api-to-pydantic-utils 0.1.1

Utilities for JSON schema extraction and Pydantic model generation

API to Pydantic Utils

Core utilities for JSON schema extraction and Pydantic model generation

A Python package that provides utilities for processing JSON data and extracting schemas for Pydantic model generation. Originally developed as part of the API to Pydantic project.

Installation

pip install api-to-pydantic-utils

Features

• Schema Extraction: Intelligent JSON schema extraction with 80-95% compression
• Type Detection: Automatic detection of emails, URLs, timestamps, and other patterns
• Model Testing: Utilities for validating Pydantic models against JSON data
• JSON Validation: Simple JSON file validation utilities

Quick Start

from api_to_pydantic_utils import extract_schema, validate_json_file, create_test_file

# Extract schema from JSON data
schema = extract_schema({"name": "John", "email": "[e-mail]"})
print(schema)

# Validate a JSON file
is_valid = validate_json_file("data.json")

# Create test file for a Pydantic model
test_file = create_test_file("model.py", "test_data.json")

API Reference

extract_schema(data, max_depth=10)

Extracts a compressed schema from JSON data.

Parameters:

• data: JSON data (dict, list, or primitive)
• max_depth: Maximum nesting depth to process

Returns: Compressed schema representation

process_json_file(file_path, max_depth=10)

Processes a JSON file and extracts its schema.

Parameters:

• file_path: Path to JSON file
• max_depth: Maximum nesting depth to process

Returns: Tuple of (schema, compression_stats)

validate_json_file(file_path)

Validates if a file contains valid JSON.

Parameters:

• file_path: Path to JSON file

Returns: Boolean indicating if file is valid JSON

create_test_file(model_file_path, json_file_path)

Creates a test file for validating Pydantic models.

Parameters:

• model_file_path: Path to Python file with Pydantic model
• json_file_path: Path to JSON file to test against

Returns: Path to created test file

Type Detection

The package automatically detects various data patterns:

• Email addresses: [e-mail] → EmailStr
• URLs: https://example.com → HttpUrl
• Timestamps: 2023-01-01T00:00:00Z → datetime
• YouTube IDs: Channel and video ID patterns
• Usernames: @username patterns
• Text content: Long text with punctuation

Requirements

• Python 3.8+
• Pydantic 2.0+

License

MIT License

Contributing

This package is part of the API to Pydantic project. Please report issues and contribute there.