Integrating Pydantic Models with FastAPI¶

FastAPI is an enjoyable tool for building web applications in Python. It is well known for its integration with Pydantic models, which makes defining and validating data structures straightforward and efficient. In this guide, we explore how simple functions that return Pydantic models can seamlessly integrate with FastAPI.

Why Choose FastAPI and Pydantic?¶

• FastAPI is a modern, high-performance web framework for building APIs with Python.
• Supports OpenAPI and JSON Schema for automatic documentation and validation.
• Supports AsyncIO for asynchronous programming leveraging the AsyncOpenAI() client

Code Example: Starting a FastAPI App with a POST Request¶

The following code snippet demonstrates how to start a FastAPI app with a POST endpoint. This endpoint accepts and returns data defined by a Pydantic model.

import instructor

from fastapi import FastAPI
from pydantic import BaseModel
from openai import AsyncOpenAI

# Enables response_model
client = instructor.from_openai(AsyncOpenAI())
app = FastAPI()


class UserData(BaseModel):
    # This can be the model for the input data
    query: str


class UserDetail(BaseModel):
    name: str
    age: int


@app.post("/endpoint", response_model=UserDetail)
async def endpoint_function(data: UserData) -> UserDetail:
    user_detail = await client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=UserDetail,
        messages=[
            {"role": "user", "content": f"Extract: `{data.query}`"},
        ],
    )
    return user_detail

Streaming Responses with FastAPI¶

FastAPI supports streaming responses, which is useful for returning large amounts of data. This feature is particularly useful when working with large language models (LLMs) that generate a large amount of data.

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from typing import Iterable
from pydantic import BaseModel

app = FastAPI()


class UserData(BaseModel):
    query: str


class UserDetail(BaseModel):
    name: str
    age: int


# Route to handle SSE events and return users
@app.post("/extract", response_class=StreamingResponse)
async def extract(data: UserData):
    users = await client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=Iterable[UserDetail],
        stream=True,
        messages=[
            {"role": "user", "content": data.query},
        ],
    )

    async def generate():
        async for user in users:
            resp_json = user.model_dump_json()
            yield f"data: {resp_json}"
        yield "data: [DONE]"

    return StreamingResponse(generate(), media_type="text/event-stream")

Automatic Documentation with FastAPI¶

FastAPI leverages the OpenAPI specification to automatically generate a dynamic and interactive documentation page, commonly referred to as the /docs page. This feature is incredibly useful for developers, as it offers a live environment to test API endpoints directly through the browser.

To explore the capabilities of your API, follow these steps:

1. Run the API using the Uvicorn command: uvicorn main:app --reload.
2. Open your web browser and navigate to http://127.0.0.1:8000/docs.
3. You will find an interactive UI where you can send different requests to your API and see the responses in real-time.