Follow our detailed guide to build a custom AI coding assistant, leveraging Deepseek models to enhance your development capabilities.

Introduction

In today’s fast-paced development landscape, enhancing productivity and maintaining code quality are paramount. Whether you’re part of a startup, an enterprise, or a product management team, leveraging AI can significantly streamline your workflows. In this tutorial, we will walk you through creating your own DIY AI assistant, a custom coding companion designed to meet your specific development needs.

Why Create a DIY AI Assistant?

While tools like GitHub Copilot offer AI-powered coding assistance, they may not always align perfectly with your project’s unique requirements. By building a DIY AI assistant, you gain full control over your development environment, ensuring data security and tailored functionalities. Moreover, a custom assistant can adapt to your existing codebase, enhancing productivity without compromising on code quality.

Benefits of a DIY AI assistant include:

Cost Efficiency: Eliminate recurring subscription fees by utilizing open-source tools.
Data Security: Maintain complete control over your data, ensuring sensitive information remains confidential.
Customization: Tailor the assistant to understand and work with your specific codebase and development practices.
Integration: Seamlessly integrate with tools like Visual Studio Code and other development environments.

Step-by-Step Guide to Building Your AI Coding Assistant

1. Install Ollama

To begin, you need to install Ollama, an open-source alternative to LM Studio. Ollama allows you to run AI models locally, ensuring data privacy and reducing dependency on external services.

# Install Ollama on your machine
# Visit https://ollama.com/ for installation instructions

After installation, verify it by running:

ollama --help

You should see confirmation that Ollama is installed correctly.

2. Download and Install the AI Model

For this guide, we will use the qwen2.5-coder model, a 7 billion parameter AI designed for coding tasks.

ollama pull qwen2.5-coder:7b

This command downloads the model to your local machine. Ensure you have at least 8 GB of available RAM to run the model efficiently.

To test the model, execute:

ollama run qwen2.5-coder:7b

You should see a prompt indicating that the model is running correctly and ready to assist with your coding tasks.

3. Integrate with Visual Studio Code

To leverage your AI assistant within your development environment, integrate it with Visual Studio Code using the Continue extension.

Install the Continue Extension:

Open Visual Studio Code and navigate to the Extensions marketplace. Search for and install the Continue extension.

Configure the Extension:

Once installed, click the Continue button in the bottom right corner of VS Code and select Configure autocomplete options. Modify the config.json file to point to your local AI model:

json { "models": [ { "model": "qwen2.5-coder:7b", "provider": "ollama", "title": "Qwen-coder" } ], "tabAutocompleteModel": { "title": "Qwen-coder", "provider": "ollama", "model": "qwen2.5-coder:7b" } }

Save the config.json file to apply the changes.

4. Test Your AI Coding Assistant

With the setup complete, your DIY AI assistant is ready to assist you. Open a coding project in VS Code and start typing. The AI assistant will provide suggestions, autocomplete code, and help streamline your development process.

Example in Action:

Imagine you’re tasked with creating a new frontend component. Simply type a brief description or partial code, and your AI assistant will generate production-grade code snippets, saving you valuable time and ensuring consistency across your codebase.

Enhancing Your Development Workflow with CodeAI

While building your DIY AI assistant empowers you with customization and control, platforms like CodeAI take this concept further. CodeAI integrates advanced AI capabilities into your frontend development process, automating code generation and enabling non-technical team members to contribute effectively.

Key Features of CodeAI:

Agentic Context Engine (ACE): Understands your specific codebase, producing relevant and high-quality code.
Seamless Integration: Easily integrates with existing workflows and codebases.
Continuous Learning: Improves over time by learning from each project, enhancing future code generation.
Enhanced Collaboration: Bridges gaps between designers, developers, and product managers, fostering a more collaborative environment.

By combining a DIY AI assistant with advanced platforms like CodeAI, you can significantly enhance your development speed, maintain high code quality, and foster a more inclusive and efficient team dynamic.

Conclusion

Creating your own DIY AI assistant is an empowering way to take control of your development environment, ensuring that your coding practices are both efficient and secure. By following this step-by-step guide, you can set up a tailored AI companion that integrates seamlessly with your existing tools, enhancing your productivity and code quality.

For more advanced AI development tools and to explore how platforms like CodeAI can revolutionize your frontend development process, visit AutonomyAI.