import type { BaseLanguageModel } from '@langchain/core/language_models/base';
import type {
	IExecuteFunctions,
	INodeExecutionData,
	INodeType,
	INodeTypeDescription,
} from 'n8n-workflow';
import { NodeApiError, NodeConnectionType, NodeOperationError } from 'n8n-workflow';

import { getPromptInputByType } from '@utils/helpers';
import { getOptionalOutputParser } from '@utils/output_parsers/N8nOutputParser';

// Import from centralized module
import {
	executeChain,
	formatResponse,
	getInputs,
	nodeProperties,
	type MessageTemplate,
} from './methods';
import {
	getCustomErrorMessage as getCustomOpenAiErrorMessage,
	isOpenAiError,
} from '../../vendors/OpenAi/helpers/error-handling';

/**
 * Basic LLM Chain Node Implementation
 * Allows connecting to language models with optional structured output parsing
 */
export class ChainLlm implements INodeType {
	description: INodeTypeDescription = {
		displayName: 'Basic LLM Chain',
		name: 'chainLlm',
		icon: 'fa:link',
		iconColor: 'black',
		group: ['transform'],
		version: [1, 1.1, 1.2, 1.3, 1.4, 1.5],
		description: 'A simple chain to prompt a large language model',
		defaults: {
			name: 'Basic LLM Chain',
			color: '#909298',
		},
		codex: {
			alias: ['LangChain'],
			categories: ['AI'],
			subcategories: {
				AI: ['Chains', 'Root Nodes'],
			},
			resources: {
				primaryDocumentation: [
					{
						url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/',
					},
				],
			},
		},
		inputs: `={{ ((parameter) => { ${getInputs.toString()}; return getInputs(parameter) })($parameter) }}`,
		outputs: [NodeConnectionType.Main],
		credentials: [],
		properties: nodeProperties,
	};

	/**
	 * Main execution method for the node
	 */
	async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
		this.logger.debug('Executing Basic LLM Chain');
		const items = this.getInputData();
		const returnData: INodeExecutionData[] = [];

		// Process each input item
		for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
			try {
				// Get the language model
				const llm = (await this.getInputConnectionData(
					NodeConnectionType.AiLanguageModel,
					0,
				)) as BaseLanguageModel;

				// Get output parser if configured
				const outputParser = await getOptionalOutputParser(this);

				// Get user prompt based on node version
				let prompt: string;

				if (this.getNode().typeVersion <= 1.3) {
					prompt = this.getNodeParameter('prompt', itemIndex) as string;
				} else {
					prompt = getPromptInputByType({
						ctx: this,
						i: itemIndex,
						inputKey: 'text',
						promptTypeKey: 'promptType',
					});
				}

				// Validate prompt
				if (prompt === undefined) {
					throw new NodeOperationError(this.getNode(), "The 'prompt' parameter is empty.");
				}

				// Get chat messages if configured
				const messages = this.getNodeParameter(
					'messages.messageValues',
					itemIndex,
					[],
				) as MessageTemplate[];

				// Execute the chain
				const responses = await executeChain({
					context: this,
					itemIndex,
					query: prompt,
					llm,
					outputParser,
					messages,
				});

				// Process each response and add to return data
				responses.forEach((response) => {
					returnData.push({
						json: formatResponse(response),
					});
				});
			} catch (error) {
				// Handle OpenAI specific rate limit errors
				if (error instanceof NodeApiError && isOpenAiError(error.cause)) {
					const openAiErrorCode: string | undefined = (error.cause as any).error?.code;
					if (openAiErrorCode) {
						const customMessage = getCustomOpenAiErrorMessage(openAiErrorCode);
						if (customMessage) {
							error.message = customMessage;
						}
					}
				}

				// Continue on failure if configured
				if (this.continueOnFail()) {
					returnData.push({ json: { error: error.message }, pairedItem: { item: itemIndex } });
					continue;
				}

				throw error;
			}
		}

		return [returnData];
	}
}