Configuration

Relevant source files

The following files were used as context for generating this wiki page:

• config/README.md
• PRODUCTION_CHECKLIST.md
• ENV_SETUP_GUIDE.md
• DOCKER_MCP_DEPLOYMENT.md
• TRANSFORMERS_INTEGRATION_ANALYSIS.md
• UNICORN_QUICK_START_CHECKLIST.md

The DevSkyy platform utilizes a unified, enterprise-grade configuration system designed for security, clarity, and maintainability. It provides a single source of truth for all application settings, leveraging Pydantic for validation and type safety. This system ensures that no secrets are hardcoded and that configuration loading follows a clear and predictable order of precedence.

The core design principles are immutability (configurations are frozen after creation), type safety, and self-documentation through descriptive fields. This approach is critical for production readiness and aligns with the project's "Truth Protocol," which mandates explicit, verifiable settings.

Sources: config/README.md, PRODUCTION_CHECKLIST.md:20-24

Configuration Loading and Precedence

The system loads settings from multiple sources in a specific order, with later sources overriding earlier ones. This provides flexibility for different environments (development, testing, production) while maintaining a clear hierarchy.

The following diagram illustrates the configuration loading flow.

The precedence is as follows:

1. Environment Variables: Highest priority. These are ideal for production and CI/CD environments.
2. .env File: Values loaded from a .env file in the project root. Useful for local development.
3. Runtime Overrides: Programmatic overrides.
4. Config Class Defaults: Lowest priority. These are the default values defined within the Pydantic configuration models.

Sources: config/README.md:14-20

Unified Configuration Structure

The configuration is managed through a central UnifiedConfig class which contains nested Pydantic models for different sections of the application. This modular structure organizes settings logically and provides type safety for each group of parameters.

Sources: PRODUCTION_CHECKLIST.md:21-23, config/README.md:58-64

The diagram below shows the class structure of the unified configuration system.

Modern Python Configuration (pyproject.toml)

In addition to the application's runtime configuration, the project leverages pyproject.toml for modern Python project management, including tool configuration for quality assurance. This aligns with enterprise standards observed in repositories like SkyyRoseLLC/transformers.

[tool.ruff]
target-version = "py311"
line-length = 119
src = ["src"]

[tool.ruff.lint]
select = ["E", "F", "I", "W", "C", "UP", "RUF"]
ignore = ["E501"]

[tool.ruff.format]
quote-style = "double"
indent-style = "space"

This snippet shows the recommended Ruff linter and formatter configuration for DevSkyy.

Sources: TRANSFORMERS_INTEGRATION_ANALYSIS.md:121-134

Environment Variables Quick Reference

All configuration can be controlled via environment variables. The system is designed to be "secure by default," requiring critical values like SECRET_KEY to be set explicitly in production environments.

Sources: config/README.md:133-137, ENV_SETUP_GUIDE.md:11-17

General

Variable	Description	Default
ENVIRONMENT	The runtime environment.	development
LOG_LEVEL	The application log level.	INFO
DEBUG	Enables or disables debug mode.	false

Sources: DOCKER_MCP_DEPLOYMENT.md:188-192, config/README.md:89-91

Security Configuration

A strong SECRET_KEY is mandatory for production. It is used for signing JWTs and other cryptographic operations.

Variable	Description	Default
SECRET_KEY	Required in production. 32+ character key for JWTs.	Auto-generated in dev
JWT_ALGORITHM	Algorithm for JWT signing.	HS256
ACCESS_TOKEN_EXPIRE_MINUTES	Expiry time for access tokens.	30
PASSWORD_HASH_ALGORITHM	Hashing algorithm for passwords.	argon2id
MIN_PASSWORD_LENGTH	Minimum required password length.	12

Sources: config/README.md:76-87, UNICORN_QUICK_START_CHECKLIST.md:120-123

Database Configuration

The application supports multiple database backends, which can be configured with a single URL or individual parameters.

Variable	Description	Default
DATABASE_URL	Full connection string for the database.	sqlite:///./devskyy.db
NEON_DATABASE_URL	Connection string for a Neon serverless DB.	None
SUPABASE_DATABASE_URL	Connection string for a Supabase DB.	None
DB_POOL_SIZE	Number of connections to keep in the pool.	5
DB_MAX_OVERFLOW	Max overflow connections allowed.	10

Sources: config/README.md:66-74, ENV_SETUP_GUIDE.md:32-55

AI Services Configuration

API keys for external AI services are managed via environment variables.

Variable	Description	Source
ANTHROPIC_API_KEY	API key for Anthropic (Claude) models.	console.anthropic.com
OPENAI_API_KEY	API key for OpenAI (GPT) models.	platform.openai.com
AI_DEFAULT_MODEL	The default model to use for requests.	claude-sonnet-4-5
AI_MAX_TOKENS	Default max tokens for model responses.	4096
AI_TEMPERATURE	Default sampling temperature for models.	0.7

Sources: config/README.md:125-131, ENV_SETUP_GUIDE.md:20-29, 60-67

Redis Configuration

Variable	Description	Default
REDIS_URL	Connection URL for the Redis server.	redis://localhost:6379
REDIS_MAX_CONNECTIONS	Max connections in the Redis pool.	50
REDIS_DEFAULT_TTL	Default time-to-live for cached items (seconds).	3600

Sources: config/README.md:103-107, UNICORN_QUICK_START_CHECKLIST.md:119

Production Validation

The system includes a validation function to ensure that all critical configurations are set correctly for a production environment. This check is a key part of the Production Readiness Checklist.

The validate_production_config() function checks for:

• A non-default, securely generated SECRET_KEY.
• A production-ready DATABASE_URL (i.e., not SQLite).
• The ENVIRONMENT variable is set to production.

This validation helps prevent common misconfigurations when deploying to production.

Sources: PRODUCTION_CHECKLIST.md:21-24, 29-30, config/README.md:43-51

Summary

The DevSkyy configuration system is a robust and secure foundation for the application. By centralizing settings, enforcing type safety with Pydantic, and prioritizing environment variables for sensitive data, it adheres to modern best practices. This unified approach simplifies management across different deployment environments, enhances security by eliminating hardcoded secrets, and improves overall maintainability, which is crucial for an enterprise-grade platform.

Sources: config/README.md:1-12, PRODUCTION_CHECKLIST.md:20-24