Chat Agent

Natural Language Understanding

Component Overview

The Chat Agent serves as Kaizen AI's intelligent conversational interface, responsible for understanding user queries, routing requests to appropriate analytical agents, and generating comprehensive, contextually-aware responses. This agent acts as the orchestration layer that transforms natural language input into actionable intelligence operations and presents complex analytical results in an accessible, conversational format.

Core Responsibilities:

• Natural language processing and intent classification

• Multi-agent query routing and orchestration

• Context management and conversation state tracking

• Multi-LLM integration for optimal response generation

• Real-time streaming responses and interactive analysis

• User session management and personalization

• Response synthesis from multiple analytical sources

Architecture Philosophy: The Chat Agent employs a sophisticated natural language understanding pipeline combined with intelligent agent orchestration to provide users with seamless access to Kaizen AI's analytical capabilities. The system prioritizes accuracy, context awareness, and user experience while maintaining the technical depth required for professional crypto analysis.

Natural Language Processing Pipeline

Advanced NLP Architecture

// Core interfaces for natural language understanding
interface NLUPipeline {
  preprocess(input: string): Promise<PreprocessedInput>;
  classifyIntent(input: PreprocessedInput): Promise<IntentClassification>;
  extractEntities(input: PreprocessedInput): Promise<EntityExtractionResult>;
  analyzeContext(input: PreprocessedInput, session: ChatSession): Promise<ContextAnalysis>;
  validateQuery(intent: IntentClassification, entities: EntityExtractionResult): Promise<QueryValidation>;
}

interface ChatQueryProcessor {
  sessionId: string;
  userId: string;
  processQuery(query: string): Promise<ChatResponse>;
  streamResponse(query: string): AsyncGenerator<ChatStreamChunk>;
  handleFollowUp(query: string, context: ConversationContext): Promise<ChatResponse>;
}

export class ChatOrchestrator {
  private nluPipeline: NLUPipeline;
  private intentRouter: IntentRouter;
  private agentCoordinator: AgentCoordinator;
  private contextManager: ConversationContextManager;
  private responseGenerator: ResponseGenerator;
  private sessionManager: SessionManager;
  private analyticsTracker: ChatAnalyticsTracker;

  constructor(
    private config: ChatConfig,
    private agentRegistry: AgentRegistry,
    private llmProvider: LLMProvider,
    private database: DatabaseInterface,
    private cache: CacheInterface
  ) {
    this.nluPipeline = new NLUPipeline(config.nlu);
    this.intentRouter = new IntentRouter(config.routing);
    this.agentCoordinator = new AgentCoordinator(agentRegistry);
    this.contextManager = new ConversationContextManager(config.context);
    this.responseGenerator = new ResponseGenerator(llmProvider, config.response);
    this.sessionManager = new SessionManager(database, cache);
    this.analyticsTracker = new ChatAnalyticsTracker(config.analytics);
    
    this.setupRealTimeCapabilities();
  }

  async processUserQuery(
    sessionId: string,
    userId: string,
    query: string,
    metadata?: QueryMetadata
  ): Promise<ChatResponse> {
    const startTime = Date.now();
    
    try {
      // Get or create session
      const session = await this.sessionManager.getOrCreateSession(sessionId, userId);
      
      // Update session with new query
      await this.sessionManager.addMessage(sessionId, {
        type: 'user',
        content: query,
        timestamp: new Date(),
        metadata
      });

      // Process through NLP pipeline
      const nluResult = await this.processNLP(query, session);
      
      // Route to appropriate agents
      const agentResponse = await this.routeToAgents(nluResult, session);
      
      // Generate conversational response
      const response = await this.generateResponse(nluResult, agentResponse, session);
      
      // Update session with response
      await this.sessionManager.addMessage(sessionId, {
        type: 'assistant',
        content: response.content,
        timestamp: new Date(),
        analyticalData: agentResponse,
        confidence: response.confidence
      });

      // Track analytics
      this.analyticsTracker.trackQuery({
        sessionId,
        userId,
        query,
        intent: nluResult.intent,
        processingTime: Date.now() - startTime,
        agentsInvolved: agentResponse.agentsUsed,
        responseType: response.type
      });

      return response;

    } catch (error) {
      logger.error('Chat query processing failed:', error);
      
      // Generate error response
      const errorResponse = await this.generateErrorResponse(query, error);
      
      await this.sessionManager.addMessage(sessionId, {
        type: 'assistant',
        content: errorResponse.content,
        timestamp: new Date(),
        isError: true
      });

      return errorResponse;
    }
  }

  private async processNLP(query: string, session: ChatSession): Promise<NLUResult> {
    // Preprocess input
    const preprocessed = await this.nluPipeline.preprocess(query);
    
    // Classify intent
    const intentClassification = await this.nluPipeline.classifyIntent(preprocessed);
    
    // Extract entities
    const entities = await this.nluPipeline.extractEntities(preprocessed);
    
    // Analyze context from conversation history
    const contextAnalysis = await this.nluPipeline.analyzeContext(preprocessed, session);
    
    // Validate query completeness
    const validation = await this.nluPipeline.validateQuery(intentClassification, entities);

    return {
      originalQuery: query,
      preprocessed,
      intent: intentClassification,
      entities,
      context: contextAnalysis,
      validation,
      confidence: this.calculateNLUConfidence(intentClassification, entities, validation)
    };
  }
}

Intent Classification System

Advanced Intent Recognition

// Sophisticated intent classification with domain-specific understanding
export class IntentClassifier {
  private models: Map<string, IntentModel> = new Map();
  private ruleEngine: IntentRuleEngine;
  private contextualClassifier: ContextualIntentClassifier;
  private confidenceCalculator: ConfidenceCalculator;

  constructor(private config: IntentConfig) {
    this.ruleEngine = new IntentRuleEngine(config.rules);
    this.contextualClassifier = new ContextualIntentClassifier(config.contextual);
    this.confidenceCalculator = new ConfidenceCalculator();
    
    this.initializeModels();
  }

  private async initializeModels() {
    // Load pre-trained intent classification models
    this.models.set('primary', await this.loadModel('crypto-intent-classifier', {
      modelPath: 'models/crypto-intent-bert',
      vocabulary: 'models/crypto-intent-vocab.json',
      intents: [
        'project_analysis',
        'security_check',
        'price_inquiry',
        'comparison_request',
        'portfolio_review',
        'market_sentiment',
        'technical_analysis',
        'fund_flow_investigation',
        'risk_assessment',
        'general_question',
        'help_request'
      ]
    }));

    // Secondary model for edge cases
    this.models.set('fallback', await this.loadModel('general-intent-classifier', {
      modelPath: 'models/general-intent-roberta',
      fallbackIntents: ['clarification_needed', 'out_of_scope', 'chitchat']
    }));
  }

  async classifyIntent(input: PreprocessedInput): Promise<IntentClassification> {
    try {
      // Multi-model classification
      const primaryResult = await this.classifyWithModel('primary', input);
      const ruleBasedResult = await this.ruleEngine.classify(input);
      const contextualResult = await this.contextualClassifier.classify(input);

      // Ensemble classification
      const ensembleResult = this.combineClassifications([
        { result: primaryResult, weight: 0.6 },
        { result: ruleBasedResult, weight: 0.25 },
        { result: contextualResult, weight: 0.15 }
      ]);

      // Validate classification confidence
      if (ensembleResult.confidence < this.config.minConfidenceThreshold) {
        const fallbackResult = await this.classifyWithModel('fallback', input);
        
        if (fallbackResult.confidence > ensembleResult.confidence) {
          return this.enhanceClassification(fallbackResult, input);
        }
      }

      return this.enhanceClassification(ensembleResult, input);

    } catch (error) {
      logger.error('Intent classification failed:', error);
      
      // Return safe fallback
      return {
        primaryIntent: 'general_question',
        confidence: 0.3,
        alternativeIntents: [],
        requiresClarification: true,
        explanation: 'Classification error - defaulting to general question'
      };
    }
  }

  private async classifyWithModel(modelName: string, input: PreprocessedInput): Promise<ClassificationResult> {
    const model = this.models.get(modelName);
    if (!model) {
      throw new Error(`Model not found: ${modelName}`);
    }

    // Prepare input features
    const features = await this.extractFeatures(input);
    
    // Get model prediction
    const prediction = await model.predict(features);
    
    return {
      intent: prediction.intent,
      confidence: prediction.confidence,
      probabilities: prediction.probabilities,
      modelUsed: modelName
    };
  }

  private combineClassifications(classifications: WeightedClassification[]): ClassificationResult {
    const intentScores: Map<string, number> = new Map();
    let totalWeight = 0;

    // Aggregate weighted scores
    for (const { result, weight } of classifications) {
      const adjustedWeight = weight * result.confidence;
      totalWeight += adjustedWeight;

      // Add primary intent score
      const currentScore = intentScores.get(result.intent) || 0;
      intentScores.set(result.intent, currentScore + (result.confidence * adjustedWeight));

      // Add alternative intent scores
      if (result.probabilities) {
        for (const [intent, probability] of Object.entries(result.probabilities)) {
          const currentAltScore = intentScores.get(intent) || 0;
          intentScores.set(intent, currentAltScore + (probability * adjustedWeight * 0.5));
        }
      }
    }

    // Normalize scores
    for (const [intent, score] of intentScores) {
      intentScores.set(intent, score / totalWeight);
    }

    // Find top intent
    const sortedIntents = Array.from(intentScores.entries())
      .sort(([, a], [, b]) => b - a);

    const topIntent = sortedIntents[0];
    const alternativeIntents = sortedIntents.slice(1, 4); // Top 3 alternatives

    return {
      intent: topIntent[0],
      confidence: topIntent[1],
      probabilities: Object.fromEntries(intentScores),
      alternativeIntents: alternativeIntents.map(([intent, score]) => ({ intent, score }))
    };
  }

  private enhanceClassification(result: ClassificationResult, input: PreprocessedInput): IntentClassification {
    return {
      primaryIntent: result.intent,
      confidence: result.confidence,
      alternativeIntents: result.alternativeIntents || [],
      requiresClarification: result.confidence < 0.7,
      explanation: this.generateIntentExplanation(result, input),
      parameters: this.extractIntentParameters(result.intent, input),
      suggestedFollowUps: this.generateFollowUpSuggestions(result.intent)
    };
  }

  private generateIntentExplanation(result: ClassificationResult, input: PreprocessedInput): string {
    const intent = result.intent;
    const confidence = (result.confidence * 100).toFixed(1);

    const explanations = {
      'project_analysis': `Request for comprehensive project analysis (${confidence}% confidence)`,
      'security_check': `Security and risk assessment inquiry (${confidence}% confidence)`,
      'price_inquiry': `Price or market data request (${confidence}% confidence)`,
      'comparison_request': `Project comparison analysis (${confidence}% confidence)`,
      'portfolio_review': `Portfolio analysis and review (${confidence}% confidence)`,
      'market_sentiment': `Market sentiment and social analysis (${confidence}% confidence)`,
      'fund_flow_investigation': `Transaction and fund flow investigation (${confidence}% confidence)`,
      'help_request': `Help or guidance request (${confidence}% confidence)`
    };

    return explanations[intent] || `General query classification (${confidence}% confidence)`;
  }

  private extractIntentParameters(intent: string, input: PreprocessedInput): IntentParameters {
    const parameters: IntentParameters = {};

    switch (intent) {
      case 'project_analysis':
      case 'security_check':
        parameters.depth = this.extractAnalysisDepth(input);
        parameters.focus = this.extractAnalysisFocus(input);
        break;
        
      case 'comparison_request':
        parameters.comparisonType = this.extractComparisonType(input);
        parameters.metrics = this.extractComparisonMetrics(input);
        break;
        
      case 'portfolio_review':
        parameters.timeframe = this.extractTimeframe(input);
        parameters.analysisType = this.extractPortfolioAnalysisType(input);
        break;
    }

    return parameters;
  }

  private extractAnalysisDepth(input: PreprocessedInput): AnalysisDepth {
    const quickIndicators = ['quick', 'fast', 'brief', 'summary'];
    const deepIndicators = ['detailed', 'comprehensive', 'thorough', 'deep dive'];
    
    const text = input.tokens.join(' ').toLowerCase();
    
    if (quickIndicators.some(indicator => text.includes(indicator))) {
      return 'quick';
    } else if (deepIndicators.some(indicator => text.includes(indicator))) {
      return 'comprehensive';
    }
    
    return 'standard';
  }
}

Entity Extraction Engine

Crypto-Specific Entity Recognition

// Advanced entity extraction specialized for crypto domain
export class CryptoEntityExtractor {
  private nerModel: NamedEntityRecognitionModel;
  private cryptoPatterns: CryptoPatternMatcher;
  private contextResolver: EntityContextResolver;
  private validationEngine: EntityValidationEngine;

  constructor(private config: EntityExtractionConfig) {
    this.nerModel = new NamedEntityRecognitionModel(config.model);
    this.cryptoPatterns = new CryptoPatternMatcher(config.patterns);
    this.contextResolver = new EntityContextResolver(config.context);
    this.validationEngine = new EntityValidationEngine(config.validation);
  }

  async extractEntities(input: PreprocessedInput): Promise<EntityExtractionResult> {
    try {
      // Extract entities using multiple methods
      const modelEntities = await this.nerModel.extract(input.text);
      const patternEntities = await this.cryptoPatterns.extract(input.text);
      const contextEntities = await this.contextResolver.resolve(input, modelEntities, patternEntities);

      // Merge and deduplicate entities
      const mergedEntities = this.mergeEntities(modelEntities, patternEntities, contextEntities);

      // Validate extracted entities
      const validatedEntities = await this.validateEntities(mergedEntities);

      // Resolve entity relationships
      const relationships = await this.resolveEntityRelationships(validatedEntities);

      return {
        entities: validatedEntities,
        relationships,
        confidence: this.calculateExtractionConfidence(validatedEntities),
        metadata: {
          extractionMethods: ['ml_model', 'pattern_matching', 'context_resolution'],
          totalEntitiesFound: validatedEntities.length,
          highConfidenceEntities: validatedEntities.filter(e => e.confidence > 0.8).length
        }
      };

    } catch (error) {
      logger.error('Entity extraction failed:', error);
      throw new EntityExtractionError('Entity extraction failed', error);
    }
  }

  private async validateEntities(entities: ExtractedEntity[]): Promise<ValidatedEntity[]> {
    const validatedEntities: ValidatedEntity[] = [];

    for (const entity of entities) {
      try {
        const validation = await this.validationEngine.validate(entity);
        
        if (validation.isValid) {
          validatedEntities.push({
            ...entity,
            validation,
            normalizedValue: validation.normalizedValue || entity.value,
            metadata: {
              ...entity.metadata,
              validationMethod: validation.method,
              validationConfidence: validation.confidence
            }
          });
        } else if (validation.confidence > 0.5) {
          // Include with lower confidence if partially valid
          validatedEntities.push({
            ...entity,
            confidence: entity.confidence * validation.confidence,
            validation,
            normalizedValue: validation.normalizedValue || entity.value
          });
        }

      } catch (error) {
        logger.warn(`Entity validation failed for ${entity.value}:`, error);
        // Include entity with reduced confidence
        validatedEntities.push({
          ...entity,
          confidence: entity.confidence * 0.5,
          validation: { isValid: false, error: error.message }
        });
      }
    }

    return validatedEntities;
  }
}

// Crypto pattern matcher for domain-specific entities
export class CryptoPatternMatcher {
  private patterns: Map<string, EntityPattern> = new Map();

  constructor(config: PatternConfig) {
    this.initializePatterns();
  }

  private initializePatterns() {
    // Contract address patterns
    this.patterns.set('ethereum_address', {
      regex: /0x[a-fA-F0-9]{40}/g,
      type: 'contract_address',
      network: 'ethereum',
      validator: this.validateEthereumAddress.bind(this)
    });

    this.patterns.set('solana_address', {
      regex: /[1-9A-HJ-NP-Za-km-z]{32,44}/g,
      type: 'contract_address',
      network: 'solana',
      validator: this.validateSolanaAddress.bind(this)
    });

    // Token symbol patterns
    this.patterns.set('token_symbol', {
      regex: /\$([A-Z]{2,10})\b/g,
      type: 'token_symbol',
      normalizer: this.normalizeTokenSymbol.bind(this)
    });

    // Transaction hash patterns
    this.patterns.set('transaction_hash', {
      regex: /0x[a-fA-F0-9]{64}/g,
      type: 'transaction_hash',
      network: 'ethereum'
    });

    // Monetary amounts
    this.patterns.set('monetary_amount', {
      regex: /\$[\d,]+(?:\.\d{2})?|\d+(?:\.\d+)?\s*(USD|ETH|BTC|SOL|USDC|USDT)/gi,
      type: 'monetary_amount',
      normalizer: this.normalizeMonetaryAmount.bind(this)
    });

    // Percentage values
    this.patterns.set('percentage', {
      regex: /\d+(?:\.\d+)?%/g,
      type: 'percentage',
      normalizer: this.normalizePercentage.bind(this)
    });

    // Timeframe expressions
    this.patterns.set('timeframe', {
      regex: /(?:last|past|next)?\s*(\d+)\s*(minute|hour|day|week|month|year)s?/gi,
      type: 'timeframe',
      normalizer: this.normalizeTimeframe.bind(this)
    });
  }

  async extract(text: string): Promise<ExtractedEntity[]> {
    const entities: ExtractedEntity[] = [];

    for (const [patternName, pattern] of this.patterns) {
      const matches = text.matchAll(pattern.regex);
      
      for (const match of matches) {
        const value = match[0];
        const startPos = match.index || 0;
        const endPos = startPos + value.length;

        // Validate if validator exists
        if (pattern.validator && !await pattern.validator(value)) {
          continue;
        }

        // Normalize if normalizer exists
        const normalizedValue = pattern.normalizer 
          ? await pattern.normalizer(value, match)
          : value;

        entities.push({
          type: pattern.type,
          value,
          normalizedValue,
          startPosition: startPos,
          endPosition: endPos,
          confidence: 0.9, // High confidence for pattern matches
          source: 'pattern_matching',
          metadata: {
            pattern: patternName,
            network: pattern.network,
            matchGroups: match.slice(1)
          }
        });
      }
    }

    return entities;
  }

  private async validateEthereumAddress(address: string): Promise<boolean> {
    // Basic format validation
    if (!/^0x[a-fA-F0-9]{40}$/.test(address)) {
      return false;
    }

    // Checksum validation (EIP-55)
    return this.isValidChecksumAddress(address);
  }

  private async validateSolanaAddress(address: string): Promise<boolean> {
    try {
      // Use Solana's base58 validation
      const decoded = this.base58Decode(address);
      return decoded.length === 32;
    } catch {
      return false;
    }
  }

  private async normalizeTokenSymbol(value: string, match: RegExpMatchArray): Promise<string> {
    // Remove $ prefix and normalize to uppercase
    return value.replace('$', '').toUpperCase();
  }

  private async normalizeMonetaryAmount(value: string, match: RegExpMatchArray): Promise<MonetaryAmount> {
    const cleanValue = value.replace(/[$,]/g, '');
    const match_result = cleanValue.match(/^(\d+(?:\.\d+)?)\s*([A-Z]+)?$/i);
    
    if (!match_result) {
      return { amount: 0, currency: 'USD' };
    }

    const amount = parseFloat(match_result[1]);
    const currency = match_result[2]?.toUpperCase() || 'USD';

    return { amount, currency };
  }

  private async normalizeTimeframe(value: string, match: RegExpMatchArray): Promise<TimeframeValue> {
    const groups = match.slice(1);
    const amount = parseInt(groups[0]);
    const unit = groups[1].toLowerCase();

    // Convert to milliseconds
    const multipliers = {
      'minute': 60 * 1000,
      'hour': 60 * 60 * 1000,
      'day': 24 * 60 * 60 * 1000,
      'week': 7 * 24 * 60 * 60 * 1000,
      'month': 30 * 24 * 60 * 60 * 1000,
      'year': 365 * 24 * 60 * 60 * 1000
    };

    const milliseconds = amount * (multipliers[unit] || multipliers['day']);

    return {
      amount,
      unit,
      milliseconds,
      readable: `${amount} ${unit}${amount > 1 ? 's' : ''}`
    };
  }
}

Multi-LLM Integration

Advanced Language Model Orchestration

// Sophisticated multi-LLM integration with intelligent routing
export class LLMOrchestrator {
  private models: Map<string, LLMProvider> = new Map();
  private routingEngine: LLMRoutingEngine;
  private responseOptimizer: ResponseOptimizer;
  private qualityAssurance: ResponseQualityAssurance;
  private costOptimizer: CostOptimizer;

  constructor(private config: LLMConfig) {
    this.routingEngine = new LLMRoutingEngine(config.routing);
    this.responseOptimizer = new ResponseOptimizer(config.optimization);
    this.qualityAssurance = new ResponseQualityAssurance(config.quality);
    this.costOptimizer = new CostOptimizer(config.cost);
    
    this.initializeModels();
  }

  private async initializeModels() {
    // Initialize GPT-4 for complex analysis
    this.models.set('gpt4', new OpenAIProvider({
      model: 'gpt-4-turbo-preview',
      apiKey: this.config.openai.apiKey,
      organization: this.config.openai.organization,
      capabilities: {
        strengths: ['complex_analysis', 'detailed_explanations', 'mathematical_reasoning'],
        maxTokens: 4096,
        costPerToken: 0.00003,
        latency: 'medium'
      }
    }));

    // Initialize Claude for technical accuracy
    this.models.set('claude', new AnthropicProvider({
      model: 'claude-3-sonnet-20240229',
      apiKey: this.config.anthropic.apiKey,
      capabilities: {
        strengths: ['technical_accuracy', 'safety', 'structured_responses'],
        maxTokens: 4096,
        costPerToken: 0.000015,
        latency: 'low'
      }
    }));

    // Initialize Mistral for speed and efficiency
    this.models.set('mistral', new MistralProvider({
      model: 'mistral-large-latest',
      apiKey: this.config.mistral.apiKey,
      capabilities: {
        strengths: ['speed', 'efficiency', 'code_generation'],
        maxTokens: 8192,
        costPerToken: 0.000008,
        latency: 'very_low'
      }
    }));

    // Local model for privacy-sensitive queries
    this.models.set('local', new LocalLLMProvider({
      model: 'crypto-analyst-7b',
      modelPath: './models/crypto-analyst-7b',
      capabilities: {
        strengths: ['privacy', 'domain_specific', 'low_cost'],
        maxTokens: 2048,
        costPerToken: 0,
        latency: 'low'
      }
    }));
  }

  async generateResponse(
    prompt: string,
    context: ConversationContext,
    requirements: ResponseRequirements
  ): Promise<LLMResponse> {
    try {
      // Determine optimal model for this request
      const selectedModel = await this.routingEngine.selectModel(prompt, context, requirements);
      
      // Optimize prompt for selected model
      const optimizedPrompt = await this.responseOptimizer.optimizePrompt(
        prompt,
        selectedModel,
        context
      );

      // Generate response
      const response = await this.generateWithModel(selectedModel, optimizedPrompt, context);
      
      // Quality assurance check
      const qualityCheck = await this.qualityAssurance.validateResponse(response, requirements);
      
      if (!qualityCheck.isAcceptable && qualityCheck.suggestedFallback) {
        // Try fallback model
        const fallbackResponse = await this.generateWithModel(
          qualityCheck.suggestedFallback,
          optimizedPrompt,
          context
        );
        
        return this.selectBestResponse([response, fallbackResponse], requirements);
      }

      return response;

    } catch (error) {
      logger.error('LLM response generation failed:', error);
      
      // Generate fallback response
      return this.generateFallbackResponse(prompt, context, error);
    }
  }

  private async generateWithModel(
    modelName: string,
    prompt: string,
    context: ConversationContext
  ): Promise<LLMResponse> {
    const provider = this.models.get(modelName);
    if (!provider) {
      throw new Error(`Model not available: ${modelName}`);
    }

    const startTime = Date.now();

    try {
      const completion = await provider.complete({
        prompt,
        context: context.conversationHistory,
        temperature: this.getOptimalTemperature(modelName, context),
        maxTokens: this.getOptimalMaxTokens(modelName, context),
        systemPrompt: this.buildSystemPrompt(modelName, context)
      });

      const responseTime = Date.now() - startTime;

      return {
        content: completion.content,
        model: modelName,
        confidence: completion.confidence || 0.8,
        responseTime,
        tokenUsage: completion.usage,
        cost: this.calculateCost(modelName, completion.usage),
        metadata: {
          temperature: completion.temperature,
          finishReason: completion.finishReason,
          safetyScores: completion.safetyScores
        }
      };

    } catch (error) {
      logger.error(`Model ${modelName} generation failed:`, error);
      throw error;
    }
  }

  private buildSystemPrompt(modelName: string, context: ConversationContext): string {
    const basePrompt = `You are Kaizen AI, an expert cryptocurrency analyst and security researcher. Your role is to help users understand crypto projects, assess risks, and make informed decisions.

Core principles:
- Provide accurate, data-driven analysis
- Always include confidence levels and caveats
- Explain complex concepts clearly
- Prioritize user safety and security
- Be transparent about limitations

Current context:
- User experience level: ${context.userProfile?.experienceLevel || 'unknown'}
- Risk tolerance: ${context.userProfile?.riskTolerance || 'unknown'}
- Previous conversation topics: ${context.topics.slice(-3).join(', ')}`;

    // Model-specific adjustments
    const modelAdjustments = {
      'gpt4': '\n\nUse your advanced reasoning capabilities for complex multi-step analysis. Provide detailed explanations and consider edge cases.',
      'claude': '\n\nFocus on technical accuracy and safety. Structure your responses clearly with proper sections and bullet points when appropriate.',
      'mistral': '\n\nBe concise and efficient while maintaining accuracy. Prioritize direct answers with essential details.',
      'local': '\n\nLeverage your crypto domain expertise. Use technical terminology appropriately for the user\'s experience level.'
    };

    return basePrompt + (modelAdjustments[modelName] || '');
  }

  async streamResponse(
    prompt: string,
    context: ConversationContext,
    requirements: ResponseRequirements
  ): AsyncGenerator<StreamChunk> {
    try {
      const selectedModel = await this.routingEngine.selectModel(prompt, context, requirements);
      const provider = this.models.get(selectedModel);
      
      if (!provider?.supportsStreaming) {
        // Fallback to non-streaming
        const response = await this.generateResponse(prompt, context, requirements);
        yield { type: 'content', content: response.content, isComplete: true };
        return;
      }

      const optimizedPrompt = await this.responseOptimizer.optimizePrompt(
        prompt,
        selectedModel,
        context
      );

      const stream = provider.streamComplete({
        prompt: optimizedPrompt,
        context: context.conversationHistory,
        temperature: this.getOptimalTemperature(selectedModel, context),
        systemPrompt: this.buildSystemPrompt(selectedModel, context)
      });

      let accumulatedContent = '';

      for await (const chunk of stream) {
        accumulatedContent += chunk.content;
        
        yield {
          type: 'content',
          content: chunk.content,
          isComplete: false,
          metadata: {
            model: selectedModel,
            tokenCount: chunk.tokenCount
          }
        };

        // Periodic quality checks during streaming
        if (accumulatedContent.length > 500 && accumulatedContent.length % 500 === 0) {
          const qualityCheck = await this.qualityAssurance.validatePartialResponse(
            accumulatedContent,
            requirements
          );
          
          if (qualityCheck.shouldStop) {
            yield {
              type: 'error',
              content: 'Response quality degraded - stopping generation',
              isComplete: true
            };
            return;
          }
        }
      }

      yield {
        type: 'complete',
        content: '',
        isComplete: true,
        metadata: {
          model: selectedModel,
          totalTokens: accumulatedContent.length
        }
      };

    } catch (error) {
      logger.error('Streaming response failed:', error);
      
      yield {
        type: 'error',
        content: 'Failed to generate streaming response',
        isComplete: true,
        error: error.message
      };
    }
  }
}

// LLM routing engine for optimal model selection
export class LLMRoutingEngine {
  constructor(private config: RoutingConfig) {}

  async selectModel(
    prompt: string,
    context: ConversationContext,
    requirements: ResponseRequirements
  ): Promise<string> {
    // Analyze prompt characteristics
    const promptAnalysis = await this.analyzePrompt(prompt);
    
    // Consider context factors
    const contextFactors = this.analyzeContext(context);
    
    // Apply routing rules
    const routingScore = this.calculateRoutingScores(promptAnalysis, contextFactors, requirements);
    
    // Select best model
    const selectedModel = this.selectBestModel(routingScore);
    
    logger.debug('Model routing decision', {
      prompt: prompt.substring(0, 100),
      selectedModel,
      scores: routingScore,
      factors: { promptAnalysis, contextFactors, requirements }
    });

    return selectedModel;
  }

  private async analyzePrompt(prompt: string): Promise<PromptAnalysis> {
    return {
      complexity: this.calculateComplexity(prompt),
      length: prompt.length,
      requiresReasoning: this.requiresReasoning(prompt),
      requiresAccuracy: this.requiresAccuracy(prompt),
      requiresSpeed: this.requiresSpeed(prompt),
      requiresPrivacy: this.requiresPrivacy(prompt),
      domain: this.identifyDomain(prompt),
      urgency: this.assessUrgency(prompt)
    };
  }

  private calculateComplexity(prompt: string): number {
    let complexity = 0;
    
    // Multi-step reasoning indicators
    const reasoningIndicators = ['analyze', 'compare', 'evaluate', 'calculate', 'determine'];
    complexity += reasoningIndicators.filter(indicator => 
      prompt.toLowerCase().includes(indicator)
    ).length * 0.2;
    
    // Technical depth indicators
    const technicalIndicators = ['smart contract', 'blockchain', 'defi', 'tokenomics'];
    complexity += technicalIndicators.filter(indicator => 
      prompt.toLowerCase().includes(indicator)
    ).length * 0.15;
    
    // Question complexity
    const questionCount = (prompt.match(/\?/g) || []).length;
    complexity += Math.min(questionCount * 0.1, 0.3);
    
    // Conditional logic indicators
    const conditionalIndicators = ['if', 'when', 'unless', 'provided that'];
    complexity += conditionalIndicators.filter(indicator => 
      prompt.toLowerCase().includes(indicator)
    ).length * 0.1;

    return Math.min(complexity, 1.0);
  }

  private selectBestModel(scores: ModelScores): string {
    const models = Object.entries(scores);
    models.sort(([, a], [, b]) => b - a);
    
    const bestModel = models[0][0];
    const bestScore = models[0][1];
    
    // Ensure minimum threshold is met
    if (bestScore < this.config.minSelectionThreshold) {
      return this.config.defaultModel || 'claude';
    }
    
    return bestModel;
  }

  private calculateRoutingScores(
    promptAnalysis: PromptAnalysis,
    contextFactors: ContextFactors,
    requirements: ResponseRequirements
  ): ModelScores {
    const scores: ModelScores = {
      'gpt4': 0,
      'claude': 0,
      'mistral': 0,
      'local': 0
    };

    // GPT-4 scoring
    scores.gpt4 += promptAnalysis.complexity * 0.4;
    scores.gpt4 += promptAnalysis.requiresReasoning ? 0.3 : 0;
    scores.gpt4 += contextFactors.isComplexConversation ? 0.2 : 0;
    scores.gpt4 -= requirements.prioritizeSpeed ? 0.3 : 0;
    scores.gpt4 -= requirements.costSensitive ? 0.4 : 0;

    // Claude scoring
    scores.claude += promptAnalysis.requiresAccuracy ? 0.4 : 0;
    scores.claude += contextFactors.requiresSafety ? 0.3 : 0;
    scores.claude += requirements.prioritizeAccuracy ? 0.3 : 0;
    scores.claude += promptAnalysis.domain === 'technical' ? 0.2 : 0;

    // Mistral scoring
    scores.mistral += requirements.prioritizeSpeed ? 0.5 : 0;
    scores.mistral += requirements.costSensitive ? 0.3 : 0;
    scores.mistral += promptAnalysis.length < 500 ? 0.2 : 0;
    scores.mistral -= promptAnalysis.complexity > 0.7 ? 0.4 : 0;

    // Local model scoring
    scores.local += promptAnalysis.requiresPrivacy ? 0.6 : 0;
    scores.local += requirements.costSensitive ? 0.4 : 0;
    scores.local += promptAnalysis.domain === 'crypto' ? 0.3 : 0;
    scores.local -= promptAnalysis.complexity > 0.8 ? 0.5 : 0;

    // Normalize scores
    const maxScore = Math.max(...Object.values(scores));
    if (maxScore > 0) {
      for (const model in scores) {
        scores[model] = scores[model] / maxScore;
      }
    }

    return scores;
  }
}

Context Management System

Advanced Conversation Context Tracking

// Sophisticated conversation context management
export class ConversationContextManager {
  private contextStore: Map<string, ConversationContext> = new Map();
  private contextAnalyzer: ContextAnalyzer;
  private memoryManager: ConversationMemoryManager;
  private personalizationEngine: PersonalizationEngine;

  constructor(
    private config: ContextConfig,
    private database: DatabaseInterface,
    private cache: CacheInterface
  ) {
    this.contextAnalyzer = new ContextAnalyzer(config.analysis);
    this.memoryManager = new ConversationMemoryManager(config.memory);
    this.personalizationEngine = new PersonalizationEngine(config.personalization);
  }

  async getContext(sessionId: string): Promise<ConversationContext> {
    // Check in-memory cache first
    if (this.contextStore.has(sessionId)) {
      const context = this.contextStore.get(sessionId)!;
      
      // Refresh if stale
      if (this.isContextStale(context)) {
        return await this.refreshContext(sessionId);
      }
      
      return context;
    }

    // Load from persistent storage
    return await this.loadContext(sessionId);
  }

  async updateContext(
    sessionId: string,
    message: ChatMessage,
    analysis?: AnalysisResult
  ): Promise<ConversationContext> {
    const context = await this.getContext(sessionId);
    
    // Add message to history
    context.conversationHistory.push(message);
    
    // Analyze message for context updates
    const messageAnalysis = await this.contextAnalyzer.analyzeMessage(message, context);
    
    // Update topics and entities
    this.updateTopics(context, messageAnalysis.topics);
    this.updateEntities(context, messageAnalysis.entities);
    
    // Update user profile insights
    await this.updateUserProfile(context, messageAnalysis, analysis);
    
    // Manage memory efficiently
    await this.memoryManager.optimizeMemory(context);
    
    // Update timestamps
    context.lastUpdated = new Date();
    context.messageCount++;
    
    // Persist updates
    await this.persistContext(sessionId, context);
    
    // Update in-memory cache
    this.contextStore.set(sessionId, context);
    
    return context;
  }

  private async updateUserProfile(
    context: ConversationContext,
    messageAnalysis: MessageAnalysis,
    analysis?: AnalysisResult
  ): Promise<void> {
    const profile = context.userProfile;
    
    // Update experience level based on query sophistication
    if (messageAnalysis.technicalComplexity > 0.7) {
      profile.experienceLevel = this.adjustExperienceLevel(
        profile.experienceLevel,
        'increase'
      );
    }
    
    // Update risk tolerance based on queries and reactions
    if (messageAnalysis.riskToleranceIndicators) {
      profile.riskTolerance = this.updateRiskTolerance(
        profile.riskTolerance,
        messageAnalysis.riskToleranceIndicators
      );
    }
    
    // Update interests based on topics
    this.updateInterests(profile, messageAnalysis.topics);
    
    // Update preferences based on response feedback
    if (analysis?.userFeedback) {
      this.updatePreferences(profile, analysis.userFeedback);
    }
  }

  private updateTopics(context: ConversationContext, newTopics: string[]): void {
    // Add new topics with timestamp and relevance
    for (const topic of newTopics) {
      const existingTopic = context.topics.find(t => t.name === topic);
      
      if (existingTopic) {
        existingTopic.frequency++;
        existingTopic.lastMentioned = new Date();
        existingTopic.relevance = this.calculateTopicRelevance(existingTopic);
      } else {
        context.topics.push({
          name: topic,
          frequency: 1,
          firstMentioned: new Date(),
          lastMentioned: new Date(),
          relevance: 1.0
        });
      }
    }
    
    // Decay relevance of old topics
    this.decayTopicRelevance(context.topics);
    
    // Keep only top N topics
    context.topics = context.topics
      .sort((a, b) => b.relevance - a.relevance)
      .slice(0, this.config.maxTopics || 20);
  }

  private calculateTopicRelevance(topic: ConversationTopic): number {
    const timeSinceLastMention = Date.now() - topic.lastMentioned.getTime();
    const hoursSinceLastMention = timeSinceLastMention / (1000 * 60 * 60);
    
    // Base relevance from frequency
    let relevance = Math.log(topic.frequency + 1) / Math.log(10);
    
    // Decay based on time
    const decayFactor = Math.exp(-hoursSinceLastMention / 24); // 24-hour half-life
    relevance *= decayFactor;
    
    return Math.max(0.1, Math.min(1.0, relevance));
  }

  async generateContextSummary(sessionId: string): Promise<ContextSummary> {
    const context = await this.getContext(sessionId);
    
    return {
      sessionId,
      messageCount: context.messageCount,
      duration: Date.now() - context.createdAt.getTime(),
      topTopics: context.topics.slice(0, 5),
      userProfile: {
        experienceLevel: context.userProfile.experienceLevel,
        riskTolerance: context.userProfile.riskTolerance,
        primaryInterests: context.userProfile.interests.slice(0, 3)
      },
      conversationPhase: this.determineConversationPhase(context),
      nextActions: await this.suggestNextActions(context),
      lastActivity: context.lastUpdated
    };
  }

  private determineConversationPhase(context: ConversationContext): ConversationPhase {
    if (context.messageCount <= 2) {
      return 'introduction';
    } else if (context.messageCount <= 10) {
      return 'exploration';
    } else if (this.hasDeepAnalysisQueries(context)) {
      return 'deep_analysis';
    } else {
      return 'ongoing_discussion';
    }
  }

  private async suggestNextActions(context: ConversationContext): Promise<string[]> {
    const suggestions: string[] = [];
    
    // Based on conversation topics
    const topTopics = context.topics.slice(0, 3);
    
    for (const topic of topTopics) {
      if (topic.name.includes('token') || topic.name.includes('project')) {
        suggestions.push(`Analyze ${topic.name} security and fundamentals`);
      } else if (topic.name.includes('portfolio')) {
        suggestions.push('Review portfolio risk assessment');
      }
    }
    
    // Based on user profile
    if (context.userProfile.experienceLevel === 'beginner') {
      suggestions.push('Learn about crypto security basics');
    } else if (context.userProfile.experienceLevel === 'advanced') {
      suggestions.push('Explore advanced DeFi analysis techniques');
    }
    
    // Based on recent entities
    const recentTokens = context.entities
      .filter(e => e.type === 'token_symbol')
      .slice(0, 3);
      
    for (const token of recentTokens) {
      suggestions.push(`Compare ${token.value} with similar projects`);
    }
    
    return suggestions.slice(0, 5); // Return top 5 suggestions
  }
}

Real-Time Chat Capabilities

WebSocket Integration and Streaming

// Real-time chat capabilities with WebSocket integration
export class RealTimeChatManager {
  private wsConnections: Map<string, WebSocketConnection> = new Map();
  private chatOrchestrator: ChatOrchestrator;
  private streamingManager: StreamingManager;
  private presenceManager: PresenceManager;
  private analyticsTracker: RealTimeAnalyticsTracker;

  constructor(
    private config: RealTimeChatConfig,
    chatOrchestrator: ChatOrchestrator,
    private sessionManager: SessionManager
  ) {
    this.chatOrchestrator = chatOrchestrator;
    this.streamingManager = new StreamingManager(config.streaming);
    this.presenceManager = new PresenceManager(config.presence);
    this.analyticsTracker = new RealTimeAnalyticsTracker(config.analytics);
  }

  async handleWebSocketConnection(ws: WebSocket, sessionId: string, userId: string): Promise<void> {
    try {
      // Initialize connection
      const connection = new WebSocketConnection(ws, sessionId, userId, {
        heartbeatInterval: this.config.heartbeatInterval || 30000,
        messageTimeout: this.config.messageTimeout || 60000,
        maxMessageSize: this.config.maxMessageSize || 65536
      });

      await connection.initialize();
      
      // Store connection
      this.wsConnections.set(sessionId, connection);
      
      // Update presence
      await this.presenceManager.setUserOnline(userId, sessionId);
      
      // Set up event handlers
      this.setupConnectionHandlers(connection);
      
      // Send welcome message
      await connection.send({
        type: 'welcome',
        sessionId,
        timestamp: new Date().toISOString(),
        capabilities: this.getConnectionCapabilities()
      });

      logger.info(`WebSocket connection established for session ${sessionId}`);

    } catch (error) {
      logger.error('WebSocket connection setup failed:', error);
      ws.close(1011, 'Connection setup failed');
    }
  }

  private setupConnectionHandlers(connection: WebSocketConnection): void {
    // Handle incoming messages
    connection.on('message', async (message: WebSocketMessage) => {
      await this.handleIncomingMessage(connection, message);
    });

    // Handle typing indicators
    connection.on('typing', async (data: TypingData) => {
      await this.handleTypingIndicator(connection, data);
    });

    // Handle connection close
    connection.on('close', async (code: number, reason: string) => {
      await this.handleConnectionClose(connection, code, reason);
    });

    // Handle errors
    connection.on('error', async (error: Error) => {
      await this.handleConnectionError(connection, error);
    });

    // Handle ping/pong for keep-alive
    connection.on('ping', () => {
      connection.pong();
    });
  }

  private async handleIncomingMessage(
    connection: WebSocketConnection,
    message: WebSocketMessage
  ): Promise<void> {
    try {
      const startTime = Date.now();
      
      // Validate message
      const validation = await this.validateMessage(message);
      if (!validation.isValid) {
        await connection.send({
          type: 'error',
          error: validation.error,
          messageId: message.id
        });
        return;
      }

      // Track message
      this.analyticsTracker.trackMessage(connection.sessionId, message);

      switch (message.type) {
        case 'chat_query':
          await this.handleChatQuery(connection, message);
          break;
          
        case 'stream_request':
          await this.handleStreamRequest(connection, message);
          break;
          
        case 'context_update':
          await this.handleContextUpdate(connection, message);
          break;
          
        case 'feedback':
          await this.handleFeedback(connection, message);
          break;
          
        default:
          await connection.send({
            type: 'error',
            error: `Unknown message type: ${message.type}`,
            messageId: message.id
          });
      }

      const processingTime = Date.now() - startTime;
      this.analyticsTracker.trackProcessingTime(connection.sessionId, processingTime);

    } catch (error) {
      logger.error('Message handling failed:', error);
      await connection.send({
        type: 'error',
        error: 'Message processing failed',
        messageId: message.id
      });
    }
  }

  private async handleChatQuery(
    connection: WebSocketConnection,
    message: ChatQueryMessage
  ): Promise<void> {
    try {
      // Send acknowledgment
      await connection.send({
        type: 'query_received',
        messageId: message.id,
        timestamp: new Date().toISOString()
      });

      // Check if streaming is requested
      if (message.options?.stream) {
        await this.handleStreamingQuery(connection, message);
      } else {
        await this.handleRegularQuery(connection, message);
      }

    } catch (error) {
      logger.error('Chat query handling failed:', error);
      await connection.send({
        type: 'query_error',
        messageId: message.id,
        error: 'Query processing failed'
      });
    }
  }

  private async handleStreamingQuery(
    connection: WebSocketConnection,
    message: ChatQueryMessage
  ): Promise<void> {
    try {
      // Start streaming response
      await connection.send({
        type: 'stream_start',
        messageId: message.id,
        timestamp: new Date().toISOString()
      });

      // Get streaming response
      const responseStream = this.chatOrchestrator.streamResponse(
        connection.sessionId,
        connection.userId,
        message.query,
        message.options
      );

      // Stream chunks to client
      for await (const chunk of responseStream) {
        await connection.send({
          type: 'stream_chunk',
          messageId: message.id,
          chunk: chunk.content,
          metadata: chunk.metadata,
          isComplete: chunk.isComplete
        });

        if (chunk.isComplete) break;
      }

      // Send completion
      await connection.send({
        type: 'stream_complete',
        messageId: message.id,
        timestamp: new Date().toISOString()
      });

    } catch (error) {
      logger.error('Streaming query failed:', error);
      await connection.send({
        type: 'stream_error',
        messageId: message.id,
        error: 'Streaming failed'
      });
    }
  }

  private async handleRegularQuery(
    connection: WebSocketConnection,
    message: ChatQueryMessage
  ): Promise<void> {
    try {
      // Process query
      const response = await this.chatOrchestrator.processUserQuery(
        connection.sessionId,
        connection.userId,
        message.query,
        message.options
      );

      // Send response
      await connection.send({
        type: 'query_response',
        messageId: message.id,
        response: response.content,
        confidence: response.confidence,
        analysisData: response.analysisData,
        suggestions: response.suggestions,
        timestamp: new Date().toISOString()
      });

    } catch (error) {
      logger.error('Regular query failed:', error);
      await connection.send({
        type: 'query_error',
        messageId: message.id,
        error: 'Query processing failed'
      });
    }
  }

  async broadcastUpdate(update: BroadcastUpdate): Promise<void> {
    const relevantConnections = Array.from(this.wsConnections.values())
      .filter(conn => this.isConnectionRelevant(conn, update));

    const broadcastPromises = relevantConnections.map(async (connection) => {
      try {
        await connection.send({
          type: 'broadcast_update',
          updateType: update.type,
          data: update.data,
          timestamp: new Date().toISOString()
        });
      } catch (error) {
        logger.warn(`Broadcast failed for session ${connection.sessionId}:`, error);
      }
    });

    await Promise.all(broadcastPromises);
    
    this.analyticsTracker.trackBroadcast(update.type, relevantConnections.length);
  }

  async generateUsageReport(timeframe: number = 86400000): Promise<ChatUsageReport> {
    const endTime = Date.now();
    const startTime = endTime - timeframe;

    const metrics = await this.analyticsTracker.getMetrics(startTime, endTime);
    const activeConnections = this.wsConnections.size;
    const presenceData = await this.presenceManager.getPresenceStats(startTime, endTime);

    return {
      timeframe: { start: new Date(startTime), end: new Date(endTime) },
      totalMessages: metrics.totalMessages,
      activeConnections,
      averageResponseTime: metrics.averageResponseTime,
      streamingRequestsPercentage: metrics.streamingRequests / Math.max(metrics.totalMessages, 1),
      errorRate: metrics.errorRate,
      presenceStats: presenceData,
      topQueryTypes: metrics.topQueryTypes,
      peakConcurrentUsers: metrics.peakConcurrentUsers,
      messageDistribution: metrics.messageDistribution
    };
  }
}

// WebSocket connection management
class WebSocketConnection extends EventEmitter {
  private heartbeatTimer: NodeJS.Timer | null = null;
  private lastActivity: Date = new Date();
  private messageQueue: MessageQueue;

  constructor(
    private ws: WebSocket,
    public readonly sessionId: string,
    public readonly userId: string,
    private config: ConnectionConfig
  ) {
    super();
    this.messageQueue = new MessageQueue(config.queueSize || 100);
  }

  async initialize(): Promise<void> {
    // Set up WebSocket event handlers
    this.ws.on('message', (data: Buffer) => {
      try {
        const message = JSON.parse(data.toString());
        this.handleMessage(message);
      } catch (error) {
        this.emit('error', new Error('Invalid message format'));
      }
    });

    this.ws.on('close', (code: number, reason: Buffer) => {
      this.cleanup();
      this.emit('close', code, reason.toString());
    });

    this.ws.on('error', (error: Error) => {
      this.emit('error', error);
    });

    this.ws.on('pong', () => {
      this.lastActivity = new Date();
    });

    // Start heartbeat
    this.startHeartbeat();
  }

  async send(message: any): Promise<void> {
    if (this.ws.readyState !== WebSocket.OPEN) {
      throw new Error('WebSocket is not open');
    }

    try {
      const serialized = JSON.stringify(message);
      
      if (serialized.length > this.config.maxMessageSize) {
        throw new Error('Message too large');
      }

      this.ws.send(serialized);
      this.lastActivity = new Date();
      
    } catch (error) {
      logger.error('Failed to send WebSocket message:', error);
      throw error;
    }
  }

  private startHeartbeat(): void {
    this.heartbeatTimer = setInterval(() => {
      if (this.ws.readyState === WebSocket.OPEN) {
        this.ws.ping();
        
        // Check for timeout
        const timeSinceActivity = Date.now() - this.lastActivity.getTime();
        if (timeSinceActivity > this.config.messageTimeout) {
          this.ws.close(1000, 'Timeout');
        }
      }
    }, this.config.heartbeatInterval);
  }

  private cleanup(): void {
    if (this.heartbeatTimer) {
      clearInterval(this.heartbeatTimer);
      this.heartbeatTimer = null;
    }
  }

  private handleMessage(message: any): void {
    this.lastActivity = new Date();
    
    // Add to queue for processing
    if (!this.messageQueue.enqueue(message)) {
      this.emit('error', new Error('Message queue full'));
      return;
    }
    
    // Emit message event
    this.emit('message', message);
  }

  pong(): void {
    if (this.ws.readyState === WebSocket.OPEN) {
      this.ws.pong();
    }
  }
}

This comprehensive Chat Agent documentation provides the technical foundation for understanding and implementing sophisticated conversational AI capabilities that serve as the user-facing interface for Kaizen AI's analytical platform.