FastAPI

FastAPI is a modern, high-performance web framework for building APIs with Python. It is based on standard Python type hints and provides automatic data validation, serialization, and interactive API documentation (Swagger UI and ReDoc). FastAPI is designed for speed and ease of development, making it an excellent choice for our AI-first backend services.

Key Features for Our Prompt-Generation Service

FastAPI’s design principles directly support the needs of our prompt-generation service:

1. Performance

FastAPI is built on Starlette for the web parts and Pydantic for data parts, which makes it very fast, comparable to Node.js and Go. This is crucial for a service that will interact with external LLMs and needs to handle many requests efficiently.

2. Type Hints and Data Validation with Pydantic

FastAPI leverages Python’s standard type hints. This means you declare the shape of your request bodies, query parameters, and response models using standard Python syntax. Under the hood, FastAPI uses Pydantic to:

Validate Data: Automatically checks if incoming request data conforms to the specified types. If not, it returns clear, detailed error messages.
Serialize Data: Converts Python objects to JSON and vice-versa, ensuring your API responses are correctly formatted.
Infer Types: Provides excellent editor support and autocompletion due to strong typing.

Example: Defining a Request Body

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

# 1. Define a Pydantic model for the request body
class PromptRequest(BaseModel):
    prompt_text: str
    max_tokens: int = 100 # Optional field with a default value

@app.post("/generate-prompt/")
async def create_prompt(request: PromptRequest):
    # request.prompt_text and request.max_tokens are automatically validated
    # and typed based on the PromptRequest model.
    print(f"Received prompt: {request.prompt_text} with max tokens: {request.max_tokens}")
    return {"message": "Prompt received", "prompt": request.prompt_text}

In this example, if a request to /generate-prompt/ does not include prompt_text as a string or max_tokens as an integer, FastAPI will automatically return a 422 Unprocessable Entity error with clear details.

3. Automatic Interactive API Documentation

FastAPI automatically generates interactive API documentation from your code, using Swagger UI and ReDoc. This documentation is accessible at /docs and /redoc by default when your application is running.

Swagger UI: Provides a user-friendly interface to visualize and interact with your API endpoints directly in the browser. You can test your endpoints, see expected request formats, and understand response structures.
ReDoc: Offers an alternative, more concise documentation style.

This feature simplifies collaboration within the team and with other teams that might consume your API.

4. Dependency Injection System

FastAPI has a robust dependency injection system that allows you to:

Manage Database Connections: Inject database sessions directly into your route functions.
Handle Authentication: Easily inject authenticated user objects into protected routes.
Improve Testability: Mock dependencies for isolated unit and integration testing.

5. Asynchronous Support (`async`/`await`)

Python’s async and await keywords are fully supported, allowing you to write highly concurrent code. This is particularly useful when your API needs to make external calls to LLMs, databases, or other services, preventing your server from blocking while waiting for these operations to complete.