feat: Add production logging system + project structure improvements

[lkronecker13]

🏗️ Major Feature: Production-Ready Logging System

This PR transforms the AI Base Template with a complete enterprise-grade logging infrastructure plus essential project structure improvements.

🚀 Major Additions

📊 Comprehensive Logging System (src/logging.py)

• ✅ Structured logging with Structlog - Industry-standard logging framework
• ✅ Dual output formats: JSON for production, human-readable for development
• ✅ Context propagation - Correlation IDs and request tracing across services
• ✅ Environment-based configuration with sensible defaults
• ✅ Professional architecture - Clean class-based HumanReadableFormatter
• ✅ 225 lines of production-ready code with full type hints and documentation

🧪 Extensive Test Coverage (tests/test_logging.py)

• ✅ 14 functionality-driven tests (355+ lines) following modern testing practices
• ✅ End-to-end workflow testing - Real request simulation across services
• ✅ Concurrent request isolation - Ensures context doesn't leak between requests
• ✅ Edge case coverage - Long values, special characters, empty values
• ✅ Modern naming conventions - test__component__behavior pattern
• ✅ Professional organization - Clear section headers matching code structure

📁 Project Structure Improvements

Directory Standardization

• 📁 Renamed: ai_base_template/ → src/ (Python packaging standard)
• 🔧 Updated references across 14+ files (Makefile, pyproject.toml, tests, docs)
• ⚡ Added make run command for easy application execution
• 📚 Enhanced main module with logging demonstration and proper entry point

Developer Experience Enhancements

• ✨ Professional code organization with consistent section headers
• 📖 Clear documentation with configuration explanations
• 🎯 Modern Python practices throughout the codebase
• 🔧 Improved Makefile with new application runner

📊 Impact & Statistics

Metric	Before	After	Improvement
Total Tests	4	18	+350%
Logging Tests	0	14	New Feature
Lines of Code	~258	~858	+600 net lines
Test Coverage	100%	100%	Maintained
Functionality	Basic	Production-Ready	Enterprise-Grade

🔧 Technical Features

Logging Capabilities

# Context propagation across services
bind_context_vars(correlation_id="req-123", user_id="user-456")

# Structured logging with automatic context
logger.info("Processing request", endpoint="/api/users", method="POST")
# → JSON: {"correlation_id": "req-123", "user_id": "user-456", "endpoint": "/api/users"}

# Human-readable format for development  
# → "14:30:45 [INFO] api.users: Processing request [endpoint=/api/users] [id:req-123]"

Environment Configuration

# Production (JSON logs)
LOGGING_LEVEL=INFO python -m src.main

# Development (human-readable)  
configure_structlog(testing=True)

Real-World Testing

def test__end_to_end_logging_workflow__maintains_context_and_formats_correctly():
    """Tests complete request lifecycle with context propagation"""
    # Simulates auth → database → API response flow
    # Validates context isolation and format consistency

✅ Quality Assurance

• 🧪 All 18 tests passing (14 logging + 4 main module)
• 🔍 100% test coverage maintained
• 🎨 Pre-commit hooks successful (formatting, linting, type checking)
• 📝 Type hints throughout with strict mypy compliance
• 🏗️ Clean architecture following separation of concerns
• 📚 Comprehensive documentation with practical examples

🎯 Benefits for Template Users

1. 📈 Production-Ready Infrastructure - Enterprise logging from day one
2. 🔧 Easy Integration - Drop-in logging with minimal configuration
3. 🐛 Better Debugging - Human-readable logs in development
4. 📊 Observability - Structured logs for monitoring and analysis
5. 🚀 Scalable Architecture - Context propagation supports microservices
6. ✅ Battle-Tested - Comprehensive test coverage ensures reliability

🏁 Migration & Usage

For New Projects

make run  # See logging in action
# Logs demonstrate correlation IDs, context propagation, and formatting

Integration Pattern

from src.logging import get_logger, bind_context_vars, configure_structlog

# Configure for your environment
configure_structlog(testing=False)  # JSON for production

# Set request context
bind_context_vars(correlation_id="req-123", user_id="user-456")

# Use throughout your application
logger = get_logger(__name__)
logger.info("Processing started", operation="data_processing")

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🎉 This PR elevates the AI Base Template from a basic project structure to a production-ready foundation with enterprise-grade logging capabilities.

🤖 Generated with Claude Code