chore: pin scene-close-on-cancel behavior + comment rationale (T74.3)
Phase 2 T44 review noted that scene close still runs when a primary turn is cancelled mid-stream and asked the implementer to review. Review finding: the existing behavior is correct, not a bug. The close-detection branch in post_turn consumes ONLY the user's prose (fully appended to the event_log BEFORE streaming starts) and the current container name. It does NOT consume the bot's output. A user who types "we're done here, fade out" and then hits Stop mid-stream still meant to close — the cancelled bot beat doesn't invalidate that intent. - Document the rationale with an inline comment near the close-detection branch in chat/web/turns.py. - Add regression test test_cancelled_turn_still_closes_scene_when_user_prose_signals_close that drives a stream raising CancelledError on first iteration and asserts the scene_closed event still lands.
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@@ -668,6 +668,15 @@ async def post_turn(
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# close in the same chat) — we have nothing to close. T13 (kickoff)
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# is the only scene-opener path in v1; Phase 2-3 will handle
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# automatic re-opening with the next container.
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#
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# T74.3: this branch deliberately runs even when ``cancelled`` is
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# True. Close detection consumes only the user's prose (which is
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# fully appended to the event_log BEFORE streaming starts) and the
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# current container name; it does NOT consume the bot's output.
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# A user who types "we're done here, fade out" and then hits Stop
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# mid-stream still meant to close the scene — the cancelled bot
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# beat doesn't invalidate that intent. Pinned by
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# test_cancelled_turn_still_closes_scene_when_user_prose_signals_close.
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if scene is not None and prose.strip():
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container = None
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if scene.get("container_id") is not None:
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@@ -715,6 +715,127 @@ def test_addressee_detection_routes_to_named_bot(app_state_setup, tmp_path):
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assert interjection_payload["interjection_of"] == "bot_b"
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def test_cancelled_turn_still_closes_scene_when_user_prose_signals_close(
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app_state_setup, tmp_path
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):
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"""T74.3 regression: a cancelled primary stream still triggers scene
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close when the user prose carries a hard close signal.
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Rationale (also documented in turns.py near the close-detection
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branch): close detection only consumes the user's prose, which is
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fully appended to the event_log BEFORE streaming starts. The
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cancelled bot beat doesn't invalidate the user's intent to close.
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Implementation: install a MockLLMClient whose ``stream`` raises
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CancelledError on the first iteration. The classifier calls (parse,
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addressee, scene_close, per-POV summaries) are still served from
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the canned queue. The post_turn route ultimately re-raises
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CancelledError after recording the partial — TestClient surfaces
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that as an exception, so we drive the request inside ``with
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pytest.raises``. Despite the exception, the scene_closed event
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must land in the event_log.
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"""
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from typing import AsyncIterator, Sequence
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_seed_chat_with_guest(tmp_path / "test.db")
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canned_parse = json.dumps(
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{"segments": [{"kind": "narration", "text": "we are done here, fade out"}]}
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)
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pov_payload = json.dumps(
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{
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"summary": "BotA noticed the day winding down.",
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"knowledge_facts": [],
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"relationship_summary": "warmer",
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}
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)
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pov_payload_guest = json.dumps(
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{
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"summary": "BotB watched the scene close.",
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"knowledge_facts": [],
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"relationship_summary": "warmer",
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}
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)
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# Canned queue: parse + addressee + 6 state-updates +
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# scene_close=True + 2 per-POV summaries. NO interjection slot
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# because the cancel path short-circuits the interjection branch.
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canned = [
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canned_parse,
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json.dumps(
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{"addressee_id": "bot_a", "confidence": "medium", "reason": "host"}
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),
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# NOTE: no narrative slot — the stream is hijacked below to
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# raise CancelledError on first iteration; it never pulls a
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# canned response.
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_zero_state(), _zero_state(), _zero_state(),
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_zero_state(), _zero_state(), _zero_state(),
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json.dumps({"should_close": True, "reason": "fade out signaled"}),
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pov_payload,
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pov_payload_guest,
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]
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class _CancelOnStreamMock:
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"""Mock LLM client that serves ``generate`` from a canned queue
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and raises CancelledError on the FIRST iteration of ``stream``.
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Mirrors :class:`chat.llm.mock.MockLLMClient` for ``generate`` but
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diverges on ``stream`` to simulate a mid-stream cancel.
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"""
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def __init__(self, canned: list[str]) -> None:
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self._canned = list(canned)
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async def generate(
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self, messages: Sequence, *, model: str, **params
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) -> str:
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return self._canned.pop(0)
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async def stream(
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self, messages: Sequence, *, model: str, **params
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) -> AsyncIterator[str]:
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# Yield a CancelledError on first iteration to simulate the
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# /turns/cancel route firing mid-stream.
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raise asyncio.CancelledError
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yield # pragma: no cover — keeps this an async generator.
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from chat.web.kickoff import get_llm_client
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mock = _CancelOnStreamMock(canned=list(canned))
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app.dependency_overrides[get_llm_client] = lambda: mock
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try:
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# FastAPI/Starlette handles the re-raised CancelledError as an
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# internal failure — TestClient surfaces it as a 500 response.
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# We don't assert on the status here; the regression is whether
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# the scene_closed event still landed in the event_log.
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try:
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app_state_setup.post(
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"/chats/chat_bot_a/turns",
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data={"prose": "we are done here, fade out"},
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)
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except BaseException:
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# Some Starlette/asyncio versions propagate the
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# CancelledError out of the test client; that's fine — the
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# partial-record + scene-close still ran before the raise.
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pass
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finally:
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app.dependency_overrides.clear()
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with open_db(tmp_path / "test.db") as conn:
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scene_close_count = conn.execute(
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"SELECT COUNT(*) FROM event_log WHERE kind = 'scene_closed'"
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).fetchone()[0]
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assistant_payload = conn.execute(
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"SELECT payload_json FROM event_log "
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"WHERE kind = 'assistant_turn' ORDER BY id"
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).fetchall()
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# Scene close lands despite the cancel.
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assert scene_close_count == 1
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# The cancelled assistant_turn was still recorded (truncated=True).
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assert len(assistant_payload) == 1
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assert json.loads(assistant_payload[0][0])["truncated"] is True
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def test_interjection_enqueues_significance_job(app_state_setup, tmp_path):
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"""T74.2: when an interjection fires, the interjection memory is
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enqueued for significance scoring just like the primary memory.
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