Django REST Framework Integration

Overview

This guide explains how to integrate Pydantic2 with Django REST Framework (DRF).

For client configuration options, see the Configuration Guide.

Pydantic2 provides seamless integration with Django REST Framework using drf-pydantic for proper serializer support.

Data Flow

graph LR
    Client[Client] --> DRF[DRF View]
    DRF --> AI[PydanticAIClient]
    AI --> PM[Pydantic Model]
    PM --> Ser[DRF Serializer]
    Ser --> Client

    style AI fill:#f9f,stroke:#333
    style PM fill:#f9f,stroke:#333

Installation

pip install drf-pydantic

Example Usage

from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import serializers
from pydantic import Field
from drf_pydantic import BaseModel
from typing import List
from pydantic2 import PydanticAIClient, ModelSettings


class FeedbackAnalysis(BaseModel):
    summary: str = Field(..., description="Summary of the feedback")
    sentiment: str = Field(..., description="Detected sentiment")
    key_points: List[str] = Field(..., description="Key points from the feedback")


class FeedbackSerializer(serializers.Serializer):
    feedback = FeedbackAnalysis.drf_serializer()


class FeedbackView(APIView):
    def post(self, request):
        feedback = request.data.get('feedback', '')

        client = PydanticAIClient(
            model_name="openai/gpt-4o-mini-2024-07-18",
            client_id="demo_client",
            user_id=str(request.user.id),
            verbose=False,
            retries=3,
            online=True,
            max_budget=1,
            model_settings=ModelSettings(
                max_tokens=1000,
                temperature=0.7,
                top_p=1,
                frequency_penalty=0,
            )
        )

        # Set up the conversation with system message
        client.message_handler.add_message_system(
            "You are a helpful AI assistant. Be concise but informative."
        )

        client.message_handler.add_message_block('FEEDBACK', feedback)

        response: FeedbackAnalysis = client.generate(
            result_type=FeedbackAnalysis
        )

        serializer = FeedbackSerializer(data={
            "feedback": response.model_dump()
        })
        if serializer.is_valid():
            return Response(serializer.data)
        else:
            return Response(serializer.errors, status=400)

This example demonstrates:

Using drf_pydantic.BaseModel for proper DRF serializer integration
Automatic conversion of Pydantic fields to DRF serializer fields
Using message handler for structured prompts
Proper response validation and serialization

Additional Features

The drf-pydantic library automatically converts Pydantic field properties to their DRF equivalents: - description → help_text - title → label - min_length/max_length for string constraints - pattern → uses RegexField - ge/gt → min_value - le/lt → max_value