Title Copilot - Polarion

The main purpose of this project is to generate titles from work items descriptions from Polarion using Mistral.

This script is a combination of 2 main components for Windows :

• A python script that get and send Polarion work items to a LLM.
• A LLM that can be used to rephrase Titles from work items.

Installation

The installation is half-manual, half-automated.
The manual part is the installation of the required software.
The automated part is the installation of each library, and specific changes done to them.

(You also need a /certif repo in the root path of the project with a ca-certificate for polarion webservice.)

Required installations

Windows

1. Python (3.8+): Python for Windows
   • Make sure to check the box "Add python.exe to PATH" during the installation
2. Git, if it isn't already installed, to clone this repository: Git
   • You can click Next for each step.
3. [Optional] A good terminal to have a more user-friendly experience.
   • You can use the new Windows Terminal for exemple: Windows Terminal

Linux

1. Python (3.8+):

   sudo apt-get install python3 python3-venv

2. Git, if it isn't already installed, to clone this repository:

   sudo apt-get install git

Requirements

1. Python, minimum version 3.8.10
   • Make sure to check the box "Add python.exe to PATH" during the installation.
2. Git, to cline this repository: Git
   • You can click Next for each step

Activate and fill your environment (Important steps)

Using a virtual environment is a good practice to avoid conflicts between libraries and versions. And also to keep your main Python installation clean.

Windows

1. Find a suitable location for the repository

   cd <DIRECTORY>

2. Create and activate the virtual environment

   py -m venv .venv
   .\.venv\Scripts\activate

   If you run into an error with execution policy, check your own execution policy with:

    Get-ExecutionPolicy

   remember it if you want to put it back later. Then, change it to RemoteSigned and try to activate the environment again:

    Set-ExecutionPolicy RemoteSigned

3. Clone the repository

   git clone https://gitlab.sw.goiba.net/req-test-tools/polarion-copilot/title_copilot.git
   cd title_copilot

4. Install the required libraries

   pip install -r requirements.txt

Linux

1. Find a suitable location for the repository

   cd /your/directory/

2. Clone the repository

   git clone https://gitlab.sw.goiba.net/req-test-tools/polarion-copilot/title_copilot.git
   cd title_copilot

Before any further steps, you need to fill the environment variables in the .env file.

1. Fill the .env file with the following content, each value must be between quotes "" (the .env file is located at the root path of the project) :

   base_url=<URL> # The URL of your Polarion server (e.g. https://polarion.example.com/polarion)
   openai_api=<URL> # The URL of your OpenAI like API (has to finish with "/v1")
   # It depends on which server you want to modify work items titles.
   polarion_url_dev=<URL> 
   polarion_user=<USERNAME> # The username to access the Polarion server
   polarion_password=<PASSWORD> # The password to access the Polarion server
   polarion_token=<TOKEN> # The user token to access the Polarion server

   Replace <URL>, <USERNAME>, and <TOKEN> with your own values. .env file contains sensitive information, make sure to not share it.

Tensordock virtual machine

1. Open TensorDock
2. Get a GPU with at least 48Gb of VRAM
3. 1GPU, 8Gb of RAM, 2CPU and 30Gb SSD
4. Select one of the available locations
5. Choose Ubuntu as operating system
6. Put a password and a machine name
7. Deploy
8. SSH into the machine :

   ssh -p xxxxx user@host -L 22027:localhost:8080 -L 22028:localhost:8000

9. Run the two docker images :

   docker run -d --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface --env "HUGGING_FACE_HUB_TOKEN=<secret>" -p 8000:8000 --ipc=host vllm/vllm-openai:latest --model mistralai/Mistral-7B-Instruct-v0.2 --max-model-len 2048

   docker run -d --gpus all -p 8080:80 -v $PWD/data:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id intfloat/multilingual-e5-large-instruct

10. Create a ssh key (without password)

ssh-keygen -t rsa -b 4096

11. Copy/Paste the pub key in TensorDock SSH

12. Add the private key to your ssh agent

ssh-add ~/.ssh/id_rsa_tensordock

We use an SSH key to automatically establish an SSH tunnel to the remote server without needing to enter a password.

Setup a Linux daemon for automatic containers start.

Warning: You must have created the container by pulling the images on point 9. When the two images are booted up, you can proceed.

1. Create a new file in /etc/systemd/system/ :

   sudo nano /etc/systemd/system/containers.service

2. Fill the file with the following content :

   [Unit]
   Description=Starts all containers service
   After=network.target
   Requires=docker.service
   
   [Service]
   Type=oneshot
   ExecStart=/bin/sh -c '/usr/bin/docker start $(/usr/bin/docker ps -aq)'
   ExecStop=/bin/sh -c '/usr/bin/docker stop $(/usr/bin/docker ps -aq)'
   RemainAfterExit=true
   

3. Reload the daemon and start the service :

   sudo systemctl daemon-reload
   sudo systemctl start containers
   

4. Now you can restart the virtual machine without having to restart the containers manually.

Use the Code

1. Run the launcher for title copilot

   py run_app.py

2. Enjoy the ride!