Building a Cursor-like AI Code Assistant: A Comprehensive Implementation Guide

Modern developers face the overwhelming challenge of navigating large, complex codebases. Traditional IDEs offer powerful features like symbol navigation and autocomplete, but they often lack the ability to semantically understand project structures or answer open-ended questions like "How does this module work?" or "What are all the authentication mechanisms in this codebase?"

Enter next-generation developer tools like Cursor, which revolutionize coding experiences by integrating artificial intelligence directly into the development environment. This comprehensive guide explores how to build your own AI-powered code assistant by combining three powerful technologies:

Language Server Protocol (LSP) for syntactic and structural intelligence
Code Embeddings for semantic similarity and intelligent retrieval
Large Language Models (LLMs) for reasoning, transformation, and natural language explanation

By the end of this article, you'll understand both the theory and practical implementation details needed to build an intelligent coding assistant that deeply understands your TypeScript projects.

Understanding the Architecture
Setting Up the Language Server Protocol
Implementing Code Embeddings
Integrating Large Language Models
Building the User Interface
Putting It All Together
Advanced Features and Optimizations
Deployment Considerations
Future Directions
Conclusion

Understanding the Architecture

Before diving into implementation details, let's understand the high-level architecture of our AI code assistant:

┌───────────────────────────────────────┐
│           IDE / Editor Client         │
│  ┌─────────────┐    ┌──────────────┐  │
│  │   LSP       │    │  UI Components│  │
│  │  Client     │    │  - Hovers    │  │
│  │             │    │  - Actions   │  │
│  └─────────────┘    └──────────────┘  │
└───────────┬─────────────────┬─────────┘
            │                 │
            ▼                 ▼
┌───────────────────┐  ┌──────────────────┐
│ Language Server   │  │ AI Assistant     │
│ ┌───────────────┐ │  │ ┌──────────────┐ │
│ │ Type Checker  │ │  │ │ Embeddings   │ │
│ │ Symbol Lookup │◄┼──┼─┤ Vector Store │ │
│ │ AST Parser    │ │  │ │ LLM Connector│ │
│ └───────────────┘ │  │ └──────────────┘ │
└───────────────────┘  └──────────────────┘

This architecture combines:

Client Components: IDE plugins or extensions that provide the UI for interactions
Language Server: Provides code intelligence through the Language Server Protocol
AI Assistant Backend: Combines embeddings and LLM capabilities

Now, let's break down each component in detail and show how to implement them.

Setting Up the Language Server Protocol

The Language Server Protocol (LSP) provides standardized communication between development tools and language-specific analyzers. For our TypeScript assistant, we'll use the TypeScript Language Server.

Installing and Configuring TypeScript Language Server

First, you'll need to install the TypeScript Language Server:

bash

npm install -g typescript-language-server typescript

Next, you'll need to create a language server client in your tool. Here's how to initialize it in a Node.js application:

typescript

import {
  LanguageClient,
  LanguageClientOptions,
  ServerOptions,
  TransportKind
} from 'vscode-languageclient/node';
import * as path from 'path';

// Define the server executable options (TypeScript Language Server)
const serverModule = 'typescript-language-server';
const serverOptions: ServerOptions = {
  run: { 
    command: serverModule, 
    args: ['--stdio'],
    transport: TransportKind.stdio 
  },
  debug: {
    command: serverModule,
    args: ['--stdio', '--log-level=4'],
    transport: TransportKind.stdio
  }
};

// Configure the client
const clientOptions: LanguageClientOptions = {
  documentSelector: [{ scheme: 'file', language: 'typescript' }],
  synchronize: {
    fileEvents: workspace.createFileSystemWatcher('**/*.ts')
  }
};

// Create and start the client
const client = new LanguageClient(
  'typescriptLanguageServer',
  'TypeScript Language Server',
  serverOptions,
  clientOptions
);

client.start();

Extracting Metadata from TypeScript Files

Now that we have our language server running, let's implement functions to extract useful metadata:

typescript

async function getSymbolInformation(uri: string, position: Position): Promise<SymbolInformation | null> {
  // Request symbol information from the language server
  const result = await client.sendRequest('textDocument/hover', {
    textDocument: { uri },
    position
  });
  
  return result;
}

async function getDefinition(uri: string, position: Position): Promise<Location | null> {
  // Get definition location
  const result = await client.sendRequest('textDocument/definition', {
    textDocument: { uri },
    position
  });
  
  return result && result[0];
}

async function getReferences(uri: string, position: Position): Promise<Location[]> {
  // Find all references to the symbol
  const result = await client.sendRequest('textDocument/references', {
    textDocument: { uri },
    position,
    context: { includeDeclaration: false }
  });
  
  return result || [];
}

Building an Abstract Syntax Tree (AST) Parser

To deeply understand code structure, we need to parse TypeScript into an AST:

typescript

import * as ts from 'typescript';
import * as fs from 'fs';

function parseTypeScriptFile(filePath: string): ts.SourceFile {
  const program = ts.createProgram([filePath], {
    target: ts.ScriptTarget.Latest,
    module: ts.ModuleKind.CommonJS
  });
  
  return program.getSourceFile(filePath)!;
}

function extractFunctionsFromAST(sourceFile: ts.SourceFile): ts.FunctionDeclaration[] {
  const functions: ts.FunctionDeclaration[] = [];
  
  function visit(node: ts.Node) {
    if (ts.isFunctionDeclaration(node)) {
      functions.push(node);
    }
    ts.forEachChild(node, visit);
  }
  
  visit(sourceFile);
  return functions;
}

// Example usage:
const sourceFile = parseTypeScriptFile('./src/example.ts');
const functions = extractFunctionsFromAST(sourceFile);
console.log(`Found ${functions.length} functions`);

With these LSP and AST utilities, our assistant now understands the structure of the codebase, but not yet its meaning. That's where embeddings come in.

Implementing Code Embeddings

Code embeddings translate source code into high-dimensional vectors that capture semantic meaning. This allows us to find similar code snippets, related functions, or relevant documentation.

Setting Up an Embedding Model

We'll use OpenAI's embedding model, but you could also use open-source alternatives like CodeBERT:

typescript

import { OpenAI } from 'openai';

// Initialize OpenAI client
const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

async function generateEmbedding(text: string): Promise<number[]> {
  try {
    const response = await openai.embeddings.create({
      model: 'text-embedding-3-small',
      input: text
    });
    
    return response.data[0].embedding;
  } catch (error) {
    console.error('Error generating embedding:', error);
    throw error;
  }
}

Building a Vector Database for Code

Next, we'll set up a vector database to store and query our embeddings. We'll use Qdrant, but you could also use alternatives like Weaviate, Pinecone, or Milvus:

typescript

import { QdrantClient } from '@qdrant/js-client-rest';

// Initialize Qdrant client
const qdrant = new QdrantClient({
  url: process.env.QDRANT_URL || 'http://localhost:6333'
});

// Configure collection (do this once)
async function setupVectorDatabase() {
  // Check if collection exists
  const collections = await qdrant.getCollections();
  
  if (!collections.collections.find(c => c.name === 'code_snippets')) {
    // Create collection for code snippets
    await qdrant.createCollection('code_snippets', {
      vectors: {
        size: 1536, // Dimension of text-embedding-3-small vectors
        distance: 'Cosine'
      }
    });
  }
}

// Store code snippets with their embeddings
async function indexCodeSnippet(
  snippet: string,
  filePath: string,
  startLine: number,
  endLine: number,
  symbolName?: string
): Promise<void> {
  // Generate embedding for the code snippet
  const embedding = await generateEmbedding(snippet);
  
  // Store in vector database
  await qdrant.upsert('code_snippets', {
    points: [{
      id: `${filePath}:${startLine}-${endLine}`, // Unique ID
      vector: embedding,
      payload: {
        snippet,
        filePath,
        startLine,
        endLine,
        symbolName,
        timestamp: new Date().toISOString()
      }
    }]
  });
}

// Search for similar code snippets
async function findSimilarCode(query: string, limit: number = 5): Promise<any[]> {
  // Generate embedding for the query
  const queryEmbedding = await generateEmbedding(query);
  
  // Search in vector database
  const searchResults = await qdrant.search('code_snippets', {
    vector: queryEmbedding,
    limit,
    with_payload: true
  });
  
  return searchResults.map(result => ({
    ...result.payload,
    score: result.score
  }));
}

Indexing Your Codebase

Now, let's create a function to scan and index an entire codebase:

typescript

import * as glob from 'glob';
import * as path from 'path';
import * as fs from 'fs';

async function indexCodebase(rootDir: string): Promise<void> {
  // Find all TypeScript files
  const files = glob.sync(path.join(rootDir, '**/*.ts'), {
    ignore: ['**/node_modules/**', '**/dist/**', '**/build/**']
  });
  
  console.log(`Found ${files.length} TypeScript files to index`);
  
  // Process each file
  let indexedCount = 0;
  for (const file of files) {
    try {
      const content = fs.readFileSync(file, 'utf-8');
      const sourceFile = parseTypeScriptFile(file);
      
      // Extract functions, classes, and interfaces
      const functions = extractFunctionsFromAST(sourceFile);
      
      // Index each function separately
      for (const func of functions) {
        const startPos = func.getStart();
        const endPos = func.getEnd();
        const startLine = sourceFile.getLineAndCharacterOfPosition(startPos).line;
        const endLine = sourceFile.getLineAndCharacterOfPosition(endPos).line;
        const snippet = content.substring(startPos, endPos);
        const symbolName = func.name?.getText() || 'anonymous';
        
        await indexCodeSnippet(snippet, file, startLine, endLine, symbolName);
        indexedCount++;
      }
      
      // You'd also add similar code for classes, interfaces, etc.
      
    } catch (error) {
      console.error(`Error processing file ${file}:`, error);
    }
  }
  
  console.log(`Successfully indexed ${indexedCount} code snippets`);
}

// Usage
await setupVectorDatabase();
await indexCodebase('./src');

With our embedding system in place, we can now find semantically similar code across the entire codebase. Next, we'll add the LLM reasoning component.

Integrating Large Language Models

LLMs provide the "intelligence" in our assistant, helping to explain code, suggest improvements, answer questions, and generate new code. We'll use OpenAI's GPT-4, but you could also use open-source alternatives like Llama 3 or Claude 3.

Setting Up the LLM Client

typescript

// We'll use the same OpenAI client as before
import { OpenAI } from 'openai';

// Initialize OpenAI client (if not already done)
const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

// Function to get an LLM response
async function getAIAssistance(
  prompt: string,
  contextSnippets: Array<{ snippet: string; filePath: string; }>
): Promise<string> {
  // Build system message with context
  const contextContent = contextSnippets
    .map(ctx => `File: ${ctx.filePath}\n\`\`\`typescript\n${ctx.snippet}\n\`\`\``)
    .join('\n\n');
  
  const systemMessage = `You are an expert TypeScript coding assistant. 
You have access to the following code snippets from the user's codebase:

${contextContent}

Provide clear, concise, and helpful responses based on this context.`;

  try {
    const response = await openai.chat.completions.create({
      model: 'gpt-4-turbo',
      messages: [
        { role: 'system', content: systemMessage },
        { role: 'user', content: prompt }
      ],
      temperature: 0.3, // Lower temperature for more precise coding answers
      max_tokens: 1500
    });
    
    return response.choices[0].message.content || 'No response generated';
  } catch (error) {
    console.error('Error getting AI assistance:', error);
    return 'Sorry, I encountered an error while processing your request.';
  }
}

Creating Specialized LLM Functions

Now, let's create specialized functions for common developer tasks:

typescript

// Explain a code snippet
async function explainCode(codeSnippet: string, filePath: string): Promise<string> {
  // Find relevant code snippets for additional context
  const similarSnippets = await findSimilarCode(codeSnippet, 3);
  
  return getAIAssistance(
    `Please explain this TypeScript code snippet in simple terms:
    
\`\`\`typescript
${codeSnippet}
\`\`\`

Focus on:
1. The purpose of this code
2. How it works
3. Any potential issues or improvements`,
    [{ snippet: codeSnippet, filePath }, ...similarSnippets]
  );
}

// Suggest improvements
async function suggestImprovements(codeSnippet: string, filePath: string): Promise<string> {
  const similarSnippets = await findSimilarCode(codeSnippet, 3);
  
  return getAIAssistance(
    `Please suggest improvements for this TypeScript code:
    
\`\`\`typescript
${codeSnippet}
\`\`\`

Consider:
1. Performance optimizations
2. Code readability
3. Modern TypeScript practices
4. Potential bugs
5. Security considerations`,
    [{ snippet: codeSnippet, filePath }, ...similarSnippets]
  );
}

// Generate unit tests
async function generateUnitTests(codeSnippet: string, filePath: string): Promise<string> {
  return getAIAssistance(
    `Please generate comprehensive Jest unit tests for this TypeScript code:
    
\`\`\`typescript
${codeSnippet}
\`\`\`

Include:
1. Test cases for normal functionality
2. Edge cases
3. Error conditions`,
    [{ snippet: codeSnippet, filePath }]
  );
}

// Answer a natural language question about the codebase
async function answerCodebaseQuestion(question: string): Promise<string> {
  // Find relevant code snippets based on the question
  const relevantSnippets = await findSimilarCode(question, 5);
  
  return getAIAssistance(
    `Please answer this question about the codebase: "${question}"`,
    relevantSnippets
  );
}

These functions provide the core AI capabilities that make our assistant intelligent. Next, we'll build a user interface to bring it all together.

Building the User Interface

For our assistant to be useful, it needs to integrate into the developer's workflow. We'll build a VS Code extension as an example, but similar principles apply to other IDEs.

Setting Up a VS Code Extension

First, create a new VS Code extension project:

bash

npm install -g yo generator-code
yo code

Then, update the extension.ts file:

typescript

import * as vscode from 'vscode';
import {
  getSymbolInformation,
  getDefinition,
  getReferences,
  explainCode,
  suggestImprovements,
  generateUnitTests,
  answerCodebaseQuestion,
  indexCodebase
} from './services'; // Import our previously created functions

export function activate(context: vscode.ExtensionContext) {
  console.log('AI Code Assistant is now active!');
  
  // Index the current workspace
  const workspaceFolders = vscode.workspace.workspaceFolders;
  if (workspaceFolders && workspaceFolders.length > 0) {
    const rootPath = workspaceFolders[0].uri.fsPath;
    indexCodebase(rootPath);
  }
  
  // Command to explain the selected code
  const explainCommand = vscode.commands.registerCommand(
    'aiCodeAssistant.explainCode',
    async () => {
      const editor = vscode.window.activeTextEditor;
      if (!editor) return;
      
      const selection = editor.selection;
      const code = editor.document.getText(selection);
      
      if (!code) {
        vscode.window.showInformationMessage('No code selected to explain.');
        return;
      }
      
      // Show loading indicator
      vscode.window.withProgress({
        location: vscode.ProgressLocation.Notification,
        title: 'AI Assistant: Analyzing code...',
        cancellable: false
      }, async () => {
        const explanation = await explainCode(code, editor.document.uri.fsPath);
        
        // Show explanation in a webview panel
        const panel = vscode.window.createWebviewPanel(
          'codeExplanation',
          'AI Code Explanation',
          vscode.ViewColumn.Beside,
          {}
        );
        
        panel.webview.html = `
          <!DOCTYPE html>
          <html>
          <head>
            <meta charset="UTF-8">
            <style>
              body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; padding: 20px; }
              pre { background-color: #f5f5f5; padding: 10px; border-radius: 5px; }
            </style>
          </head>
          <body>
            <h2>Code Explanation</h2>
            <div>${explanation.replace(/\n/g, '<br>')}</div>
          </body>
          </html>
        `;
      });
    }
  );
  
  // Hover provider to show code insights on hover
  const hoverProvider = vscode.languages.registerHoverProvider(
    { language: 'typescript' },
    {
      async provideHover(document, position, token) {
        // Get the symbol under cursor
        const range = document.getWordRangeAtPosition(position);
        if (!range) return null;
        
        const symbol = document.getText(range);
        
        // Get the entire function/method containing this position
        // (This is a simplified approach; a real implementation would use AST parsing)
        let start = position.line;
        while (start > 0 && !document.lineAt(start).text.includes('function')) {
          start--;
        }
        
        let end = position.line;
        while (end < document.lineCount - 1 && !document.lineAt(end).text.includes('}')) {
          end++;
        }
        
        const codeSnippet = document.getText(
          new vscode.Range(
            new vscode.Position(start, 0),
            new vscode.Position(end, document.lineAt(end).text.length)
          )
        );
        
        // Get a quick insight about this symbol/code
        const insight = await explainCode(codeSnippet, document.uri.fsPath);
        
        // Return a simplified insight (first paragraph only for hover)
        const firstParagraph = insight.split('\n\n')[0];
        return new vscode.Hover(firstParagraph);
      }
    }
  );
  
  // Register commands and providers
  context.subscriptions.push(explainCommand, hoverProvider);
  
  // Add more commands for improvements, tests, etc.
  // ...
}

Adding a Chat Interface

Next, let's add a chat interface for more complex interactions:

typescript

function activateChatPanel(context: vscode.ExtensionContext) {
  const provider = new ChatViewProvider(context.extensionUri);
  
  context.subscriptions.push(
    vscode.window.registerWebviewViewProvider(
      ChatViewProvider.viewType,
      provider,
      { webviewOptions: { retainContextWhenHidden: true } }
    )
  );
}

class ChatViewProvider implements vscode.WebviewViewProvider {
  public static readonly viewType = 'aiCodeAssistant.chatView';
  
  constructor(private readonly _extensionUri: vscode.Uri) {}
  
  resolveWebviewView(
    webviewView: vscode.WebviewView,
    context: vscode.WebviewViewResolveContext,
    _token: vscode.CancellationToken
  ) {
    webviewView.webview.options = {
      enableScripts: true,
      localResourceRoots: [this._extensionUri]
    };
    
    webviewView.webview.html = this._getHtmlForWebview(webviewView.webview);
    
    // Handle messages from the webview
    webviewView.webview.onDidReceiveMessage(async (data) => {
      if (data.type === 'askQuestion') {
        const response = await answerCodebaseQuestion(data.question);
        
        // Send response back to webview
        webviewView.webview.postMessage({
          type: 'response',
          text: response
        });
      }
    });
  }
  
  private _getHtmlForWebview(webview: vscode.Webview) {
    // Create HTML for chat interface
    return `
      <!DOCTYPE html>
      <html>
      <head>
        <meta charset="UTF-8">
        <style>
          /* Chat styles */
          body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; margin: 0; padding: 10px; }
          #chat-container { display: flex; flex-direction: column; height: 100vh; }
          #messages { flex: 1; overflow-y: auto; margin-bottom: 10px; }
          .message { margin-bottom: 10px; padding: 8px; border-radius: 5px; }
          .user { background-color: #e1f5fe; align-self: flex-end; }
          .assistant { background-color: #f5f5f5; }
          #input-area { display: flex; }
          #question-input { flex: 1; padding: 8px; border: 1px solid #ddd; border-radius: 4px; }
          #send-button { margin-left: 5px; padding: 8px; background-color: #0078d4; color: white; border: none; border-radius: 4px; cursor: pointer; }
        </style>
      </head>
      <body>
        <div id="chat-container">
          <div id="messages"></div>
          <div id="input-area">
            <input id="question-input" type="text" placeholder="Ask about your codebase..." />
            <button id="send-button">Send</button>
          </div>
        </div>
        
        <script>
          const vscode = acquireVsCodeApi();
          const messagesContainer = document.getElementById('messages');
          const questionInput = document.getElementById('question-input');
          const sendButton = document.getElementById('send-button');
          
          // Send a message
          function sendMessage() {
            const question = questionInput.value.trim();
            if (!question) return;
            
            // Add user message to chat
            const userMsg = document.createElement('div');
            userMsg.className = 'message user';
            userMsg.textContent = question;
            messagesContainer.appendChild(userMsg);
            
            // Clear input and scroll to bottom
            questionInput.value = '';
            messagesContainer.scrollTop = messagesContainer.scrollHeight;
            
            // Show typing indicator
            const typingMsg = document.createElement('div');
            typingMsg.id = 'typing-indicator';
            typingMsg.className = 'message assistant';
            typingMsg.textContent = 'Thinking...';
            messagesContainer.appendChild(typingMsg);
            
            // Send to extension
            vscode.postMessage({
              type: 'askQuestion',
              question: question
            });
          }
          
          // Set up event listeners
          sendButton.addEventListener('click', sendMessage);
          questionInput.addEventListener('keydown', (e) => {
            if (e.key === 'Enter') sendMessage();
          });
          
          // Listen for messages from the extension
          window.addEventListener('message', (event) => {
            const message = event.data;
            
            if (message.type === 'response') {
              // Remove typing indicator
              const typingIndicator = document.getElementById('typing-indicator');
              if (typingIndicator) {
                messagesContainer.removeChild(typingIndicator);
              }
              
              // Add assistant response
              const assistantMsg = document.createElement('div');
              assistantMsg.className = 'message assistant';
              assistantMsg.innerHTML = message.text.replace(/\n/g, '<br>');
              messagesContainer.appendChild(assistantMsg);
              
              // Scroll to bottom
              messagesContainer.scrollTop = messagesContainer.scrollHeight;
            }
          });
        </script>
      </body>
      </html>
    `;
  }
}

Putting It All Together

Now that we've built each component individually, let's connect them to create a complete AI code assistant workflow:

Complete Workflow

Here's what happens during a typical interaction with our AI code assistant:

Initialization:
- When the extension activates, it indexes the codebase
- It sets up LSP, embedding model, and LLM connections
- UI components (hover, chat panel, commands) are registered
User Interaction Examples:
a. Code Hovering:
- User hovers over a function or variable
- LSP provides symbol information
- Assistant retrieves relevant context from vector database
- LLM generates a concise explanation
- Explanation appears in a hover tooltip
b. Code Selection & Explanation:
- User selects code and runs "Explain Code" command
- Extension sends selected code to AI assistant
- Vector search finds relevant related code
- LLM generates a detailed explanation with references
- Results appear in a webview panel
c. Natural Language Question:
- User types a question in chat panel
- Question is used to find relevant code snippets via vector search
- LLM answers the question using retrieved code context
- Response appears in the chat interface
Continuous Improvement:
- As the user writes new code, it's indexed
- User feedback improves response quality
- Extension adapts to user's codebase and preferences

Enhancing User Experience

To make the assistant more effective, consider adding:

Progressive disclosure: Start with simple explanations and let users drill deeper
Contextual actions: Suggest relevant actions based on the current code
Keyboard shortcuts: For quick access to common features
Personalization: Remember user preferences and coding style
Offline mode: Basic functionality when cloud services unavailable

Advanced Features and Optimizations

To take your AI code assistant to the next level, consider implementing these advanced features:

1. Incremental Indexing

Instead of re-indexing the entire codebase, use Git or file system events to detect changes:

typescript

async function incrementalIndexing(filePath: string) {
  // Check if file is already indexed
  const isIndexed = await checkFileIndexed(filePath);
  
  if (!isIndexed) {
    // Process new or modified file
    const content = fs.readFileSync(filePath, 'utf-8');
    const sourceFile = parseTypeScriptFile(filePath);
    
    // Extract and index code snippets
    // ...
  }
}

// Hook into file system events
vscode.workspace.onDidSaveTextDocument((document) => {
  incrementalIndexing(document.uri.fsPath);
});

2. Custom Fine-tuned Models

Consider fine-tuning your own code-specific LLM using your company's codebase:

typescript

// Use a fine-tuned model for company-specific code patterns
async function getCompanySpecificAssistance(prompt: string, context: string): Promise<string> {
  try {
    const response = await openai.chat.completions.create({
      model: 'ft:gpt-4:company-code-assistant:2024-01-01', // Fine-tuned model
      messages: [
        { role: 'system', content: `You are a coding assistant specialized in our company's codebase. ${context}` },
        { role: 'user', content: prompt }
      ]
    });
    
    return response.choices[0].message.content || '';
  } catch (error) {
    console.error('Error with fine-tuned model:', error);
    // Fall back to standard model
    return getAIAssistance(prompt, [{ snippet: context, filePath: '' }]);
  }
}

3. Local LLM Deployment

To address privacy concerns and reduce latency, consider running LLMs locally:

typescript

import { Ollama } from 'ollama';

const ollama = new Ollama({
  host: 'http://localhost:11434'
});

async function getLocalLLMResponse(prompt: string, context: string): Promise<string> {
  try {
    const response = await ollama.chat({
      model: 'codellama:7b',
      messages: [
        { role: 'system', content: `You are a coding assistant. ${context}` },
        { role: 'user', content: prompt }
      ]
    });
    
    return response.message.content;
  } catch (error) {
    console.error('Error with local LLM:', error);
    return 'Error processing your request locally.';
  }
}

4. Rate Limiting and Caching

To optimize performance and manage costs:

typescript

import NodeCache from 'node-cache';

// Set up cache with 1-hour TTL
const responseCache = new NodeCache({ stdTTL: 3600 });

async function getCachedResponse(
  functionName: string,
  args: any[],
  callback: (...args: any[]) => Promise<string>
): Promise<string> {
  // Create a cache key from function name and arguments
  const cacheKey = `${functionName}:${JSON.stringify(args)}`;
  
  // Check cache first
  const cachedResult = responseCache.get<string>(cacheKey);
  if (cachedResult) {
    console.log(`Cache hit for ${cacheKey}`);
    return cachedResult;
  }
  
  // Not in cache, call function
  const result = await callback(...args);
  
  // Store in cache
  responseCache.set(cacheKey, result);
  
  return result;
}

// Usage
const explanation = await getCachedResponse(
  'explainCode',
  [codeSnippet, filePath],
  explainCode
);

Deployment Considerations

When deploying your AI code assistant, consider these important factors:

1. Security and Privacy

typescript

// Configure LLM to strip sensitive information
const securityConfig = {
  // Don't send these patterns to external LLMs
  exclusionPatterns: [
    /password\s*=\s*['"][^'"]*['"]/, // Passwords
    /api[_\s]?key\s*=\s*['"][^'"]*['"]/, // API keys
    /secret\s*=\s*['"][^'"]*['"]/ // Secrets
  ],
  
  // Replace matches with placeholder
  sanitizeContent(content: string): string {
    let sanitized = content;
    for (const pattern of this.exclusionPatterns) {
      sanitized = sanitized.replace(pattern, match => {
        return match.replace(/['"][^'"]*['"]/, '["REDACTED"]');
      });
    }
    return sanitized;
  }
};

// Use in LLM calls
async function secureLLMCall(prompt: string, context: string): Promise<string> {
  const sanitizedPrompt = securityConfig.sanitizeContent(prompt);
  const sanitizedContext = securityConfig.sanitizeContent(context);
  
  return getAIAssistance(sanitizedPrompt, [{ snippet: sanitizedContext, filePath: '' }]);
}

2. Cost Management

Track usage to keep costs under control:

typescript

// Simple usage tracker
class UsageTracker {
  private static instance: UsageTracker;
  private usageData: {
    tokenCount: number;
    requests: number;
    costEstimate: number;
    lastReset: Date;
  };
  
  private constructor() {
    this.usageData = {
      tokenCount: 0,
      requests: 0,
      costEstimate: 0,
      lastReset: new Date()
    };
  }
  
  static getInstance(): UsageTracker {
    if (!UsageTracker.instance) {
      UsageTracker.instance = new UsageTracker();
    }
    return UsageTracker.instance;
  }
  
  recordUsage(tokens: number, modelType: string): void {
    this.usageData.tokenCount += tokens;
    this.usageData.requests += 1;
    
    // Very rough cost estimate (adjust rates as needed)
    const ratePerThousandTokens = modelType === 'gpt-4' ? 0.06 : 0.002;
    this.usageData.costEstimate += (tokens / 1000) * ratePerThousandTokens;
    
    // Persist usage data
    this.persistUsageData();
  }
  
  getUsageReport(): string {
    return `
      Usage since ${this.usageData.lastReset.toLocaleDateString()}:
      - Total requests: ${this.usageData.requests}
      - Total tokens: ${this.usageData.tokenCount}
      - Estimated cost: $${this.usageData.costEstimate.toFixed(2)}
    `;
  }
  
  private persistUsageData(): void {
    // Save to disk, DB, or telemetry service
    // ...
  }
}

// Use it in API calls
async function trackedLLMCall(prompt: string, context: string): Promise<string> {
  const tracker = UsageTracker.getInstance();
  
  // Estimate tokens (very rough estimate)
  const estimatedTokens = (prompt.length + context.length) / 4;
  
  const result = await getAIAssistance(prompt, [{ snippet: context, filePath: '' }]);
  
  // Record actual usage (in a real implementation, get this from the API response)
  tracker.recordUsage(estimatedTokens + (result.length / 4), 'gpt-4');
  
  return result;
}

3. Error Handling and Resilience

Implement robust error handling and fallbacks:

typescript

async function resilientLLMCall(prompt: string, context: string, retries = 3): Promise<string> {
  let lastError: any;
  
  for (let attempt = 0; attempt < retries; attempt++) {
    try {
      // Try different LLM providers in sequence
      try {
        return await getAIAssistance(prompt, [{ snippet: context, filePath: '' }]);
      } catch (openaiError) {
        console.warn('OpenAI call failed, trying local model:', openaiError);
        return await getLocalLLMResponse(prompt, context);
      }
    } catch (error) {
      console.error(`Attempt ${attempt + 1} failed:`, error);
      lastError = error;
      
      // Wait before retrying (exponential backoff)
      await new Promise(resolve => setTimeout(resolve, Math.pow(2, attempt) * 1000));
    }
  }
  
  // All attempts failed, return graceful error
  console.error('All LLM attempts failed:', lastError);
  return `I'm having trouble processing that request right now. Please try again later. 
  
  You can try these alternatives:
  1. Rephrase your question
  2. Select a smaller code snippet
  3. Check your network connection`;
}

Future Directions

As AI technology rapidly evolves, several exciting capabilities could enhance your code assistant:

1. Multi-Repository Understanding

Extend beyond a single codebase to understand dependencies and related projects:

typescript

// Configuration for cross-repository knowledge
const repositories = [
  { name: 'main-app', path: '/path/to/main-app', priority: 1 },
  { name: 'shared-lib', path: '/path/to/shared-lib', priority: 0.8 },
  { name: 'api-server', path: '/path/to/api-server', priority: 0.7 }
];

// Index multiple repositories
async function indexAllRepositories() {
  for (const repo of repositories) {
    await indexCodebase(repo.path);
    console.log(`Indexed repository: ${repo.name}`);
  }
}

// When searching, prioritize by repository
async function enhancedSimilarCodeSearch(query: string, limit: number = 5): Promise<any[]> {
  const queryEmbedding = await generateEmbedding(query);
  
  // Get results from vector database
  const searchResults = await qdrant.search('code_snippets', {
    vector: queryEmbedding,
    limit: limit * 2, // Get more results than needed for filtering
    with_payload: true
  });
  
  // Adjust scores based on repository priority
  const enhancedResults = searchResults.map(result => {
    const filePath = result.payload.filePath;
    const repo = repositories.find(r => filePath.includes(r.path));
    const priorityMultiplier = repo ? repo.priority : 0.5;
    
    return {
      ...result.payload,
      score: result.score * priorityMultiplier
    };
  });
  
  // Sort by adjusted score and return top results
  return enhancedResults
    .sort((a, b) => b.score - a.score)
    .slice(0, limit);
}

Enable navigating code by natural language concepts rather than just symbols:

typescript

// Find code implementing a concept
async function findCodeByConcept(concept: string): Promise<any[]> {
  const matchingSnippets = await findSimilarCode(concept, 10);
  
  // Group by file for better organization
  const resultsByFile: Record<string, any[]> = {};
  
  for (const snippet of matchingSnippets) {
    if (!resultsByFile[snippet.filePath]) {
      resultsByFile[snippet.filePath] = [];
    }
    resultsByFile[snippet.filePath].push(snippet);
  }
  
  return Object.entries(resultsByFile).map(([filePath, snippets]) => ({
    filePath,
    snippets
  }));
}

// Usage in command
vscode.commands.registerCommand('aiCodeAssistant.findConcept', async () => {
  const concept = await vscode.window.showInputBox({
    prompt: 'What concept are you looking for?',
    placeHolder: 'e.g., "authentication", "caching", "database connection"'
  });
  
  if (!concept) return;
  
  vscode.window.withProgress({
    location: vscode.ProgressLocation.Notification,
    title: `Finding code related to "${concept}"...`,
    cancellable: false
  }, async () => {
    const results = await findCodeByConcept(concept);
    
    // Display results in tree view
    // ...
  });
});

3. Automated Refactoring and Code Generation

Beyond explaining code, actually modify and generate it:

typescript

// Refactor a code snippet
async function refactorCode(
  codeSnippet: string,
  instruction: string,
  filePath: string
): Promise<string> {
  // Get similar code for context
  const similarSnippets = await findSimilarCode(codeSnippet, 3);
  
  const systemPrompt = `You are an expert TypeScript refactoring assistant. 
  Your task is to refactor the given code according to the user's instructions.
  
  Always maintain the same functionality while improving the code.
  Return ONLY the refactored code with no explanations.`;
  
  const userPrompt = `Refactor this code: 
  
\`\`\`typescript
${codeSnippet}
\`\`\`

Instructions: ${instruction}

Example patterns from this codebase:
${similarSnippets.map(s => `\`\`\`typescript\n${s.snippet}\n\`\`\``).join('\n\n')}`;

  try {
    const response = await openai.chat.completions.create({
      model: 'gpt-4-turbo',
      messages: [
        { role: 'system', content: systemPrompt },
        { role: 'user', content: userPrompt }
      ],
      temperature: 0.2 // Lower temperature for more predictable refactoring
    });
    
    const refactoredCode = response.choices[0].message.content || '';
    
    // Extract just the code if it's wrapped in backticks
    const codeMatch = refactoredCode.match(/```(?:typescript|ts|js)?\n([\s\S]*?)```/);
    return codeMatch ? codeMatch[1].trim() : refactoredCode.trim();
  } catch (error) {
    console.error('Error refactoring code:', error);
    return codeSnippet; // Return original on error
  }
}

// Command to refactor selected code
vscode.commands.registerCommand('aiCodeAssistant.refactorCode', async () => {
  const editor = vscode.window.activeTextEditor;
  if (!editor) return;
  
  const selection = editor.selection;
  const code = editor.document.getText(selection);
  
  if (!code) {
    vscode.window.showInformationMessage('No code selected to refactor.');
    return;
  }
  
  const instruction = await vscode.window.showInputBox({
    prompt: 'How would you like to refactor this code?',
    placeHolder: 'e.g., "Convert to async/await", "Extract to function", "Use ES6 features"'
  });
  
  if (!instruction) return;
  
  vscode.window.withProgress({
    location: vscode.ProgressLocation.Notification,
    title: 'AI Assistant: Refactoring code...',
    cancellable: false
  }, async () => {
    const refactored = await refactorCode(code, instruction, editor.document.uri.fsPath);
    
    // Apply the refactored code
    editor.edit(editBuilder => {
      editBuilder.replace(selection, refactored);
    });
  });
});

Penify: Automation-First Approach

While this guide focuses on building an AI code assistant, it's worth mentioning that Penify is building something similar but with a different philosophy. Instead of just providing assistance, Penify is focused on automation—aiming to make code generation and modification workflows 99.9% reliable. This is a challenging journey, but the vision is that in the future, if someone creates a task item in Jira, Penify could automatically provide a ready-to-use code template for that task.

You can learn more about Penify's mission and progress at penify.dev.

Conclusion

Building a Cursor-like AI code assistant is an ambitious but achievable project that combines multiple cutting-edge technologies:

Language Server Protocol provides structural understanding of code
Code Embeddings enable semantic search and retrieval
Large Language Models provide intelligence and natural language capabilities

The architecture we've outlined allows for a modular implementation, where you can start with basic features and progressively enhance the capabilities. This approach also makes it easier to swap components as better technologies emerge.

The most powerful aspect of this system is how it combines explicit structure (from LSP) with semantic understanding (from embeddings and LLMs) to create a truly intelligent coding companion. This hybrid approach addresses the limitations of traditional IDE tools while avoiding the inaccuracies of pure LLM-based solutions.

As you implement your own AI code assistant, remember that the goal is to enhance developer productivity, not replace developer judgment. The best assistants act as collaborative partners, providing context, suggestions, and explanations while leaving final decisions to the human developer.

Resources for Getting Started

LSP Implementation:
- LSP Specification
- vscode-languageserver
Vector Databases:
- Qdrant Documentation
- Weaviate Guides
LLM Integration:
- OpenAI API Reference
- Ollama for Local LLMs
VS Code Extension Development:
- VS Code Extension API
- Your First Extension Guide

By integrating these technologies, you can create an AI-powered coding assistant that truly understands your codebase and helps developers navigate, understand, and improve their code more efficiently.

Would you like to see a working demo or get a GitHub-ready template for this system? Let me know in the comments!

For more, visit Penify Blog.

This article was last updated on May 1, 2025, and reflects the current state of AI code assistant technology.

Building a Cursor-like AI Code Assistant: A Comprehensive Implementation Guide

Table of Contents

Understanding the Architecture

Setting Up the Language Server Protocol

Installing and Configuring TypeScript Language Server

Extracting Metadata from TypeScript Files

Building an Abstract Syntax Tree (AST) Parser

Implementing Code Embeddings

Setting Up an Embedding Model

Building a Vector Database for Code

Indexing Your Codebase

Integrating Large Language Models

Setting Up the LLM Client

Creating Specialized LLM Functions

Building the User Interface

Setting Up a VS Code Extension

Adding a Chat Interface

Putting It All Together

Complete Workflow

Enhancing User Experience

Advanced Features and Optimizations

1. Incremental Indexing

2. Custom Fine-tuned Models

3. Local LLM Deployment

4. Rate Limiting and Caching

Deployment Considerations

1. Security and Privacy

2. Cost Management

3. Error Handling and Resilience

Future Directions

1. Multi-Repository Understanding

2. Semantic Code Navigation

3. Automated Refactoring and Code Generation

Penify: Automation-First Approach

Conclusion

Resources for Getting Started

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Building a Cursor-like AI Code Assistant: A Comprehensive Implementation Guide ​

Table of Contents ​

Understanding the Architecture ​

Setting Up the Language Server Protocol ​

Installing and Configuring TypeScript Language Server ​

Extracting Metadata from TypeScript Files ​

Building an Abstract Syntax Tree (AST) Parser ​

Implementing Code Embeddings ​

Setting Up an Embedding Model ​

Building a Vector Database for Code ​

Indexing Your Codebase ​

Integrating Large Language Models ​

Setting Up the LLM Client ​

Creating Specialized LLM Functions ​

Building the User Interface ​

Setting Up a VS Code Extension ​

Adding a Chat Interface ​

Putting It All Together ​

Complete Workflow ​

Enhancing User Experience ​

Advanced Features and Optimizations ​

1. Incremental Indexing ​

2. Custom Fine-tuned Models ​

3. Local LLM Deployment ​

4. Rate Limiting and Caching ​

Deployment Considerations ​

1. Security and Privacy ​

2. Cost Management ​

3. Error Handling and Resilience ​

Future Directions ​

1. Multi-Repository Understanding ​

2. Semantic Code Navigation ​

3. Automated Refactoring and Code Generation ​

Penify: Automation-First Approach ​

Conclusion ​

Resources for Getting Started ​

Building a Cursor-like AI Code Assistant: A Comprehensive Implementation Guide

Table of Contents

Understanding the Architecture

Setting Up the Language Server Protocol

Installing and Configuring TypeScript Language Server

Extracting Metadata from TypeScript Files

Building an Abstract Syntax Tree (AST) Parser

Implementing Code Embeddings

Setting Up an Embedding Model

Building a Vector Database for Code

Indexing Your Codebase

Integrating Large Language Models

Setting Up the LLM Client

Creating Specialized LLM Functions

Building the User Interface

Setting Up a VS Code Extension

Adding a Chat Interface

Putting It All Together

Complete Workflow

Enhancing User Experience

Advanced Features and Optimizations

1. Incremental Indexing

2. Custom Fine-tuned Models

3. Local LLM Deployment

4. Rate Limiting and Caching

Deployment Considerations

1. Security and Privacy

2. Cost Management

3. Error Handling and Resilience

Future Directions

1. Multi-Repository Understanding

2. Semantic Code Navigation

3. Automated Refactoring and Code Generation

Penify: Automation-First Approach

Conclusion

Resources for Getting Started