/**
 * Background embedding: the write-time embed of a new memory row (T5387) and
 * the bounded backfill of rows without a current vector (T13126).
 *
 * A one-shot process stores memory rows unembedded (see `brain-host.ts`).
 * This helper embeds a bounded batch of them, loading the model once for the
 * batch instead of once per observe. It runs from:
 * - the detached `cleo session end` worker, once per session;
 * - the sentient tick, in an opted-in long-lived host.
 *
 * It does nothing when no row is pending: the pending count is a plain
 * SQL read, so the model is never loaded for an empty backlog. It runs under
 * the governor's `background-autonomous` class, which grants a slot only when
 * memory pressure is `ok`. A deferral skips the batch, and the next session end
 * or tick retries.
 *
 * @module
 * @task T13126
 */
/** Memory rows embedded per bounded batch unless the caller says otherwise. */
export declare const DEFAULT_EMBEDDING_BACKFILL_LIMIT = 50;
/** Outcome of {@link runBoundedEmbeddingBackfill}. */
export interface BoundedEmbeddingBackfillResult {
    /** Whether a batch ran (the model was asked to embed). */
    readonly ran: boolean;
    /** Why no batch ran, when {@link ran} is `false`. */
    readonly skipped?: 'none-pending' | 'deferred' | 'unavailable';
    /** Memory rows without a current vector found before the batch. */
    readonly pending: number;
    /** Rows embedded by this batch. */
    readonly processed: number;
    /** Rows that failed to embed in this batch. */
    readonly errors: number;
}
/** Options for {@link runBoundedEmbeddingBackfill}. */
export interface BoundedEmbeddingBackfillOptions {
    /**
     * Maximum memory rows to embed, newest first.
     * @defaultValue {@link DEFAULT_EMBEDDING_BACKFILL_LIMIT}
     */
    readonly limit?: number;
}
/**
 * SQL selecting every current memory row that needs a vector: observations,
 * decisions, learnings and patterns with no embedding, or one from a model
 * other than the bound `?` (T13740). Columns: `id`, `text`, `created_at`.
 *
 * The texts match what `searchSimilar` shows for each kind, so a hit is
 * judged on the text it was embedded from.
 *
 * @returns The SELECT, with one `?` parameter: the current model tag.
 * @task T13740
 */
export declare function pendingEmbeddingsSql(): string;
/**
 * Count current memory rows that have no embedding from the current model.
 *
 * A plain SQL read: registering the provider is free, and the model is never
 * loaded here.
 *
 * @param projectRoot - Project root directory.
 * @returns The pending count, or `0` when the brain store or its vector table
 *   is unavailable.
 */
export declare function countUnembeddedMemories(projectRoot: string): Promise<number>;
/**
 * Embed a bounded batch of memory rows without a current vector, when any exist and the
 * governor admits background work.
 *
 * Never throws: the backfill is best-effort enrichment of rows that are already
 * stored and findable by BM25.
 *
 * @param projectRoot - Project root directory.
 * @param options - Batch bound.
 * @returns What ran and how many rows were embedded.
 *
 * @example
 * ```ts
 * const r = await runBoundedEmbeddingBackfill('/my/project', { limit: 50 });
 * if (r.ran) console.log(`embedded ${r.processed} of ${r.pending}`);
 * ```
 */
export declare function runBoundedEmbeddingBackfill(projectRoot: string, options?: BoundedEmbeddingBackfillOptions): Promise<BoundedEmbeddingBackfillResult>;
/**
 * Embed one freshly written memory row in the background — long-lived hosts only.
 *
 * T13126: loading the local embedding model costs ~280 MB, and a one-shot
 * process paid it on every write for a single vector. A one-shot process
 * leaves the row unembedded; `populateEmbeddings` fills it later (an opted-in
 * host's tick, the `cleo session end` background batch, or `cleo backfill`).
 * Until then the row is found by BM25/FTS5 only.
 *
 * @param projectRoot - Project whose brain holds the row.
 * @param id - Row id (`O-…`, `D…`, `L-…`, `P-…`).
 * @param text - Text to embed.
 * @task T5387
 * @task T13740
 */
export declare function scheduleEmbedding(projectRoot: string, id: string, text: string): void;
/**
 * Stop scheduling embeddings in this isolate and wait for the ones in flight
 * (T14069). The brain writer worker calls it before it is terminated: an
 * embedding loads `@huggingface/transformers` and its native onnxruntime addon,
 * and terminating the isolate mid-load or mid-inference can abort the WHOLE
 * process (`terminate called after throwing an instance of Napi::Error`).
 *
 * @returns Once every scheduled embedding has settled.
 *
 * @task T14069
 */
export declare function drainScheduledEmbeddings(): Promise<void>;
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