pyconfigre 0.2.0rc1

Flexible configuration management with Pydantic validation, YAML/JSON/TOML support, and fluent API

🚀 PyConfigre

Configuration management for Python applications. Elegant, type-safe, and built for real-world needs.

PyConfigre bridges the gap between manual configuration management and heavyweight tools. It combines Pydantic's robust validation with support for multiple formats (YAML, JSON, TOML), environment variables, and a fluent API that reads like natural Python. Need the pipeline without a schema? PyConfigre has you covered with RawConfigBuilder. Whether you're building microservices, web applications, or CLI tools, PyConfigre handles configuration without getting in the way.

✨ Why PyConfigre?

Configuration shouldn't be complicated. Most tools either do too much or too little. PyConfigre fits the middle ground:

• For teams who want type safety without sacrificing simplicity
• For projects that need to merge configurations from multiple sources
• For developers who prefer explicit, readable code over magic configuration files

🎯 Core Features

• 📋 Multi-Format Loading: YAML, JSON, TOML, environment variables, or Python dictionaries
• ✓ Type-Safe Validation: Full Pydantic integration for bulletproof configuration
• 🔀 Smart Priority Handling: Effortlessly merge multiple sources (file → env → dict)
• ⛓️ Fluent API: Chainable methods that read naturally
• 🎯 Format Auto-Detection: Automatically identify and parse files
• 🐍 Modern Python: Full type hints and Python 3.10+ syntax
• ** Schema-Free Option**: Use RawConfigBuilder when you just need a merged dict — no Pydantic required
• 🪶 Lightweight: Only pydantic and pyyaml required for core functionality

🚀 Getting Started

Installation

# Basic installation (JSON & YAML support)
pip install pyconfigre

# Add TOML support
pip install pyconfigre[toml]

# With uv support
uv add pyconfigre

30-Second Example

from pydantic import BaseModel
from pyconfigre import ConfigBuilder

class AppConfig(BaseModel):
    """Your application configuration schema."""
    debug: bool = False
    port: int = 8000
    host: str = "0.0.0.0"
    database_url: str

# Load from file → environment → runtime overrides
config = (
    ConfigBuilder(AppConfig)
    .from_file("config.yaml")
    .from_env("MYAPP_")
    .from_dict({"debug": True})
    .build()
)

# Type-safe access with IDE autocomplete
print(f"Server: {config.host}:{config.port}")

That's it. Three lines of configuration loading. Three sources merged intelligently. Full type safety throughout.

Schema-Free Alternative

Don't need typed validation? Use RawConfigBuilder to get a plain dictionary:

from pyconfigre import RawConfigBuilder

raw_config = (
    RawConfigBuilder()
    .from-file("config.yaml")
    .from_env("MYAPP_")
    .set("debug", True)  # Explicit override
    .build_dict()
)

# config is a plain dict — no schema needed
print(f"Port: {raw_config['port']}")

Same pipeline, same priority system, same multi-format support — just without the Pydantic model.

� Common Patterns

Pattern 1: File + Environment + Overrides

The most common real-world pattern:

config = (
    ConfigBuilder(AppConfig)
    .from_file("config/default.yaml")      # Base configuration
    .from_env("MYAPP_")                    # Environment overrides
    .from_dict(runtime_overrides)          # Runtime values (highest priority)
    .build()
)

Priority order (lowest to highest): File → Environment → Dict → Explicit .set() method

Pattern 2: Environment-Specific Configuration

Load different configurations per environment:

import os

environment = os.getenv("ENV", "development")
config = (
    ConfigBuilder(AppConfig)
    .from_file(f"config/{environment}.yaml")        # Environment-specific base
    .from_file("config/local.yaml", optional=True)  # Local dev overrides
    .from_env("MYAPP_")
    .build()
)

Pattern 3: Configuration with Nested Models

Structure complex configurations with nested Pydantic models:

from pydantic import BaseModel, Field

class DatabaseConfig(BaseModel):
    host: str
    port: int = 5432
    username: str
    password: str
    database: str

class CacheConfig(BaseModel):
    enabled: bool = True
    ttl_seconds: int = 3600
    backend: str = "redis"

class AppConfig(BaseModel):
    app_name: str
    debug: bool = False
    database: DatabaseConfig
    cache: CacheConfig = Field(default_factory=CacheConfig)

config = ConfigBuilder(AppConfig).from_file("config.yaml").build()

# Access nested values with full type safety
print(f"DB: {config.database.host}:{config.database.port}")
print(f"Cache TTL: {config.cache.ttl_seconds}s")

Pattern 4: Schema-Free Configuration

use RawConfigBuilder when you need the pipeline but not the validation — scripts, CLI tools, migrations, or quick prototypes:

from pyconfigre import RawConfigBuilder

# Full pipeline, plain dict output
raw_config = (
    RawConfigBuilder()
    .from_file("config/default.yaml")
    .from_env("MYAPP_")
    .from_dict({"timeout": 30})
    .build_dict()
)

# inspect mid-chain without finalising
raw_builder = RawConfigBuilder().from_file("config.yaml")
print(raw_builder.peek())  # snapshot of current state
raw_builder.set("port", 9000)
raw_config = raw_builder.build_dict()

RawConfigBuilder shares the exact same loading, merging, and priority logic as ConfigBuilder — it just skips the Pydantic validation step.

Pattern 5: Validation with Constraints

Let Pydantic enforce your business rules:

from pydantic import BaseModel, Field

class AppConfig(BaseModel):
    port: int = Field(default=8000, ge=1, le=65535)      # Port range
    workers: int = Field(default=4, ge=1, le=256)        # Worker count
    timeout: float = Field(default=30.0, gt=0)           # Positive timeout
    environment: str = Field(default="production", pattern="^(dev|staging|production)$")

# Invalid config raises validation error
config = ConfigBuilder(AppConfig).from_dict({"port": 99999}).build()  # ❌ Fails validation

🎯 Configuration Priority

PyConfigre implements a simple, predictable priority system. Later sources override earlier ones:

File → Environment Variables → Dictionary → Explicit .set() method
↑                                            ↑
Lowest Priority                              Highest Priority

Example:

# YAML file has: debug: false, port: 8000
# ENV var: MYAPP_DEBUG=true
# Dict: {"port": 3000}

config = (
    ConfigBuilder(AppConfig)
    .from_file("config.yaml")  # debug=false, port=8000
    .from_env("MYAPP_")        # debug=true (overrides file)
    .from_dict({"port": 3000}) # port=3000 (overrides both)
    .build()
)

# Result: debug=true, port=3000

This pattern matches how most systems expect configuration to work—sensible defaults in files, environment-specific overrides via env vars, and runtime tweaks via dictionaries.

📦 Supported Formats

Format	File Extension	Dependency	Notes
YAML	.yaml, .yml	pyyaml	Human-readable, widely used
JSON	.json	Built-in (stdlib)	Strict, universally supported
TOML	.toml	tomllib (3.11+) or tomli	Clean syntax, configuration standard
Environment Variables	—	Built-in	Perfect for secrets and overrides
Python Dicts	—	Built-in	Runtime configuration

🏗️ Real-World Example: API Service

Here's a complete, production-like example:

from pydantic import BaseModel, Field
from typing import Optional
from pyconfigre import ConfigBuilder

# Configuration schema
class DatabaseConfig(BaseModel):
    host: str = "localhost"
    port: int = Field(default=5432, ge=1, le=65535)
    username: str
    password: str
    database: str

class LoggingConfig(BaseModel):
    level: str = Field(default="INFO", pattern="^(DEBUG|INFO|WARNING|ERROR|CRITICAL)$")
    format: str = "json"
    output: str = "stdout"

class AppConfig(BaseModel):
    app_name: str = "my-api"
    version: str = "1.0.0"
    debug: bool = False
    host: str = "0.0.0.0"
    port: int = Field(default=8000, ge=1, le=65535)
    database: DatabaseConfig
    logging: LoggingConfig = Field(default_factory=LoggingConfig)
    secret_key: Optional[str] = None

# Load configuration in your application startup
def setup_config() -> AppConfig:
    """Load and validate application configuration."""
    return (
        ConfigBuilder(AppConfig)
        .from_file("config/default.yaml")           # Defaults
        .from_file(f"config/{os.getenv('ENV')}.yaml", optional=True)
        .from_env("MYAPI_")                         # Override with env vars
        .build()
    )

# Usage
config = setup_config()
print(f"Starting {config.app_name} v{config.version}")
print(f"Listening on {config.host}:{config.port}")
print(f"Database: {config.database.host}")

Configuration file (config/default.yaml):

app_name: my-api
version: 1.0.0
debug: false
database:
  host: localhost
  username: app_user
  password: ${DB_PASSWORD}  # Can use env vars or load separately
  database: app_db
logging:
  level: INFO

Environment override (config/production.yaml):

debug: false
database:
  host: prod-db.example.com
  username: prod_user
logging:
  level: WARNING

This approach gives you:

• ✅ Type-safe configuration with validation
• ✅ Environment-specific configurations
• ✅ Secrets via environment variables
• ✅ Sensible defaults that developers can override locally
• ✅ Production ready

🧪 Testing Configuration

import pytest
from pydantic import BaseModel, ValidationError
from pyconfigre import ConfigBuilder

class TestConfig(BaseModel):
    feature_enabled: bool = False
    api_key: str

def test_config_validation():
    """Test that invalid config raises ValidationError."""
    with pytest.raises(ValidationError):
        ConfigBuilder(TestConfig).from_dict({}).build()  # Missing required api_key

def test_config_building():
    """Test successful configuration building."""
    config = (
        ConfigBuilder(TestConfig)
        .from_dict({"api_key": "test-key"})
        .set("feature_enabled", True)
        .build()
    )
    assert config.feature_enabled is True
    assert config.api_key == "test-key"

def test_config_priority():
    """Test configuration priority (file < env < dict)."""
    config = (
        ConfigBuilder(TestConfig)
        .from_dict({"api_key": "from-dict", "feature_enabled": False})
        .from_dict({"api_key": "from-second-dict"})  # Overrides
        .build()
    )
    assert config.api_key == "from-second-dict"  # Later dict wins

✅ Best Practices

1. Define configuration schemas early – Use Pydantic models as your configuration contract
2. Use environment variables for secrets – Never hardcode API keys, passwords, or tokens
3. Provide sensible defaults – Make common values optional so developers have fewer things to configure
4. Fail fast on invalid config – Call .build() early in your app startup, not lazily
5. Test your configuration – Write tests that verify config loading from different sources
6. Version your schema – Document breaking changes to your config structure
7. Use type hints everywhere – Let IDE autocomplete guide developers using your app

🔐 Security Considerations

Never hardcode secrets:

# ❌ DON'T DO THIS
config = ConfigBuilder(AppConfig).from_dict({
    "api_key": "sk-1234567890abcdef"
}).build()

# ✅ DO THIS
config = (
    ConfigBuilder(AppConfig)
    .from_file("config.yaml")  # No secrets in file
    .from_env("MYAPP_")        # Get secrets from environment
    .build()
)

Use strong validation:

from pydantic import BaseModel, Field

class SecureConfig(BaseModel):
    api_key: str = Field(min_length=32)                        # Enforce minimum length
    timeout: float = Field(gt=0)                               # Must be positive
    environment: str = Field(pattern="^(dev|staging|prod)$")   # Whitelist values

# Invalid config is rejected before it can cause problems
config = ConfigBuilder(SecureConfig).from_env("APP_").build()  # Fails fast if invalid

Restrict file permissions:

# Ensure config files with sensitive data are readable only by your app
chmod 600 config/production.yaml

📊 Project Status

Aspect	Status
Version	0.2.0rc1
Python Support	3.10, 3.11, 3.12, 3.13, 3.14
Test Coverage	~100% (200 tests)
Type Checking	Full MyPy strict compliance
CI/CD	GitHub Actions (multi-version testing)

🤝 Contributing

Contributions are welcome! Whether it's:

• Bug reports or feature requests (open an issue)
• Documentation improvements
• Code contributions (fork and submit a PR)

Please follow the existing code style and include tests for new features.

📄 License

MIT License - see LICENSE file for details.

👨‍💻 Author

👤 Aditya Barik

---

Questions or feedback? Open an issue on GitHub.

Built with ❤️ for teams who care about clean, maintainable, type-safe Python code.