stringent 0.1.0

Parse strings into Pydantic models using pattern matching

stringent

stringent is a powerful Python library that seamlessly parses strings into Pydantic models using flexible pattern matching. Whether you're working with pipe-separated values, space-separated data, JSON strings, or custom formats, stringent makes it easy to convert unstructured strings into validated, type-safe Python objects.

Features

✨ Flexible Pattern Matching - Parse strings using format-like patterns (e.g., {name} | {age} | {city})

🔗 Pattern Chaining - Chain multiple patterns using the | operator to try patterns in order until one matches

🔄 Automatic Input Handling - Seamlessly handles both dictionary and string inputs without code changes

🎯 Pydantic Integration - Built on Pydantic 2.0+ for robust validation and type safety

📦 JSON Support - Built-in JSON parsing with automatic fallback to pattern matching

🔀 Union Types - Organize parsing strategies using union types for maximum flexibility

🧬 Inheritance Support - Parse patterns are inherited and can be overridden in subclasses

Installation

pip install stringent

Quick Start

from pydantic import BaseModel, EmailStr
from typing import Literal
from stringent import parse, parse_json, ParsableModel

class Info(BaseModel):
    name: str
    age: int
    city: str

class Record(ParsableModel):
    id: int
    info: Info = parse_json() | parse('{name} | {age} | {city}') | parse('{name} {age} {city}')
    email: EmailStr
    status: Literal['Active', 'Inactive']

# Parse the data - handles dicts, strings, and JSON automatically
data = [
    {'id': 1, 'info': {'name': 'Alice', 'age': 30, 'city': 'New York'}, 'email': '[e-mail]', 'status': 'Active'},
    {'id': 3, 'info': 'Charlie | 27 | Chicago', 'email': '[e-mail]', 'status': 'Active'},
    {'id': 5, 'info': 'Eve 35 Dallas', 'email': '[e-mail]', 'status': 'Inactive'},
    {'id': 8, 'info': '{"name": "Joe", "age": 55, "city": "Tampa"}', 'email': '[e-mail]', 'status': 'Active'},
]

for item in data:
    record = Record(**item)
    print(record)

Output:

id=1 info=Info(name='Alice', age=30, city='New York') email='[e-mail]' status='Active'
id=3 info=Info(name='Charlie', age=27, city='Chicago') email='[e-mail]' status='Active'
id=5 info=Info(name='Eve', age=35, city='Dallas') email='[e-mail]' status='Inactive'
id=8 info=Info(name='Joe', age=55, city='Tampa') email='[e-mail]' status='Active'

Why stringent?

Working with mixed data formats is a common challenge in data processing. You might receive:

• Dictionary objects from APIs
• Pipe-separated strings from legacy systems
• Space-separated values from log files
• JSON strings from message queues

stringent eliminates the need for manual parsing logic by automatically handling all these formats with a single, declarative definition.

Key Use Cases

• API Integration - Handle inconsistent data formats from different endpoints
• Data Migration - Parse legacy data formats while maintaining type safety
• Log Processing - Parse structured log entries into validated models
• ETL Pipelines - Transform unstructured strings into typed data structures
• Configuration Parsing - Support multiple configuration formats with fallback patterns

Documentation

Comprehensive documentation is available in the docs directory:

• Getting Started - Installation and basic concepts
• Basic Usage - Field-level parsing, pattern chaining, and common patterns
• Advanced Patterns - Union types, inheritance, and complex scenarios
• API Reference - Complete API documentation
• Documentation Index - Overview and quick links

Requirements

• Python 3.10 or higher
• Pydantic 2.0 or higher
• parse 1.20 or higher

Dependencies

• pydantic>=2.0.0 - For Pydantic model integration and validation
• parse>=1.20.0 - For string parsing functionality

Examples

Pattern Chaining

Try multiple patterns in order until one matches:

from stringent import parse, ParsableModel
from pydantic import BaseModel

class Info(BaseModel):
    name: str
    age: int
    city: str

class Record(ParsableModel):
    info: Info = parse('{name} | {age} | {city}') | parse('{name} {age} {city}')

# Both formats work automatically
record1 = Record(info="Alice | 30 | NYC")
record2 = Record(info="Bob 25 Chicago")

JSON Parsing

Automatically parse JSON strings with fallback to pattern matching:

from stringent import parse_json, parse, ParsableModel
from pydantic import BaseModel

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

class Record(ParsableModel):
    info: Info = parse_json() | parse('{name} | {age}')

# JSON string
record1 = Record(info='{"name": "Alice", "age": 30}')

# Pattern string (fallback)
record2 = Record(info="Bob | 25")

Union Types

Use union types to organize parsing strategies:

from typing import Union
from stringent import ParsableModel
from pydantic import BaseModel

class Info(ParsableModel):
    name: str
    age: int

class PipeInfo(Info):
    _model_parse_pattern = '{name} | {age}'

class SpaceInfo(Info):
    _model_parse_pattern = '{name} {age}'

class Record(ParsableModel):
    info: Union[PipeInfo, SpaceInfo]

# Automatically selects the correct type
record1 = Record(info="Alice | 30")  # Uses PipeInfo
record2 = Record(info="Bob 25")      # Uses SpaceInfo

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development

To set up a development environment:

# Clone the repository
git clone https://github.com/eddiethedean/stringent.git
cd stringent

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check .
ruff format .

# Run type checking
mypy stringent/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Odos Matthews

• GitHub: @eddiethedean
• Email: [e-mail]

Acknowledgments

• Built on Pydantic for robust data validation
• Uses parse for flexible string parsing

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