Refact.ai: open source AI programming assistant to improve code writing efficiency

Refact.ai is an open source AI programming assistant designed for developers, supporting major IDEs such as Visual Studio Code and JetBrains, which dramatically improves programming productivity through intelligent code completion, code refactoring, and natural language interactions. refact.ai uses advanced language models, such as Qwen2.5-Coder, and supports a variety of open and closed source models. and supports a wide range of open and closed source models. Users can choose between cloud services or local deployment to ensure code privacy and data security. It is suitable for both individual developers and enterprise teams, and supports more than 25 programming languages, including Python, JavaScript, and Java. Refact.ai's core strengths are its context-awareness and seamless integration with tools such as GitHub and databases, making it suitable for rapid development, debugging, and code optimization.

Function List

• Intelligent Code Completion: Predicts and generates code snippets, such as functions or classes, in real time based on the code context.
• Code refactoring: optimize code structure, improve readability and performance, support multiple programming languages.
• Natural Language Generated Code: Generate code by describing requirements with support for fuzzy input and spelling errors.
• Integrated IDE Chat: No need to switch windows, talk to the AI directly in the IDE and get code suggestions.
• Local Deployment: Supports self-hosting, protects code privacy, and is suitable for enterprise-level security needs.
• External tool integration: seamlessly connect with GitHub, PostgreSQL, Docker, and more to automate multi-step tasks.
• Code review automation: analyze code, provide bug fixes and optimization recommendations.
• Code Explanation: Quickly parse unfamiliar code and generate detailed explanations.
• Multiple language model support: Users can use models such as Claude, GPT-4o, etc. via BYOK (Bring Your Own Key).

Using Help

Installation process

To use Refact.ai, users need to first install the plugin in the IDE or deploy the self-hosted version. Below are the detailed steps:

Visual Studio Code Installation

1. Open VS Code and go to the Extensions Marketplace.
2. look for sth. Refact.ai, find the official extension.
3. Click "Install" and wait for the extension to finish loading.
4. After installation, log in to your Refact.ai account or configure an API key (BYOK is supported, such as OpenAI or Claude).
5. In the VS Code sidebar, click the Refact.ai icon to go to the settings page and configure code completion and chat features.

JetBrains Installation

1. Open a JetBrains IDE (such as IntelliJ IDEA or PyCharm).
2. Go to the "Plug-ins" marketplace and search for Refact.aiThe
3. Click "Install" and restart the IDE to complete the setup.
4. Find Refact.ai in the toolbar, bind an account or enter an API key.
5. Optionally, you can enable cloud mode or connect to a local server.

Local deployment (business users)

1. Ensure that Docker is installed on your system, refer to the <CONTRIBUTING.md> Documentation.
2. From the GitHub repository (https://github.com/smallcloudai/refact) Download the pre-built Docker image.
3. Run command:

   docker pull smallcloudai/refact
   docker run -p 8008:8008 smallcloudai/refact
   

4. interviews http://localhost:8008The Web UI configuration is completed.
5. Configure the local server address in the IDE to ensure that data is not exported.

Function Operation Guide

Intelligent Code Completion

Refact.ai's code completion feature is based on the Qwen2.5-Coder model combined with RAG (Retrieval Augmented Generation) technology. As the user writes code, the AI analyzes the current file and project context to suggest code snippets in real time. For example, enter def calculate_sum, Refact.ai will predict the function body and generate the full code. Operation Steps:

1. Open the code file in the IDE.
2. Start typing the code and the complementary suggestions will pop up automatically.
3. check or refer to Tab Accept the suggestion, or use your mouse to select another option.
4. If you need to adjust the complementary accuracy, you can select a more powerful model (e.g. GPT-4o) in the settings.

code refactoring

Refact.ai optimizes lengthy or inefficient code. Usage:

1. Check the code snippet that needs to be optimized.
2. In the Refact.ai panel in the IDE sidebar, type /shorter maybe /refactor Command.
3. The AI generates cleaner code and presents it as a difference comparison (diff).
4. Click the "Apply" button to replace the optimized code with the original code.

natural language-generated code (NLG)

Users can generate code by describing requirements in natural language. For example, want to create a Python web application:

1. In the Refact.ai chat window type, "Create a Flask application that supports user login and registration."
2. The AI generates the complete code framework, including routing, database connections, and HTML templates.
3. Copy the code to the project, or click "Apply" to insert it directly.
4. Support fuzzy input, such as "build a GUI interface", AI will automatically parse and generate code.

Integrated IDE Chat

Refact.ai's chat feature allows users to ask questions directly in the IDE. For example:

1. Select the code snippet to open the chat window.
2. Enter a question such as "How can this code be optimized for performance?" .
3. The AI provides detailed answers and suggests modifications based on the context.
4. Users can apply the recommendations directly or continue the dialog to adjust the program.

Local Deployment and Privacy

For enterprise users, local deployment is a core feature. After deployment, code data will not be uploaded to the cloud. Users can set file access rights in the Web UI to ensure that sensitive code is not accessed by AI. Operation steps:

1. Log in to the Web UI of the local server.
2. In Privacy Settings, specify the project folders that AI is allowed to access.
3. Test the AI function to ensure that only specified files are processed.

External tool integration

Refact.ai can connect to GitHub, databases, and CI/CD pipelines. For example, connect to GitHub:

1. Bind a GitHub account in your Refact.ai settings.
2. Authorize Refact.ai to access the target repository.
3. AI can automatically analyze the repository code and generate patch suggestions or perform tasks such as "Fix spelling errors in README".

caveat

• Ensure network stability for the best cloud experience.
• Local deployments require at least 16GB of RAM and a 4-core CPU.
• Regularly update the plugin for the latest model support.

application scenario

1. Rapid prototyping
   Refact.ai helps developers rapidly prototype applications. For example, if a UX team needs an IoT cloud app, Refact.ai can generate 99.9% of code in 30 minutes, saving weeks of development time.
2. Code debugging and optimization
   Refact.ai enables developers to analyze complex code, locate bugs and optimize performance. For example, after connecting to a MySQL database, AI can fix a WordPress plugin issue in less than 30 minutes.
3. Enterprise Code Privacy
   Business users can protect sensitive code with a local deployment, suitable for the financial or healthcare industries, to ensure that no data is leaked.
4. New Developer Support
   Zero-based developers can quickly build web applications or GUI interfaces with natural language code generation, reducing the learning curve.

QA

1. Is Refact.ai free?
   Refact.ai offers free code completion and basic features, while premium features require the purchase of coins starting at $5. Premium features require the purchase of coins, which start at $5, with $1 equaling 1,000 coins. Business users can choose to deploy locally and are responsible for their own server costs.
2. How do you ensure code privacy?
   Refact.ai supports local deployment, code is not uploaded to the cloud. Users can restrict AI access to specific files through the Web UI to ensure data security.
3. What programming languages are supported?
   Refact.ai supports more than 25 languages, including Python, JavaScript, Java, C++, Rust, PHP, and more, for a wide range of development scenarios.
4. How to choose a language model?
   Users can select Claude, GPT-4o or other models through the BYOK function, or use the default Qwen2.5-Coder model.