Redis Integration

Rate limiting, session cache, and job queues — all via Redis.

Redis powers three distinct systems in FastAPI AI Kit: per-key rate limiting, session/response caching, and the Celery task queue for background jobs. All configured with sensible defaults.

Setup in 4 steps

1Set REDIS_URL in your .env
2Rate limiting is active automatically on decorated endpoints
3Celery worker starts via docker-compose or railway up
4Cache TTLs configurable in config.py

What's included

Per-API-key rate limiting with sliding window counters
Response caching for deterministic LLM endpoints
Celery broker + result backend for background jobs
Session store for multi-turn conversation history
Pub/sub for real-time job status updates

Code example

example.py

# Rate limiting stored in Redis — automatic
@rate_limit(per_minute=60, per_day=5000)
async def my_endpoint(...):
    ...

# Manual cache usage
from app.cache import cache

@cache(ttl=300)  # 5-minute cache on deterministic results
async def get_embedding(text: str) -> list[float]:
    return await openai.embeddings.create(input=text)

Full documentation: /docs/configuration

Redis integration pre-wired and ready.

FastAPI AI Kit ships with Redis configured out of the box. No manual setup required.

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