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feat: Optimise langchain calls in batching mode (#15243)

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This commit is contained in:
Benjamin Schroth
2025-05-13 13:58:38 +02:00
committed by GitHub
parent 8591c2e0d1
commit ff156930c5
35 changed files with 2946 additions and 1171 deletions
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163
packages/@n8n/nodes-langchain/nodes/chains/ChainLLM/ChainLlm.node.ts
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@@ -1,23 +1,16 @@
import type { BaseLanguageModel } from '@langchain/core/language_models/base';
import type {
IExecuteFunctions,
INodeExecutionData,
INodeType,
INodeTypeDescription,
} from 'n8n-workflow';
import { NodeApiError, NodeConnectionTypes, NodeOperationError } from 'n8n-workflow';
import { NodeApiError, NodeConnectionTypes, NodeOperationError, sleep } from 'n8n-workflow';
import { getPromptInputByType } from '@utils/helpers';
import { getOptionalOutputParser } from '@utils/output_parsers/N8nOutputParser';
// Import from centralized module
import {
executeChain,
formatResponse,
getInputs,
nodeProperties,
type MessageTemplate,
} from './methods';
import { formatResponse, getInputs, nodeProperties } from './methods';
import { processItem } from './methods/processItem';
import {
getCustomErrorMessage as getCustomOpenAiErrorMessage,
isOpenAiError,
@@ -34,7 +27,7 @@ export class ChainLlm implements INodeType {
icon: 'fa:link',
iconColor: 'black',
group: ['transform'],
version: [1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
version: [1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7],
description: 'A simple chain to prompt a large language model',
defaults: {
name: 'Basic LLM Chain',
@@ -67,83 +60,97 @@ export class ChainLlm implements INodeType {
this.logger.debug('Executing Basic LLM Chain');
const items = this.getInputData();
const returnData: INodeExecutionData[] = [];
const outputParser = await getOptionalOutputParser(this);
// If the node version is 1.6(and LLM is using `response_format: json_object`) or higher or an output parser is configured,
// we unwrap the response and return the object directly as JSON
const shouldUnwrapObjects = this.getNode().typeVersion >= 1.6 || !!outputParser;
// Process each input item
for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
try {
// Get the language model
const llm = (await this.getInputConnectionData(
NodeConnectionTypes.AiLanguageModel,
0,
)) as BaseLanguageModel;
const batchSize = this.getNodeParameter('batching.batchSize', 0, 5) as number;
const delayBetweenBatches = this.getNodeParameter(
'batching.delayBetweenBatches',
0,
0,
) as number;
// Get output parser if configured
const outputParser = await getOptionalOutputParser(this);
// Get user prompt based on node version
let prompt: string;
if (this.getNode().typeVersion <= 1.3) {
prompt = this.getNodeParameter('prompt', itemIndex) as string;
} else {
prompt = getPromptInputByType({
ctx: this,
i: itemIndex,
inputKey: 'text',
promptTypeKey: 'promptType',
});
}
// Validate prompt
if (prompt === undefined) {
throw new NodeOperationError(this.getNode(), "The 'prompt' parameter is empty.");
}
// Get chat messages if configured
const messages = this.getNodeParameter(
'messages.messageValues',
itemIndex,
[],
) as MessageTemplate[];
// Execute the chain
const responses = await executeChain({
context: this,
itemIndex,
query: prompt,
llm,
outputParser,
messages,
if (this.getNode().typeVersion >= 1.7 && batchSize > 1) {
// Process items in batches
for (let i = 0; i < items.length; i += batchSize) {
const batch = items.slice(i, i + batchSize);
const batchPromises = batch.map(async (_item, batchItemIndex) => {
return await processItem(this, i + batchItemIndex);
});
// If the node version is 1.6(and LLM is using `response_format: json_object`) or higher or an output parser is configured,
// we unwrap the response and return the object directly as JSON
const shouldUnwrapObjects = this.getNode().typeVersion >= 1.6 || !!outputParser;
// Process each response and add to return data
responses.forEach((response) => {
returnData.push({
json: formatResponse(response, shouldUnwrapObjects),
const batchResults = await Promise.allSettled(batchPromises);
batchResults.forEach((promiseResult, batchItemIndex) => {
const itemIndex = i + batchItemIndex;
if (promiseResult.status === 'rejected') {
const error = promiseResult.reason as Error;
// Handle OpenAI specific rate limit errors
if (error instanceof NodeApiError && isOpenAiError(error.cause)) {
const openAiErrorCode: string | undefined = (error.cause as any).error?.code;
if (openAiErrorCode) {
const customMessage = getCustomOpenAiErrorMessage(openAiErrorCode);
if (customMessage) {
error.message = customMessage;
}
}
}
if (this.continueOnFail()) {
returnData.push({
json: { error: error.message },
pairedItem: { item: itemIndex },
});
return;
}
throw new NodeOperationError(this.getNode(), error);
}
const responses = promiseResult.value;
responses.forEach((response: unknown) => {
returnData.push({
json: formatResponse(response, shouldUnwrapObjects),
});
});
});
} catch (error) {
// Handle OpenAI specific rate limit errors
if (error instanceof NodeApiError && isOpenAiError(error.cause)) {
const openAiErrorCode: string | undefined = (error.cause as any).error?.code;
if (openAiErrorCode) {
const customMessage = getCustomOpenAiErrorMessage(openAiErrorCode);
if (customMessage) {
error.message = customMessage;
if (i + batchSize < items.length && delayBetweenBatches > 0) {
await sleep(delayBetweenBatches);
}
}
} else {
// Process each input item
for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
try {
const responses = await processItem(this, itemIndex);
// Process each response and add to return data
responses.forEach((response) => {
returnData.push({
json: formatResponse(response, shouldUnwrapObjects),
});
});
} catch (error) {
// Handle OpenAI specific rate limit errors
if (error instanceof NodeApiError && isOpenAiError(error.cause)) {
const openAiErrorCode: string | undefined = (error.cause as any).error?.code;
if (openAiErrorCode) {
const customMessage = getCustomOpenAiErrorMessage(openAiErrorCode);
if (customMessage) {
error.message = customMessage;
}
}
}
}
// Continue on failure if configured
if (this.continueOnFail()) {
returnData.push({ json: { error: error.message }, pairedItem: { item: itemIndex } });
continue;
}
// Continue on failure if configured
if (this.continueOnFail()) {
returnData.push({ json: { error: error.message }, pairedItem: { item: itemIndex } });
continue;
}
throw error;
throw error;
}
}
}