interface AnalyticsDataSources {
  chatSessions: CopilotChatSession[];
  toolCallRounds: ToolCallRound[];
  fileReferences: ContentReference[];
  workspaceContext: WorkspaceMetadata;
  userPreferences: UserSettings;
}

interface ModelStats {
  modelId: string;
  usage: {
    totalRequests: number;
    percentage: number;
    avgLatency: number;
    costPerRequest: number;
  };
  performance: {
    editRatio: number;
    acceptanceRate: number;
    errorRate: number;
  };
  contexts: {
    languages: string[];
    taskTypes: TaskType[];
    fileTypes: string[];
  };
}

interface LatencyMetrics {
  modelId: string;
  percentiles: {
    p50: number;  // Median response time
    p90: number;  // 90th percentile
    p99: number;  // 99th percentile
  };
  taskType: TaskType;
  contextSize: 'small' | 'medium' | 'large';
}

interface QualityScore {
  accuracy: number;      // 0-100: Correctness of suggestions
  relevance: number;     // 0-100: Contextual appropriateness
  completeness: number;  // 0-100: Thoroughness of response
  usability: number;     // 0-100: Immediate applicability
  composite: number;     // Weighted average
}

interface SessionPatternAnalysis {
  sessionId: string;
  detectedPatterns: UsagePattern[];
  confidence: number;
  indicators: PatternIndicator[];
  recommendations: OptimizationRecommendation[];
}

enum UsagePattern {
  ARCHITECTURE_DESIGN = 'architecture_design',
  CODE_GENERATION = 'code_generation', 
  REFACTORING = 'refactoring',
  DEBUGGING = 'debugging',
  RESEARCH_LEARNING = 'research_learning',
  DOCUMENTATION = 'documentation',
  TESTING = 'testing',
  MAINTENANCE = 'maintenance'
}

function detectArchitecturePattern(session: ChatSession): PatternDetection {
  const indicators = {
    designKeywords: countDesignTerms(session.messages),
    fileSpan: analyzeFileReferences(session.contentReferences),
    questionComplexity: assessQuestionComplexity(session.userMessages),
    responseLength: calculateAvgResponseLength(session.assistantMessages),
    toolUsage: analyzeToolUsage(session.toolCallRounds)
  };
  
  return {
    pattern: UsagePattern.ARCHITECTURE_DESIGN,
    confidence: calculateConfidence(indicators),
    evidence: buildEvidence(indicators)
  };
}

function detectCodeGenerationPattern(session: ChatSession): PatternDetection {
  const codeBlocks = extractCodeBlocks(session.messages);
  const editOperations = countEditOperations(session.toolCallRounds);
  const implementationKeywords = countImplementationTerms(session.messages);
  
  return {
    pattern: UsagePattern.CODE_GENERATION,
    confidence: Math.min(
      (codeBlocks.length / session.turns) * 100,
      (editOperations / session.turns) * 100
    ),
    characteristics: {
      codeBlockDensity: codeBlocks.length / session.turns,
      editRatio: editOperations / session.turns,
      implementationFocus: implementationKeywords > 10
    }
  };
}

function detectRefactoringPattern(session: ChatSession): PatternDetection {
  const refactorTerms = countRefactoringKeywords(session.messages);
  const codeAnalysis = detectCodeAnalysisActivities(session.toolCallRounds);
  const existingCodeRefs = countExistingCodeReferences(session.contentReferences);
  const improvementDiscussions = detectQualityDiscussions(session.messages);
  
  return {
    pattern: UsagePattern.REFACTORING,
    confidence: calculateRefactoringConfidence({
      refactorTerms,
      codeAnalysis,
      existingCodeRefs,
      improvementDiscussions
    }),
    scope: determineRefactoringScope(session)
  };
}

function detectResearchPattern(session: ChatSession): PatternDetection {
  const questionWords = countQuestionWords(session.userMessages);
  const explanationRequests = countExplanationRequests(session.messages);
  const editRatio = calculateEditRatio(session.toolCallRounds);
  const conceptualTerms = countConceptualDiscussion(session.messages);
  
  return {
    pattern: UsagePattern.RESEARCH_LEARNING,
    confidence: calculateLearningConfidence({
      questionDensity: questionWords / session.turns,
      lowEditRatio: editRatio < 0.3,
      explanationFocus: explanationRequests > 5,
      conceptualDepth: conceptualTerms > 15
    }),
    learningArea: identifyLearningDomain(session.messages)
  };
}

function detectDebuggingPattern(session: ChatSession): PatternDetection {
  const errorTerms = countErrorRelatedTerms(session.messages);
  const diagnosticTools = countDiagnosticToolUsage(session.toolCallRounds);
  const iterativeAttempts = detectIterativeDebugging(session.messages);
  const problemResolution = detectProblemResolution(session);
  
  return {
    pattern: UsagePattern.DEBUGGING,
    confidence: calculateDebuggingConfidence({
      errorLanguage: errorTerms > 3,
      diagnosticActivity: diagnosticTools > 2,
      iterativeNature: iterativeAttempts > 1,
      resolutionAchieved: problemResolution
    }),
    problemType: classifyProblemType(session.messages)
  };
}

class SessionPatternClassifier {
  private patterns: PatternDetector[] = [
    new ArchitectureDetector(),
    new CodeGenerationDetector(), 
    new RefactoringDetector(),
    new ResearchDetector(),
    new DebuggingDetector()
  ];

  async classifySession(session: ChatSession): Promise<SessionClassification> {
    const results = await Promise.all(
      this.patterns.map(detector => detector.analyze(session))
    );
    
    return {
      primaryPattern: this.selectPrimaryPattern(results),
      secondaryPatterns: this.selectSecondaryPatterns(results),
      confidence: this.calculateOverallConfidence(results),
      timeline: this.analyzePatternProgression(session),
      recommendations: this.generateRecommendations(results)
    };
  }

  private selectPrimaryPattern(results: PatternDetection[]): UsagePattern {
    return results.reduce((prev, current) => 
      current.confidence > prev.confidence ? current : prev
    ).pattern;
  }
}

interface PatternProgression {
  timeline: {
    start: number;
    end: number;
    pattern: UsagePattern;
    confidence: number;
  }[];
  transitions: PatternTransition[];
  dominantPattern: UsagePattern;
  complexity: 'simple' | 'moderate' | 'complex';
}

interface ContextualFactors {
  timeOfDay: 'morning' | 'afternoon' | 'evening';
  dayOfWeek: 'weekday' | 'weekend';
  projectPhase: 'planning' | 'implementation' | 'testing' | 'maintenance';
  teamCollaboration: boolean;
  deadline proximity: 'low' | 'medium' | 'high';
}

interface CopilotSessionFile {
  version: number;
  requesterUsername: string;
  responderUsername: string;
  initialLocation: string;
  requests: SessionRequest[];
}

interface SessionRequest {
  requestId: string;
  message: {
    parts: MessagePart[];
    text: string;
  };
  variableData: {
    variables: ContextVariable[];
  };
  response: ResponseComponent[];
}

class SessionAnalyzer {
  private patterns = new Map<UsagePattern, PatternMatcher>();

  constructor() {
    this.patterns.set(UsagePattern.REFACTORING, new RefactoringMatcher());
    this.patterns.set(UsagePattern.ARCHITECTURE_DESIGN, new ArchitectureMatcher());
    this.patterns.set(UsagePattern.CODE_GENERATION, new CodeGenerationMatcher());
    this.patterns.set(UsagePattern.DEBUGGING, new DebuggingMatcher());
    this.patterns.set(UsagePattern.RESEARCH_LEARNING, new ResearchMatcher());
  }

  async analyzeSessionFile(filePath: string): Promise<SessionAnalysis> {
    const sessionData = await this.loadSessionFile(filePath);
    const results = await Promise.all([...this.patterns.entries()].map(
      async ([pattern, matcher]) => ({
        pattern,
        score: await matcher.calculateScore(sessionData),
        evidence: await matcher.gatherEvidence(sessionData)
      })
    ));

    return {
      sessionId: this.extractSessionId(filePath),
      primaryPattern: this.selectPrimaryPattern(results),
      allScores: results,
      recommendations: this.generateRecommendations(results),
      metadata: this.extractMetadata(sessionData)
    };
  }

  private extractSessionId(filePath: string): string {
    const filename = path.basename(filePath);
    const match = filename.match(/chatSessions_([a-f0-9-]+)\.json$/);
    return match ? match[1] : 'unknown';
  }
}

class HistoricalAnalyzer {
  async processSessionDirectory(directoryPath: string): Promise<PatternReport> {
    const sessionFiles = await this.findSessionFiles(directoryPath);
    const analyses = await Promise.all(
      sessionFiles.map(file => this.analyzer.analyzeSessionFile(file))
    );

    return {
      totalSessions: analyses.length,
      patternDistribution: this.calculatePatternDistribution(analyses),
      temporalTrends: this.analyzeTemporalTrends(analyses),
      userBehaviorProfile: this.buildUserProfile(analyses),
      optimizationOpportunities: this.identifyOptimizations(analyses)
    };
  }

  private calculatePatternDistribution(analyses: SessionAnalysis[]): PatternDistribution {
    const counts = new Map<UsagePattern, number>();
    
    analyses.forEach(analysis => {
      const pattern = analysis.primaryPattern;
      counts.set(pattern, (counts.get(pattern) || 0) + 1);
    });

    const total = analyses.length;
    return Object.fromEntries(
      [...counts.entries()].map(([pattern, count]) => [
        pattern,
        {
          count,
          percentage: (count / total) * 100,
          avgConfidence: this.calculateAvgConfidence(analyses, pattern)
        }
      ])
    );
  }
}

interface ToolUsageAnalysis {
  sessionPattern: UsagePattern;
  toolSequence: string[];
  toolFrequency: Map<string, number>;
  effectiveness: number; // Based on successful edits/responses
}

// Example tool sequences by pattern:
const PATTERN_TOOL_SIGNATURES = {
  REFACTORING: ['copilot_findTextInFiles', 'copilot_readFile', 'copilot_editFile'],
  ARCHITECTURE: ['copilot_readFile', 'copilot_findTextInFiles', 'semantic_search'],
  DEBUGGING: ['copilot_runTests', 'copilot_getErrors', 'copilot_readFile'],
  CODE_GENERATION: ['copilot_createFile', 'copilot_editFile', 'copilot_runTests']
};

class RealtimePatternDetector {
  private sessionBuffer: SessionRequest[] = [];
  private currentPattern: UsagePattern | null = null;
  
  async onNewRequest(request: SessionRequest): Promise<PatternUpdate> {
    this.sessionBuffer.push(request);
    
    // Analyze recent context (last 3-5 requests)
    const recentContext = this.sessionBuffer.slice(-5);
    const detectedPattern = await this.detectPattern(recentContext);
    
    if (detectedPattern !== this.currentPattern) {
      this.currentPattern = detectedPattern;
      return {
        patternChanged: true,
        newPattern: detectedPattern,
        recommendations: await this.getPatternRecommendations(detectedPattern),
        suggestedModel: this.getOptimalModel(detectedPattern)
      };
    }
    
    return { patternChanged: false };
  }
}

class LLMPatternClassifier {
  private model: LLMInterface;

  async classifySession(sessionText: string): Promise<PatternClassification> {
    const prompt = `
Analyze this Copilot chat session and classify the primary usage pattern.

Session Content:
${sessionText}

Classification Categories:
1. ARCHITECTURE_DESIGN - High-level system design, patterns, trade-offs
2. CODE_GENERATION - Creating new functions, classes, components
3. REFACTORING - Improving existing code structure, cleanup
4. DEBUGGING - Finding and fixing errors, troubleshooting
5. RESEARCH_LEARNING - Understanding concepts, tutorials, explanations
6. DOCUMENTATION - Writing docs, comments, README files
7. TESTING - Writing tests, test analysis, coverage

Respond with:
{
  "primaryPattern": "PATTERN_NAME",
  "confidence": 0.85,
  "reasoning": "Detailed explanation of classification",
  "secondaryPatterns": ["PATTERN_2"],
  "keyIndicators": ["specific phrases or behaviors"]
}`;

    return await this.model.complete(prompt);
  }
}

class EmbeddingBasedClassifier {
  private embeddings: EmbeddingModel;
  private patternExemplars: Map<UsagePattern, number[]>;

  async classifyByEmbedding(sessionText: string): Promise<PatternMatch[]> {
    const sessionEmbedding = await this.embeddings.embed(sessionText);
    
    const similarities = [...this.patternExemplars.entries()].map(
      ([pattern, exemplarEmbedding]) => ({
        pattern,
        similarity: this.cosineSimilarity(sessionEmbedding, exemplarEmbedding)
      })
    );

    return similarities.sort((a, b) => b.similarity - a.similarity);
  }

  private async buildExemplars(): Promise<void> {
    // Create representative embeddings for each pattern type
    const exemplarTexts = {
      ARCHITECTURE_DESIGN: "How should I structure this microservice? What pattern should I use for data flow?",
      CODE_GENERATION: "Create a TypeScript class for user authentication with validation methods",
      REFACTORING: "This function is too complex. Help me break it into smaller, more maintainable pieces",
      DEBUGGING: "I'm getting a TypeError when accessing user.profile.name. The stack trace shows...",
      RESEARCH_LEARNING: "Can you explain how React hooks work internally? What's the difference between useState and useReducer?"
    };

    for (const [pattern, text] of Object.entries(exemplarTexts)) {
      this.patternExemplars.set(pattern as UsagePattern, await this.embeddings.embed(text));
    }
  }
}

class HybridPatternClassifier {
  private llmClassifier: LLMPatternClassifier;
  private embeddingClassifier: EmbeddingBasedClassifier;
  private statisticalClassifier: StatisticalPatternClassifier;

  async classifySession(session: CopilotSession): Promise<ClassificationResult> {
    // Run all classifiers in parallel
    const [llmResult, embeddingResult, statsResult] = await Promise.all([
      this.llmClassifier.classify(session.conversationText),
      this.embeddingClassifier.classify(session.conversationText),
      this.statisticalClassifier.classify(session.metadata)
    ]);

    // Ensemble voting with confidence weighting
    return this.ensembleVote([
      { result: llmResult, weight: 0.5 },
      { result: embeddingResult, weight: 0.3 },
      { result: statsResult, weight: 0.2 }
    ]);
  }

  private ensembleVote(classifications: WeightedClassification[]): ClassificationResult {
    const scoreMap = new Map<UsagePattern, number>();
    
    classifications.forEach(({ result, weight }) => {
      const score = result.confidence * weight;
      const current = scoreMap.get(result.pattern) || 0;
      scoreMap.set(result.pattern, current + score);
    });

    const winner = [...scoreMap.entries()].reduce((a, b) => a[1] > b[1] ? a : b);
    
    return {
      pattern: winner[0],
      confidence: winner[1],
      reasoning: this.combineReasoning(classifications),
      alternativePatterns: this.getAlternatives(scoreMap, winner[0])
    };
  }
}

const ARCHITECTURE_KEYWORDS = [
  // Question patterns
  /how should I (structure|organize|design|architect)/i,
  /what (pattern|approach|strategy) (should|would)/i,
  
  // Design concepts
  /microservices?|monolith|pattern|architecture|design/i,
  /scalability|performance|maintainability/i,
  
  // Trade-off language
  /trade[-\s]?off|pros and cons|advantages?|disadvantages?/i
];

const CODE_GENERATION_KEYWORDS = [
  // Creation verbs
  /create|generate|build|implement|write/i,
  
  // Specific artifacts
  /class|function|method|component|interface/i,
  
  // Implementation focus
  /add (a|the)|make (a|the)|write (a|the)/i
];

class ConversationalFlowAnalyzer {
  analyzeSessionFlow(requests: SessionRequest[]): FlowAnalysis {
    const turns = requests.map(req => ({
      userMessage: req.message.text,
      toolsUsed: this.extractToolUsage(req.response),
      editsMade: this.countEdits(req.response),
      questionType: this.classifyQuestion(req.message.text)
    }));

    return {
      flowType: this.determineFlowType(turns),
      complexity: this.assessComplexity(turns),
      progression: this.analyzeProgression(turns)
    };
  }

  private determineFlowType(turns: ConversationTurn[]): FlowType {
    const patterns = {
      EXPLORATORY: turns.filter(t => t.questionType === 'open-ended').length > 0.6 * turns.length,
      DIRECTED: turns.filter(t => t.editsMade > 0).length > 0.4 * turns.length,
      ITERATIVE: this.hasIterativePattern(turns),
      LEARNING: turns.filter(t => t.questionType === 'explanatory').length > 0.5 * turns.length
    };

    return Object.entries(patterns).find(([_, matches]) => matches)?.[0] as FlowType || 'MIXED';
  }
}

interface LanguageUsagePattern {
  language: string;
  preferredModels: ModelPreference[];
  commonTasks: TaskType[];
  avgSessionLength: number;
  editRatio: number;
  costEfficiency: number;
}

interface CostMetrics {
  daily: {
    standardRequests: number;
    premiumRequests: number;
    estimatedCost: number;
  };
  weekly: {
    trend: 'increasing' | 'stable' | 'decreasing';
    budgetUtilization: number;
    projectedMonthly: number;
  };
  efficiency: {
    costPerSuccessfulEdit: number;
    valueDelivered: number;
    roi: number;
  };
}

function selectOptimalModel(context: TaskContext): ModelRecommendation {
  const factors = {
    complexity: assessComplexity(context),
    cost: getCurrentBudget(),
    urgency: getDeadlinePressure(),
    quality: getQualityRequirements()
  };
  
  return optimizeModelChoice(factors);
}

interface PerformanceThresholds {
  latencyAlert: 3000;      // ms
  editRatioMin: 0.6;       // 60%
  costEfficiencyMin: 0.8;  // 80% of baseline
  qualityScoreMin: 75;     // 0-100 scale
}

class CopilotAnalytics {
  private collector: UsageCollector;
  private analyzer: PatternAnalyzer;
  private optimizer: ModelOptimizer;
  
  async trackSession(session: CopilotSession): Promise<void> {
    await this.collector.recordSession(session);
    const patterns = await this.analyzer.analyzePatterns();
    const recommendations = await this.optimizer.generateRecommendations(patterns);
    await this.updateDashboard(recommendations);
  }
}

class ModelSelector {
  async selectModel(context: TaskContext): Promise<ModelChoice> {
    const historicalPerformance = await this.getHistoricalData(context);
    const currentConstraints = await this.getCurrentConstraints();
    const prediction = await this.predictPerformance(context, historicalPerformance);
    
    return this.optimizeChoice(prediction, currentConstraints);
  }
}