feat(scouts): real triage LLM call via scout-triage-system prompt

app/scouts/engine.py

@@ -28,6 +28,8 @@ from datetime import datetime, timedelta, timezone
 from sqlalchemy import select
 from sqlalchemy.exc import IntegrityError
 
+from app.core.langfuse_client import extract_usage, get_langfuse, get_prompt_or_fallback
+from app.core.llm import get_llm
 from app.db import async_session
 from app.models import CloudScoutConfig, ScoutTriageQueue
 from app.scouts.connectors.base import ItemContent, ItemRef, TriageVerdict
@@ -188,5 +190,81 @@ class ScoutEngine:
             await session.commit()
 
     async def _triage_llm(self, scout: CloudScoutConfig, content: ItemContent) -> TriageVerdict:
-        """Stub — real implementation in Task 24."""
+        """Call the scout-triage-system Langfuse prompt to classify an item as relevant or spam.
-        raise NotImplementedError("Real triage LLM call lands in Task 24")
+
+        Uses gpt-4o-mini with JSON mode. Wraps the LLM call in a Langfuse generation
+        observation when Langfuse is configured.
+        """
+        import json  # noqa: PLC0415
+
+        from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+
+        _TRIAGE_FALLBACK = (
+            "You are a triage classifier for an executive-assistant scout that watches a "
+            "{source_type} feed.\n"
+            'The scout\'s purpose is: "{scout_purpose}".\n\n'
+            "Given one item, decide whether it is RELEVANT (worth surfacing to the user as a "
+            "potential task / event / note / project) or SPAM (advertising, mass marketing, "
+            "phishing, bulk notifications with no actionable content).\n\n"
+            "Item:\n"
+            "  - Subject: {item_subject}\n"
+            "  - From:    {item_sender}\n"
+            "  - Body (truncated): {item_body_truncated_2k}\n\n"
+            'Return JSON only, matching this schema:\n'
+            '  {{"verdict": "relevant" | "spam", "reason": <short string>, "confidence": <0..1>}}\n\n'
+            "Be conservative on \"spam\" — if a message could plausibly be a personal/work "
+            "email, mark it relevant."
+        )
+
+        template, prompt_obj = get_prompt_or_fallback("scout-triage-system", _TRIAGE_FALLBACK)
+
+        body_trunc = (content.body_text or "")[:2000]
+        variables = dict(
+            source_type=scout.provider,
+            scout_purpose=scout.prompt_template or "",
+            item_subject=content.metadata.subject or "",
+            item_sender=content.metadata.sender or "",
+            item_body_truncated_2k=body_trunc,
+        )
+
+        if prompt_obj is not None:
+            try:
+                system_text = prompt_obj.compile(**variables)
+                if isinstance(system_text, list):
+                    system_text = "\n".join(
+                        m.get("content", "") for m in system_text if isinstance(m, dict)
+                    )
+            except Exception as exc:
+                logger.warning("scout triage: compile failed: %s", exc)
+                system_text = template.replace("{{source_type}}", variables["source_type"]) \
+                    .replace("{{scout_purpose}}", variables["scout_purpose"]) \
+                    .replace("{{item_subject}}", variables["item_subject"]) \
+                    .replace("{{item_sender}}", variables["item_sender"]) \
+                    .replace("{{item_body_truncated_2k}}", variables["item_body_truncated_2k"])
+        else:
+            system_text = template.format(**variables)
+
+        llm = get_llm(model="gpt-4o-mini", temperature=0)
+        llm_json = llm.bind(response_format={"type": "json_object"})  # type: ignore[attr-defined]
+
+        messages = [
+            SystemMessage(content=system_text),
+            HumanMessage(content="Classify this item."),
+        ]
+
+        lf = get_langfuse()
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="scout-triage",
+                model="gpt-4o-mini",
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm_json.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm_json.ainvoke(messages)
+
+        data = json.loads(response.content)
+        return TriageVerdict(**data)

tests/test_scout_engine.py

@@ -172,6 +172,59 @@ async def test_idempotent_replay(monkeypatch):
     assert len(rows) == 1, "Replay must not create duplicate queue rows"
 
 
+@pytest.mark.asyncio
+async def test_triage_llm_parses_json_response(monkeypatch):
+    """Real _triage_llm path: mock the LLM ainvoke, verify TriageVerdict parsed correctly."""
+    from unittest.mock import MagicMock  # noqa: PLC0415
+
+    from app.models import CloudScoutConfig  # noqa: PLC0415
+
+    scout = CloudScoutConfig(
+        id=str(uuid.uuid4()),
+        user_id="00000000-0000-0000-0000-000000000003",
+        provider="gmail",
+        name="test-scout",
+        data_types=[],
+        prompt_template="watch invoices and project updates",
+        schedule_cron="0 * * * *",
+        enabled=True,
+        auto_trash_spam=False,
+        device_inactivity_pause_days=14,
+    )
+    content = ItemContent(
+        metadata=ItemMetadata(subject="Invoice 42", sender="billing@acme.com"),
+        body_text="Payment of €1 200 is due on 2026-06-01. Please confirm receipt.",
+    )
+
+    # Build a fake LangChain response whose .content is valid JSON.
+    fake_response = MagicMock()
+    fake_response.content = '{"verdict": "relevant", "reason": "invoice due", "confidence": 0.92}'
+    fake_response.usage_metadata = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
+
+    # Fake LLM: .bind() returns self (or another mock with ainvoke).
+    fake_llm = MagicMock()
+    fake_llm.bind.return_value = fake_llm
+    fake_llm.ainvoke = AsyncMock(return_value=fake_response)
+
+    # Patch get_llm inside app.scouts.engine so our fake is used.
+    monkeypatch.setattr("app.scouts.engine.get_llm", lambda **kwargs: fake_llm)
+    # Disable Langfuse for this test.
+    monkeypatch.setattr("app.scouts.engine.get_langfuse", lambda: None)
+    # Use fallback prompt (no Langfuse) — patch get_prompt_or_fallback to return fallback.
+    monkeypatch.setattr(
+        "app.scouts.engine.get_prompt_or_fallback",
+        lambda name, fallback: (fallback, None),
+    )
+
+    engine = ScoutEngine(session_factory=_TestSessionLocal)
+    verdict = await engine._triage_llm(scout, content)
+
+    assert verdict.verdict == "relevant"
+    assert verdict.reason == "invoice due"
+    assert abs(verdict.confidence - 0.92) < 1e-6
+    fake_llm.ainvoke.assert_awaited_once()
+
+
 @pytest.mark.asyncio
 async def test_deliver_pending_sends_one_frame_per_queued_row(monkeypatch):
     user_id = "00000000-0000-0000-0000-000000000003"