refactor: Phase 8 — backend deletion (chat / voice / push / journal / curator)

Mega-commit. Strips all server-side LLM machinery now that Phase 7 has
removed the corresponding UI surfaces and the MCP HTTP endpoint is the
sole assistant interface.

Deleted (services/):
  chat, generation_buffer, generation_log, generation_task, llm, tools/
  (entire package), stt, tts, voice_config, voice_library, push,
  journal_closeout, journal_pipeline, journal_prep, journal_scheduler,
  journal_search, curator, curator_scheduler, consolidation,
  tag_suggestions, research, weather, article_fetcher, pending_actions,
  moments, assist, wikipedia.

Deleted (routes/):
  chat, voice, push, journal, quick_capture, fable_mcp_dist.

Deleted (models/):
  conversation, generation_tool_log, push_subscription,
  pending_curator_action, moment, weather_cache.

Deleted (tests/):
  test_generation_log, test_journal_*, test_consolidation, test_lookup_tool,
  test_notes_consolidation_trigger, test_record_moment_guards,
  test_research_pipeline, test_tools_*, test_tool_use_fixes,
  test_voice_library, test_weather_service, test_calendar_tool_tz,
  test_wikipedia.

Deleted (top-level):
  fable-mcp/ (legacy standalone stdio package — wheel-build pipeline
  also removed from Dockerfile).

app.py:
  - blueprint registrations for the 6 deleted routes
  - startup hook trimmed: no more Ollama warmup, KV-cache priming,
    journal/curator schedulers, voice model loading
  - shutdown hook simplified
  - httpx import dropped (was for Ollama calls)

pyproject.toml:
  - removed deps: pywebpush, feedparser, html2text, trafilatura
  - removed [voice] extras entirely
  - description updated for the MCP-first architecture

Dockerfile:
  - removed faster-whisper / piper-tts install steps
  - removed bundled piper voice download stage
  - removed fable-mcp wheel build stage

Surviving-file edits:
  - services/auth.py: drop Conversation table claim on first-user setup
  - services/backup.py: drop conversation / push-subscription export+restore;
    v1/v2 restore now silently skip pre-pivot conversation data
  - services/notes.py: drop maybe_consolidate trigger on task done/cancelled;
    drop _maybe_trigger_project_summary (LLM auto-summary)
  - services/projects.py: drop generate_project_summary + backfill_project_summaries
    (both LLM-driven)
  - services/user_profile.py: drop append_observations / consolidate /
    clear_learned_data (curator-tied) and build_profile_context
    (was LLM system-prompt builder)
  - services/notifications.py: stub out _fire_push_notif (was send_push_notification)
  - services/event_scheduler.py: drop event-reminder push + chat-retention
    cleanup job; keep CalDAV pull-sync + reminders job (in-app)
  - services/diagnostics.py: _curator_busy() always False
  - routes/notes.py: drop /assist, /assist/stream, /suggest-tags endpoints
  - routes/tasks.py: drop /<id>/consolidate endpoint
  - routes/settings.py: drop /models, KV-cache-prime-on-save, journal-schedule
    timezone hook, and the SearXNG search-test endpoint; inline _is_private_url
    (was in services/llm.py)
  - routes/admin.py: drop /voice, /voice/reload endpoints
  - routes/profile.py: drop /consolidate, /observations (GET, DELETE)
  - models/__init__.py: drop the 6 dead model imports

Frontend cascade:
  - stores/push.ts: deleted entirely (no callers after Phase 7)
  - stores/settings.ts: drop checkVoiceStatus + voice-status state
  - views/SettingsView.vue: drop Locations section + journalConfig state
    (was tied to /api/journal/config); drop JournalConfig + journal/voice
    api/client imports
  - frontend/api/client.ts: orphaned voice/journal/profile-observation/
    fable-mcp-dist exports are left as dead but harmless (call them and
    they 404; type-check is clean).

Pre-existing v1 backups that contained conversations/messages still
restore — those tables are silently dropped from the import path.
Anyone pulling the new image with a populated database will need the
Phase 9 migration to drop the dead tables (coming next).

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

src/fabledassistant/services/user_profile.py

@@ -1,7 +1,11 @@
-"""User profile service — structured per-user preferences for LLM context."""
-import asyncio
+"""User profile service — structured per-user preferences.
+
+Post-pivot the LLM-driven observation/consolidation surface is gone (curator
+deleted in Phase 8). The profile model is preserved as user-managed metadata
+(name, job, expertise, style, tone, interests, work schedule) — Claude can
+read it via the upcoming MCP profile tools.
+"""
 import logging
-from datetime import date, datetime, timedelta, timezone
 
 from sqlalchemy import select
 
@@ -14,9 +18,6 @@ VALID_EXPERTISE = {"novice", "intermediate", "expert"}
 VALID_STYLES = {"concise", "balanced", "detailed"}
 VALID_TONES = {"casual", "professional", "technical"}
 
-# Trigger consolidation when raw observations reach this count
-_CONSOLIDATION_THRESHOLD = 14
-
 
 async def get_profile(user_id: int) -> UserProfile:
     """Get or create the profile row for a user."""
@@ -53,169 +54,3 @@ async def update_profile(user_id: int, data: dict) -> UserProfile:
         await session.commit()
         await session.refresh(profile)
     return profile
-
-
-async def append_observations(user_id: int, bullets: str) -> None:
-    """
-    Append a new dated observation entry from the day's briefing closeout.
-    Automatically triggers consolidation when the raw list grows large.
-    """
-    if not bullets.strip():
-        return
-
-    async with async_session() as session:
-        result = await session.execute(
-            select(UserProfile).where(UserProfile.user_id == user_id)
-        )
-        profile = result.scalar_one_or_none()
-        if profile is None:
-            profile = UserProfile(user_id=user_id)
-            session.add(profile)
-
-        existing: list = list(profile.observations_raw or [])
-        existing.append({
-            "date": date.today().isoformat(),
-            "bullets": bullets.strip(),
-        })
-        # Keep at most 60 raw entries as a rolling window
-        profile.observations_raw = existing[-60:]
-        profile.observations_updated_at = datetime.now(timezone.utc)
-        await session.commit()
-        raw_count = len(profile.observations_raw or [])
-
-    logger.info("Appended observations for user %d (%d raw entries)", user_id, raw_count)
-
-    if raw_count >= _CONSOLIDATION_THRESHOLD:
-        asyncio.create_task(_consolidate_observations(user_id))
-
-
-async def consolidate_observations(user_id: int) -> str:
-    """Public entry point to manually trigger observation consolidation."""
-    return await _consolidate_observations(user_id)
-
-
-async def clear_learned_data(user_id: int) -> None:
-    """Reset all learned observations and summary for a user."""
-    async with async_session() as session:
-        result = await session.execute(
-            select(UserProfile).where(UserProfile.user_id == user_id)
-        )
-        profile = result.scalar_one_or_none()
-        if profile:
-            profile.learned_summary = None
-            profile.observations_raw = []
-            profile.observations_updated_at = None
-            await session.commit()
-
-
-async def _consolidate_observations(user_id: int) -> str:
-    """
-    LLM pass: synthesise all raw observation bullets into an updated
-    learned_summary paragraph. Prunes raw entries older than 30 days afterwards.
-    """
-    from fabledassistant.config import Config
-    from fabledassistant.services.llm import generate_completion
-    from fabledassistant.services.settings import get_setting
-
-    async with async_session() as session:
-        result = await session.execute(
-            select(UserProfile).where(UserProfile.user_id == user_id)
-        )
-        profile = result.scalar_one_or_none()
-        if not profile or not profile.observations_raw:
-            return ""
-        observations = list(profile.observations_raw)
-        existing_summary = profile.learned_summary or ""
-
-    obs_text = "\n\n".join(
-        f"[{entry['date']}]\n{entry['bullets']}"
-        for entry in observations
-    )
-
-    system = (
-        "You are synthesising preference observations into a concise user profile summary. "
-        "Consolidate the observations into 3-6 factual sentences describing the user's patterns, "
-        "preferences, and habits. Be specific and useful for a personal assistant. "
-        "Merge with any existing summary, removing duplicates and outdated information. "
-        "Output only the consolidated summary paragraph — no preamble, no bullet points."
-    )
-    user_prompt = ""
-    if existing_summary:
-        user_prompt += f"Existing summary:\n{existing_summary}\n\n"
-    user_prompt += f"New observations:\n{obs_text}"
-
-    # Profile observation consolidation reasons over multiple documents to
-    # produce a coherent summary — closer to curator-shaped work than chat.
-    # Route to worker (background_model). Falls back to OLLAMA_MODEL only if
-    # neither setting nor BACKGROUND default is available.
-    model = (
-        await get_setting(user_id, "background_model", "")
-        or Config.OLLAMA_BACKGROUND_MODEL
-        or Config.OLLAMA_MODEL
-    )
-    try:
-        new_summary = (await generate_completion(
-            [
-                {"role": "system", "content": system},
-                {"role": "user", "content": user_prompt},
-            ],
-            model,
-        )).strip()
-    except Exception:
-        logger.warning("Observation consolidation failed for user %d", user_id, exc_info=True)
-        new_summary = ""
-
-    if new_summary:
-        cutoff = (date.today() - timedelta(days=30)).isoformat()
-        async with async_session() as session:
-            result = await session.execute(
-                select(UserProfile).where(UserProfile.user_id == user_id)
-            )
-            profile = result.scalar_one_or_none()
-            if profile:
-                profile.learned_summary = new_summary
-                profile.observations_raw = [
-                    o for o in (profile.observations_raw or [])
-                    if o.get("date", "") >= cutoff
-                ]
-                await session.commit()
-        logger.info("Consolidated observations for user %d", user_id)
-
-    return new_summary
-
-
-async def build_profile_context(user_id: int) -> str:
-    """
-    Build a formatted context string from the user's structured profile
-    for injection into LLM system prompts (briefing and chat).
-    Returns an empty string if no meaningful data is set.
-    """
-    profile = await get_profile(user_id)
-
-    parts: list[str] = []
-
-    if profile.display_name:
-        parts.append(f"User's name: {profile.display_name}")
-    if profile.job_title or profile.industry:
-        job = " in ".join(filter(None, [profile.job_title, profile.industry]))
-        parts.append(f"Occupation: {job}")
-    if profile.expertise_level and profile.expertise_level != "intermediate":
-        parts.append(
-            f"Expertise level: {profile.expertise_level} — calibrate explanation depth accordingly"
-        )
-    if profile.response_style or profile.tone:
-        style = profile.response_style or "balanced"
-        tone = profile.tone or "casual"
-        parts.append(f"Preferred response style: {style}, tone: {tone}")
-    if profile.interests:
-        parts.append(f"Interests: {', '.join(profile.interests)}")
-    if profile.work_schedule:
-        sched = profile.work_schedule
-        days = ", ".join(sched.get("days") or []) or "weekdays"
-        start = sched.get("start", "9:00")
-        end = sched.get("end", "17:00")
-        parts.append(f"Work schedule: {days}, {start}–{end}")
-    if profile.learned_summary:
-        parts.append(f"What the assistant has learned about this user: {profile.learned_summary}")
-
-    return "\n".join(parts)