import { Redis } from "ioredis";
import { BaseCache } from "@langchain/core/caches";
import { Generation } from "@langchain/core/outputs";
/**
 * Cache LLM results using Redis.
 * @example
 * ```typescript
 * const model = new ChatOpenAI({
 *   model: "gpt-4o-mini",
 *   cache: new RedisCache(new Redis(), { ttl: 60 }),
 * });
 *
 * // Invoke the model with a prompt
 * const response = await model.invoke("Do something random!");
 * console.log(response);
 *
 * // Remember to disconnect the Redis client when done
 * await redisClient.disconnect();
 * ```
 */
export declare class RedisCache extends BaseCache {
    protected redisClient: Redis;
    protected ttl?: number;
    constructor(redisClient: Redis, config?: {
        ttl?: number;
    });
    /**
     * Retrieves data from the Redis server using a prompt and an LLM key. If
     * the data is not found, it returns null.
     * @param prompt The prompt used to find the data.
     * @param llmKey The LLM key used to find the data.
     * @returns The corresponding data as an array of Generation objects, or null if not found.
     */
    lookup(prompt: string, llmKey: string): Promise<Generation[] | null>;
    /**
     * Updates the data in the Redis server using a prompt and an LLM key.
     * @param prompt The prompt used to store the data.
     * @param llmKey The LLM key used to store the data.
     * @param value The data to be stored, represented as an array of Generation objects.
     */
    update(prompt: string, llmKey: string, value: Generation[]): Promise<void>;
}
