Coder DB - AI Memory Enhancement System

A structured memory system for AI assistants to enhance coding capabilities using database integration utilizing Claude Desktop and MCP Servers.

Overview

This system leverages multiple database types to create a comprehensive memory system for coding assistance:

1. Qdrant Vector Database: For semantic search and retrieval of code patterns
2. SQLite Database: For structured algorithm storage and versioning
3. Knowledge Graph: For representing relationships between coding concepts

Database Usage Guide

Qdrant Memory Storage

For storing and retrieving code snippets, patterns, and solutions by semantic meaning.

What to store:

• Reusable code patterns with explanations
• Solutions to complex problems
• Best practices and design patterns
• Documentation fragments and explanations

Enhanced Metadata:

• Language and framework details
• Complexity level (simple, intermediate, advanced)
• Dependencies and requirements
• Quality metrics (cyclomatic complexity, documentation coverage)
• User feedback and ratings

Example Usage:

# Storing a code pattern
information = {
    "type": "code_pattern",
    "language": "python",
    "name": "Context Manager Pattern",
    "code": "class MyContextManager:\n    def __enter__(self):\n        # Setup code\n        return self\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        # Cleanup code\n        pass",
    "explanation": "Context managers provide a clean way to manage resources like file handles.",
    "tags": ["python", "resource management", "context manager"],
    "complexity": "intermediate",
    "quality_metrics": {
        "cyclomatic_complexity": 2,
        "documentation_coverage": 0.85
    },
    "user_rating": 4.5
}
# Store in Qdrant

SQLite Algorithm Database

For maintaining a structured catalog of algorithms with proper versioning.

Database Schema:

• algorithms: Basic algorithm information (name, description)
• algorithm_versions: Different versions of algorithm implementations
• algorithm_categories: Categories like Sorting, Searching, Graph, etc.
• performance_metrics: Performance data for different implementations
• improvements: Tracked improvements between versions
• change_logs: Detailed logs of changes with rationale and context

Version Diffing:

• Store diffs between algorithm versions
• Track performance improvements across versions
• Document rationale behind changes

Example Query:

-- Find all sorting algorithms with performance metrics
SELECT a.name, a.description, v.version_number, p.time_complexity, p.space_complexity
FROM algorithms a
JOIN algorithm_versions v ON a.id = v.algorithm_id
JOIN performance_metrics p ON v.id = p.version_id
JOIN algorithm_category_mapping m ON a.id = m.algorithm_id
JOIN algorithm_categories c ON m.category_id = c.id
WHERE c.name = 'Sorting'
ORDER BY a.name, v.version_number DESC;

-- Get change logs for a specific algorithm
SELECT v.version_number, c.change_description, c.rationale, c.created_at
FROM algorithm_versions v
JOIN change_logs c ON v.id = c.version_id
WHERE v.algorithm_id = 5
ORDER BY v.version_number;

Knowledge Graph Integration

For representing complex relationships between coding concepts, patterns, and solutions.

Advanced Ontology:

• Algorithm
• DesignPattern
• CodeConcept
• ProblemType
• Solution
• Framework
• Library
• Language

Rich Relation Types:

• IMPLEMENTS (Algorithm → CodeConcept)
• SOLVES (DesignPattern → ProblemType)
• OPTIMIZES (Algorithm → Performance)
• RELATED_TO (Any → Any)
• IMPROVES_UPON (Solution → Solution)
• ALTERNATIVELY_SOLVES (Solution → ProblemType)
• EXTENDS (Pattern → Pattern)
• DEPENDS_ON (Solution → Library)
• COMPATIBLE_WITH (Framework → Language)

Graph Analytics:

• Identify frequently co-occurring patterns
• Discover emerging trends in coding practices
• Map problem domains to solution approaches

Usage Workflows

1. Enhanced Problem-Solving Workflow

When facing a new coding problem:

1. Context Gathering:

   • Clearly define the problem and constraints
   • Identify performance requirements and environment details
   • Document project-specific considerations

2. Memory Querying:

   • Break down the problem using sequential thinking
   • Query Qdrant for similar solutions: qdrant-find-memories("efficient way to traverse binary tree")
   • Filter results by language, complexity, and quality metrics
   • Check algorithm database for relevant algorithms: SELECT * FROM algorithms WHERE name LIKE '%tree%'
   • Explore knowledge graph for related concepts and alternative approaches

3. Solution Application:

   • Test and verify solution in REPL
   • Document performance characteristics
   • Compare against alternatives

4. Feedback Loop:

   • Store successful solution back in Qdrant with detailed metadata
   • Log performance metrics and usage context
   • Update knowledge graph connections

2. Pattern Learning & Storage

When discovering a useful pattern:

1. Automated Documentation:

   • Generate initial documentation using AI tools
   • Include detailed usage examples
   • Document edge cases and limitations

2. Quality Assessment:

   • Run linters and static analyzers to ensure code quality
   • Calculate and store quality metrics
   • Validate against best practices

3. Metadata Enrichment:

   • Document the pattern with clear examples
   • Add comprehensive metadata (language, complexity, dependencies)
   • Apply consistent tagging from controlled vocabulary

4. Knowledge Integration:

   • Store in Qdrant with appropriate tags and explanation
   • Create knowledge graph connections to related concepts
   • Add to SQL database if it's an algorithm implementation
   • Suggest automatic connections based on content similarity

3. Project Setup & Boilerplate

When starting a new project:

1. Template Selection:

   • Choose from library of project templates
   • Customize based on project requirements
   • Select language, framework, and testing tools

2. Automated Setup:

   • Generate project structure with proper directory layout
   • Set up version control with appropriate .gitignore
   • Configure linting and code quality tools
   • Initialize testing framework

3. Best Practices Integration:

   • Query memory system for relevant boilerplate code
   • Retrieve best practices for the specific project type
   • Use stored documentation templates for initial setup
   • Configure CI/CD based on project requirements

Security & Data Integrity

1. Access Controls:

   • Role-based access for sensitive code repositories
   • Permissions for viewing vs. modifying memories

2. Backup & Recovery:

   • Regular backups of Qdrant and SQLite databases
   • Version control for knowledge graph
   • Recovery procedures for data corruption

3. Sensitive Information:

   • Sanitize code examples to remove sensitive data
   • Validate code snippets before storage
   • Flag and restrict access to sensitive patterns

Monitoring & Analytics

1. Usage Tracking:

   • Monitor which patterns are most frequently retrieved
   • Track search query patterns to identify knowledge gaps
   • Log user ratings and feedback

2. Performance Metrics:

   • Monitor database response times
   • Track memory usage and scaling requirements
   • Optimize queries based on usage patterns

Maintenance Guidelines

1. Quality over Quantity: Only store high-quality, well-documented code
2. Regular Review: Periodically review and update stored patterns
3. Contextual Storage: Include usage context with each stored pattern
4. Versioning: Track improvements and versions in SQLite
5. Tagging Consistency: Use controlled vocabulary for better retrieval
6. Performance Optimization: Regularly optimize database queries
7. Feedback Integration: Update patterns based on usage feedback

Getting Started

1. Store your first code memory:

   qdrant-store-memory(json.dumps({
       "type": "code_pattern",
       "name": "Python decorator pattern",
       "code": "def my_decorator(func):\n    def wrapper(*args, **kwargs):\n        # Do something before\n        result = func(*args, **kwargs)\n        # Do something after\n        return result\n    return wrapper",
       "explanation": "Decorators provide a way to modify functions without changing their code.",
       "tags": ["python", "decorator", "metaprogramming"],
       "complexity": "intermediate"
   }))
   

2. Retrieve it later:

   qdrant-find-memories("python decorator pattern")
   

Future Enhancements

• Advanced code quality assessment before storage
• Integration with version control systems
• Learning from usage patterns to improve retrieval
• Automated documentation generation
• Custom IDE plugins for seamless access
• Multi-modal storage (code, diagrams, explanations)
• Natural language interface for querying
• Performance benchmark database
• Install script for MCP Servers and DB