refactor(Question and Answer Chain Node): Use new LangChain's syntax (#13868)

packages/@n8n/nodes-langchain/nodes/chains/ChainRetrievalQA/ChainRetrievalQa.node.ts

@@ -6,15 +6,15 @@ import {
 	PromptTemplate,
 } from '@langchain/core/prompts';
 import type { BaseRetriever } from '@langchain/core/retrievers';
-import { RetrievalQAChain } from 'langchain/chains';
+import { createStuffDocumentsChain } from 'langchain/chains/combine_documents';
+import { createRetrievalChain } from 'langchain/chains/retrieval';
+import { NodeConnectionType, NodeOperationError, parseErrorMetadata } from 'n8n-workflow';
 import {
-	NodeConnectionType,
+	type INodeProperties,
 	type IExecuteFunctions,
 	type INodeExecutionData,
 	type INodeType,
 	type INodeTypeDescription,
-	NodeOperationError,
-	parseErrorMetadata,
 } from 'n8n-workflow';
 
 import { promptTypeOptions, textFromPreviousNode } from '@utils/descriptions';
@@ -22,10 +22,24 @@ import { getPromptInputByType, isChatInstance } from '@utils/helpers';
 import { getTemplateNoticeField } from '@utils/sharedFields';
 import { getTracingConfig } from '@utils/tracing';
 
-const SYSTEM_PROMPT_TEMPLATE = `Use the following pieces of context to answer the users question.
+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: {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,
+	},
+};
 
 export class ChainRetrievalQa implements INodeType {
 	description: INodeTypeDescription = {
@@ -34,7 +48,7 @@ export class ChainRetrievalQa implements INodeType {
 		icon: 'fa:link',
 		iconColor: 'black',
 		group: ['transform'],
-		version: [1, 1.1, 1.2, 1.3, 1.4],
+		version: [1, 1.1, 1.2, 1.3, 1.4, 1.5],
 		description: 'Answer questions about retrieved documents',
 		defaults: {
 			name: 'Question and Answer Chain',
@@ -146,14 +160,21 @@ export class ChainRetrievalQa implements INodeType {
 				placeholder: 'Add Option',
 				options: [
 					{
-						displayName: 'System Prompt Template',
-						name: 'systemPromptTemplate',
-						type: 'string',
-						default: SYSTEM_PROMPT_TEMPLATE,
-						description:
-							'Template string used for the system prompt. This should include the variable `{context}` for the provided context. For text completion models, you should also include the variable `{question}` for the user’s query.',
-						typeOptions: {
-							rows: 6,
+						...systemPromptOption,
+						description: `Template string used for the system prompt. This should include the variable \`{context}\` for the provided context. For text completion models, you should also include the variable \`{${LEGACY_INPUT_TEMPLATE_KEY}}\` for the user’s query.`,
+						displayOptions: {
+							show: {
+								'@version': [{ _cnd: { lt: 1.5 } }],
+							},
+						},
+					},
+					{
+						...systemPromptOption,
+						description: `Template string used for the system prompt. This should include the variable \`{context}\` for the provided context. For text completion models, you should also include the variable \`{${INPUT_TEMPLATE_KEY}}\` for the user’s query.`,
+						displayOptions: {
+							show: {
+								'@version': [{ _cnd: { gte: 1.5 } }],
+							},
 						},
 					},
 				],
@@ -166,6 +187,7 @@ export class ChainRetrievalQa implements INodeType {
 
 		const items = this.getInputData();
 		const returnData: INodeExecutionData[] = [];
+
 		// Run for each item
 		for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
 			try {
@@ -200,35 +222,62 @@ export class ChainRetrievalQa implements INodeType {
 					systemPromptTemplate?: string;
 				};
 
-				const chainParameters = {} as {
-					prompt?: PromptTemplate | ChatPromptTemplate;
-				};
+				let templateText = options.systemPromptTemplate ?? SYSTEM_PROMPT_TEMPLATE;
 
-				if (options.systemPromptTemplate !== undefined) {
-					if (isChatInstance(model)) {
-						const messages = [
-							SystemMessagePromptTemplate.fromTemplate(options.systemPromptTemplate),
-							HumanMessagePromptTemplate.fromTemplate('{question}'),
-						];
-						const chatPromptTemplate = ChatPromptTemplate.fromMessages(messages);
-
-						chainParameters.prompt = chatPromptTemplate;
-					} else {
-						const completionPromptTemplate = new PromptTemplate({
-							template: options.systemPromptTemplate,
-							inputVariables: ['context', 'question'],
-						});
-
-						chainParameters.prompt = completionPromptTemplate;
-					}
+				// 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}}`,
+					);
 				}
 
-				const chain = RetrievalQAChain.fromLLM(model, retriever, chainParameters);
+				// 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:' : '';
 
-				const response = await chain
-					.withConfig(getTracingConfig(this))
-					.invoke({ query }, { signal: this.getExecutionCancelSignal() });
-				returnData.push({ json: { response } });
+					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(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);

packages/@n8n/nodes-langchain/nodes/chains/ChainRetrievalQA/test/ChainRetrievalQa.node.test.ts

@@ -0,0 +1,229 @@
+import { Document } from '@langchain/core/documents';
+import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+import type { BaseRetriever } from '@langchain/core/retrievers';
+import { FakeChatModel, FakeLLM, FakeRetriever } from '@langchain/core/utils/testing';
+import get from 'lodash/get';
+import type { IDataObject, IExecuteFunctions } from 'n8n-workflow';
+import { NodeConnectionType, NodeOperationError, UnexpectedError } from 'n8n-workflow';
+
+import { ChainRetrievalQa } from '../ChainRetrievalQa.node';
+
+const createExecuteFunctionsMock = (
+	parameters: IDataObject,
+	fakeLlm: BaseLanguageModel,
+	fakeRetriever: BaseRetriever,
+	version: number,
+) => {
+	return {
+		getExecutionCancelSignal() {
+			return new AbortController().signal;
+		},
+		getNodeParameter(parameter: string) {
+			return get(parameters, parameter);
+		},
+		getNode() {
+			return {
+				typeVersion: version,
+			};
+		},
+		getInputConnectionData(type: NodeConnectionType) {
+			if (type === NodeConnectionType.AiLanguageModel) {
+				return fakeLlm;
+			}
+			if (type === NodeConnectionType.AiRetriever) {
+				return fakeRetriever;
+			}
+			return null;
+		},
+		getInputData() {
+			return [{ json: {} }];
+		},
+		getWorkflow() {
+			return {
+				name: 'Test Workflow',
+			};
+		},
+		getExecutionId() {
+			return 'test_execution_id';
+		},
+		continueOnFail() {
+			return false;
+		},
+		logger: { debug: jest.fn() },
+	} as unknown as IExecuteFunctions;
+};
+
+describe('ChainRetrievalQa', () => {
+	let node: ChainRetrievalQa;
+	const testDocs = [
+		new Document({
+			pageContent: 'The capital of France is Paris. It is known for the Eiffel Tower.',
+		}),
+		new Document({
+			pageContent:
+				'Paris is the largest city in France with a population of over 2 million people.',
+		}),
+	];
+
+	const fakeRetriever = new FakeRetriever({ output: testDocs });
+
+	beforeEach(() => {
+		node = new ChainRetrievalQa();
+	});
+
+	it.each([1.3, 1.4, 1.5])(
+		'should process a query using a chat model (version %s)',
+		async (version) => {
+			// Mock a chat model that returns a predefined answer
+			const mockChatModel = new FakeChatModel({});
+
+			const params = {
+				promptType: 'define',
+				text: 'What is the capital of France?',
+				options: {},
+			};
+
+			const result = await node.execute.call(
+				createExecuteFunctionsMock(params, mockChatModel, fakeRetriever, version),
+			);
+
+			// Check that the result contains the expected response (FakeChatModel returns the query as response)
+			expect(result).toHaveLength(1);
+			expect(result[0]).toHaveLength(1);
+			expect(result[0][0].json.response).toBeDefined();
+
+			let responseText = result[0][0].json.response;
+			if (version < 1.5 && typeof responseText === 'object') {
+				responseText = (responseText as { text: string }).text;
+			}
+
+			expect(responseText).toContain('You are an assistant for question-answering tasks'); // system prompt
+			expect(responseText).toContain('The capital of France is Paris.'); // context
+			expect(responseText).toContain('What is the capital of France?'); // query
+		},
+	);
+
+	it.each([1.3, 1.4, 1.5])(
+		'should process a query using a text completion model (version %s)',
+		async (version) => {
+			// Mock a text completion model that returns a predefined answer
+			const mockTextModel = new FakeLLM({ response: 'Paris is the capital of France.' });
+
+			const modelCallSpy = jest.spyOn(mockTextModel, '_call');
+
+			const params = {
+				promptType: 'define',
+				text: 'What is the capital of France?',
+				options: {},
+			};
+
+			const result = await node.execute.call(
+				createExecuteFunctionsMock(params, mockTextModel, fakeRetriever, version),
+			);
+
+			// Check model was called with the correct query
+			expect(modelCallSpy).toHaveBeenCalled();
+			expect(modelCallSpy.mock.calls[0][0]).toEqual(
+				expect.stringContaining('Question: What is the capital of France?'),
+			);
+
+			// Check that the result contains the expected response
+			expect(result).toHaveLength(1);
+			expect(result[0]).toHaveLength(1);
+
+			if (version < 1.5) {
+				expect((result[0][0].json.response as { text: string }).text).toContain(
+					'Paris is the capital of France.',
+				);
+			} else {
+				expect(result[0][0].json).toEqual({
+					response: 'Paris is the capital of France.',
+				});
+			}
+		},
+	);
+
+	it.each([1.3, 1.4, 1.5])(
+		'should use a custom system prompt if provided (version %s)',
+		async (version) => {
+			const customSystemPrompt = `You are a geography expert. Use the following context to answer the question.
+			----------------
+			Context: {context}`;
+
+			// The chat model will return a response indicating it received the custom prompt
+			const mockChatModel = new FakeChatModel({});
+
+			const params = {
+				promptType: 'define',
+				text: 'What is the capital of France?',
+				options: {
+					systemPromptTemplate: customSystemPrompt,
+				},
+			};
+
+			const result = await node.execute.call(
+				createExecuteFunctionsMock(params, mockChatModel, fakeRetriever, version),
+			);
+
+			expect(result).toHaveLength(1);
+			expect(result[0]).toHaveLength(1);
+			if (version < 1.5) {
+				expect((result[0][0].json.response as { text: string }).text).toContain(
+					'You are a geography expert.',
+				);
+			} else {
+				expect(result[0][0].json.response).toContain('You are a geography expert.');
+			}
+		},
+	);
+
+	it.each([1.3, 1.4, 1.5])(
+		'should throw an error if the query is undefined (version %s)',
+		async (version) => {
+			const mockChatModel = new FakeChatModel({});
+
+			const params = {
+				promptType: 'define',
+				text: undefined, // undefined query
+				options: {},
+			};
+
+			await expect(
+				node.execute.call(
+					createExecuteFunctionsMock(params, mockChatModel, fakeRetriever, version),
+				),
+			).rejects.toThrow(NodeOperationError);
+		},
+	);
+
+	it.each([1.3, 1.4, 1.5])(
+		'should add error to json if continueOnFail is true (version %s)',
+		async (version) => {
+			// Create a model that will throw an error
+			class ErrorLLM extends FakeLLM {
+				async _call(): Promise<string> {
+					throw new UnexpectedError('Model error');
+				}
+			}
+
+			const errorModel = new ErrorLLM({});
+
+			const params = {
+				promptType: 'define',
+				text: 'What is the capital of France?',
+				options: {},
+			};
+
+			// Override continueOnFail to return true
+			const execMock = createExecuteFunctionsMock(params, errorModel, fakeRetriever, version);
+			execMock.continueOnFail = () => true;
+
+			const result = await node.execute.call(execMock);
+
+			expect(result).toHaveLength(1);
+			expect(result[0]).toHaveLength(1);
+			expect(result[0][0].json).toHaveProperty('error');
+			expect(result[0][0].json.error).toContain('Model error');
+		},
+	);
+});