/**
 * Knowledge Base (RAG) - Retrieval Augmented Generation
 */
export interface Document {
    id: string;
    content: string;
    metadata?: Record<string, any>;
    embedding?: number[];
}
export interface SearchResult {
    document: Document;
    score: number;
}
export interface EmbeddingProvider {
    embed(text: string): Promise<number[]>;
    embedBatch(texts: string[]): Promise<number[][]>;
}
export interface KnowledgeBaseConfig {
    embeddingProvider?: EmbeddingProvider;
    similarityThreshold?: number;
    maxResults?: number;
}
/**
 * Simple in-memory vector store for RAG
 */
export declare class KnowledgeBase {
    private documents;
    private embeddingProvider?;
    private similarityThreshold;
    private maxResults;
    constructor(config?: KnowledgeBaseConfig);
    /**
     * Add a document to the knowledge base
     */
    add(doc: Omit<Document, 'embedding'>): Promise<Document>;
    /**
     * Add multiple documents
     */
    addBatch(docs: Array<Omit<Document, 'embedding'>>): Promise<Document[]>;
    /**
     * Get a document by ID
     */
    get(id: string): Document | undefined;
    /**
     * Delete a document
     */
    delete(id: string): boolean;
    /**
     * Search for similar documents
     */
    search(query: string, limit?: number): Promise<SearchResult[]>;
    /**
     * Simple text-based search fallback
     */
    private textSearch;
    /**
     * Calculate cosine similarity between two vectors
     */
    private cosineSimilarity;
    /**
     * Get all documents
     */
    list(): Document[];
    /**
     * Clear all documents
     */
    clear(): void;
    /**
     * Get document count
     */
    get size(): number;
    /**
     * Build context from search results for RAG
     */
    buildContext(results: SearchResult[]): string;
}
/**
 * Create a knowledge base
 */
export declare function createKnowledgeBase(config?: KnowledgeBaseConfig): KnowledgeBase;