/**
 * Local Enterprise Privacy Provider (Phase 6)
 *
 * Implements Ollama integration to run PRSense completely on-premise
 * without sending proprietary source code or PR context to OpenAI.
 *
 * Supports both RAG Generation (LLMProvider) and Text Embedding (Embedder).
 */
import type { LLMProvider } from '../rag/queryEngine.js';
import type { Embedder } from '../embeddingPipeline.js';
export interface OllamaConfig {
    /**
     * The base URL of the Ollama server.
     * @default 'http://localhost:11434'
     */
    baseUrl?: string;
    /**
     * The LLM model to use for text generation (e.g., 'llama3', 'mistral', 'codellama').
     * @default 'llama3'
     */
    model?: string;
    /**
     * The model to use for embeddings (e.g., 'nomic-embed-text').
     * @default 'nomic-embed-text'
     */
    embeddingModel?: string;
}
export declare class OllamaProvider implements LLMProvider, Embedder {
    private baseUrl;
    private model;
    private embeddingModel;
    constructor(config?: OllamaConfig);
    /**
     * Generate text using the configured Ollama LLM.
     * Used natively by the RAGQueryEngine for answering questions.
     */
    generate(prompt: string): Promise<string>;
    /**
     * Embed standard text (like PR bodies or codebase chunks).
     */
    embedText(text: string): Promise<Float32Array>;
    /**
     * Embed raw diffs natively.
     */
    embedDiff(diff: string): Promise<Float32Array>;
    private getOllamaEmbedding;
}
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