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big ui and intel updates

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This commit is contained in:
Logan
2026-04-19 16:48:55 -04:00
parent 0df53df92e
commit 303c5b13cf
11 changed files with 527 additions and 169 deletions
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drb-c2-core/app/internal/incident_correlator.py
+10 -4
View File
@@ -44,6 +44,7 @@ async def correlate_call(
tags: list[str],
incident_type: Optional[str],
location: Optional[str] = None,
location_coords: Optional[dict] = None,
) -> Optional[str]:
"""
Link call_id to an existing incident or create a new one.
@@ -113,10 +114,10 @@ async def correlate_call(
# ----------------------------------------------------------------
if matched_incident:
incident_id = matched_incident["incident_id"]
await _update_incident(matched_incident, call_id, talkgroup_id, system_id, tags, location, now)
await _update_incident(matched_incident, call_id, talkgroup_id, system_id, tags, location, location_coords, now)
else:
incident_id = await _create_incident(
call_id, incident_type, talkgroup_id, talkgroup_name, system_id, tags, location, now
call_id, incident_type, talkgroup_id, talkgroup_name, system_id, tags, location, location_coords, now
)
# Back-link the call
@@ -131,6 +132,7 @@ async def _update_incident(
system_id: Optional[str],
tags: list[str],
location: Optional[str],
location_coords: Optional[dict],
now: datetime,
) -> None:
incident_id = inc["incident_id"]
@@ -151,11 +153,11 @@ async def _update_incident(
merged_tags = list(dict.fromkeys(inc.get("tags", []) + tags))
# Location — append to mentions; update display location if new one is non-null
# TODO: replace "newest wins" with Maps geocoding bbox comparison for true specificity
location_mentions = inc.get("location_mentions", [])
if location and location not in location_mentions:
location_mentions.append(location)
best_location = location if location else inc.get("location")
best_coords = location_coords if location_coords else inc.get("location_coords")
# Update centroid embedding
embedding_updates = await _merge_embedding(inc, call_id)
@@ -172,6 +174,8 @@ async def _update_incident(
}
if best_location:
updates["location"] = best_location
if best_coords:
updates["location_coords"] = best_coords
await fstore.doc_set("incidents", incident_id, updates)
logger.info(f"Correlator: linked call {call_id} to existing incident {incident_id}")
@@ -185,10 +189,11 @@ async def _create_incident(
system_id: Optional[str],
tags: list[str],
location: Optional[str],
location_coords: Optional[dict],
now: datetime,
) -> str:
incident_id = str(uuid.uuid4())
tg_label = talkgroup_name or "Unknown Talkgroup"
tg_label = talkgroup_name or (f"TGID {talkgroup_id}" if talkgroup_id else "Unknown Talkgroup")
call_doc = await fstore.doc_get("calls", call_id)
call_embedding = call_doc.get("embedding") if call_doc else None
@@ -202,6 +207,7 @@ async def _create_incident(
"type": incident_type,
"status": "active",
"location": location,
"location_coords": location_coords,
"location_mentions": [location] if location else [],
"call_ids": [call_id],
"talkgroup_ids": [str(talkgroup_id)] if talkgroup_id is not None else [],
drb-c2-core/app/internal/intelligence.py
+76 -24
View File
@@ -1,10 +1,10 @@
"""
Gemini-powered intelligence extraction from call transcripts.
GPT-4o-mini intelligence extraction from call transcripts.
Sends the transcript to Gemini Flash with a tight JSON schema prompt.
Sends the transcript to GPT-4o mini with a tight JSON schema prompt.
Returns structured data: incident type, tags, location, vehicles, units, severity.
Falls back gracefully if Gemini is unavailable or returns malformed output.
Falls back gracefully if the API is unavailable or returns malformed output.
"""
import asyncio
import json
@@ -36,6 +36,9 @@ System: {system_id}
Talkgroup: {talkgroup_name}
{transcript_block}"""
# Nominatim viewbox half-width in degrees (~35 km at mid-latitudes)
_GEO_DELTA = 0.3
async def extract_tags(
call_id: str,
@@ -44,38 +47,52 @@ async def extract_tags(
talkgroup_id: Optional[int] = None,
system_id: Optional[str] = None,
segments: Optional[list[dict]] = None,
) -> tuple[list[str], Optional[str], Optional[str]]:
node_id: Optional[str] = None,
) -> tuple[list[str], Optional[str], Optional[str], Optional[dict]]:
"""
Extract incident tags, type, location, and corrected transcript via Gemini.
Extract incident tags, type, location, and corrected transcript via GPT-4o mini.
Geocodes the extracted location string via Nominatim using the node's position as bias.
Returns:
(tags, primary_type, location)
(tags, primary_type, location_str, location_coords)
where location_coords is {"lat": float, "lng": float} or None.
Side-effect: updates calls/{call_id} in Firestore with tags, location,
vehicles, units, severity, transcript_corrected; also stores the call embedding.
location_coords, vehicles, units, severity, transcript_corrected; also stores embedding.
"""
result = await asyncio.to_thread(_sync_extract, transcript, talkgroup_name, talkgroup_id, system_id, segments)
result = await asyncio.to_thread(
_sync_extract, transcript, talkgroup_name, talkgroup_id, system_id, segments
)
tags: list[str] = result.get("tags") or []
tags: list[str] = result.get("tags") or []
incident_type: Optional[str] = result.get("incident_type") or None
location: Optional[str] = result.get("location") or None
vehicles: list[str] = result.get("vehicles") or []
units: list[str] = result.get("units") or []
severity: str = result.get("severity") or "unknown"
location: Optional[str] = result.get("location") or None
vehicles: list[str] = result.get("vehicles") or []
units: list[str] = result.get("units") or []
severity: str = result.get("severity") or "unknown"
transcript_corrected: Optional[str] = result.get("transcript_corrected") or None
if incident_type in ("unknown", "other", ""):
incident_type = None
# Geocode the location string if we have one and a node to bias toward
location_coords: Optional[dict] = None
if location and node_id:
node_doc = await fstore.doc_get("nodes", node_id)
if node_doc:
node_lat = node_doc.get("lat")
node_lon = node_doc.get("lon")
if node_lat is not None and node_lon is not None:
location_coords = await _geocode_location(location, node_lat, node_lon)
# Store embedding alongside structured data
embedding = await asyncio.to_thread(_sync_embed, _embed_text(transcript, incident_type))
updates: dict = {
"tags": tags,
"severity": severity,
}
updates: dict = {"tags": tags, "severity": severity}
if location:
updates["location"] = location
if location_coords:
updates["location_coords"] = location_coords
if vehicles:
updates["vehicles"] = vehicles
if units:
@@ -92,10 +109,49 @@ async def extract_tags(
logger.info(
f"Intelligence: call {call_id} → type={incident_type}, "
f"tags={tags}, location={location!r}, severity={severity}, "
f"tags={tags}, location={location!r}, coords={location_coords}, severity={severity}, "
f"corrected={transcript_corrected is not None}"
)
return tags, incident_type, location
return tags, incident_type, location, location_coords
async def _geocode_location(
location_str: str, node_lat: float, node_lon: float
) -> Optional[dict]:
"""
Geocode a location string using Nominatim, biased toward the node's area.
Returns {"lat": float, "lng": float} or None if geocoding fails.
"""
import httpx
viewbox = (
f"{node_lon - _GEO_DELTA},{node_lat - _GEO_DELTA},"
f"{node_lon + _GEO_DELTA},{node_lat + _GEO_DELTA}"
)
params = {
"q": location_str,
"format": "json",
"limit": 1,
"viewbox": viewbox,
"bounded": 1,
}
headers = {"User-Agent": "DRB-Dispatch/1.0 (public-safety radio monitor)"}
try:
async with httpx.AsyncClient(timeout=5.0) as client:
r = await client.get(
"https://nominatim.openstreetmap.org/search",
params=params,
headers=headers,
)
r.raise_for_status()
results = r.json()
if results:
coords = {"lat": float(results[0]["lat"]), "lng": float(results[0]["lon"])}
logger.info(f"Geocoded '{location_str}' → {coords}")
return coords
except Exception as e:
logger.warning(f"Geocoding failed for '{location_str}': {e}")
return None
def _build_transcript_block(transcript: str, segments: Optional[list[dict]]) -> str:
@@ -154,10 +210,7 @@ def _sync_embed(text: str) -> Optional[list[float]]:
try:
client = OpenAI(api_key=settings.openai_api_key)
result = client.embeddings.create(
model="text-embedding-3-small",
input=text,
)
result = client.embeddings.create(model="text-embedding-3-small", input=text)
return result.data[0].embedding
except Exception as e:
logger.warning(f"Embedding generation failed: {e}")
@@ -165,6 +218,5 @@ def _sync_embed(text: str) -> Optional[list[float]]:
def _embed_text(transcript: str, incident_type: Optional[str]) -> str:
"""Build the text string to embed — transcript + type context."""
prefix = f"[{incident_type}] " if incident_type else ""
return f"{prefix}{transcript}"
drb-c2-core/app/routers/upload.py
+7 -2
View File
@@ -95,9 +95,10 @@ 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 = await intelligence.extract_tags(
tags, incident_type, location, location_coords = await intelligence.extract_tags(
call_id, transcript, talkgroup_name,
talkgroup_id=talkgroup_id, system_id=system_id, segments=segments,
node_id=node_id,
)
if incident_type:
@@ -110,6 +111,7 @@ async def _run_extraction_pipeline(
tags=tags,
incident_type=incident_type,
location=location,
location_coords=location_coords,
)
await alerter.check_and_dispatch(
@@ -150,10 +152,12 @@ async def _run_intelligence_pipeline(
tags: list[str] = []
incident_type: Optional[str] = None
location: Optional[str] = None
location_coords: Optional[dict] = None
if transcript:
tags, incident_type, location = await intelligence.extract_tags(
tags, incident_type, location, location_coords = await intelligence.extract_tags(
call_id, transcript, talkgroup_name,
talkgroup_id=talkgroup_id, system_id=system_id, segments=segments,
node_id=node_id,
)
# Step 3: Incident correlation
@@ -167,6 +171,7 @@ async def _run_intelligence_pipeline(
tags=tags,
incident_type=incident_type,
location=location,
location_coords=location_coords,
)
# Step 4: Alert dispatch (always runs — talkgroup ID rules don't need a transcript)