FastAPI — Build AI Backend APIs in Minutes

What it is

FastAPI is a modern Python web framework built for building APIs with automatic validation, documentation, and async support. It combines Python type hints with Pydantic for request validation and generates OpenAPI documentation automatically.

FastAPI is best for backend engineers building AI services, REST APIs, and microservices in Python who need production-grade performance with minimal boilerplate.

How it saves time or tokens

FastAPI generates interactive API documentation (Swagger UI and ReDoc) from your type annotations without extra code. Pydantic models validate request and response data automatically, catching type errors before they reach your business logic. Async support lets you handle concurrent LLM API calls efficiently. The estimated token cost for this workflow is around 3,800 tokens.

How to use

1. Install FastAPI and Uvicorn:

pip install fastapi uvicorn

1. Create your API file:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ChatRequest(BaseModel):
    message: str
    model: str = 'gpt-4'

@app.post('/chat')
async def chat(req: ChatRequest):
    return {'reply': f'Received: {req.message}'}

1. Run the server:

uvicorn main:app --reload

1. Open http://localhost:8000/docs for interactive API docs

Example

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional

app = FastAPI(title='AI Service')

class CompletionRequest(BaseModel):
    prompt: str
    max_tokens: int = 500
    temperature: Optional[float] = 0.7

class CompletionResponse(BaseModel):
    text: str
    tokens_used: int

@app.post('/v1/completions', response_model=CompletionResponse)
async def complete(req: CompletionRequest):
    if not req.prompt.strip():
        raise HTTPException(status_code=400, detail='Prompt cannot be empty')
    result = await call_llm(req.prompt, req.max_tokens)
    return CompletionResponse(text=result, tokens_used=len(result.split()))

Related on TokRepo

• AI coding tools — frameworks and tools for AI development
• API tools — API development and management resources

Common pitfalls

• Forgetting to use async def for endpoints that make external API calls, blocking the event loop
• Not setting up CORS middleware when serving a frontend from a different origin
• Skipping Pydantic model validation by using raw dicts, losing the main advantage of FastAPI