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

packages/@n8n/nodes-langchain/nodes/chains/ChainLLM/methods/processItem.ts

@@ -0,0 +1,54 @@
+import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+import { type IExecuteFunctions, NodeConnectionTypes, NodeOperationError } from 'n8n-workflow';
+
+import { getPromptInputByType } from '@utils/helpers';
+import { getOptionalOutputParser } from '@utils/output_parsers/N8nOutputParser';
+
+import { executeChain } from './chainExecutor';
+import { type MessageTemplate } from './types';
+
+export const processItem = async (ctx: IExecuteFunctions, itemIndex: number) => {
+	const llm = (await ctx.getInputConnectionData(
+		NodeConnectionTypes.AiLanguageModel,
+		0,
+	)) as BaseLanguageModel;
+
+	// Get output parser if configured
+	const outputParser = await getOptionalOutputParser(ctx);
+
+	// Get user prompt based on node version
+	let prompt: string;
+
+	if (ctx.getNode().typeVersion <= 1.3) {
+		prompt = ctx.getNodeParameter('prompt', itemIndex) as string;
+	} else {
+		prompt = getPromptInputByType({
+			ctx,
+			i: itemIndex,
+			inputKey: 'text',
+			promptTypeKey: 'promptType',
+		});
+	}
+
+	// Validate prompt
+	if (prompt === undefined) {
+		throw new NodeOperationError(ctx.getNode(), "The 'prompt' parameter is empty.");
+	}
+
+	// Get chat messages if configured
+	const messages = ctx.getNodeParameter(
+		'messages.messageValues',
+		itemIndex,
+		[],
+	) as MessageTemplate[];
+
+	// Execute the chain
+	return await executeChain({
+		context: ctx,
+		itemIndex,
+		query: prompt,
+		llm,
+		outputParser,
+		messages,
+	});
+};