@n8n/n8n-nodes-langchain
Version:
215 lines • 9.17 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.ChainSummarizationV1 = void 0;
const prompts_1 = require("@langchain/core/prompts");
const chains_1 = require("@langchain/classic/chains");
const n8n_workflow_1 = require("n8n-workflow");
const ai_utilities_1 = require("@n8n/ai-utilities");
const prompt_1 = require("../prompt");
class ChainSummarizationV1 {
constructor(baseDescription) {
this.description = {
...baseDescription,
version: 1,
defaults: {
name: 'Summarization Chain',
color: '#909298',
},
inputs: [
n8n_workflow_1.NodeConnectionTypes.Main,
{
displayName: 'Model',
maxConnections: 1,
type: n8n_workflow_1.NodeConnectionTypes.AiLanguageModel,
required: true,
},
{
displayName: 'Document',
maxConnections: 1,
type: n8n_workflow_1.NodeConnectionTypes.AiDocument,
required: true,
},
],
outputs: [n8n_workflow_1.NodeConnectionTypes.Main],
credentials: [],
properties: [
(0, ai_utilities_1.getTemplateNoticeField)(1951),
{
displayName: 'Type',
name: 'type',
type: 'options',
description: 'The type of summarization to run',
default: 'map_reduce',
options: [
{
name: 'Map Reduce (Recommended)',
value: 'map_reduce',
description: 'Summarize each document (or chunk) individually, then summarize those summaries',
},
{
name: 'Refine',
value: 'refine',
description: 'Summarize the first document (or chunk). Then update that summary based on the next document (or chunk), and repeat.',
},
{
name: 'Stuff',
value: 'stuff',
description: 'Pass all documents (or chunks) at once. Ideal for small datasets.',
},
],
},
{
displayName: 'Options',
name: 'options',
type: 'collection',
default: {},
placeholder: 'Add Option',
options: [
{
displayName: 'Final Prompt to Combine',
name: 'combineMapPrompt',
type: 'string',
hint: 'The prompt to combine individual summaries',
displayOptions: {
show: {
'/type': ['map_reduce'],
},
},
default: prompt_1.DEFAULT_PROMPT_TEMPLATE,
typeOptions: {
rows: 6,
},
},
{
displayName: 'Individual Summary Prompt',
name: 'prompt',
type: 'string',
default: prompt_1.DEFAULT_PROMPT_TEMPLATE,
hint: 'The prompt to summarize an individual document (or chunk)',
displayOptions: {
show: {
'/type': ['map_reduce'],
},
},
typeOptions: {
rows: 6,
},
},
{
displayName: 'Prompt',
name: 'prompt',
type: 'string',
default: prompt_1.DEFAULT_PROMPT_TEMPLATE,
displayOptions: {
show: {
'/type': ['stuff'],
},
},
typeOptions: {
rows: 6,
},
},
{
displayName: 'Subsequent (Refine) Prompt',
name: 'refinePrompt',
type: 'string',
displayOptions: {
show: {
'/type': ['refine'],
},
},
default: prompt_1.REFINE_PROMPT_TEMPLATE,
hint: 'The prompt to refine the summary based on the next document (or chunk)',
typeOptions: {
rows: 6,
},
},
{
displayName: 'Initial Prompt',
name: 'refineQuestionPrompt',
type: 'string',
displayOptions: {
show: {
'/type': ['refine'],
},
},
default: prompt_1.DEFAULT_PROMPT_TEMPLATE,
hint: 'The prompt for the first document (or chunk)',
typeOptions: {
rows: 6,
},
},
],
},
],
};
}
async execute() {
this.logger.debug('Executing Vector Store QA Chain');
const type = this.getNodeParameter('type', 0);
const model = (await this.getInputConnectionData(n8n_workflow_1.NodeConnectionTypes.AiLanguageModel, 0));
const documentInput = (await this.getInputConnectionData(n8n_workflow_1.NodeConnectionTypes.AiDocument, 0));
const options = this.getNodeParameter('options', 0, {});
const chainArgs = {
type,
};
if (type === 'map_reduce') {
const mapReduceArgs = chainArgs;
if (options.combineMapPrompt) {
mapReduceArgs.combineMapPrompt = new prompts_1.PromptTemplate({
template: options.combineMapPrompt,
inputVariables: ['text'],
});
}
if (options.prompt) {
mapReduceArgs.combinePrompt = new prompts_1.PromptTemplate({
template: options.prompt,
inputVariables: ['text'],
});
}
}
if (type === 'stuff') {
const stuffArgs = chainArgs;
if (options.prompt) {
stuffArgs.prompt = new prompts_1.PromptTemplate({
template: options.prompt,
inputVariables: ['text'],
});
}
}
if (type === 'refine') {
const refineArgs = chainArgs;
if (options.refinePrompt) {
refineArgs.refinePrompt = new prompts_1.PromptTemplate({
template: options.refinePrompt,
inputVariables: ['existing_answer', 'text'],
});
}
if (options.refineQuestionPrompt) {
refineArgs.questionPrompt = new prompts_1.PromptTemplate({
template: options.refineQuestionPrompt,
inputVariables: ['text'],
});
}
}
const chain = (0, chains_1.loadSummarizationChain)(model, chainArgs);
const items = this.getInputData();
const returnData = [];
for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
let processedDocuments;
if (documentInput instanceof ai_utilities_1.N8nJsonLoader || documentInput instanceof ai_utilities_1.N8nBinaryLoader) {
processedDocuments = await documentInput.processItem(items[itemIndex], itemIndex);
}
else {
processedDocuments = documentInput;
}
const response = await chain.call({
input_documents: processedDocuments,
});
returnData.push({ json: { response } });
}
return [returnData];
}
}
exports.ChainSummarizationV1 = ChainSummarizationV1;
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