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257 changes: 257 additions & 0 deletions
257
packages/@n8n/nodes-langchain/nodes/chains/SentimentAnalysis/SentimentAnalysis.node.ts
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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; | ||
| } | ||
| } |
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