Welcome to the future of coding assistance! QA-Pilot is an innovative chat interface designed to help you navigate and understand GitHub repositories. This guide will take you through step-by-step instructions to get your QA-Pilot up and running smoothly. So, grab your coding gear, and let’s dive in!

Key Features of QA-Pilot

• Interact with GitHub public repositories seamlessly.
• Store and manage your chat history.
• Easy configuration settings.
• Support for multiple chat sessions with quick session location through a search feature.
• Integration with CodeGraph to view Python files.
• Support for various LLM models, including Ollama, OpenAI GPT, MistralAI, and more.

Steps to Deploy QA-Pilot

Follow these detailed steps to set up your QA-Pilot:

Step 1: Clone the Repository

Open your terminal and clone the QA-Pilot repository:

git clone https://github.com/reid41/QA-Pilot.git
cd QA-Pilot

Step 2: Set Up Your Environment

You’ll need to install conda to manage your virtual environment. Create and activate a new virtual environment:

conda create -n QA-Pilot python=3.10.14
conda activate QA-Pilot

Step 3: Install Required Dependencies

Install the necessary dependencies by running:

pip install -r requirements.txt

Step 4: Install PyTorch

Follow the instructions on the PyTorch website to install the appropriate version for your system.

Step 5: Set Up LLM Providers

For setting up different LLM providers:

• Use Ollama to manage your local LLM by pulling models like so:

ollama pull model_name
ollama list

• For LocalAI, set the base URL in your config file. You can run LocalAI using:

docker run -p 8080:8080 --name local-ai -ti localai/localai:latest

Step 6: Configure Your Database

Set up your database to store your chat sessions:

CREATE DATABASE qa_pilot_chatsession_db;
CREATE USER qa_pilot_user WITH ENCRYPTED PASSWORD 'qa_pilot_p';
GRANT ALL PRIVILEGES ON DATABASE qa_pilot_chatsession_db TO qa_pilot_user;

Step 7: Run the Backend

To start the QA-Pilot, execute:

python qa_pilot_run.py

Understanding the Code: An Analogy

Think of the deployment process as setting up a new kitchen: your repository is the kitchen’s structure, the environment is the range of utensils and appliances you need to handle various cooking tasks, and the dependencies are the ingredients needed for your recipes. Each step ensures that you have the right tools and materials in place before you start cooking up some delicious code interactions!

Troubleshooting Tips

If you encounter any difficulties during your setup, consider the following troubleshooting ideas:

• Ensure that all required dependencies are installed correctly. You can verify them by running the installation commands again.
• If you have GPU compatibility issues, check the CUDA version you are using.
• Verify your connection settings in the config.ini file.
• If your chat history isn’t saving, check the permissions on your database.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Now, with your QA-Pilot deployed, you’re ready to dive into code like never before. Happy coding!