FastAPI (Modern Python Backends)
Definition
FastAPI is a modern Python web framework built on ASGI (async), type hints, and Pydantic data validation. Declaring request/response models as typed Python classes gives you automatic validation, serialization, and OpenAPI/Swagger documentation. It is the de-facto standard for Python API services, and in particular for services that wrap machine-learning models and LLMs.
Why it matters for AI jobs
The AI ecosystem runs on Python, and FastAPI is how Python services are shipped: model-serving endpoints, RAG APIs, agent backends, and internal tools. Job posts that say "Python + FastAPI + Postgres" describe the default AI-startup backend. For an engineer with Spring Boot/ASP.NET experience, FastAPI maps directly: routers ≈ controllers, Pydantic ≈ DTO validation, dependency injection included.
Key concepts
- Async endpoints —
async defhandlers, the event loop, and when sync vs async matters. - Pydantic models — typed request/response schemas with validation and serialization.
- Dependency injection —
Depends()for auth, DB sessions, and configuration. - Auto OpenAPI docs — generated Swagger UI as living API documentation.
- Streaming responses — Server-Sent Events (SSE) for LLM token streaming.
- Background work — background tasks vs task queues (Celery, ARQ) for long jobs.
- Deployment — uvicorn workers, Docker images, health checks, and observability.
Learning path
- Build a CRUD API with FastAPI + SQLAlchemy + Postgres, with Pydantic schemas and auto docs.
- Add an LLM endpoint that streams a model’s answer over SSE — the core pattern of every AI backend.
- Combine with pgvector to serve a RAG search API (see the RAG and pgvector entries).
- Add auth (OAuth2/JWT via Depends), rate limiting, and structured logging.
- Containerize with Docker and deploy to a cloud service; add pytest-based API tests.