feat(journal): run_for_user orchestrates closeout extraction

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/fabledassistant/services/journal_closeout.py

@@ -7,6 +7,20 @@ the bullets to user_profiles.observations_raw via append_observations.
 """
 from __future__ import annotations
 
+import datetime
+import logging
+
+from sqlalchemy import or_, select
+
+from fabledassistant.config import Config
+from fabledassistant.models import async_session
+from fabledassistant.models.conversation import Conversation, Message
+from fabledassistant.services.llm import generate_completion
+from fabledassistant.services.settings import get_setting
+from fabledassistant.services.user_profile import append_observations
+
+logger = logging.getLogger(__name__)
+
 # Message kinds whose content must NEVER be sent to the closeout LLM.
 # These are assistant-authored auto-blocks that would otherwise dominate
 # attention and leak back into "what the assistant has learned."
@@ -54,3 +68,66 @@ SYSTEM_PROMPT = (
     "- Write 2-5 short bullet points. Be specific and factual.\n"
     "- If nothing notable, output only: (nothing to note)"
 )
+
+
+async def run_for_user(user_id: int, yesterday: datetime.date) -> None:
+    """Extract preference observations from yesterday's journal conversation.
+
+    Skips silently when there is nothing meaningful to extract.
+    """
+    async with async_session() as session:
+        conv_result = await session.execute(
+            select(Conversation).where(
+                Conversation.user_id == user_id,
+                Conversation.conversation_type == "journal",
+                Conversation.day_date == yesterday,
+            )
+        )
+        conv = conv_result.scalar_one_or_none()
+        if conv is None:
+            logger.debug("closeout: no journal conv for user %d on %s", user_id, yesterday)
+            return
+
+        msg_result = await session.execute(
+            select(Message)
+            .where(
+                Message.conversation_id == conv.id,
+                Message.role.in_(("user", "assistant")),
+                or_(
+                    Message.msg_metadata.is_(None),
+                    ~Message.msg_metadata["kind"].astext.in_(EXCLUDED_KINDS),
+                ),
+            )
+            .order_by(Message.created_at)
+        )
+        messages = list(msg_result.scalars().all())
+
+    # Defensive second-pass filter (covers any message with metadata the
+    # SQL JSON path can't reach, e.g. older rows where kind nesting differs).
+    messages = _filter_messages(messages)
+
+    if len(messages) < 2:
+        logger.debug("closeout: not enough messages for user %d (%d)", user_id, len(messages))
+        return
+
+    transcript = _build_transcript(messages)
+    model = await get_setting(user_id, "background_model", Config.OLLAMA_BACKGROUND_MODEL)
+
+    try:
+        output = (await generate_completion(
+            [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": transcript},
+            ],
+            model,
+        )).strip()
+    except Exception:
+        logger.warning("closeout LLM failed for user %d", user_id, exc_info=True)
+        return
+
+    if not output or "(nothing to note)" in output.lower():
+        logger.debug("closeout: nothing to note for user %d", user_id)
+        return
+
+    await append_observations(user_id, output)
+    logger.info("closeout: appended observations for user %d (%s)", user_id, yesterday)