import type { BaseResponse } from '../../../core/base-client';
import { type OpenSearchEmbeddingParams } from '../schemas';
import type { AgrSiteClient } from '../client';
type ExecuteRequest = AgrSiteClient['executeRequest'];
/**
 * Creates the openSearch resource methods
 * OpenAPI Path: /open-search → openSearch.*
 * @description Advanced search embedding and indexing functionality for agricultural content discovery
 */
export declare function createOpenSearchResource(executeRequest: ExecuteRequest): {
    /**
     * Text embedding operations
     */
    embedding: {
        /**
         * Generate text embedding for search optimization
         * @description Creates vector embeddings for agricultural content to enable semantic search and content discovery
         * @fullPath api.agrSite.openSearch.embedding.get
         * @service agr-site
         * @domain search-optimization
         * @dataMethod openSearchData.embedding.get - returns only the embedding data without metadata
         * @discoverable true
         * @searchTerms ["embedding", "vector search", "semantic search", "text embedding", "opensearch", "search optimization", "content indexing"]
         * @relatedEndpoints ["api.agrSite.fyxerTranscript.create", "api.agrSite.fyxerTranscript.update", "api.agrSite.settings.list"]
         * @commonPatterns ["Generate content embeddings", "Optimize search index", "Create semantic vectors", "Agricultural content analysis"]
         * @workflow ["search-optimization", "content-indexing", "ai-processing", "agricultural-content-analysis"]
         * @prerequisites ["Valid authentication token", "Search integration permissions", "Valid text content"]
         * @nextSteps ["Index embeddings in search system", "Use embeddings for content discovery"]
         * @businessRules ["Optimizes embeddings for agricultural content", "Rate limited for large-scale operations", "Content filtered for relevance"]
         * @functionalArea "site-content-and-ai-processing"
         * @caching "Embeddings cached for 24 hours, invalidate on content changes"
         * @performance "AI processing may take 2-5 seconds depending on content length"
         * @param params Text content and embedding configuration parameters
         * @returns Promise<BaseResponse<unknown>> Complete response with generated embedding vectors and metadata
         * @example
         * ```typescript
         * const embedding = await client.openSearch.embedding.get({
         *   text: 'Sustainable farming practices for crop rotation and soil health',
         *   model: 'agricultural-optimized',
         *   dimensions: 384
         * });
         *
         * // Get just the embedding data
         * const embeddingData = await client.openSearchData.embedding.get({
         *   text: 'Organic farming techniques for sustainable agriculture'
         * });
         * ```
         */
        get: (params: OpenSearchEmbeddingParams) => Promise<BaseResponse<unknown>>;
    };
};
/**
 * Creates the openSearchData resource methods (data-only versions)
 */
export declare function createOpenSearchDataResource(openSearch: ReturnType<typeof createOpenSearchResource>): {
    embedding: {
        /**
         * Get embedding data without response metadata
         * @param params Text content and embedding configuration parameters
         * @returns Promise<unknown> Embedding data directly
         */
        get: (params: OpenSearchEmbeddingParams) => Promise<unknown>;
    };
};
export type OpenSearchResource = ReturnType<typeof createOpenSearchResource>;
export type OpenSearchDataResource = ReturnType<typeof createOpenSearchDataResource>;
export {};
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