CodeGini

LLM Code Suggestion Plugin for VSCode

This VSCode plugin integrates with multiple large language models (LLMs) such as Hugging Face, AWS Llama, Cohere, and OpenAI GPT-4 to provide advanced code suggestions, refactoring, and completion features directly in your IDE.

Features

• Multiple LLM Provider Support: Supports Hugging Face (StarCoder), AWS (Llama), and OpenAI (GPT-4).
• Code Suggestions: Get optimized code suggestions, completions, and refactoring recommendations from LLMs.
• Customizable Configuration: Set your preferences for maxTokens, temperature, and easily switch between different LLM providers.
• Secure API Key Handling: Uses VSCode's secret storage API to securely store and manage API keys.
• Seamless IDE Integration: Provides inline code suggestions directly within the VSCode editor.

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Table of Contents

• Installation
• Usage
• Configuration
• How It Works
• Internal Workings
• Troubleshooting

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Installation

Prerequisites

1. Ensure you have Visual Studio Code installed.
2. You'll need API keys for the LLM providers you want to use (Hugging Face, AWS, OpenAI).

Steps

1. Clone or download this repository.

   git clone https://github.com/Dakshya52/CodeGini

2. Open the project folder in Visual Studio Code.

3. Run the following command to install necessary dependencies:

   npm install

4. Press F5 to run the extension in a new VSCode window for testing and development purposes.

5. To use the extension in your main environment, build the plugin and install it directly in VSCode:

   npm run build

6. Install the resulting .vsix file in your main VSCode instance by dragging and dropping it into the Extensions panel or using the Install from VSIX option.

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Usage

Step 1: Select Code Snippet

1. Open any code file in VSCode.
2. Select the code snippet you want to optimize or refactor.

Step 2: Run the Command

1. Press Ctrl + Shift + P or Cmd + Shift + P to open the Command Palette.
2. Type CodeGini: Suggest Code and select it.

Step 3: Choose LLM Provider

1. You'll be prompted to select one of the available LLM providers:
   • Hugging Face (StarCoder)
   • AWS Llama
   • OpenAI GPT-4
   • Cohere
2. Select the provider of your choice.

Step 4: Provide API Key

• If you are using the provider for the first time, the plugin will ask you to enter your API key.
• The key will be securely stored in VSCode's secret storage.

Step 5: View the Suggested Code

• Once the plugin fetches the suggestion, it will replace the selected code with the new, optimized version.
• If no suggestion is found, an error message will be shown.

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Configuration

You can configure various settings in VSCode to control how the plugin interacts with LLMs.

1. maxTokens: Maximum number of tokens to generate in the LLM response.

   • Default: 200
   • Example: Increase to 500 tokens for larger code completions.

2. temperature: Controls the randomness of the LLM response. Higher values (closer to 1) mean more randomness, while lower values (closer to 0) provide deterministic responses.

   • Default: 0.3
   • Example: Set to 0.7 for more creative code suggestions.

How to Configure

1. Go to File > Preferences > Settings.
2. Search for LLM Plugin in the settings.
3. Adjust the maxTokens and temperature values according to your needs.

Alternatively, you can edit these settings directly in the settings.json file:

{
  "llmPlugin.maxTokens": 200,
  "llmPlugin.temperature": 0.3
}

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How It Works

Overview

This plugin communicates with various large language models (LLMs) to provide code suggestions directly inside your VSCode environment. The extension uses axios to send API requests to the selected LLM provider (Hugging Face, AWS, or OpenAI), retrieves suggestions, and replaces the selected code in the editor with the improved version.

1. Code Snippet Selection: The user selects a portion of code.
2. Command Execution: The user runs the CodeGini: Suggest Code command via the Command Palette.
3. LLM Provider Selection: The plugin prompts the user to choose an LLM provider.
4. API Key Handling: If not previously provided, the plugin asks for the API key and securely stores it using VSCode's secretStorage API.
5. API Request: The plugin sends a request to the selected LLM provider, including the selected code snippet, and receives an optimized code suggestion.
6. Suggestion Application: The suggested code replaces the selected code in the VSCode editor.

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Internal Workings

Key Components

1. Provider Selection

   • The plugin supports multiple LLM providers (Hugging Face, AWS, and OpenAI). It dynamically adjusts API URLs, request formats, and response parsing based on the selected provider.
   • The provider is chosen via the vscode.window.showQuickPick method.

2. Secure API Key Management

   • API keys are securely stored using VSCode’s secretStorage API, ensuring that sensitive information is not stored in plain text.
   • When a provider is used for the first time, the plugin prompts the user to input the API key, which is stored securely for future use.

   const secretStorage = context.secrets;
   let apiKey = await secretStorage.get(provider);
   if (!apiKey) {
     apiKey = await vscode.window.showInputBox({ prompt: `Enter your API key for ${provider}` });
     await secretStorage.store(provider, apiKey);
   }

3. API Request Construction

   • The getSuggestions function forms different API requests depending on the selected LLM provider. It handles request body structure, URL endpoints, and token limits based on user configuration.

   if (provider === 'Hugging Face') {
     apiUrl = 'https://api-inference.huggingface.co/models/bigcode/starcoder';
     requestBody = {
       inputs: `# Instruction: Provide a concise, well-optimized solution for the following code or task.\n\n${codeSnippet}`,
       parameters: { max_new_tokens: maxTokens }
     };
   } else if (provider === 'AWS Llama') {
     apiUrl = 'https://api.aws.com/llama/generate';
     requestBody = { prompt: codeSnippet, max_tokens: maxTokens };
   } else if (provider === 'OpenAI GPT-4') {
     apiUrl = 'https://api.openai.com/v1/completions';
     requestBody = {
       prompt: `Optimize and refactor the following code:\n\n${codeSnippet}`,
       max_tokens: maxTokens,
       temperature: temperature,
       model: 'gpt-4'
     };
   }

4. LLM Response Handling

   • Once the API call returns a response, the plugin handles the different response structures from each provider, extracts the suggested code, and replaces the selected snippet with the new suggestion.

   if (provider === 'Hugging Face') {
     return response.data[0].generated_text.split('/*')[0]; 
   } else {
     return response.data.choices[0].text;  // OpenAI and AWS response structure
   }

5. Configuration and Customization

   • Users can customize the number of tokens (maxTokens) and temperature for the LLM requests. These settings can be adjusted in VSCode’s settings panel or directly in settings.json.

   const config = vscode.workspace.getConfiguration('llmPlugin');
   const maxTokens = config.get('maxTokens') || 200;
   const temperature = config.get('temperature') || 0.3;

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Troubleshooting

1. No API Key Found: If you haven't provided an API key, the plugin will prompt you to enter one. Ensure that you have a valid API key for the selected LLM provider.

2. Invalid API Key: If you receive an error regarding an invalid API key, ensure that the key you entered is correct. You can update it by deleting the stored key via VSCode's command Developer: Toggle Developer Tools and rerunning the command.

3. Error Fetching Suggestions: If the plugin encounters an issue while fetching suggestions, ensure that:

   • You have an active internet connection.
   • The LLM API you're connecting to is reachable.
   • The API key and configuration are valid.

4. Inconsistent Suggestions: If the code suggestions seem too random, try lowering the temperature value in the plugin's settings.

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Future Enhancements

• Real-Time Code Suggestions: Implement real-time inline suggestions similar to IntelliSense.
• More LLM Integrations: Add support for other LLM providers like Google PaLM or Anthropic Claude.
• Debugging Assistance: Provide debugging and error-handling suggestions based on the context of the code.

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This plugin provides a powerful way to harness the capabilities of large language models directly within your coding environment, allowing for enhanced productivity and optimized code generation.