/**
 * EmbeddingService
 *
 * Computes text embeddings via any OpenAI-compatible `/embeddings` endpoint
 * (OpenAI, Ollama, LocalAI, vLLM, LM Studio, …).
 *
 * Configuration (in priority order):
 *   1. Constructor argument `EmbeddingConfig`
 *   2. Environment variables: EMBED_BASE_URL, EMBED_MODEL, EMBED_API_KEY
 *
 * The service batches texts in groups of `batchSize` (default 64) and
 * resolves all batches sequentially to avoid overloading the server.
 */
import type { OpenLoreConfig } from '../../types/index.js';
export interface EmbeddingConfig {
    /** Base URL of the OpenAI-compatible API, e.g. "http://localhost:11434/v1" */
    baseUrl: string;
    /** Embedding model name, e.g. "nomic-embed-text" or "text-embedding-3-small" */
    model: string;
    /** API key — optional for local servers */
    apiKey?: string;
    /** Maximum number of texts per API call (default: 64) */
    batchSize?: number;
    /** Disable SSL certificate verification (e.g. self-signed certs on local servers) */
    skipSslVerify?: boolean;
}
export declare class EmbeddingService {
    private baseUrl;
    private model;
    private apiKey;
    private batchSize;
    /**
     * Maximum characters per text before truncation.
     * ~24 000 chars ≈ 6 000 words ≈ 8 000 tokens — stays under the 8 192-token
     * limit of most embedding models (nomic-embed-text, text-embedding-3-small…).
     */
    private static readonly MAX_CHARS_PER_TEXT;
    constructor(config: EmbeddingConfig);
    /** The configured embedding model name (recorded in the index metadata sidecar). */
    get modelName(): string;
    /**
     * Build an EmbeddingService from environment variables.
     * Throws if EMBED_BASE_URL or EMBED_MODEL are not set.
     */
    static fromEnv(): EmbeddingService;
    /**
     * Build an EmbeddingService from a OpenLoreConfig.
     * Returns null if no embedding config is present.
     */
    static fromConfig(cfg: OpenLoreConfig): EmbeddingService | null;
    /**
     * Compute embeddings for a list of texts.
     * Returns one embedding vector per input text (same order).
     */
    embed(texts: string[]): Promise<number[][]>;
    private callEmbeddingsApi;
}
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