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Updates to intel and correlation

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Logan
2026-04-23 01:26:41 -04:00
parent bcd3406ae8
commit 317f9d2a9d
12 changed files with 468 additions and 150 deletions
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drb-c2-core/app/routers/upload.py
+75 -41
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@@ -96,35 +96,47 @@ async def _run_extraction_pipeline(
"""Run steps 2-4 of the intelligence pipeline using an existing transcript."""
from app.internal import intelligence, incident_correlator, alerter
tags, incident_type, location, location_coords, resolved = await intelligence.extract_tags(
# Step 2: Scene detection + intelligence extraction.
# Returns one scene per distinct incident detected in the recording.
scenes = await intelligence.extract_scenes(
call_id, transcript, talkgroup_name,
talkgroup_id=talkgroup_id, system_id=system_id, segments=segments,
node_id=node_id,
preserve_transcript_correction=preserve_transcript_correction,
)
incident_id = await incident_correlator.correlate_call(
call_id=call_id,
node_id=node_id,
system_id=system_id,
talkgroup_id=talkgroup_id,
talkgroup_name=talkgroup_name,
tags=tags,
incident_type=incident_type,
location=location,
location_coords=location_coords,
)
# Step 3: Correlate each scene to an incident independently.
incident_ids: list[str] = []
all_tags: list[str] = []
for scene in scenes:
all_tags.extend(scene["tags"])
incident_id = await incident_correlator.correlate_call(
call_id=call_id,
node_id=node_id,
system_id=system_id,
talkgroup_id=talkgroup_id,
talkgroup_name=talkgroup_name,
tags=scene["tags"],
incident_type=scene["incident_type"],
location=scene["location"],
location_coords=scene["location_coords"],
)
if incident_id and incident_id not in incident_ids:
incident_ids.append(incident_id)
if scene["resolved"] and incident_id:
await fstore.doc_set("incidents", incident_id, {"status": "resolved"})
logger.info(f"Auto-resolved incident {incident_id} (LLM closure detection)")
if resolved and incident_id:
await fstore.doc_set("incidents", incident_id, {"status": "resolved"})
logger.info(f"Auto-resolved incident {incident_id} (LLM closure detection)")
if incident_ids:
await fstore.doc_set("calls", call_id, {"incident_ids": incident_ids})
# Step 4: Alert dispatch — run once with merged tags from all scenes.
await alerter.check_and_dispatch(
call_id=call_id,
node_id=node_id,
talkgroup_id=talkgroup_id,
talkgroup_name=talkgroup_name,
tags=tags,
tags=list(dict.fromkeys(all_tags)),
transcript=transcript,
)
@@ -140,8 +152,8 @@ async def _run_intelligence_pipeline(
"""
Post-upload intelligence pipeline (runs as a background task):
1. Transcribe audio via Google STT
2. Extract tags/incident type from transcript
3. Correlate with existing incidents (or create new one)
2. Detect scenes + extract intelligence (one result per incident in recording)
3. Correlate each scene with existing incidents (or create new ones)
4. Check alert rules and dispatch notifications
"""
from app.internal import transcription, intelligence, incident_correlator, alerter
@@ -155,35 +167,57 @@ async def _run_intelligence_pipeline(
call_id, gcs_uri, talkgroup_name, system_id=system_id
)
# Step 2: Intelligence extraction
tags: list[str] = []
incident_type: Optional[str] = None
location: Optional[str] = None
location_coords: Optional[dict] = None
resolved: bool = False
# Step 2: Scene detection + intelligence extraction
scenes: list[dict] = []
if transcript:
tags, incident_type, location, location_coords, resolved = await intelligence.extract_tags(
scenes = await intelligence.extract_scenes(
call_id, transcript, talkgroup_name,
talkgroup_id=talkgroup_id, system_id=system_id, segments=segments,
node_id=node_id,
)
# Step 3: Incident correlation (always runs — unclassified calls can still link via talkgroup)
incident_id = await incident_correlator.correlate_call(
call_id=call_id,
node_id=node_id,
system_id=system_id,
talkgroup_id=talkgroup_id,
talkgroup_name=talkgroup_name,
tags=tags,
incident_type=incident_type,
location=location,
location_coords=location_coords,
)
# Step 3: Correlate each scene independently.
# A single recording can produce multiple incidents on a busy channel.
incident_ids: list[str] = []
all_tags: list[str] = []
for scene in scenes:
all_tags.extend(scene["tags"])
incident_id = await incident_correlator.correlate_call(
call_id=call_id,
node_id=node_id,
system_id=system_id,
talkgroup_id=talkgroup_id,
talkgroup_name=talkgroup_name,
tags=scene["tags"],
incident_type=scene["incident_type"],
location=scene["location"],
location_coords=scene["location_coords"],
)
if incident_id and incident_id not in incident_ids:
incident_ids.append(incident_id)
if scene["resolved"] and incident_id:
await fstore.doc_set("incidents", incident_id, {"status": "resolved"})
logger.info(f"Auto-resolved incident {incident_id} (LLM closure detection)")
if resolved and incident_id:
await fstore.doc_set("incidents", incident_id, {"status": "resolved"})
logger.info(f"Auto-resolved incident {incident_id} (LLM closure detection)")
# Correlator also runs for calls with no scenes (unclassified) to attempt
# talkgroup-based linking even when no transcript could be produced.
if not scenes:
incident_id = await incident_correlator.correlate_call(
call_id=call_id,
node_id=node_id,
system_id=system_id,
talkgroup_id=talkgroup_id,
talkgroup_name=talkgroup_name,
tags=[],
incident_type=None,
location=None,
location_coords=None,
)
if incident_id:
incident_ids.append(incident_id)
if incident_ids:
await fstore.doc_set("calls", call_id, {"incident_ids": incident_ids})
# Step 4: Alert dispatch (always runs — talkgroup ID rules don't need a transcript)
await alerter.check_and_dispatch(
@@ -191,6 +225,6 @@ async def _run_intelligence_pipeline(
node_id=node_id,
talkgroup_id=talkgroup_id,
talkgroup_name=talkgroup_name,
tags=tags,
tags=list(dict.fromkeys(all_tags)),
transcript=transcript,
)