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Building a RAG Extension for GitHub Copilot Chat 🤖💬

This hands-on lab will guide you through building a Retrieval-Augmented Generation (RAG) extension for GitHub Copilot Chat. This extension will enhance Copilot's capabilities by allowing it to access and leverage your own documentation or knowledge base.

Lab Overview 📋

Duration: 1-2 hours
Difficulty: Advanced
Prerequisites: - Familiarity with GitHub Copilot - Basic understanding of RAG systems - Experience with JavaScript/TypeScript - VS Code installed with GitHub Copilot extension

What is RAG? 🧩

Retrieval-Augmented Generation (RAG) is an AI framework that enhances large language models by: 1. Retrieving relevant information from external knowledge sources 2. Augmenting the model's prompts with this retrieved information 3. Generating more accurate, up-to-date, and contextually relevant responses

This approach helps overcome limitations of pre-trained models by providing them with current, domain-specific information.

Why Build a RAG Extension for GitHub Copilot? 🎯

GitHub Copilot is incredibly powerful, but it has some limitations: - It may not have knowledge of your specific codebase or documentation - Its training data has a cutoff date, so it lacks information about newer technologies - It doesn't have direct access to your organization's best practices or internal libraries

By building a RAG extension, you can: - Connect Copilot to your own documentation - Provide context-specific suggestions based on your codebase - Ensure recommendations follow your organization's coding standards - Improve the relevance and accuracy of Copilot's suggestions

What You'll Build 🏗️

In this lab, you'll create a VS Code extension that: 1. Indexes your project documentation or specified knowledge base 2. Creates embeddings for efficient semantic search 3. Intercepts GitHub Copilot Chat queries 4. Retrieves relevant information from your knowledge base 5. Augments Copilot's responses with this information

Getting Started 🚀

Step 1: Set Up Your Development Environment

First, you'll need to set up your development environment for creating VS Code extensions:

# Install Yeoman and VS Code Extension Generator
npm install -g yo generator-code

# Create a new extension project
yo code

# Select 'New Extension (TypeScript)'
# Fill in the required information

Step 2: Install Required Dependencies

Add the necessary dependencies for building the RAG extension:

# Navigate to your extension directory
cd your-extension-name

# Install dependencies
npm install @vscode/vscode-languagedetection
npm install langchain
npm install openai

Step 3: Create the Extension Structure

Your extension will need several key components:

  1. Document Loader: To read and process your documentation
  2. Text Splitter: To break documents into manageable chunks
  3. Embedding Generator: To create vector representations of text
  4. Vector Store: To store and query embeddings
  5. Retriever: To find relevant information based on queries
  6. Copilot Interceptor: To augment Copilot's responses

Step 4: Implement the RAG Pipeline

The detailed implementation will be covered in the workshop, but here's a high-level overview:

// Sample code structure (to be implemented during the workshop)
import * as vscode from 'vscode';
import { Document } from 'langchain/document';
import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { MemoryVectorStore } from 'langchain/vectorstores/memory';

// Document loading and processing
async function loadDocuments(paths: string[]): Promise<Document[]> {
  // Implementation to be added during workshop
}

// Create embeddings and vector store
async function createVectorStore(documents: Document[]): Promise<MemoryVectorStore> {
  // Implementation to be added during workshop
}

// Query the vector store
async function queryKnowledgeBase(query: string, vectorStore: MemoryVectorStore): Promise<string> {
  // Implementation to be added during workshop
}

// Intercept and augment Copilot Chat
function interceptCopilotChat() {
  // Implementation to be added during workshop
}

Step 5: Test Your Extension

You'll learn how to test your extension with:

  • Sample documentation
  • Various query types
  • Different retrieval strategies

Step 6: Package and Share Your Extension

Finally, you'll package your extension for distribution:

# Package the extension
vscode-dev-tools package

# This creates a .vsix file that can be installed in VS Code

Expected Outcomes 🎉

By the end of this lab, you'll have:

  • A working RAG extension for GitHub Copilot Chat
  • The ability to connect Copilot to your own knowledge base
  • A deeper understanding of how RAG systems work
  • Skills to further customize and enhance the extension

Resources for Further Learning 📚

Coming Soon! 🔜

This hands-on lab is currently under development and will be available in the next workshop update. Stay tuned for the complete implementation guide!

In the meantime, you can prepare by:

  1. Familiarizing yourself with VS Code extension development
  2. Learning about vector embeddings and semantic search
  3. Exploring the LangChain framework
  4. Gathering documentation you'd like to use with your RAG system

We're excited to guide you through building this powerful extension in the upcoming workshop! 🚀