/**
 * User Context Generator
 *
 * Synthesizes context paragraphs from user premises for storage in the
 * user_contexts table. Two scopes:
 *  - network-scoped: who the person is through the lens of a given network
 *  - global (networkId = null): a cohesive, network-agnostic identity paragraph
 *    that replaces the legacy synthesized user_profile projection.
 * Each scope supports cold-start (all premises at once) and incremental
 * (single premise change applied to an existing context).
 * Returns { text, embedding } for storage.
 */
import type { EmbeddingGenerator } from "../shared/interfaces/embedder.interface.js";
/** Input for cold-start context generation from a full set of premises. */
export interface UserContextInput {
    premises: Array<{
        text: string;
    }>;
    networkPrompt: string | null;
    networkTitle: string;
}
/** Input for incremental context update from a single premise change. */
export interface IncrementalContextInput {
    currentContext: string;
    changeType: 'added' | 'updated' | 'retracted' | 'expired';
    premiseText: string;
    previousPremiseText?: string;
    networkPrompt: string | null;
    networkTitle: string;
}
/** Input for cold-start global (network-agnostic) context generation. */
export interface GlobalContextInput {
    premises: Array<{
        text: string;
    }>;
}
/** Input for incremental global context update from a single premise change. */
export interface GlobalIncrementalContextInput {
    currentContext: string;
    changeType: 'added' | 'updated' | 'retracted' | 'expired';
    premiseText: string;
    previousPremiseText?: string;
}
/** Generated context paragraph with its embedding vector. */
export interface UserContextResult {
    text: string;
    embedding: number[];
}
/**
 * Generates network-scoped context paragraphs from user premises.
 * Uses LLM synthesis to produce focused context and an embedding vector.
 */
export declare class UserContextGenerator {
    private embeddingGenerator;
    private model;
    constructor(embeddingGenerator: EmbeddingGenerator);
    /**
     * Generate a context paragraph from the full set of premises (cold-start).
     *
     * @param input - Premises, network prompt, and network title
     * @returns Synthesized context text with embedding vector
     */
    generateColdStart(input: UserContextInput): Promise<UserContextResult>;
    /**
     * Update an existing context paragraph after a single premise change.
     *
     * @param input - Current context, change details, network metadata
     * @returns Updated context text with embedding vector
     */
    generateIncremental(input: IncrementalContextInput): Promise<UserContextResult>;
    /**
     * Generate a global (network-agnostic) identity paragraph from the full set
     * of premises (cold-start). This is the profile-replacing projection stored
     * as the user's global user_context row (networkId = null).
     *
     * @param input - The user's active premises
     * @returns Synthesized global identity text with embedding vector
     */
    generateGlobalColdStart(input: GlobalContextInput): Promise<UserContextResult>;
    /**
     * Update the global identity paragraph after a single premise change.
     *
     * @param input - Current global context and change details
     * @returns Updated global identity text with embedding vector
     */
    generateGlobalIncremental(input: GlobalIncrementalContextInput): Promise<UserContextResult>;
    /** Normalize an LLM message content into a plain string. */
    private extractText;
    /** Format the network context header for LLM prompts. */
    private formatNetworkContext;
    /** Describe the premise change for the incremental prompt. */
    private describeChange;
    /** Generate embedding from text, normalizing the return type. */
    private embed;
}