export { L as ListOptions, M as Memory, c as MemoryAdapter, d as MemoryAdapterFactory, a as MemoryScope, b as RecordOptions, R as RetrieveOptions } from '../../types-B_kkAV7t.mjs';
import { LanguageModel } from 'ai';

/**
 * Post-turn memory extraction.
 *
 * Two strategies, both producing a `{ upserts, deletes }` delta:
 *   1. LLM extraction (recommended) — `generateObject` against a cheap/fast
 *      model with a structured schema; can add new facts AND supersede stale
 *      ones. The system prompt forbids storing secrets/credentials/unvolunteered
 *      PII.
 *   2. Heuristic (fallback, no extra model call) — a small rule set that
 *      captures explicit imperative preferences ("always…", "I prefer…",
 *      "call me…", "remember that…") and stores them verbatim.
 *
 * Either way the output runs a redaction pass that drops obvious secret patterns
 * before persistence.
 */

interface MemoryDelta {
    upserts: {
        text: string;
        kind: string;
    }[];
    deletes: string[];
}
/** Drop any upsert that looks like it carries a secret. */
declare function redactDelta(delta: MemoryDelta): MemoryDelta;
/** Flatten a UI/Model message's text parts for the extraction prompt. */
declare function renderTurn(messages: unknown[]): string;
/** LLM extraction via `generateObject`. Returns an empty delta on any failure. */
declare function llmExtract(model: LanguageModel, turnText: string, existing: {
    id: string;
    text: string;
}[]): Promise<MemoryDelta>;
/**
 * Heuristic extraction — zero extra model call. Captures explicit imperative
 * preferences the user states. Lower recall, no cost/latency.
 */
declare function heuristicExtract(turnText: string): MemoryDelta;

export { type MemoryDelta, heuristicExtract, llmExtract, redactDelta, renderTurn };