graph TD
    A[IDE Extension] --> B[Language Server]
    B --> C[AI Model Gateway]
    C --> D[Code Context Engine]
    C --> E[Pattern Recognition]
    C --> F[Code Generation]
    D --> G[Repository Analysis]
    E --> H[Team Standards DB]
    F --> I[Template Engine]

interface CodeContext {
  currentFile: string;
  projectStructure: FileTree;
  imports: ImportStatement[];
  nearbyFunctions: Function[];
  teamStandards: CodingStandard[];
  recentChanges: GitCommit[];
}

class ContextEngine {
  async generateContext(position: Position): Promise<CodeContext> {
    return {
      currentFile: await this.getCurrentFile(position),
      projectStructure: await this.analyzeProject(),
      imports: await this.extractImports(),
      nearbyFunctions: await this.findNearbyFunctions(position),
      teamStandards: await this.getTeamStandards(),
      recentChanges: await this.getRecentCommits()
    };
  }
}

// Example: Auto-completion for API endpoint
// User types: "app.get('/api/users"
// Copilot suggests:
app.get('/api/users/:id', async (req, res) => {
  try {
    const { id } = req.params;
    const user = await UserService.findById(id);
    
    if (!user) {
      return res.status(404).json({ 
        error: 'User not found' 
      });
    }
    
    res.json(user);
  } catch (error) {
    logger.error('Error fetching user:', error);
    res.status(500).json({ 
      error: 'Internal server error' 
    });
  }
});

# Simplified training pipeline
class CodeModelTrainer:
    def prepare_training_data(self):
        # Extract code patterns from repositories
        patterns = self.extract_patterns(repositories)
        
        # Create training examples
        examples = self.create_examples(patterns)
        
        # Add team-specific preferences
        examples = self.add_team_context(examples)
        
        return examples
    
    def fine_tune_model(self, base_model, training_data):
        # Fine-tune on company-specific code
        model = base_model.fine_tune(
            training_data=training_data,
            validation_split=0.2,
            epochs=10,
            learning_rate=0.0001
        )
        
        return model

{
  "vscode_extension": {
    "activation": "onStartup",
    "commands": [
      "freddy.generateCode",
      "freddy.explainCode", 
      "freddy.optimizeCode",
      "freddy.generateTests"
    ],
    "keybindings": [
      {
        "command": "freddy.generateCode",
        "key": "ctrl+shift+g"
      }
    ]
  }
}

class CodeValidator {
  async validateGenerated(code: string): Promise<ValidationResult> {
    const lintResults = await this.runESLint(code);
    const styleCheck = await this.checkCodingStyle(code);
    const securityScan = await this.securityAnalysis(code);
    
    return {
      isValid: lintResults.valid && styleCheck.valid && securityScan.safe,
      suggestions: [...lintResults.fixes, ...styleCheck.improvements],
      warnings: securityScan.warnings
    };
  }
}

class ContextOptimizer {
  selectRelevantContext(fullContext: CodeContext, position: Position): OptimizedContext {
    return {
      // Most relevant 20 lines around cursor
      immediate: fullContext.nearbyLines.slice(-10, 10),
      
      // Related functions and imports
      related: this.findRelatedFunctions(position),
      
      // Essential type definitions
      types: this.extractRelevantTypes(fullContext),
      
      // Team conventions summary
      standards: this.summarizeStandards(fullContext.teamStandards)
    };
  }
}

interface DeveloperFeedback {
  productivity: 4.7/5;
  codeQuality: 4.5/5;
  learningCurve: 4.3/5;
  overallSatisfaction: 4.6/5;
}

const feedback: DeveloperFeedback = {
  productivity: 4.7,
  codeQuality: 4.5, 
  learningCurve: 4.3,
  overallSatisfaction: 4.6
};

// 1. Set up the basic extension structure
export class AICodeAssistant {
  private contextEngine: ContextEngine;
  private aiClient: AIClient;
  private cache: CacheManager;
  
  constructor() {
    this.contextEngine = new ContextEngine();
    this.aiClient = new AIClient(process.env.AI_API_KEY);
    this.cache = new CacheManager();
  }
  
  async provideSuggestions(document: TextDocument, position: Position): Promise<Suggestion[]> {
    // Get context for current position
    const context = await this.contextEngine.generateContext(position);
    
    // Check cache first
    const cacheKey = this.generateCacheKey(context, position);
    const cached = await this.cache.get(cacheKey);
    if (cached) return cached;
    
    // Generate AI suggestions
    const suggestions = await this.aiClient.generateSuggestions(context);
    
    // Cache and return
    await this.cache.set(cacheKey, suggestions, { ttl: 300 });
    return suggestions;
  }
}

# Model training pipeline
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

class CodeModelTrainer:
    def __init__(self, model_name="microsoft/CodeGPT-small-py"):
        self.tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        self.model = GPT2LMHeadModel.from_pretrained(model_name)
        
    def prepare_code_data(self, repositories):
        """Extract and prepare training data from repositories"""
        training_data = []
        
        for repo in repositories:
            for file in repo.get_source_files():
                # Extract functions, classes, and patterns
                patterns = self.extract_code_patterns(file)
                training_data.extend(patterns)
                
        return training_data
    
    def fine_tune(self, training_data, epochs=5):
        """Fine-tune the model on company-specific code"""
        # Training implementation
        pass