/**
 * EmbeddingProvider implementation backed by Google's Gemini embeddings API.
 *
 * Uses native `fetch` so it works in Cloudflare Workers + Node + browsers
 * without an SDK dependency. Templates pass in the API key (and optionally
 * a custom `baseUrl` for proxies).
 *
 * Models supported by default:
 *   - `gemini-embedding-001` — 3072d, multilingual, current recommendation.
 *     Supports Matryoshka representation learning (MRL) — request a smaller
 *     output dimension via `outputDimensionality` to reduce storage cost.
 *   - `text-embedding-004` — 768d, multilingual, legacy stable.
 *
 * See `docs/architecture/catalog-architecture.md` for the design.
 */
import { chunkForBatch, type EmbeddingProvider } from "./contract.js";
/**
 * Known Gemini embedding models. `dimensions` is the *native* size; when
 * `outputDimensionality` is set in the provider options, the effective
 * vector size is whatever the caller requested (Gemini truncates server-side
 * via MRL on `gemini-embedding-001`).
 */
declare const GEMINI_MODELS: {
    readonly "gemini-embedding-001": {
        readonly dimensions: 3072;
        readonly maxTokensPerInput: 2048;
        readonly maxBatchSize: 100;
        readonly multilingual: true;
        readonly supportsOutputDimensionality: true;
    };
    readonly "text-embedding-004": {
        readonly dimensions: 768;
        readonly maxTokensPerInput: 2048;
        readonly maxBatchSize: 100;
        readonly multilingual: true;
        readonly supportsOutputDimensionality: false;
    };
};
export type GeminiEmbeddingModel = keyof typeof GEMINI_MODELS;
/**
 * Tasks Gemini optimizes embeddings for. `RETRIEVAL_DOCUMENT` is the right
 * default for indexed catalog docs; switch to `RETRIEVAL_QUERY` when
 * embedding a search query at read time. The provider keeps a single task
 * type per instance — wire two providers if you index and query separately.
 */
export type GeminiTaskType = "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | "QUESTION_ANSWERING" | "FACT_VERIFICATION" | "CODE_RETRIEVAL_QUERY";
export interface GeminiEmbeddingProviderOptions {
    /**
     * API key to authenticate with. In `auth: "google"` mode, this is the
     * Google AI Studio key. In `auth: "bearer"` mode (e.g. when routing
     * through the Voyant Cloud AI gateway), this is the gateway's bearer
     * token.
     */
    apiKey: string;
    /**
     * How to attach the API key to outbound requests.
     *  - `"google"` (default) — `x-goog-api-key: <apiKey>`. Use when
     *    talking directly to `generativelanguage.googleapis.com`.
     *  - `"bearer"` — `Authorization: Bearer <apiKey>`. Use when routing
     *    through the Voyant Cloud `/ai/v1/gemini` gateway, which forwards
     *    to Google with the org's saved provider key.
     */
    auth?: "google" | "bearer";
    /**
     * Embedding model to use. Default: `gemini-embedding-001`.
     * Switching models is a deliberate `bulkReindex` operation — the catalog
     * plane scopes vector queries to documents matching the active
     * `embedding_model_id`, so mid-migration mixes are handled cleanly.
     */
    model?: GeminiEmbeddingModel;
    /**
     * Output vector size for MRL-capable models. When omitted, the model's
     * native dimension is used. Only `gemini-embedding-001` supports this.
     * Smaller dims reduce storage / query cost at some quality loss.
     */
    outputDimensionality?: number;
    /**
     * Task type the embedded text will be used for. Default:
     * `RETRIEVAL_DOCUMENT` (right for ingestion). Use `RETRIEVAL_QUERY` for
     * read-side query embedding.
     */
    taskType?: GeminiTaskType;
    /**
     * Override the API base URL — useful for a corporate proxy or a custom
     * Vertex-compatible deployment. Default:
     * `https://generativelanguage.googleapis.com/v1beta`.
     */
    baseUrl?: string;
    /**
     * Optional `fetch` override for testing or custom transport. Default:
     * the global `fetch`. Must follow the standard Fetch API contract.
     */
    fetchImpl?: typeof fetch;
    /**
     * Override the model id stamped onto search-index documents. Defaults
     * to `gemini/<model>/v1` — keep this stable across deployments so
     * documents stay queryable across instances.
     */
    modelId?: string;
}
/**
 * Build a Gemini-backed EmbeddingProvider.
 */
export declare function createGeminiEmbeddingProvider(options: GeminiEmbeddingProviderOptions): EmbeddingProvider;
/**
 * Re-export the chunking helper alongside the Gemini provider so callers
 * can `embedBatched(provider, texts)` for very large inputs.
 */
export { chunkForBatch, GEMINI_MODELS };