Add eval system Phase 4 — read endpoints and background runner ...

REST surface for the debug UI:
- GET /eval/sessions  — overview list with eval status / latest avg /
  feedback counts (single SQL: sessions ⨝ feedback ⨝ latest run)
- GET /eval/sessions/{id} — session detail with all evaluations
- GET /eval/stats — weekly per-axis means; optional complexity-bucket split
- POST /eval/run — fire-and-forget background eval, returns run_id
- GET /eval/run/{id}, GET /eval/runs — poll progress and history

Pulled the runner loop out of cli into runner.py so both the CLI and
the REST endpoint share the same loop. State for in-flight runs lives
in an in-memory registry (single-process, cleared on restart).

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

Add eval system Phase 1 — message feedback signal ...

Spec at docs/eval_system.md describes the full LLM-as-judge plan;
this commit lands only the in-app feedback layer:
- debug/eval/ Python package with EvalDB (asyncpg) and FastAPI router
  exposing /eval/feedback (set / clear / list)
- message_feedback postgres table keyed by (session_id, message_index)
- thumbs up / down on each completed assistant block in the webclient,
  optimistic update with rollback on failure

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