feat(briefing): add briefing_preferences service for RSS scoring and filtering

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/fabledassistant/services/briefing_preferences.py

@@ -0,0 +1,110 @@
+"""
+Briefing preferences: load topic settings, aggregate reaction scores,
+filter and rank RSS items for briefing inclusion.
+"""
+
+import json
+import logging
+from datetime import datetime, timezone
+
+from fabledassistant.models import async_session
+
+logger = logging.getLogger(__name__)
+
+
+async def load_topic_preferences(user_id: int) -> tuple[list[str], list[str]]:
+    """
+    Return (include_topics, exclude_topics) from user settings.
+    """
+    from fabledassistant.services.settings import get_setting
+
+    raw_include = await get_setting(user_id, "briefing_include_topics", "[]")
+    raw_exclude = await get_setting(user_id, "briefing_exclude_topics", "[]")
+
+    def _parse(raw) -> list[str]:
+        try:
+            val = json.loads(raw) if isinstance(raw, str) else raw
+            return [str(t) for t in val] if isinstance(val, list) else []
+        except Exception:
+            return []
+
+    return _parse(raw_include), _parse(raw_exclude)
+
+
+async def load_topic_reaction_scores(user_id: int) -> dict[str, float]:
+    """
+    Aggregate per-topic reaction scores from the last 30 days.
+    Returns a dict of topic -> net_score (positive = liked, negative = disliked).
+    Uses rss_item_reactions joined to rss_items.topics.
+    """
+    try:
+        from sqlalchemy import text as _text
+
+        async with async_session() as session:
+            result = await session.execute(
+                _text("""
+                    SELECT unnest(i.topics) AS topic,
+                           SUM(CASE r.reaction WHEN 'up' THEN 1 ELSE -1 END) AS score
+                    FROM rss_item_reactions r
+                    JOIN rss_items i ON i.id = r.rss_item_id
+                    WHERE r.user_id = :uid
+                      AND r.created_at > NOW() - INTERVAL '30 days'
+                    GROUP BY topic
+                """).bindparams(uid=user_id)
+            )
+            return {row.topic: float(row.score) for row in result}
+    except Exception:
+        logger.warning("Failed to load topic reaction scores", exc_info=True)
+        return {}
+
+
+def score_and_filter_items(
+    items: list[dict],
+    include_topics: list[str],
+    exclude_topics: list[str],
+    topic_scores: dict[str, float],
+    max_items: int = 10,
+) -> list[dict]:
+    """
+    Score, filter, and rank RSS items for briefing inclusion.
+
+    Scoring:
+    - Hard-exclude: any item tagged with an excluded topic is removed.
+    - Base score: 0.0
+    - +2.0 per topic that appears in include_topics
+    - +1.0 / -1.0 per topic based on reaction score (clamped per topic)
+    - Tiebreak: newer published_at wins
+
+    Returns up to max_items items, highest score first.
+    Items with classified_at=None (unclassified) pass through with score=0.
+    """
+    include_set = set(include_topics)
+    exclude_set = set(exclude_topics)
+    scored = []
+
+    for item in items:
+        item_topics = item.get("topics") or []
+
+        # Hard exclude
+        if exclude_set and any(t in exclude_set for t in item_topics):
+            continue
+
+        score = 0.0
+        for topic in item_topics:
+            if topic in include_set:
+                score += 2.0
+            if topic in topic_scores:
+                score += max(-1.0, min(1.0, topic_scores[topic]))
+
+        # Parse published_at for tiebreak
+        pub_str = item.get("published_at") or ""
+        try:
+            pub_ts = datetime.fromisoformat(pub_str).timestamp() if pub_str else 0.0
+        except ValueError:
+            pub_ts = 0.0
+
+        scored.append((score, pub_ts, item))
+
+    # Sort: highest score first, then newest first
+    scored.sort(key=lambda x: (x[0], x[1]), reverse=True)
+    return [item for _, _, item in scored[:max_items]]