feat(eval): memoir A/B chapter judging and eval-web parity with dialogue

- Judge baseline excerpt and library chapter separately; build_memoir_compare_summary for gate, nine-dim and leaf deltas.

- Memoir SSE chapter payload: baseline_judge, compare_summary, baseline_judge_error.

- MemoirJudgeOutput: loose score coercion and post-validate clamp; memoir judge prompt caps from settings.

- app-eval-web: two-column MemoirScoreCard layout, MemoirCompareSummary, chapter blocks and CSS.

- Add memoir_compare_summary, log_events, celery_log_context, memoir_pipeline_progress; tests and migration 0014.

- Misc: memory/evidence and enrichment paths, task/orchestrator updates, internal-eval docs, env examples.

api/app/tasks/memory_compaction_tasks.py

@@ -28,6 +28,7 @@ logger = get_logger(__name__)
 @shared_task
 def memory_compaction_sweep() -> dict[str, Any]:
     """Beat：为近期有记忆写入的用户调度 compaction（debounce 仍由 schedule 合并）。"""
+    t0 = time.perf_counter()
     if not settings.memory_compaction_enabled:
         return {"skipped": True, "reason": "disabled"}
     hours = int(settings.memory_compaction_sweep_recent_hours)
@@ -36,8 +37,13 @@ def memory_compaction_sweep() -> dict[str, Any]:
     ctx_base: dict[str, Any] = {"trigger_source": "beat", "sweep_hours": hours}
     for uid in user_ids:
         schedule_memory_compaction_run(uid, dict(ctx_base))
+    ms = (time.perf_counter() - t0) * 1000
     logger.info(
-        "memory_compaction_sweep hours={} scheduled_users={}", hours, len(user_ids)
+        "event=memory_compaction_sweep_done hours={} scheduled_users={} duration_ms={:.1f} "
+        "msg=记忆压缩定时扫描已调度",
+        hours,
+        len(user_ids),
+        ms,
     )
     return {"scheduled": len(user_ids), "user_ids": user_ids}
 
@@ -46,6 +52,7 @@ def memory_compaction_sweep() -> dict[str, Any]:
 def memory_compaction_run(
     self, user_id: str, context: dict[str, Any] | None = None
 ) -> dict[str, Any]:
+    run_t0 = time.perf_counter()
     if not settings.memory_compaction_enabled:
         return {"skipped": True, "reason": "disabled"}
 
@@ -61,9 +68,12 @@ def memory_compaction_run(
         ttl_seconds=settings.memory_compaction_lock_ttl_seconds,
     )
     if lock is None:
+        ms = (time.perf_counter() - run_t0) * 1000
         logger.info(
-            "memory_compaction_skipped user_id={} skipped_reason=lock_not_acquired",
+            "event=memory_compaction_skipped user_id={} reason=lock_not_acquired "
+            "duration_ms={:.1f} msg=记忆压缩跳过（未拿到锁）",
             user_id,
+            ms,
         )
         out = {"skipped": True, "reason": "lock_not_acquired"}
         finalize_memory_compaction_run(
@@ -89,10 +99,23 @@ def memory_compaction_run(
             observed_deadline_ts=deadline,
             context=ctx,
         )
+        ms = (time.perf_counter() - run_t0) * 1000
+        logger.info(
+            "event=memory_compaction_done user_id={} duration_ms={:.1f} msg=记忆压缩运行完成",
+            user_id,
+            ms,
+        )
         return out
     except Exception as exc:
-        logger.warning("memory_compaction_run failed user_id={} err={}", user_id, exc)
+        ms = (time.perf_counter() - run_t0) * 1000
+        logger.warning(
+            "event=memory_compaction_failed user_id={} duration_ms={:.1f} err={} "
+            "msg=记忆压缩运行失败",
+            user_id,
+            ms,
+            exc,
+        )
         release_scheduler_gate(user_id)
-        raise self.retry(exc=exc)
+        raise self.retry(exc=exc) from exc
     finally:
         release_redis_lock(lock)