Pydantic + LLM Structured Outputs Cheat Sheet

Sat, 21 Jun 2025 00:00:00 +0000

What is Pydantic?

A Python library for data validation and serialization that automatically:

Validates data types
Converts types when possible
Provides clear error messages
Serializes to/from JSON, dictionaries

Basic Pydantic Model

from pydantic import BaseModel

class Evaluation(BaseModel):
 is_acceptable: bool
 feedback: str

Using with LLMs

The Problem

LLMs return unstructured text
Hard to parse and validate
Inconsistent formats

The Solution

Use Pydantic models to enforce structure!

response = gemini.beta.chat.completions.parse(
 model="gemini-2.0-flash", 
 messages=messages, 
 response_format=Evaluation # Your Pydantic model
)

# Returns validated Evaluation object
print(response.is_acceptable) # bool
print(response.feedback) # str

How It Works Under the Hood

Your Pydantic Model → JSON Schema (automatic conversion)
JSON Schema → LLM API (as instructions)
LLM generates → JSON matching schema
Library parses → Validated Pydantic object

Example JSON Schema Generated

{
 "type": "object",
 "properties": {
 "is_acceptable": {"type": "boolean"},
 "feedback": {"type": "string"}
 },
 "required": ["is_acceptable", "feedback"]
}

Benefits

✅ Guaranteed structure - Always get expected fields
✅ Type safety - Fields are correct types
✅ Error handling - Validation catches format errors
✅ Easy processing - Use data immediately in code
✅ No manual parsing - Library handles conversion

Llm on Principles