import { a as GQPPlugin, l as GQPEngine, n as GQPNode, Q as QueryFilters, P as PluginContext } from '../../types-CqJN-Ev5.js';

/**
 * Vector Search Plugin for GQP
 * Enables semantic search on text fields
 */

/**
 * Vector provider configuration
 */
interface VectorProviderConfig {
    /** Pinecone configuration */
    pinecone?: {
        apiKey: string;
        index: string;
        environment?: string;
    };
    /** OpenAI embedding configuration */
    openai?: {
        apiKey: string;
        model?: string;
    };
    /** Custom embedding function */
    customEmbed?: (text: string) => Promise<number[]>;
    /** Custom search function */
    customSearch?: (namespace: string, embedding: number[], limit: number) => Promise<Array<{
        id: string;
        score: number;
    }>>;
}
/**
 * Auto-indexing configuration
 */
interface AutoIndexConfig {
    /** Enable auto-indexing */
    enabled: boolean;
    /** Fields to index (e.g., ['Order.customerNotes', 'Product.description']) */
    fields?: string[];
    /** Batch size for indexing */
    batchSize?: number;
}
/**
 * Vector plugin configuration
 */
interface VectorPluginConfig {
    /** Vector provider */
    provider: "pinecone" | "custom";
    /** Provider-specific configuration */
    config: VectorProviderConfig;
    /** Similarity threshold (0-1) */
    threshold?: number;
    /** Max results from vector search before SQL filtering */
    maxResults?: number;
    /** Enable hybrid search (vector + SQL) */
    hybrid?: boolean;
    /** Auto-indexing configuration */
    autoIndex?: AutoIndexConfig;
}
/**
 * Vector Search Plugin
 *
 * @example
 * ```typescript
 * import { GQP } from '@mzhub/gqp';
 * import { VectorPlugin } from '@mzhub/gqp/vector';
 *
 * const graph = new GQP({
 *   sources: { db: fromPrisma(prisma) },
 *   plugins: [
 *     new VectorPlugin({
 *       provider: 'pinecone',
 *       config: {
 *         pinecone: {
 *           apiKey: process.env.PINECONE_KEY,
 *           index: 'products'
 *         },
 *         openai: {
 *           apiKey: process.env.OPENAI_KEY
 *         }
 *       }
 *     })
 *   ]
 * });
 * ```
 */
declare class VectorPlugin implements GQPPlugin {
    name: string;
    private config;
    private _indexedFields;
    constructor(config: VectorPluginConfig);
    /**
     * Initialize plugin
     */
    onInit(_engine: GQPEngine): Promise<void>;
    /**
     * Pre-query hook - handle semantic search
     */
    onPreQuery(node: GQPNode, filters: QueryFilters, context: PluginContext): Promise<QueryFilters>;
    /**
     * Perform vector search
     */
    private vectorSearch;
    /**
     * Generate embedding for text
     */
    private embed;
    /**
     * Search vector store
     */
    private search;
    /**
     * Embed text using OpenAI
     */
    private embedWithOpenAI;
    /**
     * Search using Pinecone
     */
    private searchWithPinecone;
    /**
     * Index a field for vector search
     */
    indexField(fieldPath: string, getData: () => Promise<Array<{
        id: string;
        text: string;
    }>>, options?: {
        batchSize?: number;
        onProgress?: (percent: number) => void;
    }): Promise<void>;
    /**
     * Upsert vectors to store
     */
    private upsertVectors;
    /**
     * Check if a field is indexed
     */
    isIndexed(fieldPath: string): boolean;
}
/**
 * Factory function
 */
declare function createVectorPlugin(config: VectorPluginConfig): VectorPlugin;

export { type AutoIndexConfig, VectorPlugin, type VectorPluginConfig, type VectorProviderConfig, createVectorPlugin };