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

packages/@n8n/nodes-langchain/nodes/chains/ChainRetrievalQA/processItem.ts

@@ -0,0 +1,100 @@
+import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+import {
+	ChatPromptTemplate,
+	HumanMessagePromptTemplate,
+	PromptTemplate,
+	SystemMessagePromptTemplate,
+} 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 { type IExecuteFunctions, NodeConnectionTypes, NodeOperationError } from 'n8n-workflow';
+
+import { getPromptInputByType, isChatInstance } from '@utils/helpers';
+import { getTracingConfig } from '@utils/tracing';
+
+import { INPUT_TEMPLATE_KEY, LEGACY_INPUT_TEMPLATE_KEY, SYSTEM_PROMPT_TEMPLATE } from './constants';
+
+export const processItem = async (
+	ctx: IExecuteFunctions,
+	itemIndex: number,
+): Promise<Record<string, unknown>> => {
+	const model = (await ctx.getInputConnectionData(
+		NodeConnectionTypes.AiLanguageModel,
+		0,
+	)) as BaseLanguageModel;
+
+	const retriever = (await ctx.getInputConnectionData(
+		NodeConnectionTypes.AiRetriever,
+		0,
+	)) as BaseRetriever;
+
+	let query;
+
+	if (ctx.getNode().typeVersion <= 1.2) {
+		query = ctx.getNodeParameter('query', itemIndex) as string;
+	} else {
+		query = getPromptInputByType({
+			ctx,
+			i: itemIndex,
+			inputKey: 'text',
+			promptTypeKey: 'promptType',
+		});
+	}
+
+	if (query === undefined) {
+		throw new NodeOperationError(ctx.getNode(), 'The ‘query‘ parameter is empty.');
+	}
+
+	const options = ctx.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 (ctx.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,
+	});
+
+	// Create the retrieval chain that handles the retrieval and then passes to the combine docs chain
+	const retrievalChain = await createRetrievalChain({
+		combineDocsChain,
+		retriever,
+	});
+
+	// Execute the chain with tracing config
+	const tracingConfig = getTracingConfig(ctx);
+	return await retrievalChain
+		.withConfig(tracingConfig)
+		.invoke({ input: query }, { signal: ctx.getExecutionCancelSignal() });
+};