/**
 * Agentic QE v3 - Semantic Analyzer Service
 * Semantic code analysis using vector embeddings and HNSW search
 */
import { Result } from '../../../shared/types';
import { MemoryBackend } from '../../../kernel/interfaces';
import { NomicEmbedderConfig, IEmbeddingProvider } from '../../../shared/embeddings';
import { SearchRequest, SearchResults, SearchResult } from '../interfaces';
/**
 * Interface for the semantic analyzer service
 */
export interface ISemanticAnalyzerService {
    /** Perform semantic search across codebase */
    search(request: SearchRequest): Promise<Result<SearchResults, Error>>;
    /** Index code for semantic search */
    indexCode(file: string, content: string): Promise<Result<void, Error>>;
    /** Find semantically similar code */
    findSimilar(code: string, limit?: number): Promise<Result<SearchResult[], Error>>;
    /** Analyze code semantics */
    analyze(code: string): Promise<Result<SemanticAnalysis, Error>>;
    /** Get code embeddings */
    getEmbedding(code: string): Promise<number[]>;
}
/**
 * Semantic analysis result
 */
export interface SemanticAnalysis {
    concepts: string[];
    patterns: string[];
    complexity: CodeComplexity;
    dependencies: string[];
    suggestions: string[];
}
/**
 * Code complexity metrics
 */
export interface CodeComplexity {
    cyclomatic: number;
    cognitive: number;
    halstead: HalsteadMetrics;
}
/**
 * Halstead complexity metrics
 */
export interface HalsteadMetrics {
    vocabulary: number;
    length: number;
    difficulty: number;
    effort: number;
    time: number;
    bugs: number;
}
/**
 * Configuration for the semantic analyzer
 */
export interface SemanticAnalyzerConfig {
    /** Embedding vector dimension (768 for Nomic, 384 for fallback) */
    embeddingDimension: number;
    /** Minimum similarity score threshold */
    minScore: number;
    /** Maximum search results */
    maxResults: number;
    /** Storage namespace */
    namespace: string;
    /** Enable embedding caching */
    enableCaching: boolean;
    /** Cache size limit */
    cacheSize: number;
    /** Use Nomic embeddings via Ollama when available */
    useNomicEmbeddings: boolean;
    /** Nomic embedder configuration */
    nomicConfig?: NomicEmbedderConfig;
    /** Custom embedding provider (for testing/DI) */
    embeddingProvider?: IEmbeddingProvider;
}
/**
 * Semantic Analyzer Service Implementation
 * Provides O(log n) semantic search using HNSW vector index
 */
export declare class SemanticAnalyzerService implements ISemanticAnalyzerService {
    private readonly memory;
    private readonly config;
    private readonly embeddingCache;
    private readonly analysisCache;
    private embedder;
    private embedderInitialized;
    constructor(memory: MemoryBackend, config?: Partial<SemanticAnalyzerConfig>);
    /**
     * Initialize the Nomic embedder lazily
     */
    private initializeEmbedder;
    /**
     * Perform semantic search across indexed code
     */
    search(request: SearchRequest): Promise<Result<SearchResults, Error>>;
    /**
     * Index code content for semantic search
     */
    indexCode(file: string, content: string): Promise<Result<void, Error>>;
    /**
     * Find semantically similar code
     */
    findSimilar(code: string, limit?: number): Promise<Result<SearchResult[], Error>>;
    /**
     * Analyze code semantics
     */
    analyze(code: string): Promise<Result<SemanticAnalysis, Error>>;
    /**
     * Get embedding vector for code
     * Uses Nomic embeddings via Ollama when available, falls back to pseudo-embeddings
     */
    getEmbedding(code: string): Promise<number[]>;
    /**
     * Check if using real Nomic embeddings
     */
    isUsingNomicEmbeddings(): boolean;
    private semanticSearch;
    private exactSearch;
    private fuzzySearch;
    private extractConcepts;
    private detectPatterns;
    private calculateComplexity;
    private extractDependencies;
    private generateSuggestions;
    private generateCodeEmbedding;
    private extractCodeMetadata;
    private detectLanguage;
    private tokenize;
    private calculateFuzzyScore;
    private matchesFilters;
    private extractSnippet;
    private extractSnippetAround;
    private getLineNumber;
    private findHighlights;
    private findSemanticHighlights;
    private fileToKey;
    private hashCode;
    private cacheEmbedding;
    private cacheAnalysis;
}
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