mirror of
https://github.com/Abdulazizzn/n8n-enterprise-unlocked.git
synced 2025-12-17 10:02:05 +00:00
487 lines
14 KiB
TypeScript
487 lines
14 KiB
TypeScript
import type { Document } from '@langchain/core/documents';
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import type { BaseLanguageModel } from '@langchain/core/language_models/base';
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import type { ChainValues } from '@langchain/core/utils/types';
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import type { TextSplitter } from '@langchain/textsplitters';
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import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
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import { loadSummarizationChain } from 'langchain/chains';
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import type {
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INodeTypeBaseDescription,
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IExecuteFunctions,
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INodeExecutionData,
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INodeType,
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INodeTypeDescription,
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IDataObject,
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INodeInputConfiguration,
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} from 'n8n-workflow';
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import { NodeConnectionTypes, sleep } from 'n8n-workflow';
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import { N8nBinaryLoader } from '@utils/N8nBinaryLoader';
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import { N8nJsonLoader } from '@utils/N8nJsonLoader';
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import { getTemplateNoticeField } from '@utils/sharedFields';
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import { getTracingConfig } from '@utils/tracing';
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import { getChainPromptsArgs } from '../helpers';
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import { REFINE_PROMPT_TEMPLATE, DEFAULT_PROMPT_TEMPLATE } from '../prompt';
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function getInputs(parameters: IDataObject) {
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const chunkingMode = parameters?.chunkingMode;
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const operationMode = parameters?.operationMode;
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const inputs: INodeInputConfiguration[] = [
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{ displayName: '', type: 'main' },
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{
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displayName: 'Model',
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maxConnections: 1,
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type: 'ai_languageModel',
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required: true,
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},
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];
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if (operationMode === 'documentLoader') {
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inputs.push({
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displayName: 'Document',
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type: 'ai_document',
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required: true,
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maxConnections: 1,
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});
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return inputs;
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}
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if (chunkingMode === 'advanced') {
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inputs.push({
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displayName: 'Text Splitter',
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type: 'ai_textSplitter',
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required: false,
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maxConnections: 1,
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});
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return inputs;
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}
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return inputs;
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}
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export class ChainSummarizationV2 implements INodeType {
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description: INodeTypeDescription;
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constructor(baseDescription: INodeTypeBaseDescription) {
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this.description = {
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...baseDescription,
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version: [2],
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defaults: {
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name: 'Summarization Chain',
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color: '#909298',
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},
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// eslint-disable-next-line n8n-nodes-base/node-class-description-inputs-wrong-regular-node
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inputs: `={{ ((parameter) => { ${getInputs.toString()}; return getInputs(parameter) })($parameter) }}`,
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outputs: [NodeConnectionTypes.Main],
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credentials: [],
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properties: [
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getTemplateNoticeField(1951),
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{
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displayName: 'Data to Summarize',
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name: 'operationMode',
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noDataExpression: true,
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type: 'options',
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description: 'How to pass data into the summarization chain',
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default: 'nodeInputJson',
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options: [
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{
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name: 'Use Node Input (JSON)',
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value: 'nodeInputJson',
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description: 'Summarize the JSON data coming into this node from the previous one',
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},
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{
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name: 'Use Node Input (Binary)',
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value: 'nodeInputBinary',
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description: 'Summarize the binary data coming into this node from the previous one',
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},
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{
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name: 'Use Document Loader',
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value: 'documentLoader',
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description: 'Use a loader sub-node with more configuration options',
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},
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],
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},
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{
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displayName: 'Chunking Strategy',
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name: 'chunkingMode',
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noDataExpression: true,
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type: 'options',
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description: 'Chunk splitting strategy',
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default: 'simple',
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options: [
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{
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name: 'Simple (Define Below)',
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value: 'simple',
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},
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{
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name: 'Advanced',
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value: 'advanced',
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description: 'Use a splitter sub-node with more configuration options',
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},
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],
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displayOptions: {
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show: {
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'/operationMode': ['nodeInputJson', 'nodeInputBinary'],
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},
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},
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},
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{
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displayName: 'Characters Per Chunk',
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name: 'chunkSize',
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description:
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'Controls the max size (in terms of number of characters) of the final document chunk',
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type: 'number',
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default: 1000,
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displayOptions: {
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show: {
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'/chunkingMode': ['simple'],
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},
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},
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},
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{
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displayName: 'Chunk Overlap (Characters)',
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name: 'chunkOverlap',
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type: 'number',
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description: 'Specifies how much characters overlap there should be between chunks',
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default: 200,
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displayOptions: {
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show: {
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'/chunkingMode': ['simple'],
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},
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},
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},
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{
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displayName: 'Options',
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name: 'options',
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type: 'collection',
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default: {},
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placeholder: 'Add Option',
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options: [
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{
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displayName: 'Input Data Field Name',
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name: 'binaryDataKey',
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type: 'string',
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default: 'data',
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description:
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'The name of the field in the agent or chain’s input that contains the binary file to be processed',
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displayOptions: {
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show: {
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'/operationMode': ['nodeInputBinary'],
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},
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},
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},
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{
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displayName: 'Summarization Method and Prompts',
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name: 'summarizationMethodAndPrompts',
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type: 'fixedCollection',
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default: {
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values: {
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summarizationMethod: 'map_reduce',
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prompt: DEFAULT_PROMPT_TEMPLATE,
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combineMapPrompt: DEFAULT_PROMPT_TEMPLATE,
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},
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},
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placeholder: 'Add Option',
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typeOptions: {},
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options: [
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{
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name: 'values',
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displayName: 'Values',
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values: [
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{
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displayName: 'Summarization Method',
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name: 'summarizationMethod',
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type: 'options',
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description: 'The type of summarization to run',
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default: 'map_reduce',
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options: [
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{
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name: 'Map Reduce (Recommended)',
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value: 'map_reduce',
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description:
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'Summarize each document (or chunk) individually, then summarize those summaries',
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},
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{
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name: 'Refine',
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value: 'refine',
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description:
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'Summarize the first document (or chunk). Then update that summary based on the next document (or chunk), and repeat.',
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},
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{
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name: 'Stuff',
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value: 'stuff',
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description:
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'Pass all documents (or chunks) at once. Ideal for small datasets.',
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},
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],
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},
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{
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displayName: 'Individual Summary Prompt',
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name: 'combineMapPrompt',
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type: 'string',
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hint: 'The prompt to summarize an individual document (or chunk)',
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displayOptions: {
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hide: {
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'/options.summarizationMethodAndPrompts.values.summarizationMethod': [
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'stuff',
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'refine',
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],
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},
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},
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default: DEFAULT_PROMPT_TEMPLATE,
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typeOptions: {
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rows: 9,
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},
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},
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{
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displayName: 'Final Prompt to Combine',
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name: 'prompt',
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type: 'string',
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default: DEFAULT_PROMPT_TEMPLATE,
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hint: 'The prompt to combine individual summaries',
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displayOptions: {
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hide: {
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'/options.summarizationMethodAndPrompts.values.summarizationMethod': [
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'stuff',
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'refine',
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],
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},
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},
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typeOptions: {
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rows: 9,
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},
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},
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{
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displayName: 'Prompt',
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name: 'prompt',
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type: 'string',
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default: DEFAULT_PROMPT_TEMPLATE,
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displayOptions: {
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hide: {
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'/options.summarizationMethodAndPrompts.values.summarizationMethod': [
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'refine',
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'map_reduce',
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],
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},
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},
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typeOptions: {
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rows: 9,
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},
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},
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{
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displayName: 'Subsequent (Refine) Prompt',
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name: 'refinePrompt',
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type: 'string',
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displayOptions: {
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hide: {
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'/options.summarizationMethodAndPrompts.values.summarizationMethod': [
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'stuff',
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'map_reduce',
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],
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},
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},
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default: REFINE_PROMPT_TEMPLATE,
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hint: 'The prompt to refine the summary based on the next document (or chunk)',
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typeOptions: {
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rows: 9,
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},
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},
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{
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displayName: 'Initial Prompt',
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name: 'refineQuestionPrompt',
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type: 'string',
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displayOptions: {
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hide: {
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'/options.summarizationMethodAndPrompts.values.summarizationMethod': [
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'stuff',
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'map_reduce',
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],
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},
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},
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default: DEFAULT_PROMPT_TEMPLATE,
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hint: 'The prompt for the first document (or chunk)',
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typeOptions: {
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rows: 9,
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},
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},
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],
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},
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],
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},
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{
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displayName: 'Batch Processing',
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name: 'batching',
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type: 'collection',
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description: 'Batch processing options for rate limiting',
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default: {},
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options: [
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{
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displayName: 'Batch Size',
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name: 'batchSize',
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default: 100,
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type: 'number',
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description:
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'How many items to process in parallel. This is useful for rate limiting.',
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},
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{
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displayName: 'Delay Between Batches',
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name: 'delayBetweenBatches',
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default: 0,
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type: 'number',
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description:
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'Delay in milliseconds between batches. This is useful for rate limiting.',
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},
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],
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},
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],
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},
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],
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};
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}
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async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
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this.logger.debug('Executing Summarization Chain V2');
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const operationMode = this.getNodeParameter('operationMode', 0, 'nodeInputJson') as
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| 'nodeInputJson'
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| 'nodeInputBinary'
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| 'documentLoader';
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const chunkingMode = this.getNodeParameter('chunkingMode', 0, 'simple') as
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| 'simple'
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| 'advanced';
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const items = this.getInputData();
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const returnData: INodeExecutionData[] = [];
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const { batchSize, delayBetweenBatches } = this.getNodeParameter('options.batching', 0, {
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batchSize: 100,
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delayBetweenBatches: 0,
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}) as {
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batchSize: number;
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delayBetweenBatches: number;
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};
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for (let i = 0; i < items.length; i += batchSize) {
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const batch = items.slice(i, i + batchSize);
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const batchPromises = batch.map(async (_item, batchIndex) => {
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const itemIndex = i + batchIndex;
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const model = (await this.getInputConnectionData(
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NodeConnectionTypes.AiLanguageModel,
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0,
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)) as BaseLanguageModel;
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const summarizationMethodAndPrompts = this.getNodeParameter(
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'options.summarizationMethodAndPrompts.values',
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itemIndex,
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{},
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) as {
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prompt?: string;
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refineQuestionPrompt?: string;
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refinePrompt?: string;
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summarizationMethod: 'map_reduce' | 'stuff' | 'refine';
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combineMapPrompt?: string;
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};
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const chainArgs = getChainPromptsArgs(
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summarizationMethodAndPrompts.summarizationMethod ?? 'map_reduce',
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summarizationMethodAndPrompts,
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);
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const chain = loadSummarizationChain(model, chainArgs);
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const item = items[itemIndex];
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let processedDocuments: Document[];
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let output: ChainValues = {};
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// Use dedicated document loader input to load documents
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if (operationMode === 'documentLoader') {
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const documentInput = (await this.getInputConnectionData(
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NodeConnectionTypes.AiDocument,
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0,
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)) as N8nJsonLoader | Array<Document<Record<string, unknown>>>;
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const isN8nLoader =
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documentInput instanceof N8nJsonLoader || documentInput instanceof N8nBinaryLoader;
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processedDocuments = isN8nLoader
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? await documentInput.processItem(item, itemIndex)
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: documentInput;
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output = await chain.withConfig(getTracingConfig(this)).invoke({
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input_documents: processedDocuments,
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});
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}
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// Take the input and use binary or json loader
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if (['nodeInputJson', 'nodeInputBinary'].includes(operationMode)) {
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let textSplitter: TextSplitter | undefined;
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switch (chunkingMode) {
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// In simple mode we use recursive character splitter with default settings
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case 'simple':
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const chunkSize = this.getNodeParameter('chunkSize', itemIndex, 1000) as number;
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const chunkOverlap = this.getNodeParameter('chunkOverlap', itemIndex, 200) as number;
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textSplitter = new RecursiveCharacterTextSplitter({ chunkOverlap, chunkSize });
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break;
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// In advanced mode user can connect text splitter node so we just retrieve it
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case 'advanced':
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textSplitter = (await this.getInputConnectionData(
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NodeConnectionTypes.AiTextSplitter,
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0,
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)) as TextSplitter | undefined;
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break;
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default:
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break;
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}
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let processor: N8nJsonLoader | N8nBinaryLoader;
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if (operationMode === 'nodeInputBinary') {
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const binaryDataKey = this.getNodeParameter(
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'options.binaryDataKey',
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itemIndex,
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'data',
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) as string;
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processor = new N8nBinaryLoader(this, 'options.', binaryDataKey, textSplitter);
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} else {
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processor = new N8nJsonLoader(this, 'options.', textSplitter);
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}
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const processedItem = await processor.processItem(item, itemIndex);
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output = await chain.invoke(
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{
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input_documents: processedItem,
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},
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{ signal: this.getExecutionCancelSignal() },
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);
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}
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return output;
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});
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const batchResults = await Promise.allSettled(batchPromises);
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batchResults.forEach((response, index) => {
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if (response.status === 'rejected') {
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const error = response.reason as Error;
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if (this.continueOnFail()) {
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returnData.push({
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json: { error: error.message },
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pairedItem: { item: i + index },
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});
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} else {
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throw error;
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}
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} else {
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const output = response.value;
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returnData.push({ json: { output } });
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}
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});
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// Add delay between batches if not the last batch
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if (i + batchSize < items.length && delayBetweenBatches > 0) {
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await sleep(delayBetweenBatches);
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}
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}
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return [returnData];
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}
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}
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