Coder DB: AI-Driven Memory & Semantic Search

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