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FabledGrimoire/src/grimoire/rate_limit.py
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bvandeusenandClaude Opus 5.5 0f5ef0be63
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rename(547): the server is grimoire — package, image, DB defaults, backups
Step 2 of the Scribe -> Grimoire rename.

- src/scribe -> src/grimoire, and every import, patch target, module path,
  hypercorn entry point and `python -m grimoire.restore` with it; pyproject,
  uv.lock and the frontend package are named grimoire.
- Image git.fabledsword.com/bvandeusen/fabledgrimoire; CI's Postgres service
  runs as grimoire / grimoire_test.
- Compose files default the DB user, database and (prod) volume to grimoire,
  each overridable from .env, so an install created as Scribe keeps its data
  by setting POSTGRES_USER, POSTGRES_DB and PGDATA_VOLUME.
- Backups are written as grimoire-<ts>.tar with format grimoire-backup-tar.
  Archives named scribe-* with the scribe-backup-tar manifest still restore
  and are still counted at the destination (folder and S3), with tests.
- Exports, the MCP auth realm, the ASGI scope key and the persistent logger
  carry the new name.

Kept on purpose: the secret-sealing key label stays "scribe.secrets.v1",
because every stored secret was derived under it.

Plugin identity (plugin name, hooks, env vars, .scribe marker, skills) is
step 3.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-22 10:50:45 -04:00

28 lines
971 B
Python

"""In-process sliding-window rate limiter.
IMPORTANT — deployment note:
Rate limit counters are stored in memory and are lost on process restart.
When deployed behind a reverse proxy (nginx, Caddy, Traefik) you MUST set
TRUST_PROXY_HEADERS=true so that the real client IP is used as the bucket key
rather than the proxy's IP (which would cause all users to share one bucket).
"""
import asyncio
import time
from collections import defaultdict
_buckets: dict[str, list[float]] = defaultdict(list)
_lock = asyncio.Lock()
async def is_rate_limited(key: str, max_requests: int, window_seconds: int) -> bool:
"""Returns True if request should be blocked (limit exceeded)."""
async with _lock:
now = time.monotonic()
cutoff = now - window_seconds
_buckets[key] = [t for t in _buckets[key] if t > cutoff]
if len(_buckets[key]) >= max_requests:
return True
_buckets[key].append(now)
return False