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.
Last updated