Coder DB - AI Memory Enhancement System
About
An intelligent code memory system that leverages vector embeddings, structured databases, and knowledge graphs to store, retrieve, and analyze code patterns with semantic search capabilities, quality metrics, and relationship modeling. Designed to enhance programming workflows th
Details
- Author
- angrysky56
- GitHub stars
- 8
- Downloads
- 351
- Categories
- Knowledge Base, AI
Jump to
- Stores code patterns, solutions, and documentation with semantic search.
- Tracks algorithm versions with performance metrics and change logs.
- Represents relationships between coding concepts via a knowledge graph.
- Enables enhanced problem-solving, pattern learning, and project setup workflows.
- Includes security measures (access controls, backup, sanitization).
- Provides usage tracking and performance monitoring.
Install dependencies using Poetry, configure environment variables in a .env file (e.g., Qdrant host, port, API key, SQLite database path), and run the FastAPI MCP server. Store code memories using the qdrant-store-memory command and retrieve them with qdrant-find-memories. Query the SQLite algorithm database via SQL or explore the knowledge graph for relationships.
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:
```python
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