/**
 * Semantic deduplication for extracted insights.
 *
 * Before inserting a new insight into the DB, we check if a semantically
 * equivalent insight already exists. If cosine similarity > DEDUP_THRESHOLD,
 * the existing insight receives a corroboration boost (+0.1 trust, capped at
 * +0.2 total) rather than creating a duplicate card.
 *
 * Embedding is done with the same local embedder used for card search,
 * keeping all inference in-process at zero external API cost.
 */
import type { ExtractedInsight } from "./extractor.js";
export interface DeduplicatedInsight {
    insight: ExtractedInsight;
    /** ID of existing insight if this is a corroboration (no new card), undefined if new */
    corroboratesId?: string;
    /** Adjusted trust_score after corroboration boost */
    trustScore: number;
}
export interface StoredInsightEmbedding {
    id: string;
    statement: string;
    embedding: Float32Array;
    trustScore: number;
    corroborationCount: number;
}
/**
 * Computes cosine similarity between two Float32Array vectors.
 */
export declare function cosineSimilarity(a: Float32Array, b: Float32Array): number;
/**
 * Deduplicates a batch of new insights against the existing stored embeddings.
 *
 * For each new insight:
 *   - Embed the statement
 *   - Compare against existing embeddings
 *   - If similarity > DEDUP_THRESHOLD: return as corroboration (no new card)
 *   - Otherwise: return as new (insert new card)
 *
 * **Side-effect**: `existing` is mutated in-place. Newly accepted insights are
 * appended to `existing` so that later insights in the same batch can
 * corroborate them. Callers that share the `existing` array across calls must
 * be aware that it grows after each invocation.
 */
export declare function deduplicateInsights(newInsights: ExtractedInsight[], existing: StoredInsightEmbedding[]): Promise<DeduplicatedInsight[]>;
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