/**
 * Bayesian Surprisal Scoring — T1146 Wave 6 Dreamer Upgrade
 *
 * Computes a surprisal score for a brain_observation relative to prior
 * context (existing embeddings in the same project/peer scope).
 *
 * Surprisal definition: how much new information does this observation
 * add to the existing memory state?
 *
 *   score = -log(P(observation | prior_context))
 *
 * Implementation:
 *   - High score (novel, >0.7): observation is semantically distant from all
 *     stored embeddings → high priority for consolidation
 *   - Low score (redundant, <0.3): observation is very similar to an existing
 *     embedding → low priority, may be merged/skipped
 *   - Neutral (0.5): returned when embeddings unavailable (graceful degrade)
 *
 * Temporal decay and confidence weighting from BRAIN_SOURCE_CONFIDENCE are
 * applied to adjust the prior similarity.
 *
 * No external library: pure TypeScript cosine similarity + log transform.
 *
 * @task T1146
 * @epic T1146
 */
import type { DatabaseSync } from 'node:sqlite';
/** Neutral surprisal returned when embeddings are unavailable. */
export declare const NEUTRAL_SURPRISAL = 0.5;
/** An observation row with its embedding for surprisal computation. */
export interface EmbeddableObservation {
    id: string;
    embedding: number[] | null | undefined;
    createdAt?: string | null;
}
/** Options for {@link computeSurprisalScore}. */
export interface SurprisalOptions {
    /** Inject a DatabaseSync for testing. */
    db?: DatabaseSync | null;
    /** Project root (used to scope prior observations). */
    project?: string | null;
    /** Peer ID for memory isolation. Default 'global'. */
    peerId?: string;
}
/** Result of surprisal computation. */
export interface SurprisalResult {
    /** Observation ID scored. */
    id: string;
    /** Surprisal score 0.0–1.0 (higher = more novel = higher consolidation priority). */
    score: number;
    /** Whether embedding was available (false = returned neutral 0.5). */
    embeddingAvailable: boolean;
    /** Most similar prior observation ID (if found). */
    mostSimilarId?: string;
    /** Cosine similarity to most similar prior (0–1). */
    maxSimilarity?: number;
}
/**
 * Compute a Bayesian surprisal score for a single observation.
 *
 * Returns a value in [0.0, 1.0]:
 *   - >0.7 → novel (high surprisal) — high consolidation priority
 *   - <0.3 → redundant (low surprisal) — skip or merge
 *   - 0.5  → neutral (no embeddings available)
 *
 * Degrades gracefully when embeddings are unavailable:
 * returns NEUTRAL_SURPRISAL (0.5) with a console.warn, never throws.
 *
 * @param observation - The observation to score (must include embedding if available).
 * @param options     - Scope, db injection for tests.
 * @returns SurprisalResult with score and diagnostics.
 *
 * @task T1146
 */
export declare function computeSurprisalScore(observation: EmbeddableObservation, options?: SurprisalOptions): SurprisalResult;
/**
 * Compute surprisal scores for a batch of observations, sorted descending.
 *
 * High-surprisal observations are returned first — they should be processed
 * by the dreamer first (bypass normal rate limit).
 *
 * @param observations - Batch to score.
 * @param options      - Scope, db injection.
 * @returns Sorted array with highest surprisal first.
 *
 * @task T1146
 */
export declare function computeSurprisalBatch(observations: EmbeddableObservation[], options?: SurprisalOptions): SurprisalResult[];
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