/**
 * GSB AI Chat Service
 *
 * Provides functionality for interacting with AI chat models through the GSB backend.
 * This service integrates with the 'aiChat' serverless function to process AI chat requests.
 *
 * Key features:
 * - Template-based prompt generation using nunjucks (handled by backend)
 * - Multi-provider support (OpenAI, Azure, Anthropic, HuggingFace)
 * - Chat history management
 * - Context-aware conversations
 */
import { QueryParams } from '@gsb-core/core';
import { GsbAiChat, GsbAiMessage } from '../interfaces/entity-interfaces';
export interface ChatOptions {
    prompt: string;
    llmConfId: string;
    chatId?: string;
    data?: any;
    options?: any;
}
/**
 * AI Chat Service
 *
 * This service acts as a client for the GSB backend AI chat functionality.
 * It communicates with the 'aiChat' serverless function to process templates,
 * manage chat history, and generate AI responses.
 */
export declare class GsbAiChatService {
    /**
     * Singleton instance
     */
    private static instance;
    /**
     * Entity service for data operations
     */
    private entityService;
    /**
     * Runtime service for serverless function execution
     */
    private runtime;
    /**
     * Get the singleton instance of the AI Chat Service
     *
     * @param token Authentication token for API calls
     * @returns The singleton instance
     */
    static getInstance(useCache?: boolean): GsbAiChatService;
    /**
     * Constructor
     *
     * @param token Authentication token for API calls
     */
    constructor(useCache?: boolean);
    /**
     * Send a message to the AI chat and get a response
     *
     * This method calls the 'aiChat' serverless function which:
     * 1. Gets or creates a chat session
     * 2. Processes the template using nunjucks (if configured)
     * 3. Stores the user message
     * 4. Generates an AI response using the configured LLM provider
     * 5. Stores the AI response in the chat history
     * 6. Returns the AI response
     * @param prompt User's message/prompt
     * @param llmConfId LLM configuration ID (from a saved LlmConfiguration entity)
     * @param chatId Existing chat ID (if continuing a conversation) or undefined for a new chat
     * @param data Context data containing the entity,entityDefinition,entity_id and other information for template processing, matches GsbWorkflowInstance
     * @param options Additional options like testMode, system prompts, etc. matches GsbWorkflowInstance.prms
     * @returns Response object containing the AI message and updated chat
     *
     * @example
     * ```typescript
     * // Start a new chat
     * const result = await aiChatService.chat(
     *   "What's the status of our project?",
     *   "llm-config-id",  // ID of saved LlmConfiguration
     *   undefined,        // New chat
     *   { entity: projectEntity, entityDefinition: projectEntityDefinition, entity_id: projectEntityId }
     * );
     *
     * // Continue the conversation
     * const followUp = await aiChatService.chat(
     *   "What should be our next steps?",
     *   "llm-config-id",
     *   result.chat.id,   // Use existing chat ID
     *   { entity: projectEntity, entityDefinition: projectEntityDefinition, entity_id: projectEntityId }
     * );
     * ```
     */
    chat({ prompt, llmConfId, chatId, data, options }: ChatOptions, token?: string, tenantCode?: string): Promise<{
        message: string;
        chat: GsbAiChat;
    }>;
    /**
     * Get a chat by ID
     *
     * @param chatId The chat ID
     * @returns The chat entity
     */
    getChat(chatId: string, token?: string, tenantCode?: string): Promise<GsbAiChat>;
    /**
     * Get all chats
     *
     * @returns Array of chat entities
     */
    getChats(queryParams?: QueryParams<GsbAiChat>, token?: string, tenantCode?: string): Promise<GsbAiChat[]>;
    /**
     * Get messages for a specific chat
     *
     * @param chatId The chat ID
     * @returns Array of message entities
     */
    getChatMessages(chatId: string, queryParams?: QueryParams<GsbAiMessage>, token?: string, tenantCode?: string): Promise<GsbAiMessage[]>;
}