Build a Production LLM Microservice with FastAPI, Redis, and Docker
Async API, caching, rate limiting, and containerized deployment for LLM services
Build a Production LLM Microservice with FastAPI, Redis, and Docker
Async API, caching, rate limiting, and containerized deployment for LLM services
Build a scalable LLM microservice using FastAPI with async endpoints, Redis caching, rate limiting, health checks, and Docker containerization for production deployment.
Production LLM microservice architecture with FastAPI. Core setup: pip install fastapi uvicorn openai redis pydantic; main.py with lifespan async context manager for startup/shutdown. API structure: POST /v1/chat/completions (OpenAI compatible), GET /v1/models, GET /health. Key implementation: 1) Async LLM calls: use AsyncOpenAI client, avoid blocking sync calls. 2) Redis caching: hash prompt + model + params as cache key, TTL 1 hour for deterministic responses. 3) Rate limiting: use Redis with sliding window counter, return 429 with Retry-After header. 4) Streaming: use StreamingResponse with async generator yielding SSE chunks. 5) Middleware: logging with request ID, CORS, authentication. Dockerfile: FROM python:3.12-slim; COPY requirements.txt .; RUN pip install -r requirements.txt; COPY . .; CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]. Docker Compose with Redis: services: api (build: .) and redis (image: redis:alpine). Kubernetes deployment: horizontal pod autoscaler based on RPS metric, readiness probe on /health. Testing: pytest with httpx AsyncClient, mock OpenAI with respx or unittest.mock.