@n8n/n8n-nodes-langchain
Version:
341 lines • 17.4 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.SentimentAnalysis = void 0;
const messages_1 = require("@langchain/core/messages");
const prompts_1 = require("@langchain/core/prompts");
const output_parsers_1 = require("@langchain/classic/output_parsers");
const n8n_workflow_1 = require("n8n-workflow");
const zod_1 = require("zod");
const ai_utilities_1 = require("@n8n/ai-utilities");
const tracing_1 = require("../../../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, defaultCategories) => {
const options = (parameters?.options ?? {});
const categories = options?.categories ?? defaultCategories;
const categoriesArray = categories.split(',').map((cat) => cat.trim());
const ret = categoriesArray.map((cat) => ({ type: 'main', displayName: cat }));
return ret;
};
class SentimentAnalysis {
constructor() {
this.description = {
displayName: 'Sentiment Analysis',
name: 'sentimentAnalysis',
icon: 'node:sentiment-analysis',
iconColor: 'black',
group: ['transform'],
version: [1, 1.1],
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: n8n_workflow_1.NodeConnectionTypes.Main },
{
displayName: 'Model',
maxConnections: 1,
type: n8n_workflow_1.NodeConnectionTypes.AiLanguageModel,
required: true,
},
],
outputs: `={{(${configuredOutputs})($parameter, "${DEFAULT_CATEGORIES}")}}`,
builderHint: {
inputs: {
ai_languageModel: { required: true },
},
},
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)',
},
(0, ai_utilities_1.getBatchingOptionFields)({
show: {
'@version': [{ _cnd: { gte: 1.1 } }],
},
}),
],
},
],
};
}
async execute() {
const items = this.getInputData();
const llm = (await this.getInputConnectionData(n8n_workflow_1.NodeConnectionTypes.AiLanguageModel, 0));
const returnData = [];
const batchSize = this.getNodeParameter('options.batching.batchSize', 0, 5);
const delayBetweenBatches = this.getNodeParameter('options.batching.delayBetweenBatches', 0, 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 sentimentCategories = this.getNodeParameter('options.categories', itemIndex, DEFAULT_CATEGORIES);
const categories = sentimentCategories
.split(',')
.map((cat) => cat.trim())
.filter(Boolean);
if (categories.length === 0) {
return {
result: null,
itemIndex,
error: new n8n_workflow_1.NodeOperationError(this.getNode(), 'No sentiment categories provided', {
itemIndex,
}),
};
}
if (returnData.length === 0) {
returnData.push(...Array.from({ length: categories.length }, () => []));
}
const options = this.getNodeParameter('options', itemIndex, {});
const schema = zod_1.z.object({
sentiment: zod_1.z.enum(categories),
strength: zod_1.z
.number()
.min(0)
.max(1)
.describe('Strength score for sentiment in relation to the category'),
confidence: zod_1.z.number().min(0).max(1),
});
const structuredParser = output_parsers_1.StructuredOutputParser.fromZodSchema(schema);
const parser = options.enableAutoFixing
? output_parsers_1.OutputFixingParser.fromLLM(llm, structuredParser)
: structuredParser;
const escapedTemplate = (options.systemPromptTemplate ?? DEFAULT_SYSTEM_PROMPT_TEMPLATE)
.replace(/[{}]/g, (match) => match + match)
.replaceAll('{{categories}}', '{categories}');
const systemPromptTemplate = prompts_1.SystemMessagePromptTemplate.fromTemplate(`${escapedTemplate}
{format_instructions}`);
const input = this.getNodeParameter('inputText', itemIndex);
const inputPrompt = new messages_1.HumanMessage(input);
const messages = [
await systemPromptTemplate.format({
categories: sentimentCategories,
format_instructions: parser.getFormatInstructions(),
}),
inputPrompt,
];
const prompt = prompts_1.ChatPromptTemplate.fromMessages(messages);
const chain = prompt.pipe(llm).pipe(parser).withConfig((0, tracing_1.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[itemIndex] };
const sentimentAnalysis = {
category: output.sentiment,
};
if (options.includeDetailedResults) {
sentimentAnalysis.strength = output.strength;
sentimentAnalysis.confidence = output.confidence;
}
resultItem.json = {
...resultItem.json,
sentimentAnalysis,
};
return {
result: {
resultItem,
sentimentIndex,
},
itemIndex,
};
}
return {
result: {},
itemIndex,
};
}
catch (error) {
return {
result: null,
itemIndex,
error: new n8n_workflow_1.NodeOperationError(this.getNode(), 'Error during parsing of LLM output, please check your LLM model and configuration', {
itemIndex,
}),
};
}
});
const batchResults = await Promise.all(batchPromises);
batchResults.forEach(({ result, itemIndex, error }) => {
if (error) {
if (this.continueOnFail()) {
const executionErrorData = this.helpers.constructExecutionMetaData(this.helpers.returnJsonArray({ error: error.message }), { itemData: { item: itemIndex } });
returnData[0].push(...executionErrorData);
return;
}
else {
throw error;
}
}
else if (result.resultItem && result.sentimentIndex !== -1) {
const sentimentIndex = result.sentimentIndex;
const resultItem = result.resultItem;
returnData[sentimentIndex].push(resultItem);
}
});
if (i + batchSize < items.length && delayBetweenBatches > 0) {
await (0, n8n_workflow_1.sleep)(delayBetweenBatches);
}
}
}
else {
for (let i = 0; i < items.length; i++) {
try {
const sentimentCategories = this.getNodeParameter('options.categories', i, DEFAULT_CATEGORIES);
const categories = sentimentCategories
.split(',')
.map((cat) => cat.trim())
.filter(Boolean);
if (categories.length === 0) {
throw new n8n_workflow_1.NodeOperationError(this.getNode(), 'No sentiment categories provided', {
itemIndex: i,
});
}
if (returnData.length === 0) {
returnData.push(...Array.from({ length: categories.length }, () => []));
}
const options = this.getNodeParameter('options', i, {});
const schema = zod_1.z.object({
sentiment: zod_1.z.enum(categories),
strength: zod_1.z
.number()
.min(0)
.max(1)
.describe('Strength score for sentiment in relation to the category'),
confidence: zod_1.z.number().min(0).max(1),
});
const structuredParser = output_parsers_1.StructuredOutputParser.fromZodSchema(schema);
const parser = options.enableAutoFixing
? output_parsers_1.OutputFixingParser.fromLLM(llm, structuredParser)
: structuredParser;
const escapedTemplate = (options.systemPromptTemplate ?? DEFAULT_SYSTEM_PROMPT_TEMPLATE)
.replace(/[{}]/g, (match) => match + match)
.replaceAll('{{categories}}', '{categories}');
const systemPromptTemplate = prompts_1.SystemMessagePromptTemplate.fromTemplate(`${escapedTemplate}
{format_instructions}`);
const input = this.getNodeParameter('inputText', i);
const inputPrompt = new messages_1.HumanMessage(input);
const messages = [
await systemPromptTemplate.format({
categories: sentimentCategories,
format_instructions: parser.getFormatInstructions(),
}),
inputPrompt,
];
const prompt = prompts_1.ChatPromptTemplate.fromMessages(messages);
const chain = prompt.pipe(llm).pipe(parser).withConfig((0, tracing_1.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 = {
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 n8n_workflow_1.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;
}
}
exports.SentimentAnalysis = SentimentAnalysis;
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