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feat: Optimize langchain calls in batching mode (#15011)

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Co-authored-by: कारतोफ्फेलस्क्रिप्ट™ <aditya@netroy.in>
This commit is contained in:
Benjamin Schroth
2025-05-02 17:09:31 +02:00
committed by GitHub
parent a4290dcb78
commit f3e29d25ed
12 changed files with 632 additions and 205 deletions
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139
packages/@n8n/nodes-langchain/nodes/chains/TextClassifier/TextClassifier.node.ts
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@@ -2,7 +2,7 @@ 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 { NodeOperationError, NodeConnectionTypes } from 'n8n-workflow';
import { NodeOperationError, NodeConnectionTypes, sleep } from 'n8n-workflow';
import type {
IDataObject,
IExecuteFunctions,
@@ -158,6 +158,31 @@ export class TextClassifier implements INodeType {
description:
'Whether to enable auto-fixing (may trigger an additional LLM call if output is broken)',
},
{
displayName: 'Batch Processing',
name: 'batching',
type: 'collection',
description: 'Batch processing options for rate limiting',
default: {},
options: [
{
displayName: 'Batch Size',
name: 'batchSize',
default: 100,
type: 'number',
description:
'How many items to process in parallel. This is useful for rate limiting.',
},
{
displayName: 'Delay Between Batches',
name: 'delayBetweenBatches',
default: 0,
type: 'number',
description:
'Delay in milliseconds between batches. This is useful for rate limiting.',
},
],
},
],
},
],
@@ -165,6 +190,13 @@ export class TextClassifier implements INodeType {
async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
const items = this.getInputData();
const { batchSize, delayBetweenBatches } = this.getNodeParameter('options.batching', 0, {
batchSize: 100,
delayBetweenBatches: 0,
}) as {
batchSize: number;
delayBetweenBatches: number;
};
const llm = (await this.getInputConnectionData(
NodeConnectionTypes.AiLanguageModel,
@@ -223,68 +255,79 @@ export class TextClassifier implements INodeType {
{ length: categories.length + (fallback === 'other' ? 1 : 0) },
(_) => [],
);
for (let itemIdx = 0; itemIdx < items.length; itemIdx++) {
const item = items[itemIdx];
item.pairedItem = { item: itemIdx };
const input = this.getNodeParameter('inputText', itemIdx) as string;
if (input === undefined || input === null) {
if (this.continueOnFail()) {
returnData[0].push({
json: { error: 'Text to classify is not defined' },
pairedItem: { item: itemIdx },
});
continue;
} else {
for (let i = 0; i < items.length; i += batchSize) {
const batch = items.slice(i, i + batchSize);
const batchPromises = batch.map(async (_item, batchItemIndex) => {
const itemIdx = i + batchItemIndex;
const item = items[itemIdx];
item.pairedItem = { item: itemIdx };
const input = this.getNodeParameter('inputText', itemIdx) as string;
if (input === undefined || input === null) {
throw new NodeOperationError(
this.getNode(),
`Text to classify for item ${itemIdx} is not defined`,
);
}
}
const inputPrompt = new HumanMessage(input);
const inputPrompt = new HumanMessage(input);
const systemPromptTemplateOpt = this.getNodeParameter(
'options.systemPromptTemplate',
itemIdx,
SYSTEM_PROMPT_TEMPLATE,
) as string;
const systemPromptTemplate = SystemMessagePromptTemplate.fromTemplate(
`${systemPromptTemplateOpt ?? SYSTEM_PROMPT_TEMPLATE}
{format_instructions}
${multiClassPrompt}
${fallbackPrompt}`,
);
const systemPromptTemplateOpt = this.getNodeParameter(
'options.systemPromptTemplate',
itemIdx,
SYSTEM_PROMPT_TEMPLATE,
) as string;
const systemPromptTemplate = SystemMessagePromptTemplate.fromTemplate(
`${systemPromptTemplateOpt ?? SYSTEM_PROMPT_TEMPLATE}
{format_instructions}
${multiClassPrompt}
${fallbackPrompt}`,
);
const messages = [
await systemPromptTemplate.format({
categories: categories.map((cat) => cat.category).join(', '),
format_instructions: parser.getFormatInstructions(),
}),
inputPrompt,
];
const prompt = ChatPromptTemplate.fromMessages(messages);
const chain = prompt.pipe(llm).pipe(parser).withConfig(getTracingConfig(this));
const messages = [
await systemPromptTemplate.format({
categories: categories.map((cat) => cat.category).join(', '),
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);
return await chain.invoke(messages);
});
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: itemIdx },
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);
});
continue;
if (fallback === 'other' && output.fallback) returnData[returnData.length - 1].push(item);
}
});
throw error;
// Add delay between batches if not the last batch
if (i + batchSize < items.length && delayBetweenBatches > 0) {
await sleep(delayBetweenBatches);
}
}