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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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205
packages/@n8n/nodes-langchain/nodes/chains/ChainRetrievalQA/ChainRetrievalQa.node.ts
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@@ -1,16 +1,5 @@
import type { BaseLanguageModel } from '@langchain/core/language_models/base';
import { NodeConnectionTypes, parseErrorMetadata, sleep } from 'n8n-workflow';
import {
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
PromptTemplate,
} from '@langchain/core/prompts';
import type { BaseRetriever } from '@langchain/core/retrievers';
import { createStuffDocumentsChain } from 'langchain/chains/combine_documents';
import { createRetrievalChain } from 'langchain/chains/retrieval';
import { NodeConnectionTypes, NodeOperationError, parseErrorMetadata } from 'n8n-workflow';
import {
type INodeProperties,
type IExecuteFunctions,
type INodeExecutionData,
type INodeType,
@@ -18,28 +7,10 @@ import {
} from 'n8n-workflow';
import { promptTypeOptions, textFromPreviousNode } from '@utils/descriptions';
import { getPromptInputByType, isChatInstance } from '@utils/helpers';
import { getTemplateNoticeField } from '@utils/sharedFields';
import { getTracingConfig } from '@utils/tracing';
import { getBatchingOptionFields, getTemplateNoticeField } from '@utils/sharedFields';
const SYSTEM_PROMPT_TEMPLATE = `You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
Context: {context}`;
// Due to the refactoring in version 1.5, the variable name {question} needed to be changed to {input} in the prompt template.
const LEGACY_INPUT_TEMPLATE_KEY = 'question';
const INPUT_TEMPLATE_KEY = 'input';
const systemPromptOption: INodeProperties = {
displayName: 'System Prompt Template',
name: 'systemPromptTemplate',
type: 'string',
default: SYSTEM_PROMPT_TEMPLATE,
typeOptions: {
rows: 6,
},
};
import { INPUT_TEMPLATE_KEY, LEGACY_INPUT_TEMPLATE_KEY, systemPromptOption } from './constants';
import { processItem } from './processItem';
export class ChainRetrievalQa implements INodeType {
description: INodeTypeDescription = {
@@ -48,7 +19,7 @@ export class ChainRetrievalQa implements INodeType {
icon: 'fa:link',
iconColor: 'black',
group: ['transform'],
version: [1, 1.1, 1.2, 1.3, 1.4, 1.5],
version: [1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
description: 'Answer questions about retrieved documents',
defaults: {
name: 'Question and Answer Chain',
@@ -177,6 +148,11 @@ export class ChainRetrievalQa implements INodeType {
},
},
},
getBatchingOptionFields({
show: {
'@version': [{ _cnd: { gte: 1.6 } }],
},
}),
],
},
],
@@ -187,109 +163,78 @@ export class ChainRetrievalQa implements INodeType {
const items = this.getInputData();
const returnData: INodeExecutionData[] = [];
const batchSize = this.getNodeParameter('options.batching.batchSize', 0, 5) as number;
const delayBetweenBatches = this.getNodeParameter(
'options.batching.delayBetweenBatches',
0,
0,
) as number;
// Run for each item
for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
try {
const model = (await this.getInputConnectionData(
NodeConnectionTypes.AiLanguageModel,
0,
)) as BaseLanguageModel;
const retriever = (await this.getInputConnectionData(
NodeConnectionTypes.AiRetriever,
0,
)) as BaseRetriever;
let query;
if (this.getNode().typeVersion <= 1.2) {
query = this.getNodeParameter('query', itemIndex) as string;
} else {
query = getPromptInputByType({
ctx: this,
i: itemIndex,
inputKey: 'text',
promptTypeKey: 'promptType',
});
}
if (query === undefined) {
throw new NodeOperationError(this.getNode(), 'The ‘query‘ parameter is empty.');
}
const options = this.getNodeParameter('options', itemIndex, {}) as {
systemPromptTemplate?: string;
};
let templateText = options.systemPromptTemplate ?? SYSTEM_PROMPT_TEMPLATE;
// Replace legacy input template key for versions 1.4 and below
if (this.getNode().typeVersion < 1.5) {
templateText = templateText.replace(
`{${LEGACY_INPUT_TEMPLATE_KEY}}`,
`{${INPUT_TEMPLATE_KEY}}`,
);
}
// Create prompt template based on model type and user configuration
let promptTemplate;
if (isChatInstance(model)) {
// For chat models, create a chat prompt template with system and human messages
const messages = [
SystemMessagePromptTemplate.fromTemplate(templateText),
HumanMessagePromptTemplate.fromTemplate('{input}'),
];
promptTemplate = ChatPromptTemplate.fromMessages(messages);
} else {
// For non-chat models, create a text prompt template with Question/Answer format
const questionSuffix =
options.systemPromptTemplate === undefined ? '\n\nQuestion: {input}\nAnswer:' : '';
promptTemplate = new PromptTemplate({
template: templateText + questionSuffix,
inputVariables: ['context', 'input'],
});
}
// Create the document chain that combines the retrieved documents
const combineDocsChain = await createStuffDocumentsChain({
llm: model,
prompt: promptTemplate,
if (this.getNode().typeVersion >= 1.6 && batchSize >= 1) {
// Run 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);
});
// Create the retrieval chain that handles the retrieval and then passes to the combine docs chain
const retrievalChain = await createRetrievalChain({
combineDocsChain,
retriever,
const batchResults = await Promise.allSettled(batchPromises);
batchResults.forEach((response, index) => {
if (response.status === 'rejected') {
const error = response.reason;
if (this.continueOnFail()) {
const metadata = parseErrorMetadata(error);
returnData.push({
json: { error: error.message },
pairedItem: { item: index },
metadata,
});
return;
} else {
throw error;
}
}
const output = response.value;
const answer = output.answer as string;
if (this.getNode().typeVersion >= 1.5) {
returnData.push({ json: { response: answer } });
} else {
// Legacy format for versions 1.4 and below is { text: string }
returnData.push({ json: { response: { text: answer } } });
}
});
// Execute the chain with tracing config
const tracingConfig = getTracingConfig(this);
const response = await retrievalChain
.withConfig(tracingConfig)
.invoke({ input: query }, { signal: this.getExecutionCancelSignal() });
// Get the answer from the response
const answer: string = response.answer;
if (this.getNode().typeVersion >= 1.5) {
returnData.push({ json: { response: answer } });
} else {
// Legacy format for versions 1.4 and below is { text: string }
returnData.push({ json: { response: { text: answer } } });
}
} catch (error) {
if (this.continueOnFail()) {
const metadata = parseErrorMetadata(error);
returnData.push({
json: { error: error.message },
pairedItem: { item: itemIndex },
metadata,
});
continue;
// Add delay between batches if not the last batch
if (i + batchSize < items.length && delayBetweenBatches > 0) {
await sleep(delayBetweenBatches);
}
}
} else {
// Run for each item
for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
try {
const response = await processItem(this, itemIndex);
const answer = response.answer as string;
if (this.getNode().typeVersion >= 1.5) {
returnData.push({ json: { response: answer } });
} else {
// Legacy format for versions 1.4 and below is { text: string }
returnData.push({ json: { response: { text: answer } } });
}
} catch (error) {
if (this.continueOnFail()) {
const metadata = parseErrorMetadata(error);
returnData.push({
json: { error: error.message },
pairedItem: { item: itemIndex },
metadata,
});
continue;
}
throw error;
throw error;
}
}
}
return [returnData];