@cyqlelabs/mcp-dual-cycle-reasoner/build/semantic-analyzer.d.ts

MCP server implementing dual-cycle metacognitive reasoning framework for autonomous agents

export interface SemanticAnalysisResult {
    label: 'CONTRADICTION' | 'ENTAILMENT' | 'NEUTRAL';
    score: number;
    confidence: number;
}
export interface ActionAssessmentResult {
    category: 'success' | 'failure' | 'neutral';
    confidence: number;
    reasoning: string;
}
export interface SemanticSimilarityResult {
    similarity: number;
    confidence: number;
    reasoning: string;
}
export declare class SemanticAnalyzer {
    private nliClassifier;
    private embeddingModel;
    private isInitialized;
    private embeddingCache;
    private readonly maxCacheSize;
    initialize(): Promise<void>;
    analyzeTextPair(premise: string, hypothesis: string): Promise<SemanticAnalysisResult>;
    assessActionOutcome(action: string, expectedOutcome: string): Promise<ActionAssessmentResult>;
    classifyActionIntent(action: string, possibleIntents: string[]): Promise<{
        bestMatch: string;
        confidence: number;
        allScores: Array<{
            intent: string;
            score: number;
        }>;
    }>;
    /**
     * Calculate semantic similarity between two texts using NLI-based approach
     * Higher similarity indicates more related content
     */
    calculateSemanticSimilarity(text1: string, text2: string): Promise<SemanticSimilarityResult>;
    /**
     * PERFORMANCE OPTIMIZATION: Batch compute embeddings for multiple texts
     * This is 10-100x faster than individual model calls
     */
    getBatchEmbeddings(texts: string[]): Promise<number[][]>;
    /**
     * Fast cosine similarity calculation between two vectors
     */
    private cosineSimilarity;
    /**
     * PERFORMANCE OPTIMIZATION: Batch similarity matrix for multiple texts
     * Computes all pairwise similarities in one batch - much faster than individual calls
     */
    computeSimilarityMatrix(texts: string[]): Promise<number[][]>;
    /**
     * Extract semantic features from text for advanced similarity calculations
     */
    extractSemanticFeatures(text: string, customIntents?: string[]): Promise<{
        intents: string[];
        sentiment: 'positive' | 'negative' | 'neutral';
        confidence: number;
    }>;
    isReady(): boolean;
}
export declare const semanticAnalyzer: SemanticAnalyzer;