CodeArcheo

This is a Copilot Chat Extension for VSCode that helps with analyzing legacy code.

The main logic is implemented as a Visual Studio Code extension. Overall, this code is designed to interact with a Copilot language model, process its response, and update the UI accordingly, while handling errors and logging usage.

The app processes and generates a response when specific commands or user actions are triggered.

Based on the provided code snippet and context, here are the key triggers:

1. Command Execution:

   • The app listens for specific commands to be executed. For example, the command associated with CODEARCHEO_AST_COMMAND_ID will trigger the app to process and generate a response.

   • Example:

     vscode.commands.registerCommand(CODEARCHEO_AST_COMMAND_ID, async () => {

       // Command logic here

     });

2. Model Selection:

   • When a user selects a language model using vscode.lm.selectChatModels, it triggers the app to prepare and send a request to the model.

   • Example:

     const [model] = await vscode.lm.selectChatModels(MODEL_SELECTOR);

3. Button Click:

   • The app adds a button to the UI stream, and clicking this button triggers a specific command.

   • Example:

     stream.button({

       command: CODEARCHEO_AST_COMMAND_ID,

       title: vscode.l10n.t('Lets get the AST')

     });

4. Specific Commands in Requests:

   • The app processes specific commands within requests, such as 'funny' or 'dig', to generate appropriate responses.

   • Example:

     if (request.command === 'funny') {

       // Process 'funny' command

     } else if (request.command === 'dig') {

       // Process 'dig' command

     }

These triggers ensure that the app responds to user actions and commands, processes the input, and generates the appropriate responses.

Here's a breakdown of the logic:

1. Model Selection:

const [model] = await vscode.lm.selectChatModels(MODEL_SELECTOR);

• The code selects a language model using vscode.lm.selectChatModels with a MODEL_SELECTOR.

2. Message Preparation:

   if (model) {

     const messages = [
       vscode.LanguageModelChatMessage.User('You are a cat! Your job is to explain computer science concepts in the funny manner of a cat. Always start your response by stating what concept you are explaining. Always include code samples.'),
       vscode.LanguageModelChatMessage.User(topic)
     ];

• If a model is selected, it prepares a list of messages. The first message instructs the model to respond as a cat explaining computer science concepts humorously. The second message contains the topic to be explained.

3. Sending Request and Handling Response:

   const chatResponse = await model.sendRequest(messages, {}, token);

   for await (const fragment of chatResponse.text) {

     stream.markdown(fragment);

   }

• The code sends the prepared messages to the model and awaits the response. It then streams the response fragments as markdown to the stream.

4. Error Handling:

   } catch(err) {

     handleError(logger, err, stream);

   }

• If an error occurs during the process, it is caught and handled by the handleError function.

5. Button Creation:

   stream.button({

     command: CODEARCHEO_AST_COMMAND_ID,

     title: vscode.l10n.t('Lets get the AST')

   });

• A button is added to the stream with the command CODEARCHEO_AST_COMMAND_ID and the title "Lets get the AST".

6. Logging and Return:

   logger.logUsage('request', { kind: 'funny'});

   return { metadata: { command: 'funny' } };

• The usage of the request is logged with the kind 'funny', and the function returns metadata indicating the command 'funny'.

7. Handling 'dig' Command:

   } else if (request.command === 'dig') {

     stream.progress('Digging for secrets...');

     try {

       const [model] = await vscode.lm.selectChatModels(MODEL_SELECTOR);

       let documentText = '';

       if (model) {

         // Here's an example of how to use the prompt-tsx library to build a prompt

• If the command is 'dig', it shows a progress message "Digging for secrets..." and attempts to select a model again. The rest of the logic for this command is not shown in the provided excerpt.

Objective	Implementation
Create a VSCode extension for code archeology using Copilot chat	Implemented in src/extension.ts
Replace Python function definitions with cat sounds	Implemented in the activate function in src/extension.ts using fragment.replaceAll('def', 'meow')
Provide chat participants and commands for the extension	Defined in the contributes section of package.json
Compile TypeScript code	Configured in tsconfig.json and executed via npm scripts in package.json
Run and watch tests	Configured in tasks.json and launch.json
Lint TypeScript code	Configured in package.json with ESLint and TypeScript ESLint plugins

Additional Key Points

• Project Name: codearcheo

• Publisher: decyjphr

• Description: VSCode Copilot Chat Extension for Code Archeology

• Repository URL: GitHub Repository

• Main Features:

  • Chat participants for natural language interaction with code
  • Commands to use cat names and AST in the editor

• Dependencies:

  • @vscode/prompt-tsx
  • @types/node
  • typescript
  • @types/vscode
  • @typescript-eslint/eslint-plugin
  • @typescript-eslint/parser
  • eslint

• Development Scripts:

  • compile: Compiles the TypeScript code
  • lint: Lints the TypeScript code
  • watch: Watches for changes and recompiles the code

This repository aims to enhance code understanding and interaction using natural language processing.