@ async def run_incidents(run_id: str, decoded: dict = Depends(require_admin_token
"units_active": inc.get("units_active"),
"units_cleared": inc.get("units_cleared"),
"talkgroup_ids": inc.get("talkgroup_ids"),
"srcaddrs": inc.get("srcaddrs"),
"calls": rows,
})
orphans = sorted((r for r in by_id.values() if not r["incident_ids"]),
@@ -0,0 +1,68 @@
"""
Dispatch→10-8 lifecycle, as measured by the first replay (server-26#170):
0 of 19 incidents resolved on a clear although 25 transmissions said one.
Three independent breaks, each pinned here.
"""
from app.internal import incident_correlator as ic
from app.internal.intelligence import _clearance_scene, _short_clearance_unit
def test_short_clearance_names_the_unit_that_cleared():
assert _short_clearance_unit("45-9, I'm clear.") == "45-9"
assert _short_clearance_unit("Vehicle 1, clear.") == "Vehicle 1"
assert _short_clearance_unit("11 Adam, clear") == "11 Adam"
assert _short_clearance_unit("Car 12 10-8") == "Car 12"
assert _short_clearance_unit("45 9 clear") == "45-9"
assert _short_clearance_unit("Warrant 4, clear from the jail") is None or True # >5 words: GPT's job
assert _short_clearance_unit("45-9 clear, thank you") == "45-9"
def test_short_clearance_never_guesses():
for t in ("10-8, 10-8.", "CMT clear.", "10-8, I'm back now. Clear.",
"10-8, thank you.", "Show us 10-8, post 4.", "7, Charlie Central.", "10-4.",
# review findings: questions, negations, orders, places, times
"45-9, are you clear?", "Is 45-9 clear", "45-9, not clear yet.",
"Engine 5 not available.", "45-9, clear the scene.", "Medic 3, clear to transport.",
"Room 2 clear.", "Route 9 is clear.", "1400 hours, clear.", "you clear 45-9"):
assert _short_clearance_unit(t) is None, t
def test_clearance_scene_cannot_open_an_incident():
scene = _clearance_scene("45-9, I'm clear.", "45-9")
ctx = {"call_vehicles": scene["vehicles"], "coords": scene["location_coords"], "tags": scene["tags"]}
assert not ic.has_event_substance(ctx)
assert scene["severity"] == "routine" and scene["incident_type"] is None
def test_clearance_matches_a_differently_spoken_unit():
inc = {"units_active": ["11 Adam", "45-9"], "units_cleared": []}
active, cleared, resolved = ic._apply_unit_clearance(inc, ["11-Adam"])
assert active == ["45-9"]
active, cleared, resolved = ic._apply_unit_clearance(
{"units_active": active, "units_cleared": cleared}, ["45 9"])
assert active == [] and resolved
def test_a_unit_never_on_the_incident_cannot_close_it():
inc = {"units_active": ["45-9"], "units_cleared": []}
active, cleared, resolved = ic._apply_unit_clearance(inc, ["22-1"])
assert active == ["45-9"] and cleared == [] and not resolved
# an incident with no numbered unit ever active never resolves on a clear
active, cleared, resolved = ic._apply_unit_clearance({"units_active": [], "units_cleared": []}, ["22-1"])
assert not resolved
def test_clearance_only_call_skips_the_llm():
from app.internal import llm_correlator
ctx = {"call_units": ["45-9"], "call_cleared": ["45-9"], "tags": [], "location": None,
"call_vehicles": [], "incident_type": None}
assert llm_correlator._is_clearance_only(ctx)
assert not llm_correlator._is_clearance_only({**ctx, "tags": ["mva"]})
assert not llm_correlator._is_clearance_only({**ctx, "call_cleared": []})
def test_only_numbered_units_hold_an_incident_open():
for junk in ("Desk", "Central", "Division", "sergeant", "unknown", "John", "Zebra", "10-8", "10 4"):
assert not ic._is_trackable_unit(junk), junk
for real in ("45-9", "11-Adam", "Whitestone 1", "E-14", "Highway 3-4", "7"):
assert ic._is_trackable_unit(real), real
@@ -67,7 +67,10 @@ def test_clearing_a_unit_not_tracked_as_active_is_a_noop_for_active_list():
inc = _incident(units_active=["6-7"], units_cleared=[])
active, cleared, resolved = _apply_unit_clearance(inc, ["ghost-unit"])
assert active == ["6-7"]
assert cleared == ["ghost-unit"]
# A unit never active on this incident is not recorded as cleared here
# either (server-26#170 replay review): its 10-8 says nothing about this
# incident, and recording it let the all-clear gate pass on a stray clear.
assert cleared == []
assert resolved is False