import type { AIResult } from './types';
/**
 * Generate embeddings for text input.
 */
// eslint-disable-next-line pickier/no-unused-vars
export declare function createEmbedding(input: string, options?: EmbeddingOptions): Promise<number[]>;
// eslint-disable-next-line pickier/no-unused-vars
export declare function createEmbedding(input: string[], options?: EmbeddingOptions): Promise<number[][]>;
/**
 * Calculate cosine similarity between two vectors.
 */
export declare function cosineSimilarity(a: number[], b: number[]): number;
/**
 * Calculate dot product similarity between two vectors.
 */
export declare function dotProduct(a: number[], b: number[]): number;
/**
 * Calculate Euclidean distance between two vectors.
 */
export declare function euclideanDistance(a: number[], b: number[]): number;
/**
 * Perform RAG: search for relevant documents and use them as context for generation.
 */
export declare function rag(query: string, index: VectorIndex, options?: RAGOptions): Promise<RAGResult>;
/**
 * Split text into overlapping chunks for embedding.
 */
export declare function chunkText(text: string, options?: ChunkOptions): string[];
/**
 * Create a searchable index from a long text by chunking and embedding.
 */
export declare function indexText(text: string, options?: ChunkOptions & EmbeddingOptions & { idPrefix?: string }): Promise<VectorIndex>;
// ============================================================================
// Exports
// ============================================================================
export declare const search: {
  createEmbedding: typeof createEmbedding;
  cosineSimilarity: typeof cosineSimilarity;
  dotProduct: typeof dotProduct;
  euclideanDistance: typeof euclideanDistance;
  VectorIndex: typeof VectorIndex;
  rag: typeof rag;
  chunkText: typeof chunkText;
  indexText: typeof indexText
};
// ============================================================================
// Types
// ============================================================================
export declare interface EmbeddingOptions {
  provider?: 'openai' | 'ollama'
  model?: string
}
export declare interface SearchDocument {
  id: string
  content: string
  metadata?: Record<string, unknown>
}
export declare interface IndexedDocument extends SearchDocument {
  embedding: number[]
}
export declare interface SearchResult {
  document: SearchDocument
  score: number
  rank: number
}
export declare interface RAGOptions {
  provider?: 'anthropic' | 'openai' | 'ollama'
  embeddingProvider?: 'openai' | 'ollama'
  embeddingModel?: string
  model?: string
  maxTokens?: number
  temperature?: number
  topK?: number
  systemPrompt?: string
}
export declare interface RAGResult extends AIResult {
  sources: SearchResult[]
}
// ============================================================================
// Text Chunking Utilities
// ============================================================================
export declare interface ChunkOptions {
  chunkSize?: number
  chunkOverlap?: number
  separator?: string
}
/**
 * Simple in-memory vector search index.
 * For production, use a dedicated vector database.
 */
export declare class VectorIndex {
  constructor(options?: EmbeddingOptions);
  add(documents: SearchDocument[]): Promise<void>;
  addWithEmbedding(document: SearchDocument, embedding: number[]): void;
  search(query: string, topK?: number): Promise<SearchResult[]>;
  searchByVector(queryEmbedding: number[], topK?: number): SearchResult[];
  remove(id: string): boolean;
  clear(): void;
  get size(): number;
  get ids(): string[];
}