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This page shows how to validate and structure data when using Gravix Layer with PydanticAI for robust input/output validation and schema-driven workflows.

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What You’ll Learn​

  • How to validate LLM outputs with Pydantic schemas
  • How to use Gravix Layer’s API for structured data
  • Example: Enforcing output formats for data extraction

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1. Install Required Packages​

pip install pydantic-ai python-dotenv

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2. Configure Your API Key​

Add your API key to a .env file: 
GRAVIXLAYER_API_KEY=your_api_key_here

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3. Using PydanticAI with Gravix Layer​

from pydantic_ai import Agent, StructuredDict
import os
from dotenv import load_dotenv

load_dotenv()
api_key = os.environ.get("GRAVIXLAYER_API_KEY")

agent = Agent(
	model="meta-llama/llama-3.1-8b-instruct",
	output_type=StructuredDict({
		"type": "object",
		"properties": {
			"name": {"type": "string"},
			"age": {"type": "integer"}
		},
		"required": ["name", "age"]
	}),
	model_settings={
		"base_url": "https://api.gravixlayer.com/v1/inference",
		"api_key": api_key
	}
)

result = agent.run_sync("Extract name and age from: John Doe, 34 years old.")
print(result.output)
Expected Output: 
{'name': 'John Doe', 'age': 34}
Tips:
  • The model_settings argument must include the correct base_url and api_key for Gravix Layer.
PydanticAI lets you enforce structure and validation on LLM outputs. Gravix Layer provides the inference power for any schema-driven task.
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