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

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+import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+import { OutputFixingParser, StructuredOutputParser } from '@langchain/classic/output_parsers';
+import { NodeOperationError, NodeConnectionTypes, sleep } from 'n8n-workflow';
+import type {
+	IDataObject,
+	IExecuteFunctions,
+	INodeExecutionData,
+	INodeParameters,
+	INodeType,
+	INodeTypeDescription,
+} from 'n8n-workflow';
+import { z } from 'zod';
+
+import { getBatchingOptionFields } from '@n8n/ai-utilities';
+
+import { processItem } from './processItem';
+
+const SYSTEM_PROMPT_TEMPLATE =
+	"Please classify the text provided by the user into one of the following categories: {categories}, and use the provided formatting instructions below. Don't explain, and only output the json.";
+
+const configuredOutputs = (parameters: INodeParameters) => {
+	const categories = ((parameters.categories as IDataObject)?.categories as IDataObject[]) ?? [];
+	const fallback = (parameters.options as IDataObject)?.fallback as string;
+	const ret = categories.map((cat) => {
+		return { type: 'main', displayName: cat.category };
+	});
+	if (fallback === 'other') ret.push({ type: 'main', displayName: 'Other' });
+	return ret;
+};
+
+export class TextClassifier implements INodeType {
+	description: INodeTypeDescription = {
+		displayName: 'Text Classifier',
+		name: 'textClassifier',
+		icon: 'fa:tags',
+		iconColor: 'black',
+		group: ['transform'],
+		version: [1, 1.1],
+		description: 'Classify your text into distinct categories',
+		codex: {
+			categories: ['AI'],
+			subcategories: {
+				AI: ['Chains', 'Root Nodes'],
+			},
+			resources: {
+				primaryDocumentation: [
+					{
+						url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.text-classifier/',
+					},
+				],
+			},
+		},
+		defaults: {
+			name: 'Text Classifier',
+		},
+		inputs: [
+			{ displayName: '', type: NodeConnectionTypes.Main },
+			{
+				displayName: 'Model',
+				maxConnections: 1,
+				type: NodeConnectionTypes.AiLanguageModel,
+				required: true,
+			},
+		],
+		outputs: `={{(${configuredOutputs})($parameter)}}`,
+		builderHint: {
+			inputs: {
+				ai_languageModel: { required: true },
+			},
+			message:
+				'Each category defined creates a separate output branch. Output 0 corresponds to the first category, output 1 to the second, and so on. Use .output(index).to() to connect from a specific category. @example textClassifier.output(0).to(nodeA) and textClassifier.output(1).to(nodeB)',
+		},
+		properties: [
+			{
+				displayName: 'Text to Classify',
+				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: 'Categories',
+				name: 'categories',
+				placeholder: 'Add Category',
+				type: 'fixedCollection',
+				default: {},
+				typeOptions: {
+					multipleValues: true,
+				},
+				options: [
+					{
+						name: 'categories',
+						displayName: 'Categories',
+						values: [
+							{
+								displayName: 'Category',
+								name: 'category',
+								type: 'string',
+								default: '',
+								description: 'Category to add',
+								required: true,
+							},
+							{
+								displayName: 'Description',
+								name: 'description',
+								type: 'string',
+								default: '',
+								description: "Describe your category if it's not obvious",
+							},
+						],
+					},
+				],
+			},
+			{
+				displayName: 'Options',
+				name: 'options',
+				type: 'collection',
+				default: {},
+				placeholder: 'Add Option',
+				options: [
+					{
+						displayName: 'Allow Multiple Classes To Be True',
+						name: 'multiClass',
+						type: 'boolean',
+						default: false,
+					},
+					{
+						displayName: 'When No Clear Match',
+						name: 'fallback',
+						type: 'options',
+						default: 'discard',
+						description: 'What to do with items that don’t match the categories exactly',
+						options: [
+							{
+								name: 'Discard Item',
+								value: 'discard',
+								description: 'Ignore the item and drop it from the output',
+							},
+							{
+								name: "Output on Extra, 'Other' Branch",
+								value: 'other',
+								description: "Create a separate output branch called 'Other'",
+							},
+						],
+					},
+					{
+						displayName: 'System Prompt Template',
+						name: 'systemPromptTemplate',
+						type: 'string',
+						default: SYSTEM_PROMPT_TEMPLATE,
+						description: 'String to use directly as the system prompt template',
+						typeOptions: {
+							rows: 6,
+						},
+					},
+					{
+						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)',
+					},
+					getBatchingOptionFields({
+						show: {
+							'@version': [{ _cnd: { gte: 1.1 } }],
+						},
+					}),
+				],
+			},
+		],
+	};
+
+	async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
+		const items = this.getInputData();
+		const batchSize = this.getNodeParameter('options.batching.batchSize', 0, 5) as number;
+		const delayBetweenBatches = this.getNodeParameter(
+			'options.batching.delayBetweenBatches',
+			0,
+			0,
+		) as number;
+
+		const llm = (await this.getInputConnectionData(
+			NodeConnectionTypes.AiLanguageModel,
+			0,
+		)) as BaseLanguageModel;
+
+		const categories = this.getNodeParameter('categories.categories', 0, []) as Array<{
+			category: string;
+			description: string;
+		}>;
+
+		if (categories.length === 0) {
+			throw new NodeOperationError(this.getNode(), 'At least one category must be defined');
+		}
+
+		const options = this.getNodeParameter('options', 0, {}) as {
+			multiClass: boolean;
+			fallback?: string;
+			systemPromptTemplate?: string;
+			enableAutoFixing: boolean;
+		};
+		const multiClass = options?.multiClass ?? false;
+		const fallback = options?.fallback ?? 'discard';
+
+		const schemaEntries = categories.map((cat) => [
+			cat.category,
+			z
+				.boolean()
+				.describe(
+					`Should be true if the input has category "${cat.category}" (description: ${cat.description})`,
+				),
+		]);
+		if (fallback === 'other')
+			schemaEntries.push([
+				'fallback',
+				z.boolean().describe('Should be true if none of the other categories apply'),
+			]);
+		const schema = z.object(Object.fromEntries(schemaEntries));
+
+		const structuredParser = StructuredOutputParser.fromZodSchema(schema);
+
+		const parser = options.enableAutoFixing
+			? OutputFixingParser.fromLLM(llm, structuredParser)
+			: structuredParser;
+
+		const multiClassPrompt = multiClass
+			? 'Categories are not mutually exclusive, and multiple can be true'
+			: 'Categories are mutually exclusive, and only one can be true';
+
+		const fallbackPrompt = {
+			other: 'If no categories apply, select the "fallback" option.',
+			discard: 'If there is not a very fitting category, select none of the categories.',
+		}[fallback];
+
+		const returnData: INodeExecutionData[][] = Array.from(
+			{ length: categories.length + (fallback === 'other' ? 1 : 0) },
+			(_) => [],
+		);
+
+		if (this.getNode().typeVersion >= 1.1 && batchSize > 1) {
+			for (let i = 0; i < items.length; i += batchSize) {
+				const batch = items.slice(i, i + batchSize);
+				const batchPromises = batch.map(async (_item, batchItemIndex) => {
+					const itemIndex = i + batchItemIndex;
+					const item = items[itemIndex];
+
+					return await processItem(
+						this,
+						itemIndex,
+						item,
+						llm,
+						parser,
+						categories,
+						multiClassPrompt,
+						fallbackPrompt,
+					);
+				});
+
+				const batchResults = await Promise.allSettled(batchPromises);
+
+				batchResults.forEach((response, batchItemIndex) => {
+					const index = i + batchItemIndex;
+					if (response.status === 'rejected') {
+						const error = response.reason as Error;
+						if (this.continueOnFail()) {
+							returnData[0].push({
+								json: { error: error.message },
+								pairedItem: { item: index },
+							});
+							return;
+						} else {
+							throw new NodeOperationError(this.getNode(), error.message);
+						}
+					} else {
+						const output = response.value;
+						const item = items[index];
+
+						categories.forEach((cat, idx) => {
+							if (output[cat.category]) returnData[idx].push(item);
+						});
+
+						if (fallback === 'other' && output.fallback)
+							returnData[returnData.length - 1].push(item);
+					}
+				});
+
+				// Add delay between batches if not the last batch
+				if (i + batchSize < items.length && delayBetweenBatches > 0) {
+					await sleep(delayBetweenBatches);
+				}
+			}
+		} else {
+			for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
+				const item = items[itemIndex];
+
+				try {
+					const output = await processItem(
+						this,
+						itemIndex,
+						item,
+						llm,
+						parser,
+						categories,
+						multiClassPrompt,
+						fallbackPrompt,
+					);
+
+					categories.forEach((cat, idx) => {
+						if (output[cat.category]) returnData[idx].push(item);
+					});
+					if (fallback === 'other' && output.fallback) returnData[returnData.length - 1].push(item);
+				} catch (error) {
+					if (this.continueOnFail()) {
+						returnData[0].push({
+							json: { error: error.message },
+							pairedItem: { item: itemIndex },
+						});
+
+						continue;
+					}
+
+					throw error;
+				}
+			}
+		}
+
+		return returnData;
+	}
+}