Beginners often struggle to take their first steps with Jenkins’ documentation and available resources. To address this challenge, this plugin integrates an AI-powered assistant directly into the Jenkins interface. It offers quick, intuitive support to users of all experience levels through a simple conversational UI.
The plugin is designed to reduce the learning curve for newcomers while also improving accessibility and productivity for experienced users.
This plugin was developed as part of a Google Summer of Code 2025 project.
- Python: 3.11+
- Build tools:
make,cmake(≥3.14), C/C++ compiler (gcc/clang/MSVC) - Java: JDK 11+ and Maven 3.6+ (for plugin development)
# Ubuntu/Debian/WSL
sudo apt install -y make cmake gcc g++ python3.11 python3.11-venv python3.11-dev
# macOS
brew install cmake python@3.11 && xcode-select --install
There are two ways to run the API locally, depending on your use case:
Use this if you're working on the API, backend logic, data pipeline, or tests and don't need to test the actual chatbot responses.
make dev-lite
This will:
- Set up the Python environment automatically
- Install dependencies (skips the 4GB model download)
- Start the API server without loading the LLM
The API will be available at http://127.0.0.1:8000 within a few minutes.
Verify it's working:
curl -X POST http://127.0.0.1:8000/api/chatbot/sessions
What works: All API endpoints, session management, context search, data pipeline
What doesn't work: Actual chat completions (no model loaded)
Use this if you need to test the chatbot with real LLM responses or work on model-specific features.
First, complete the full setup in docs/setup.md. This includes installing llama-cpp-python and downloading the 4GB model.
Then run:
make api
The API will be available at http://127.0.0.1:8000.
What works: Everything, including actual chat completions with the local LLM
See docs/README.md for detailed explanations.
The tutorial shows how to fork the repo, set up the backend, download the LLM model, run the frontend, and verify the chatbot works.
llama-cpp-python installation fails: Ensure build tools are installed and use Python 3.11+
API crashes on startup: This may be caused by missing optional dependencies (e.g. retriv).
Try installing missing packages:
pip install llama-cpp-python retriv
General issues: Run make clean && make <target>, verify your virtual environment is activated, and ensure all dependencies from docs/setup.md are installed.
Development-related documentation can be found in the docs/ directory.
Refer to our contribution guidelines
Licensed under MIT, see LICENSE