feat: structured user profile with LLM-learned preferences

Replaces the freeform briefing-profile note with a DB-backed user_profiles
table. Users can edit job/industry/expertise/response preferences/interests/
work schedule via a new Settings → Profile tab. The LLM appends nightly
observations; at 14+ entries they are auto-consolidated into a learned_summary.
Profile context is injected into both briefing and chat system prompts.

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

src/fabledassistant/models/__init__.py

@@ -41,3 +41,4 @@ from fabledassistant.models.notification import Notification  # noqa: E402, F401
 from fabledassistant.models.rss_feed import RssFeed, RssItem  # noqa: E402, F401
 from fabledassistant.models.weather_cache import WeatherCache  # noqa: E402, F401
 from fabledassistant.models.api_key import ApiKey  # noqa: E402, F401
+from fabledassistant.models.user_profile import UserProfile  # noqa: E402, F401

src/fabledassistant/models/user_profile.py

@@ -0,0 +1,55 @@
+from datetime import datetime
+
+from sqlalchemy import DateTime, ForeignKey, Integer, Text
+from sqlalchemy.dialects.postgresql import ARRAY, JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from fabledassistant.models import Base
+from fabledassistant.models.base import TimestampMixin
+
+
+class UserProfile(Base, TimestampMixin):
+    __tablename__ = "user_profiles"
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    user_id: Mapped[int] = mapped_column(
+        Integer, ForeignKey("users.id", ondelete="CASCADE"), nullable=False, unique=True
+    )
+    display_name: Mapped[str | None] = mapped_column(Text, nullable=True)
+    job_title: Mapped[str | None] = mapped_column(Text, nullable=True)
+    industry: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # novice / intermediate / expert — calibrates explanation depth
+    expertise_level: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # concise / balanced / detailed
+    response_style: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # casual / professional / technical
+    tone: Mapped[str | None] = mapped_column(Text, nullable=True)
+    interests: Mapped[list[str] | None] = mapped_column(ARRAY(Text), nullable=True)
+    # {days: ["Mon","Tue",...], start: "09:00", end: "17:00"}
+    work_schedule: Mapped[dict | None] = mapped_column(JSONB, nullable=True)
+    # LLM-consolidated summary of learned preferences
+    learned_summary: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # [{date: "YYYY-MM-DD", bullets: "..."}, ...]
+    observations_raw: Mapped[list | None] = mapped_column(JSONB, nullable=True)
+    observations_updated_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+
+    def to_dict(self) -> dict:
+        return {
+            "display_name": self.display_name or "",
+            "job_title": self.job_title or "",
+            "industry": self.industry or "",
+            "expertise_level": self.expertise_level or "intermediate",
+            "response_style": self.response_style or "balanced",
+            "tone": self.tone or "casual",
+            "interests": self.interests or [],
+            "work_schedule": self.work_schedule or {},
+            "learned_summary": self.learned_summary or "",
+            "observations_count": len(self.observations_raw or []),
+            "observations_updated_at": (
+                self.observations_updated_at.isoformat()
+                if self.observations_updated_at
+                else None
+            ),
+        }