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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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84
packages/@n8n/nodes-langchain/nodes/chains/ChainLLM/ChainLlm.node.ts
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@@ -5,7 +5,7 @@ import type {
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';
@@ -67,19 +67,28 @@ export class ChainLlm implements INodeType {
this.logger.debug('Executing Basic LLM Chain');
const items = this.getInputData();
const returnData: INodeExecutionData[] = [];
const { batchSize, delayBetweenBatches } = this.getNodeParameter('batching', 0, {
batchSize: 100,
delayBetweenBatches: 0,
}) as {
batchSize: number;
delayBetweenBatches: number;
};
// Get output parser if configured
const outputParser = await getOptionalOutputParser(this);
// 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) => {
const itemIndex = i + batchItemIndex;
// 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;
// Get output parser if configured
const outputParser = await getOptionalOutputParser(this);
// Get user prompt based on node version
let prompt: string;
@@ -106,44 +115,53 @@ export class ChainLlm implements INodeType {
[],
) as MessageTemplate[];
// Execute the chain
const responses = await executeChain({
return (await executeChain({
context: this,
itemIndex,
query: prompt,
llm,
outputParser,
messages,
});
})) as object[];
});
// 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),
});
});
} 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;
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);
}
// Continue on failure if configured
if (this.continueOnFail()) {
returnData.push({ json: { error: error.message }, pairedItem: { item: itemIndex } });
continue;
}
const responses = promiseResult.value;
responses.forEach((response: object) => {
returnData.push({
json: formatResponse(response, this.getNode().typeVersion >= 1.6 || !!outputParser),
});
});
});
throw error;
if (i + batchSize < items.length && delayBetweenBatches > 0) {
await sleep(delayBetweenBatches);
}
}