Code Knowledge Tool

A knowledge management tool for code repositories using vector embeddings. This tool helps maintain and query knowledge about your codebase using advanced embedding techniques.

Building and Installing

1. Build the Package

First, you need to build the distribution files:

# Clone the repository git clone https://github.com/yourusername/code-knowledge-tool.git cd code-knowledge-tool # Create and activate a virtual environment python -m venv venv source venv/bin/activate # Install build tools python -m pip install --upgrade pip build # Build the package python -m build

This will create two files in the dist/ directory:

code_knowledge_tool-0.1.0-py3-none-any.whl (wheel file for installation)
code_knowledge_tool-0.1.0.tar.gz (source distribution)

2. Install the Package

Prerequisites

Ensure Ollama is installed and running:

# Install Ollama (if not already installed) curl https://ollama.ai/install.sh | sh # Start Ollama service ollama serve

Install the package:

Option 1: Install from wheel file (recommended for usage)

# Navigate to where you built the package cd /path/to/code_knowledge_tool # Install from the wheel file pip install dist/code_knowledge_tool-0.1.0-py3-none-any.whl

Option 2: Install in editable mode (recommended for development)

This option is best if you want to modify the tool or contribute to its development:

# Assuming you're already in the code-knowledge-tool directory # and have activated your virtual environment # Install in editable mode with development dependencies pip install -e ".[dev]"

Integration with RooCode/Cline

Copy the MCP configuration to your settings:

For Cline (VSCode):

# Open the settings file open ~/Library/Application\ Support/Code/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json

Add this configuration:

{ "mcpServers": { "code_knowledge": { "command": "python", "args": ["-m", "code_knowledge_tool.mcp_tool"], "env": { "PYTHONPATH": "${workspaceFolder}" } } } }

For RooCode:

# Open the settings file open ~/Library/Application\ Support/RooCode/roocode_config.json

Add the same configuration as above.

Restart RooCode/Cline to load the new tool.

Using as Memory Bank and RAG Context Provider

This tool can serve as your project's memory bank and RAG context provider. To set this up:

Copy the provided template to your project:

cp clinerules_template.md /path/to/your/project/.clinerules

Customize the rules and patterns in .clinerules for your project's needs

The template includes comprehensive instructions for:

Knowledge base management
RAG-based development workflows
Code quality guidelines
Memory management practices

See clinerules_template.md for the full configuration and usage details.

Features

Local vector storage for code knowledge
Efficient embedding generation using Ollama
Support for multiple file types
Context-aware code understanding
Integration with RooCode and Cline via MCP
RAG-based context augmentation
Persistent knowledge storage

Requirements

Python 3.8 or higher
Ollama service running locally
chromadb for vector operations

Development

Running Tests

The project follows an integration-first testing approach, focusing on end-to-end functionality and MCP contract compliance. The test suite consists of:

MCP Contract Tests
- Tool registration and execution
- Resource management
- Knowledge operations
- Error handling
Package Build Tests
- Installation verification
- Dependency resolution
- MCP server initialization
- Basic functionality

To run the tests:

# Install test dependencies pip install -e ".[dev]" # Run all tests pytest # Run specific test suites pytest tests/integration/test_mcp_contract.py -v # MCP functionality pytest tests/integration/test_package_build.py -v # Installation verification

Test Environment Requirements:

# Ensure Ollama is running ollama serve

The tests use a temporary directory (test_knowledge_store) that is cleaned up automatically between test runs.

For more details on the testing strategy and patterns, see the documentation in docs/.

Future Distribution

If you want to make this package available through pip (i.e., pip install code-knowledge-tool), you would need to:

Register an account on PyPI
Install twine: pip install twine
Upload your distribution: twine upload dist/*

However, for now, use the local build and installation methods described above.

License

MIT License

This server cannot be installed

-

security - not tested

F

license - not found

-

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Provides a project memory bank and RAG context provider for enhanced code understanding and management through vector embeddings, integrated with RooCode and Cline.

Related Resources

Reddit Discussion about this server

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