Coder DB - AI Memory Enhancement System

- 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.

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")

   

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

This project uses Poetry for dependency management and packaging.

1. Install Poetry (if you haven't already):
Follow the instructions on the Poetry website.

2. Install Dependencies:
Navigate to the directory containing pyproject.toml (this should be the mcp_server directory if you created it as a self-contained Poetry project, or the root of this repository if mcp_server is a sub-package of a larger Poetry project) and run:

    poetry install --with dev # --with dev includes testing dependencies

    

3. Environment Configuration (Optional but Recommended):
The application uses settings defined in mcp_server/core/config.py. You can override these by creating a .env file in the same directory where you run uvicorn (typically the root of the repository or mcp_server/ if running from there).
Example .env file content:

With development dependencies installed (poetry install --with dev):

From the root of the repository: