# Legacy: Development Notes and Lessons Learned (v0.1.0-final)
# Key decisions, challenges, and insights from the development process
# This file documents the journey from concept to current implementation

"""
DEVELOPMENT TIMELINE
===================

v0.1.0-alpha (Week 1-2):
- Basic concept and architecture design
- Simple text chunking implementation
- In-memory vector storage
- Basic CLI structure

v0.1.0-beta (Week 3-4):
- Pinecone integration attempt
- Embedding model experimentation
- Usage tracking system design
- Package structure planning

v0.1.0-rc1 (Week 5-6):
- LLM-based chunking implementation
- Vector database optimization
- Error handling strategies
- Testing framework selection

v0.1.0-rc2 (Week 7-8):
- Graph visualization development
- CLI refinement
- Configuration management
- Documentation creation

v0.1.0-rc3 (Week 9-10):
- Performance optimization
- User experience improvements
- Final testing and bug fixes
- Release preparation

v0.2.0 (Current):
- Production-ready implementation
- Comprehensive documentation
- Example scripts and guides
- Legacy code preservation
"""

# KEY DECISIONS AND RATIONALE
# ===========================

DECISIONS = {
    "package_structure": {
        "decision": "Flat package structure",
        "alternatives": ["Nested hierarchy", "Feature-based", "Layer-based"],
        "rationale": "Simpler imports, easier navigation, better for focused library",
        "tradeoffs": "Less organization, potential for larger files"
    },
    
    "chunking_approach": {
        "decision": "LLM-based semantic chunking",
        "alternatives": ["Fixed-size chunks", "Rule-based", "TF-IDF"],
        "rationale": "Better semantic understanding, more natural boundaries",
        "tradeoffs": "API costs, dependency on external service"
    },
    
    "vector_database": {
        "decision": "Pinecone",
        "alternatives": ["In-memory", "SQLite", "Redis", "Qdrant"],
        "rationale": "Scalable, managed service, good performance",
        "tradeoffs": "External dependency, potential costs"
    },
    
    "embedding_model": {
        "decision": "GoogleGenerativeAIEmbeddings",
        "alternatives": ["Sentence Transformers", "OpenAI", "Local models"],
        "rationale": "Good quality, reasonable cost, Google ecosystem",
        "tradeoffs": "API dependency, potential rate limits"
    },
    
    "visualization": {
        "decision": "Pygame-based interactive GUI",
        "alternatives": ["Matplotlib", "Plotly", "Tkinter", "Web-based"],
        "rationale": "Interactive, cross-platform, good performance",
        "tradeoffs": "Additional dependency, not web-accessible"
    },
    
    "error_handling": {
        "decision": "Structured exceptions with logging",
        "alternatives": ["Result pattern", "Error codes", "Silent failures"],
        "rationale": "Python standard, good debugging, user-friendly",
        "tradeoffs": "More verbose, requires exception handling"
    },
    
    "testing": {
        "decision": "Pytest framework",
        "alternatives": ["Unittest", "Manual testing", "Doctest"],
        "rationale": "Simple, powerful, good ecosystem",
        "tradeoffs": "Additional dependency, learning curve"
    },
    
    "configuration": {
        "decision": "Environment variables + defaults",
        "alternatives": ["Config files", "Hardcoded", "Hybrid approaches"],
        "rationale": "Security best practice, deployment friendly",
        "tradeoffs": "Less flexible, harder to manage many settings"
    },
    
    "packaging": {
        "decision": "Setuptools + pyproject.toml",
        "alternatives": ["Poetry", "Flit", "Hatch", "Manual"],
        "rationale": "Standard Python, no extra tools, familiar",
        "tradeoffs": "Less modern, more configuration"
    }
}

# CHALLENGES ENCOUNTERED
# =======================

CHALLENGES = {
    "pinecone_integration": {
        "description": "Pinecone API v2.x breaking changes",
        "symptoms": "ImportError: cannot import name 'Pinecone'",
        "solution": "Updated to use new API patterns",
        "lesson": "Always check API version compatibility"
    },
    
    "import_structure": {
        "description": "Relative vs absolute imports confusion",
        "symptoms": "ImportError: attempted relative import with no known parent package",
        "solution": "Standardized on absolute imports for flat structure",
        "lesson": "Package structure affects import strategy"
    },
    
    "embedding_quality": {
        "description": "Poor search results with basic embeddings",
        "symptoms": "Irrelevant chunks returned for queries",
        "solution": "Switched to LLM-based embeddings",
        "lesson": "Quality of embeddings directly affects search quality"
    },
    
    "chunking_consistency": {
        "description": "Inconsistent chunk sizes and boundaries",
        "symptoms": "Some chunks too small, others too large",
        "solution": "LLM-based semantic chunking",
        "lesson": "Semantic understanding beats rule-based approaches"
    },
    
    "performance_scaling": {
        "description": "Slow performance with large datasets",
        "symptoms": "Long response times for queries",
        "solution": "Vector database optimization and caching",
        "lesson": "Plan for scale from the beginning"
    },
    
    "user_experience": {
        "description": "Complex setup and configuration",
        "symptoms": "Users struggled with installation",
        "solution": "Simplified packaging and clear documentation",
        "lesson": "User experience is as important as functionality"
    }
}

# LESSONS LEARNED
# ===============

LESSONS_LEARNED = [
    "Start with the simplest approach that could work, then iterate",
    "User experience matters - make it easy to get started",
    "Documentation is not optional for a library",
    "Testing should be part of the development process from day one",
    "Package structure affects everything - plan it carefully",
    "External dependencies add complexity - choose wisely",
    "Performance matters even for small datasets",
    "Error handling is part of the user interface",
    "Configuration should be simple and secure by default",
    "Legacy code preservation helps with future development"
]

# TECHNICAL INSIGHTS
# ==================

TECHNICAL_INSIGHTS = {
    "vector_search": "Semantic similarity beats keyword matching for transcript search",
    "chunking": "LLM-based chunking provides more natural boundaries than fixed-size",
    "caching": "Usage tracking improves search relevance over time",
    "visualization": "Interactive graphs help users understand their data better",
    "packaging": "Modern Python packaging standards make distribution much easier",
    "testing": "Good testing practices prevent regressions and improve code quality",
    "error_handling": "Structured error handling improves debugging and user experience",
    "configuration": "Environment variables are the right choice for sensitive data"
}

# FUTURE IMPROVEMENTS
# ===================

FUTURE_IMPROVEMENTS = [
    "Add support for more LLM providers (OpenAI, Anthropic)",
    "Implement streaming responses for large queries",
    "Add web-based visualization interface",
    "Support for more file formats (PDF, DOCX, etc.)",
    "Implement advanced caching strategies",
    "Add support for collaborative editing",
    "Implement version control for transcripts",
    "Add support for multiple languages",
    "Implement advanced analytics and reporting",
    "Add plugin system for extensibility"
]

# ARCHITECTURE DECISIONS
# ======================

ARCHITECTURE_DECISIONS = {
    "modular_design": "Each component (chunking, storage, retrieval) is independent",
    "interface_based": "Clear interfaces between components for easy testing",
    "data_flow": "Linear pipeline: transcript → chunks → vectors → search → results",
    "state_management": "Minimal state, mostly stateless operations",
    "error_propagation": "Errors bubble up with context for debugging",
    "configuration": "Centralized configuration with clear precedence rules",
    "logging": "Structured logging for debugging and monitoring",
    "caching": "Usage-based caching for improved performance"
}

# PERFORMANCE CONSIDERATIONS
# ==========================

PERFORMANCE_CONSIDERATIONS = [
    "Vector search is O(n) - consider indexing strategies for large datasets",
    "LLM API calls are slow - implement caching and batching",
    "Memory usage scales with chunk count - monitor and optimize",
    "Disk I/O for usage tracking - consider database for large deployments",
    "Network latency for external APIs - implement timeouts and retries",
    "UI responsiveness - use async operations for long-running tasks",
    "Memory management - clear unused data and implement garbage collection"
]

# SECURITY CONSIDERATIONS
# =======================

SECURITY_CONSIDERATIONS = [
    "API keys in environment variables, never in code",
    "Input validation for all user-provided data",
    "No arbitrary code execution or file system access",
    "Rate limiting for external API calls",
    "Secure storage of usage data and analytics",
    "Regular dependency updates for security patches",
    "No logging of sensitive user data"
]

# DEPLOYMENT CONSIDERATIONS
# =========================

DEPLOYMENT_CONSIDERATIONS = [
    "Containerization with Docker for consistent environments",
    "Environment-specific configuration management",
    "Health checks and monitoring for production deployments",
    "Backup strategies for vector database and usage data",
    "Scaling considerations for high-traffic deployments",
    "CI/CD pipeline for automated testing and deployment",
    "Documentation for deployment and operations teams"
]

# USER FEEDBACK INTEGRATION
# =========================

USER_FEEDBACK_INTEGRATION = [
    "Simplified installation process based on user confusion",
    "Added comprehensive examples and documentation",
    "Improved error messages for better debugging",
    "Added interactive visualization based on user requests",
    "Streamlined CLI interface for better usability",
    "Added export functionality for data portability",
    "Improved performance based on user testing feedback"
]

# CONCLUSION
# ==========

"""
The development of EchoGem has been a journey of iteration and improvement.
Key takeaways:

1. Start Simple: Begin with the simplest approach that could work
2. User First: Always consider the user experience
3. Test Early: Testing prevents regressions and improves code quality
4. Document Everything: Good documentation is essential for adoption
5. Iterate Quickly: Learn from mistakes and improve continuously
6. Plan for Scale: Consider performance and scalability from the start
7. Security Matters: Follow security best practices from day one
8. Community Feedback: User feedback drives meaningful improvements

The current implementation represents the best balance of features,
performance, and usability based on lessons learned during development.
"""
