feat(Sentiment Analysis Node): Implement Sentiment Analysis node (#10184)

packages/@n8n/nodes-langchain/nodes/chains/SentimentAnalysis/SentimentAnalysis.node.ts

@@ -0,0 +1,257 @@
+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 for the output parser',
+					},
+				],
+			},
+		],
+	};
+
+	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(error)) {
+					const executionErrorData = this.helpers.constructExecutionMetaData(
+						this.helpers.returnJsonArray({ error: error.message }),
+						{ itemData: { item: i } },
+					);
+					returnData[0].push(...executionErrorData);
+					continue;
+				}
+				throw error;
+			}
+		}
+		return returnData;
+	}
+}