feat: OpenTelemetry LGTM observability, dev tooling, and memoir UX fixes (#31) (#32)

* add staging ios app build script

* feat(api): add OpenTelemetry LGTM stack for local observability

Wire OTel traces, metrics, and logs through a collector to Tempo,
Prometheus, and Loki, with custom LLM instrumentation, dev compose overlay,
Grafana provisioning, env templates, and development.sh auto-start.



* feat: expand observability, harden dev tooling, and fix expo staging UX

Add business and LLM Prometheus metrics with Grafana dashboards, alerting,
and a metrics verification script. Wire telemetry through adapters and core
LLM paths, and document the local LGTM workflow.

Fix development.sh for macOS bash 3.2, open Grafana and eval-web in Chrome,
and repair eval-web auto-open (unbound EVAL_WEB_BROWSER_SCHEDULED). Merge
internal-eval into the main dev script with improved compose handling.

Require EXPO_PUBLIC_* at build time, improve iOS HTTP ATS for staging IPs,
show memoir empty state instead of load errors when no chapters exist, and
add jest env setup plus chapter list response normalization.



* chore: enable Grafana Assistant Cursor plugin



* fix: memoir empty state and repair withdrawn 0020_chapters_book_id stamp

Show empty memoir UI when the chapter list succeeds with no items; treat auth/404 as non-fatal. Extend alembic revision repair so local dev DBs stamped with the removed 0020_chapters_book_id migration can roll back and upgrade to 0019.



---------

Co-authored-by: Kevin <kevin@brighteng.org>
Co-authored-by: Cursor <cursoragent@cursor.com>

api/app/tasks/memory_enrichment_tasks.py

@@ -11,6 +11,7 @@ from typing import Any, cast
 
 from celery import shared_task
 
+from app.core.business_telemetry import business_span
 from app.core.config import settings
 from app.core.db import AsyncSessionLocal
 from app.core.dependencies import get_embedding_provider
@@ -166,7 +167,8 @@ def embed_memory_source(
         status="running",
     )
     try:
-        result = asyncio.run(_embed_memory_source_async(user_id, source_id))
+        with business_span("memory.embed_source"):
+            result = asyncio.run(_embed_memory_source_async(user_id, source_id))
         ms = (time.perf_counter() - t0) * 1000
         logger.info(
             "event=memory_embedding_done user_id={} source_id={} duration_ms={:.1f} status={} vectors_written={} msg=记忆向量化完成",
@@ -241,7 +243,8 @@ def enrich_memory_source(
         status="running",
     )
     try:
-        asyncio.run(_enrich_memory_source_async(user_id, source_id))
+        with business_span("memory.enrich_source"):
+            asyncio.run(_enrich_memory_source_async(user_id, source_id))
         ms = (time.perf_counter() - t0) * 1000
         logger.info(
             "event=memory_enrichment_done user_id={} source_id={} duration_ms={:.1f} "