export declare const EMBEDDING_DIM: number;
export type EmbedTaskType = "query" | "document";
/**
 * Generates embeddings locally using nomic-embed-text-v1.5 (768-d) via
 * HuggingFace Transformers.js. The model (~300 MB) is downloaded and cached
 * under `~/.cache/codeprism/models/` on first use.
 *
 * Pass `taskType` to inject the Matryoshka prefix:
 *   - `"query"`:    prefixes with `"search_query: "`
 *   - `"document"`: prefixes with `"search_document: "`
 *   - omitted:      no prefix (backward-compat, avoid for new call sites)
 */
export declare class LocalEmbedder {
    private pipeline;
    private ready;
    constructor();
    private init;
    /** Embed a single text into a unit-length vector. */
    embed(text: string, taskType?: EmbedTaskType): Promise<Float32Array>;
    /**
     * Embed multiple texts in a single ONNX forward pass (true batching).
     * Splits into chunks of `chunkSize` to avoid OOM on large card sets.
     * ~25–50× faster than calling embed() one-by-one for ≥20 texts.
     */
    embedBatch(texts: string[], taskType?: EmbedTaskType, chunkSize?: number): Promise<Float32Array[]>;
}
/** Returns the shared {@link LocalEmbedder} singleton. */
export declare function getEmbedder(): LocalEmbedder;
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