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)

scripts/__init__.py

@@ -0,0 +1 @@
+"""Runnable scripts (import_docs, llm_probe) — invoked via `python -m scripts.<name>`."""

scripts/entrypoint.sh

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+#!/bin/sh
+# Production entrypoint: apply migrations, then serve.
+set -eu
+cd /app
+echo "[entrypoint] applying database migrations..."
+alembic upgrade head
+echo "[entrypoint] starting uvicorn on :${PORT:-8000}"
+exec uvicorn app.main:app --host 0.0.0.0 --port "${PORT:-8000}"

scripts/llm_probe.py

@@ -0,0 +1,63 @@
+"""Probe the self-hosted LLM endpoint (aipi).
+
+Lists available models and verifies the embedding dimension of the
+configured ``embed`` model against ``BOR_EMBEDDING_DIM`` (default 768).
+Run this before the first import if the LLM backend ever changes:
+
+    uv run python -m scripts.llm_probe
+"""
+from __future__ import annotations
+
+import os
+import sys
+
+import httpx
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def main() -> int:
+    base_url = os.environ.get("BOR_LLM_BASE_URL", "https://aipi.reeseapps.com/v1").rstrip("/")
+    api_key = (
+        os.environ.get("BOR_LLM_API_KEY")
+        or os.environ.get("AIPI_KEY")
+        or "not-needed"
+    )
+    embed_model = os.environ.get("BOR_LLM_EMBED_MODEL", "embed")
+    chat_model = os.environ.get("BOR_LLM_CHAT_MODEL", "turbo")
+    expected_dim = int(os.environ.get("BOR_EMBEDDING_DIM", "768"))
+
+    headers = {"Authorization": f"Bearer {api_key}"}
+    with httpx.Client(base_url=base_url, headers=headers, timeout=30.0) as client:
+        r = client.get("/models")
+        r.raise_for_status()
+        models = [m["id"] for m in r.json()["data"]]
+        print(f"[probe] base_url : {base_url}")
+        print(f"[probe] models   : {', '.join(models)}")
+
+        for needed in (chat_model, embed_model):
+            if needed not in models:
+                print(f"[probe] ERROR: required model '{needed}' not available")
+                return 1
+
+        r = client.post(
+            "/embeddings",
+            json={"model": embed_model, "input": "brain of reese dimension probe"},
+        )
+        r.raise_for_status()
+        dims = sorted({len(d["embedding"]) for d in r.json()["data"]})
+        print(f"[probe] dims({embed_model}): {dims}")
+
+    if dims != [expected_dim]:
+        print(
+            f"[probe] MISMATCH: expected {expected_dim}, got {dims}. "
+            "Update BOR_EMBEDDING_DIM and recreate the chunks table (see README)."
+        )
+        return 1
+    print("[probe] OK — models present, embedding dimension matches configuration.")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())