feat: scaffold Brain of Reese — FastAPI RAG chat over Postgres 17 + pgvector

Foundation (phase 01, verified):
- FastAPI app: /api/health, /api/suggestions, /api/chat (placeholder),
  static frontend served locally (no CDN)
- Postgres 17 + pgvector via db/Containerfile + compose.yaml
  (podman compose up -d db), Alembic initial migration (documents,
  chunks with vector(768), query_log)
- LLM client targeting https://aipi.reeseapps.com/v1 (turbo/embed);
  scripts/llm_probe.py verified models + 768-dim embeddings live
- Conditional debugpy: imported only when DEBUGPY=1 (attach on demand,
  :5678); logging config for clean single-line logs
- Frontend shell: mobile-first chat + Sources pages, tokens, a11y baselines
- Tests: 24 unit+integration (99% coverage on app/), ruff + pyright clean,
  Playwright smoke E2E (3 tests) against a deterministic mock LLM
- Planning: .agent/PLAN.md (architecture + LOCKED decisions), AGENTS.md,
  6 user stories, 7 phase files (one story / one phase / one Playwright
  suite each)

app/api/health.py

@@ -0,0 +1,21 @@
+"""Health & readiness endpoint."""
+from __future__ import annotations
+
+from fastapi import APIRouter
+
+from app.config import get_settings
+from app.db import db_available
+from app.schemas import HealthResponse
+
+router = APIRouter(tags=["health"])
+
+
+@router.get("/health", response_model=HealthResponse)
+def health() -> HealthResponse:
+    settings = get_settings()
+    return HealthResponse(
+        status="ok",
+        db="up" if db_available() else "down",
+        version=settings.app_version,
+        environment=settings.environment,
+    )