correlator: LLM tier reads the scene transcript, not the whole call (#102) #112
@@ -670,6 +670,7 @@ async def correlate_call(
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reassignment: bool = False,
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embedding: Optional[list] = None,
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severity: Optional[str] = None,
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transcript: Optional[str] = None,
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) ->Optional[str]:
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"""
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Link call_id to an existing incident or create a new one.
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@@ -686,7 +687,7 @@ async def correlate_call(
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system_id=system_id, talkgroup_id=talkgroup_id, talkgroup_name=talkgroup_name,
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tags=tags, incident_type=incident_type, location=location,
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reassignment=reassignment, create_if_new=create_if_new,
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embedding=embedding, severity=severity,
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embedding=embedding, severity=severity, transcript=transcript,
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)
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decision = _run_decision(ctx)
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return await _apply_and_log(decision, ctx)
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@@ -710,6 +711,7 @@ async def preview_correlation(
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reassignment: bool = False,
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embedding: Optional[list] = None,
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severity: Optional[str] = None,
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transcript: Optional[str] = None,
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) ->dict:
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"""
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Run the rules engine and return the decision WITHOUT committing to Firestore.
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@@ -730,7 +732,7 @@ async def preview_correlation(
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system_id=system_id, talkgroup_id=talkgroup_id, talkgroup_name=talkgroup_name,
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tags=tags, incident_type=incident_type, location=location,
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reassignment=reassignment, create_if_new=create_if_new,
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embedding=embedding, severity=severity,
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embedding=embedding, severity=severity, transcript=transcript,
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)
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decision = _run_decision(ctx)
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return {"decision": decision, "ctx": ctx}
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@@ -765,6 +767,7 @@ async def _build_context(
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create_if_new: bool,
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embedding: Optional[list] = None,
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severity: Optional[str] = None,
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transcript: Optional[str] = None,
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) -> dict:
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now = reference_time or datetime.now(timezone.utc)
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window = timedelta(hours=settings.correlation_window_hours)
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@@ -804,6 +807,13 @@ async def _build_context(
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call_vehicles = vehicles if vehicles is not None else (call_doc.get("vehicles") or [])
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call_cleared = cleared_units if cleared_units is not None else (call_doc.get("cleared_units") or [])
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call_severity = severity or "routine"
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# The transcript the LLM correlation tier reasons over. Prefer the SCENE's
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# own words (server-26#102) — passed by upload.py's scene loop — and fall
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# back to the call doc only when no scene text was supplied (the
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# recorrelation sweep, and single-scene calls where the two are identical).
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# Without this, every non-primary scene of a multi-scene call was judged by
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# the LLM against a transcript containing the OTHER scenes.
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scene_transcript = transcript or call_doc.get("transcript_corrected") or call_doc.get("transcript")
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# A string that is not a place is not a location anywhere downstream — not
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# in the fit tests, not in the thin-call test, not in the LLM prompt, and
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# not on the incident. Its coordinates go with it: coords are geocoded
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@@ -826,6 +836,7 @@ async def _build_context(
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return {
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"call_id": call_id, "org_id": org_id, "all_active": all_active, "recent": recent,
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"call_doc": call_doc, "call_embedding": call_embedding,
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"scene_transcript": scene_transcript,
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"call_units": call_units, "call_vehicles": call_vehicles,
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"call_cleared": call_cleared, "call_severity": call_severity,
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"coords": coords, "is_thin_call": is_thin_call, "now": now,
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@@ -172,7 +172,7 @@ async def extract_scenes(
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Each scene dict contains:
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tags, incident_type, location, location_coords, resolved,
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severity, vehicles, units, transcript_corrected,
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severity, vehicles, units, transcript, transcript_corrected,
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segment_indices, embedding
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Side-effect: updates calls/{call_id} in Firestore with merged tags,
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@@ -337,6 +337,19 @@ async def extract_scenes(
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)
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embedding = await asyncio.to_thread(_sync_embed, scene_text)
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# This scene's own words, unprefixed — corrected text if we have it,
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# else the raw segments this scene owns, else (single-scene) the whole
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# transcript. The correlator's LLM tier reads this per scene instead of
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# the call doc's whole-call transcript (server-26#102).
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if transcript_corrected:
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scene_transcript = transcript_corrected
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elif segments and segment_indices:
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scene_transcript = " ".join(
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segments[i]["text"] for i in segment_indices if i < len(segments)
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)
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else:
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scene_transcript = transcript
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processed.append({
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"tags": tags,
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"incident_type": incident_type,
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@@ -348,6 +361,7 @@ async def extract_scenes(
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"severity": severity,
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"resolved": resolved,
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"reassignment": reassignment,
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"transcript": scene_transcript,
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"transcript_corrected": transcript_corrected,
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"segment_indices": segment_indices,
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"embedding": embedding,
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@@ -61,7 +61,13 @@ def _inc_summary(inc: dict, now: datetime) -> str:
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def _call_block(ctx: dict) -> str:
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lines = []
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call_doc = ctx["call_doc"]
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transcript = call_doc.get("transcript_corrected") or call_doc.get("transcript")
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# The SCENE's own transcript, resolved in _build_context (server-26#102).
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# Falls back to the call doc for a ctx built without a scene (tests, sweep).
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transcript = (
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ctx.get("scene_transcript")
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or call_doc.get("transcript_corrected")
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or call_doc.get("transcript")
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)
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if transcript:
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lines.append(f"Transcript: {transcript[:700]}")
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if ctx["tags"]:
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@@ -108,6 +108,7 @@ async def _recorrelate_orphan(call: dict) -> bool:
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cleared_units = call.get("cleared_units") or [],
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embedding = call.get("embedding"),
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severity = call.get("severity"),
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transcript = call.get("transcript_corrected") or call.get("transcript"),
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reference_time = started_at, # anchor window to when the call happened
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create_if_new = False, # never create — link-only
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)
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@@ -116,6 +116,7 @@ async def _correlate_with_consensus(
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reassignment: bool = False,
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embedding: Optional[list] = None,
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severity: Optional[str] = None,
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transcript: Optional[str] = None,
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) -> Optional[str]:
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"""
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Consensus correlator: runs the rules engine and the cheap LLM in sequence.
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@@ -133,7 +134,7 @@ async def _correlate_with_consensus(
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tags=tags, incident_type=incident_type, location=location,
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location_coords=location_coords, units=units, vehicles=vehicles,
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cleared_units=cleared_units, reassignment=reassignment,
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embedding=embedding, severity=severity,
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embedding=embedding, severity=severity, transcript=transcript,
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)
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ctx = preview["ctx"]
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rules_decision = preview["decision"]
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@@ -226,6 +227,7 @@ async def _run_extraction_pipeline(
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reassignment=is_reassignment,
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embedding=scene.get("embedding"),
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severity=scene.get("severity"),
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transcript=scene.get("transcript"),
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)
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if incident_id and incident_id not in incident_ids:
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incident_ids.append(incident_id)
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@@ -343,6 +345,7 @@ async def _run_intelligence_pipeline(
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reassignment=is_reassignment,
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embedding=scene.get("embedding"),
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severity=scene.get("severity"),
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transcript=scene.get("transcript"),
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)
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if incident_id and incident_id not in incident_ids:
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incident_ids.append(incident_id)
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@@ -304,6 +304,39 @@ async def test_a_scene_is_judged_on_its_own_embedding_and_severity():
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assert ctx["call_severity"] == "major"
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@pytest.mark.asyncio
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async def test_the_llm_tier_reads_the_scene_transcript_not_the_whole_call():
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"""
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server-26#102. intelligence.py writes only the primary scene's corrected
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text to calls/{id}. _call_block (the LLM correlation prompt) must reason
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over the SCENE being correlated, not a whole-call transcript that also
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contains the other scenes. _build_context threads the scene's text in;
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with no scene text it falls back to the call doc (sweep / single-scene).
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"""
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with patch("app.internal.incident_correlator.fstore") as mock_fstore:
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mock_fstore.doc_get = AsyncMock(return_value={
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"transcript": "scene one about a fire. scene two about a traffic stop.",
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})
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mock_fstore.collection_list = AsyncMock(return_value=[])
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scene = await _build_context(
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call_id="call-1", units=None, vehicles=None, cleared_units=None,
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location_coords=None, reference_time=NOW,
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system_id="sys-1", talkgroup_id=383, talkgroup_name=DISPATCH_TG,
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tags=[], incident_type="police", location=None,
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reassignment=False, create_if_new=True,
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transcript="scene two about a traffic stop.",
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)
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fallback = await _build_context(
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call_id="call-1", units=None, vehicles=None, cleared_units=None,
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location_coords=None, reference_time=NOW,
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system_id="sys-1", talkgroup_id=383, talkgroup_name=DISPATCH_TG,
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tags=[], incident_type="police", location=None,
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reassignment=False, create_if_new=True,
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)
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assert scene["scene_transcript"] == "scene two about a traffic stop."
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assert fallback["scene_transcript"] == "scene one about a fire. scene two about a traffic stop."
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@pytest.mark.asyncio
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async def test_a_bare_number_never_becomes_an_incident_location_or_title():
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inc = await _create(tags=["flames"], location="49", coords=None,
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Block a user