import type {
	IDataObject,
	IExecuteFunctions,
	INodeExecutionData,
	INodeParameters,
	INodeType,
	INodeTypeDescription,
} from 'n8n-workflow';

import { NodeConnectionType, NodeOperationError } from 'n8n-workflow';

import type { BaseLanguageModel } from '@langchain/core/language_models/base';
import { HumanMessage } from '@langchain/core/messages';
import { SystemMessagePromptTemplate, ChatPromptTemplate } from '@langchain/core/prompts';
import { OutputFixingParser, StructuredOutputParser } from 'langchain/output_parsers';
import { z } from 'zod';
import { getTracingConfig } from '../../../utils/tracing';

const DEFAULT_SYSTEM_PROMPT_TEMPLATE =
	'You are highly intelligent and accurate sentiment analyzer. Analyze the sentiment of the provided text. Categorize it into one of the following: {categories}. Use the provided formatting instructions. Only output the JSON.';

const DEFAULT_CATEGORIES = 'Positive, Neutral, Negative';
const configuredOutputs = (parameters: INodeParameters, defaultCategories: string) => {
	const options = (parameters?.options ?? {}) as IDataObject;
	const categories = (options?.categories as string) ?? defaultCategories;
	const categoriesArray = categories.split(',').map((cat) => cat.trim());

	const ret = categoriesArray.map((cat) => ({ type: NodeConnectionType.Main, displayName: cat }));
	return ret;
};

export class SentimentAnalysis implements INodeType {
	description: INodeTypeDescription = {
		displayName: 'Sentiment Analysis',
		name: 'sentimentAnalysis',
		icon: 'fa:balance-scale-left',
		iconColor: 'black',
		group: ['transform'],
		version: 1,
		description: 'Analyze the sentiment of your text',
		codex: {
			categories: ['AI'],
			subcategories: {
				AI: ['Chains', 'Root Nodes'],
			},
			resources: {
				primaryDocumentation: [
					{
						url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.sentimentanalysis/',
					},
				],
			},
		},
		defaults: {
			name: 'Sentiment Analysis',
		},
		inputs: [
			{ displayName: '', type: NodeConnectionType.Main },
			{
				displayName: 'Model',
				maxConnections: 1,
				type: NodeConnectionType.AiLanguageModel,
				required: true,
			},
		],
		outputs: `={{(${configuredOutputs})($parameter, "${DEFAULT_CATEGORIES}")}}`,
		properties: [
			{
				displayName: 'Text to Analyze',
				name: 'inputText',
				type: 'string',
				required: true,
				default: '',
				description: 'Use an expression to reference data in previous nodes or enter static text',
				typeOptions: {
					rows: 2,
				},
			},
			{
				displayName:
					'Sentiment scores are LLM-generated estimates, not statistically rigorous measurements. They may be inconsistent across runs and should be used as rough indicators only.',
				name: 'detailedResultsNotice',
				type: 'notice',
				default: '',
				displayOptions: {
					show: {
						'/options.includeDetailedResults': [true],
					},
				},
			},
			{
				displayName: 'Options',
				name: 'options',
				type: 'collection',
				default: {},
				placeholder: 'Add Option',
				options: [
					{
						displayName: 'Sentiment Categories',
						name: 'categories',
						type: 'string',
						default: DEFAULT_CATEGORIES,
						description: 'A comma-separated list of categories to analyze',
						noDataExpression: true,
						typeOptions: {
							rows: 2,
						},
					},
					{
						displayName: 'System Prompt Template',
						name: 'systemPromptTemplate',
						type: 'string',
						default: DEFAULT_SYSTEM_PROMPT_TEMPLATE,
						description: 'String to use directly as the system prompt template',
						typeOptions: {
							rows: 6,
						},
					},
					{
						displayName: 'Include Detailed Results',
						name: 'includeDetailedResults',
						type: 'boolean',
						default: false,
						description:
							'Whether to include sentiment strength and confidence scores in the output',
					},
					{
						displayName: 'Enable Auto-Fixing',
						name: 'enableAutoFixing',
						type: 'boolean',
						default: true,
						description:
							'Whether to enable auto-fixing (may trigger an additional LLM call if output is broken)',
					},
				],
			},
		],
	};

	async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
		const items = this.getInputData();

		const llm = (await this.getInputConnectionData(
			NodeConnectionType.AiLanguageModel,
			0,
		)) as BaseLanguageModel;

		const returnData: INodeExecutionData[][] = [];

		for (let i = 0; i < items.length; i++) {
			try {
				const sentimentCategories = this.getNodeParameter(
					'options.categories',
					i,
					DEFAULT_CATEGORIES,
				) as string;

				const categories = sentimentCategories
					.split(',')
					.map((cat) => cat.trim())
					.filter(Boolean);

				if (categories.length === 0) {
					throw new NodeOperationError(this.getNode(), 'No sentiment categories provided', {
						itemIndex: i,
					});
				}

				// Initialize returnData with empty arrays for each category
				if (returnData.length === 0) {
					returnData.push(...Array.from({ length: categories.length }, () => []));
				}

				const options = this.getNodeParameter('options', i, {}) as {
					systemPromptTemplate?: string;
					includeDetailedResults?: boolean;
					enableAutoFixing?: boolean;
				};

				const schema = z.object({
					sentiment: z.enum(categories as [string, ...string[]]),
					strength: z
						.number()
						.min(0)
						.max(1)
						.describe('Strength score for sentiment in relation to the category'),
					confidence: z.number().min(0).max(1),
				});

				const structuredParser = StructuredOutputParser.fromZodSchema(schema);

				const parser = options.enableAutoFixing
					? OutputFixingParser.fromLLM(llm, structuredParser)
					: structuredParser;

				const systemPromptTemplate = SystemMessagePromptTemplate.fromTemplate(
					`${options.systemPromptTemplate ?? DEFAULT_SYSTEM_PROMPT_TEMPLATE}
		{format_instructions}`,
				);

				const input = this.getNodeParameter('inputText', i) as string;
				const inputPrompt = new HumanMessage(input);
				const messages = [
					await systemPromptTemplate.format({
						categories: sentimentCategories,
						format_instructions: parser.getFormatInstructions(),
					}),
					inputPrompt,
				];

				const prompt = ChatPromptTemplate.fromMessages(messages);
				const chain = prompt.pipe(llm).pipe(parser).withConfig(getTracingConfig(this));

				try {
					const output = await chain.invoke(messages);
					const sentimentIndex = categories.findIndex(
						(s) => s.toLowerCase() === output.sentiment.toLowerCase(),
					);

					if (sentimentIndex !== -1) {
						const resultItem = { ...items[i] };
						const sentimentAnalysis: IDataObject = {
							category: output.sentiment,
						};
						if (options.includeDetailedResults) {
							sentimentAnalysis.strength = output.strength;
							sentimentAnalysis.confidence = output.confidence;
						}
						resultItem.json = {
							...resultItem.json,
							sentimentAnalysis,
						};
						returnData[sentimentIndex].push(resultItem);
					}
				} catch (error) {
					throw new NodeOperationError(
						this.getNode(),
						'Error during parsing of LLM output, please check your LLM model and configuration',
						{
							itemIndex: i,
						},
					);
				}
			} catch (error) {
				if (this.continueOnFail()) {
					const executionErrorData = this.helpers.constructExecutionMetaData(
						this.helpers.returnJsonArray({ error: error.message }),
						{ itemData: { item: i } },
					);
					returnData[0].push(...executionErrorData);
					continue;
				}
				throw error;
			}
		}
		return returnData;
	}
}