/**
 * Local embedding provider using @huggingface/transformers (transformers.js v4).
 *
 * Implements the EmbeddingProvider interface for brain memory vector search.
 * Uses all-MiniLM-L6-v2 (22MB, 384 dimensions) — matches the brain_embeddings
 * vec0 table schema. The model loads from the transformers cache, and is
 * downloaded only when `brain.embedding.allowModelDownload` is true (T13716).
 *
 * @epic T134
 * @task T136
 * @why Ship vector search out-of-the-box without external API keys
 * @what Local embedding provider using @huggingface/transformers all-MiniLM-L6-v2
 * @remarks Brain embeddings are a FIRST-CLASS CLEO feature — the transformers
 *   package is a regular dependency of `@cleocode/core`, not optional.
 *   Migrated from `@xenova/transformers` v2 to `@huggingface/transformers`
 *   v4 (upstream rename, same author) which drops the deprecated
 *   `prebuild-install` transitive via `sharp@0.34+`.
 */
import type { EmbeddingProvider } from './brain-embedding.js';
/**
 * How a user opts into the one-time model download (T13716).
 *
 * @task T13716
 */
export declare const ENABLE_MODEL_DOWNLOAD_HINT = "cleo config set brain.embedding.allowModelDownload true --type boolean --scope global";
/**
 * Import transformers.js with hub access gated on the opt-in (T13716).
 *
 * The one gate for every CLEO model load (this embedding model and the
 * transcript extractor's text-generation model). transformers.js 4.0.1 still
 * fetches config and tokenizer metadata from the hub under `local_files_only`,
 * so the gate sets the process-global `env.allowRemoteModels` from
 * `brain.embedding.allowModelDownload` before every load: one rule, one value,
 * so concurrent loads cannot disagree and nothing needs restoring.
 *
 * @returns The transformers module and whether downloads are allowed.
 * @task T13716
 */
export declare function importGatedTransformers(): Promise<{
    transformers: typeof import('@huggingface/transformers');
    allowDownload: boolean;
}>;
/**
 * Has the pipeline actually been loaded in this process?
 *
 * Distinct from {@link LocalEmbeddingProvider.isAvailable}, which reports
 * CAPABILITY. This reports STATE, and exists for diagnostics — notably to warn
 * when `brain.embedding.enabled` is true but nothing was ever embedded.
 *
 * @returns `true` once a real embedding pipeline exists.
 *
 * @task T12129 (gh#1217)
 */
export declare function isEmbeddingPipelineLoaded(): boolean;
/**
 * Reset the module-level pipeline state. Test-only.
 *
 * @internal
 * @task T12129 (gh#1217)
 */
export declare function __resetLocalEmbeddingStateForTests(): void;
/**
 * Override the weights hash a load must match. Test-only.
 *
 * @param sha256 - Expected hex sha256 of `onnx/model.onnx`.
 * @internal
 * @task T13740
 */
export declare function __setExpectedModelSha256ForTests(sha256: string): void;
/**
 * Local embedding provider backed by @huggingface/transformers.
 *
 * Produces 384-dimension Float32Array vectors compatible with the
 * brain_embeddings vec0 table. The model loads from the local cache; it is
 * downloaded only when `brain.embedding.allowModelDownload` is true (T13716).
 *
 * Use {@link initDefaultProvider} (in brain-embedding.ts) to register an
 * instance when brain.embedding.enabled=true and
 * brain.embedding.provider='local'.
 */
export declare class LocalEmbeddingProvider implements EmbeddingProvider {
    /** Number of dimensions produced — must match brain_embeddings vec0 table. */
    readonly dimensions = 384;
    /** Model tag stored beside every vector this provider produces (T13740). */
    readonly model: string;
    /**
     * Whether this provider CAN produce embeddings — not whether it already has.
     *
     * ## The deadlock this fixes (gh#1217)
     *
     * This previously returned `_ready`, the "pipeline has loaded" flag. But
     * `_ready` is set only by `loadPipeline()`, which runs only inside
     * {@link embed} — and every caller of `embed` first checks `isAvailable()`.
     * In any fresh CLI process `_ready` is false, so nothing ever called
     * `embed`, so `_ready` never became true. **The readiness check could only
     * be satisfied by the call it guarded.**
     *
     * The observable result was total, silent inertness: `cleo brain
     * maintenance` reported `{processed: 0, skipped: 0, errors: 0}` forever,
     * `brain_embeddings` stayed empty after dozens of observations, new writes
     * were never embedded, and every hybrid search quietly degraded to FTS5 —
     * while `brain.embedding.enabled` was true and the provider itself was
     * perfectly healthy when called directly.
     *
     * Availability is therefore capability: this provider can load its pipeline
     * on demand, and says so until a load actually fails. The ~22 MB first-call
     * download is the expected cost of the lazy design, not a reason to refuse
     * to begin.
     *
     * @returns `true` unless loading the pipeline has already failed here.
     *
     * @task T12129 (gh#1217)
     */
    isAvailable(): boolean;
    /**
     * Convert a single text string into a 384-dimension float vector.
     * Loads the model on first call (downloading it only on opt-in, T13716).
     *
     * @param text - The text to embed.
     * @returns A Float32Array of length 384.
     */
    embed(text: string): Promise<Float32Array>;
    /**
     * Embed multiple texts in sequence, reusing the cached pipeline.
     *
     * @param texts - Array of text strings to embed.
     * @returns Array of Float32Array vectors, one per input text.
     */
    embedBatch(texts: string[]): Promise<Float32Array[]>;
}
/**
 * Get or create the shared LocalEmbeddingProvider singleton.
 *
 * @returns The shared LocalEmbeddingProvider instance.
 */
export declare function getLocalEmbeddingProvider(): LocalEmbeddingProvider;
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