import { BaseEmbeddings, EmbedTextParams, EmbedTextResult, EmbedTextsParams, EmbedTextsResult } from '@memberjunction/ai';
import { Ollama } from 'ollama';
/**
 * Ollama implementation of the BaseEmbeddings class for local embedding generation
 * Supports various embedding models like nomic-embed-text, mxbai-embed-large, etc.
 */
export declare class OllamaEmbedding extends BaseEmbeddings {
    private _client;
    private _baseUrl;
    private _keepAlive;
    constructor(apiKey?: string);
    /**
     * Read only getter method to get the Ollama client instance
     */
    get OllamaClient(): Ollama;
    /**
     * Read only getter method to get the Ollama client instance
     */
    get client(): Ollama;
    /**
     * Override SetAdditionalSettings to handle Ollama specific settings
     */
    SetAdditionalSettings(settings: Record<string, any>): void;
    /**
     * Embed a single text string using Ollama
     */
    EmbedText(params: EmbedTextParams): Promise<EmbedTextResult>;
    /**
     * Embed multiple texts in batch using Ollama
     * Note: Ollama doesn't have native batch support, so we process sequentially
     * For better performance, consider running multiple Ollama instances or using async processing
     */
    EmbedTexts(params: EmbedTextsParams): Promise<EmbedTextsResult>;
    /**
     * Ensure a model is available locally, pulling it if necessary
     */
    private ensureModelAvailable;
    /**
     * Get available embedding models
     * Required by BaseEmbeddings abstract class
     */
    GetEmbeddingModels(): Promise<any>;
    /**
     * List available embedding models in Ollama
     */
    listEmbeddingModels(): Promise<string[]>;
    /**
     * Get information about a specific embedding model
     */
    getModelInfo(modelName: string): Promise<any>;
    /**
     * Get the dimension size for a specific embedding model
     * This is useful for setting up vector databases
     */
    getEmbeddingDimension(modelName: string): Promise<number | null>;
}
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