/**
 * Embedding Module -- Tier 2.5 semantic similarity detection.
 *
 * Compares incoming text against pre-computed attack embeddings using
 * cosine similarity. Catches paraphrases, multilingual attacks, and
 * semantic variants that regex cannot detect.
 *
 * Uses all-MiniLM-L6-v2 (384 dimensions, ~22MB, runs locally in JS/WASM).
 * No API calls. Optional dependency: @xenova/transformers.
 *
 * @module agent-threat-rules/modules/embedding
 */
import type { AgentEvent } from '../types.js';
import type { ATRModule, ModuleCondition, ModuleResult } from './index.js';
import { type VectorEntry, type SearchResult } from '../embedding/vector-store.js';
import type { EmbeddingModel } from '../embedding/model-loader.js';
export interface EmbeddingModuleConfig {
    /** Pre-loaded attack vector entries */
    readonly attackVectors?: readonly VectorEntry[];
    /** Path to pre-computed attack-embeddings.json file */
    readonly attackVectorsPath?: string;
    /** Raw JSON data (alternative to file path) */
    readonly attackVectorsData?: readonly {
        id: string;
        vector: number[];
        label: string;
        category: string;
        severity: string;
    }[];
    /** Cosine similarity threshold (default: 0.65) */
    readonly similarityThreshold?: number;
    /** Top-K results to consider (default: 3) */
    readonly topK?: number;
    /** Custom embedding model (default: TransformersJSModel) */
    readonly model?: EmbeddingModel;
}
export declare class EmbeddingModule implements ATRModule {
    private readonly config;
    readonly name = "embedding";
    readonly description = "Vector similarity detection against known attack embeddings";
    readonly version = "0.1.0";
    readonly functions: {
        name: string;
        description: string;
        args: ({
            name: string;
            type: "string";
            required: boolean;
            description: string;
        } | {
            name: string;
            type: "number";
            required: boolean;
            description: string;
        })[];
    }[];
    private store;
    private model;
    private readonly threshold;
    private readonly topK;
    private initialized;
    constructor(config?: EmbeddingModuleConfig);
    initialize(): Promise<void>;
    evaluate(event: AgentEvent, condition: ModuleCondition): Promise<ModuleResult>;
    /** Get search results with full details (for debugging/testing) */
    searchDetailed(text: string, threshold?: number): Promise<readonly SearchResult[]>;
    destroy(): Promise<void>;
    /** Check if module is operational */
    isAvailable(): boolean;
}
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