mirror of
https://github.com/n8n-io/n8n.git
synced 2024-11-14 16:44:07 -08:00
518 lines
17 KiB
TypeScript
518 lines
17 KiB
TypeScript
import {
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AGENT_NODE_NAME,
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MANUAL_CHAT_TRIGGER_NODE_NAME,
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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MANUAL_TRIGGER_NODE_NAME,
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AI_MEMORY_WINDOW_BUFFER_MEMORY_NODE_NAME,
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AI_TOOL_CALCULATOR_NODE_NAME,
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AI_OUTPUT_PARSER_AUTO_FIXING_NODE_NAME,
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AI_TOOL_CODE_NODE_NAME,
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AI_TOOL_WIKIPEDIA_NODE_NAME,
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BASIC_LLM_CHAIN_NODE_NAME,
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EDIT_FIELDS_SET_NODE_NAME,
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CHAT_TRIGGER_NODE_DISPLAY_NAME,
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} from './../constants';
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import {
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closeManualChatModal,
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getManualChatMessages,
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getManualChatModal,
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getManualChatModalLogs,
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getManualChatModalLogsEntries,
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getManualChatModalLogsTree,
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sendManualChatMessage,
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} from '../composables/modals/chat-modal';
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import { setCredentialValues } from '../composables/modals/credential-modal';
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import {
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clickCreateNewCredential,
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clickExecuteNode,
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clickGetBackToCanvas,
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getNoToolsUsedCallout,
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getOutputPanelTable,
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toggleParameterCheckboxInputByName,
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} from '../composables/ndv';
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import {
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addLanguageModelNodeToParent,
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addMemoryNodeToParent,
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addNodeToCanvas,
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addOutputParserNodeToParent,
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addToolNodeToParent,
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clickExecuteWorkflowButton,
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clickManualChatButton,
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disableNode,
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getExecuteWorkflowButton,
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navigateToNewWorkflowPage,
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getNodes,
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openNode,
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getConnectionBySourceAndTarget,
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} from '../composables/workflow';
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import { NDV, WorkflowPage } from '../pages';
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import { createMockNodeExecutionData, runMockWorkflowExecution } from '../utils';
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describe('Langchain Integration', () => {
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beforeEach(() => {
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navigateToNewWorkflowPage();
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});
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it('should not open chat modal', () => {
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addNodeToCanvas(MANUAL_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(EDIT_FIELDS_SET_NODE_NAME, true);
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clickGetBackToCanvas();
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addNodeToCanvas(AGENT_NODE_NAME, true, true);
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clickGetBackToCanvas();
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addLanguageModelNodeToParent(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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AGENT_NODE_NAME,
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true,
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);
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clickGetBackToCanvas();
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clickExecuteWorkflowButton();
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getManualChatModal().should('not.exist');
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});
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it('should disable test workflow button', () => {
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addNodeToCanvas('Schedule Trigger', true);
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addNodeToCanvas(EDIT_FIELDS_SET_NODE_NAME, true);
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clickGetBackToCanvas();
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addNodeToCanvas(AGENT_NODE_NAME, true, true);
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clickGetBackToCanvas();
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addLanguageModelNodeToParent(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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AGENT_NODE_NAME,
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true,
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);
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clickGetBackToCanvas();
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disableNode('Schedule Trigger');
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getExecuteWorkflowButton().should('be.disabled');
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});
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it('should add nodes to all Agent node input types', () => {
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addNodeToCanvas(MANUAL_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(AGENT_NODE_NAME, true, true);
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toggleParameterCheckboxInputByName('hasOutputParser');
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clickGetBackToCanvas();
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addLanguageModelNodeToParent(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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AGENT_NODE_NAME,
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true,
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);
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clickGetBackToCanvas();
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addMemoryNodeToParent(AI_MEMORY_WINDOW_BUFFER_MEMORY_NODE_NAME, AGENT_NODE_NAME);
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clickGetBackToCanvas();
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addToolNodeToParent(AI_TOOL_CALCULATOR_NODE_NAME, AGENT_NODE_NAME);
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clickGetBackToCanvas();
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addOutputParserNodeToParent(AI_OUTPUT_PARSER_AUTO_FIXING_NODE_NAME, AGENT_NODE_NAME);
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clickGetBackToCanvas();
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});
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it('should add multiple tool nodes to Agent node tool input type', () => {
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addNodeToCanvas(MANUAL_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(AGENT_NODE_NAME, true);
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[
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AI_TOOL_CALCULATOR_NODE_NAME,
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AI_TOOL_CODE_NODE_NAME,
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AI_TOOL_CODE_NODE_NAME,
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AI_TOOL_CODE_NODE_NAME,
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AI_TOOL_WIKIPEDIA_NODE_NAME,
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].forEach((tool) => {
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addToolNodeToParent(tool, AGENT_NODE_NAME);
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clickGetBackToCanvas();
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});
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});
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it('should be able to open and execute Basic LLM Chain node', () => {
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addNodeToCanvas(MANUAL_CHAT_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(BASIC_LLM_CHAIN_NODE_NAME, true);
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addLanguageModelNodeToParent(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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BASIC_LLM_CHAIN_NODE_NAME,
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true,
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);
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clickCreateNewCredential();
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setCredentialValues({
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apiKey: 'sk_test_123',
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});
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clickGetBackToCanvas();
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openNode(BASIC_LLM_CHAIN_NODE_NAME);
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const inputMessage = 'Hello!';
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const outputMessage = 'Hi there! How can I assist you today?';
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clickExecuteNode();
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runMockWorkflowExecution({
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trigger: () => sendManualChatMessage(inputMessage),
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runData: [
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createMockNodeExecutionData(BASIC_LLM_CHAIN_NODE_NAME, {
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jsonData: {
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main: { output: outputMessage },
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},
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metadata: {
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subRun: [{ node: AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, runIndex: 0 }],
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},
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}),
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],
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lastNodeExecuted: BASIC_LLM_CHAIN_NODE_NAME,
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});
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getManualChatMessages().should('contain', outputMessage);
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});
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it('should be able to open and execute Agent node', () => {
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addNodeToCanvas(MANUAL_CHAT_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(AGENT_NODE_NAME, true);
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addLanguageModelNodeToParent(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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AGENT_NODE_NAME,
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true,
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);
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clickCreateNewCredential();
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setCredentialValues({
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apiKey: 'sk_test_123',
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});
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clickGetBackToCanvas();
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openNode(AGENT_NODE_NAME);
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const inputMessage = 'Hello!';
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const outputMessage = 'Hi there! How can I assist you today?';
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clickExecuteNode();
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runMockWorkflowExecution({
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trigger: () => sendManualChatMessage(inputMessage),
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runData: [
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createMockNodeExecutionData(AGENT_NODE_NAME, {
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jsonData: {
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main: { output: outputMessage },
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},
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metadata: {
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subRun: [{ node: AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, runIndex: 0 }],
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},
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}),
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],
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lastNodeExecuted: AGENT_NODE_NAME,
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});
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getManualChatMessages().should('contain', outputMessage);
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});
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it('should add and use Manual Chat Trigger node together with Agent node', () => {
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addNodeToCanvas(MANUAL_CHAT_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(AGENT_NODE_NAME, true);
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addLanguageModelNodeToParent(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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AGENT_NODE_NAME,
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true,
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);
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clickCreateNewCredential();
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setCredentialValues({
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apiKey: 'sk_test_123',
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});
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clickGetBackToCanvas();
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clickManualChatButton();
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const inputMessage = 'Hello!';
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const outputMessage = 'Hi there! How can I assist you today?';
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const runData = [
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createMockNodeExecutionData(MANUAL_CHAT_TRIGGER_NODE_NAME, {
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jsonData: {
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main: { input: inputMessage },
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},
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}),
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createMockNodeExecutionData(AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, {
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jsonData: {
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ai_languageModel: {
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response: {
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generations: [
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{
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text: `{
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"action": "Final Answer",
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"action_input": "${outputMessage}"
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}`,
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message: {
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lc: 1,
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type: 'constructor',
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id: ['langchain', 'schema', 'AIMessage'],
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kwargs: {
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content: `{
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"action": "Final Answer",
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"action_input": "${outputMessage}"
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}`,
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additional_kwargs: {},
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},
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},
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generationInfo: { finish_reason: 'stop' },
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},
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],
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llmOutput: {
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tokenUsage: {
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completionTokens: 26,
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promptTokens: 519,
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totalTokens: 545,
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},
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},
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},
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},
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},
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metadata: {
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subRun: [{ node: AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, runIndex: 0 }],
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},
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inputOverride: {
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ai_languageModel: [
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[
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{
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json: {
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messages: [
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{
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lc: 1,
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type: 'constructor',
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id: ['langchain', 'schema', 'SystemMessage'],
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kwargs: {
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content:
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'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist. However, above all else, all responses must adhere to the format of RESPONSE FORMAT INSTRUCTIONS.',
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additional_kwargs: {},
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},
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},
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{
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lc: 1,
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type: 'constructor',
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id: ['langchain', 'schema', 'HumanMessage'],
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kwargs: {
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content:
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'TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n\n\nRESPONSE FORMAT INSTRUCTIONS\n----------------------------\n\nOutput a JSON markdown code snippet containing a valid JSON object in one of two formats:\n\n**Option 1:**\nUse this if you want the human to use a tool.\nMarkdown code snippet formatted in the following schema:\n\n```json\n{\n "action": string, // The action to take. Must be one of []\n "action_input": string // The input to the action. May be a stringified object.\n}\n```\n\n**Option #2:**\nUse this if you want to respond directly and conversationally to the human. Markdown code snippet formatted in the following schema:\n\n```json\n{\n "action": "Final Answer",\n "action_input": string // You should put what you want to return to use here and make sure to use valid json newline characters.\n}\n```\n\nFor both options, remember to always include the surrounding markdown code snippet delimiters (begin with "```json" and end with "```")!\n\n\nUSER\'S INPUT\n--------------------\nHere is the user\'s input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\nHello!',
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additional_kwargs: {},
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},
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},
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],
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options: { stop: ['Observation:'], promptIndex: 0 },
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},
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},
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],
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],
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},
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}),
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createMockNodeExecutionData(AGENT_NODE_NAME, {
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jsonData: {
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main: { output: 'Hi there! How can I assist you today?' },
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},
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}),
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];
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runMockWorkflowExecution({
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trigger: () => {
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sendManualChatMessage(inputMessage);
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},
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runData,
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lastNodeExecuted: AGENT_NODE_NAME,
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});
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const messages = getManualChatMessages();
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messages.should('have.length', 2);
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messages.should('contain', inputMessage);
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messages.should('contain', outputMessage);
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getManualChatModalLogsTree().should('be.visible');
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getManualChatModalLogsEntries().should('have.length', 1);
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closeManualChatModal();
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getManualChatModalLogs().should('not.exist');
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getManualChatModal().should('not.exist');
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});
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it('should auto-add chat trigger and basic LLM chain when adding LLM node', () => {
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addNodeToCanvas(AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, true);
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getConnectionBySourceAndTarget(
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CHAT_TRIGGER_NODE_DISPLAY_NAME,
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BASIC_LLM_CHAIN_NODE_NAME,
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).should('exist');
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getConnectionBySourceAndTarget(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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BASIC_LLM_CHAIN_NODE_NAME,
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).should('exist');
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getNodes().should('have.length', 3);
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});
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it('should not auto-add nodes if AI nodes are already present', () => {
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addNodeToCanvas(AGENT_NODE_NAME, true);
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addNodeToCanvas(AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, true);
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getConnectionBySourceAndTarget(CHAT_TRIGGER_NODE_DISPLAY_NAME, AGENT_NODE_NAME).should('exist');
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getNodes().should('have.length', 3);
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});
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it('should not auto-add nodes if ChatTrigger is already present', () => {
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addNodeToCanvas(MANUAL_CHAT_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(AGENT_NODE_NAME, true);
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addNodeToCanvas(AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, true);
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getConnectionBySourceAndTarget(CHAT_TRIGGER_NODE_DISPLAY_NAME, AGENT_NODE_NAME).should('exist');
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getNodes().should('have.length', 3);
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});
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it('should render runItems for sub-nodes and allow switching between them', () => {
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const workflowPage = new WorkflowPage();
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const ndv = new NDV();
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cy.visit(workflowPage.url);
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cy.createFixtureWorkflow('In_memory_vector_store_fake_embeddings.json');
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workflowPage.actions.zoomToFit();
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workflowPage.actions.executeNode('Populate VS');
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cy.get('[data-label="25 items"]').should('exist');
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const assertInputOutputText = (text: string, assertion: 'exist' | 'not.exist') => {
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ndv.getters.outputPanel().contains(text).should(assertion);
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ndv.getters.inputPanel().contains(text).should(assertion);
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};
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workflowPage.actions.openNode('Character Text Splitter');
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ndv.getters.outputRunSelector().should('exist');
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ndv.getters.inputRunSelector().should('exist');
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ndv.getters.inputRunSelector().find('input').should('include.value', '3 of 3');
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ndv.getters.outputRunSelector().find('input').should('include.value', '3 of 3');
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assertInputOutputText('Kyiv', 'exist');
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assertInputOutputText('Berlin', 'not.exist');
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assertInputOutputText('Prague', 'not.exist');
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ndv.actions.changeOutputRunSelector('2 of 3');
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assertInputOutputText('Berlin', 'exist');
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assertInputOutputText('Kyiv', 'not.exist');
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assertInputOutputText('Prague', 'not.exist');
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ndv.actions.changeOutputRunSelector('1 of 3');
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assertInputOutputText('Prague', 'exist');
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assertInputOutputText('Berlin', 'not.exist');
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assertInputOutputText('Kyiv', 'not.exist');
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ndv.actions.toggleInputRunLinking();
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ndv.actions.changeOutputRunSelector('2 of 3');
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ndv.getters.inputRunSelector().find('input').should('include.value', '1 of 3');
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ndv.getters.outputRunSelector().find('input').should('include.value', '2 of 3');
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ndv.getters.inputPanel().contains('Prague').should('exist');
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ndv.getters.inputPanel().contains('Berlin').should('not.exist');
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ndv.getters.outputPanel().contains('Berlin').should('exist');
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ndv.getters.outputPanel().contains('Prague').should('not.exist');
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ndv.actions.toggleInputRunLinking();
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ndv.getters.inputRunSelector().find('input').should('include.value', '1 of 3');
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ndv.getters.outputRunSelector().find('input').should('include.value', '1 of 3');
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assertInputOutputText('Prague', 'exist');
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assertInputOutputText('Berlin', 'not.exist');
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assertInputOutputText('Kyiv', 'not.exist');
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});
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it('should show tool info notice if no existing tools were used during execution', () => {
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addNodeToCanvas(MANUAL_CHAT_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(AGENT_NODE_NAME, true);
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addLanguageModelNodeToParent(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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AGENT_NODE_NAME,
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true,
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);
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clickCreateNewCredential();
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setCredentialValues({
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apiKey: 'sk_test_123',
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});
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clickGetBackToCanvas();
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addToolNodeToParent(AI_TOOL_CALCULATOR_NODE_NAME, AGENT_NODE_NAME);
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clickGetBackToCanvas();
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openNode(AGENT_NODE_NAME);
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const inputMessage = 'Hello!';
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const outputMessage = 'Hi there! How can I assist you today?';
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clickExecuteNode();
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runMockWorkflowExecution({
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trigger: () => sendManualChatMessage(inputMessage),
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runData: [
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createMockNodeExecutionData(AGENT_NODE_NAME, {
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jsonData: {
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main: { output: outputMessage },
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},
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metadata: {
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subRun: [{ node: AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, runIndex: 0 }],
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},
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}),
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],
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lastNodeExecuted: AGENT_NODE_NAME,
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});
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closeManualChatModal();
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openNode(AGENT_NODE_NAME);
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getNoToolsUsedCallout().should('exist');
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});
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it('should not show tool info notice if tools were used during execution', () => {
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addNodeToCanvas(MANUAL_CHAT_TRIGGER_NODE_NAME, true);
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addNodeToCanvas(AGENT_NODE_NAME, true);
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addLanguageModelNodeToParent(
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AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME,
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AGENT_NODE_NAME,
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true,
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);
|
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clickCreateNewCredential();
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|
setCredentialValues({
|
|
apiKey: 'sk_test_123',
|
|
});
|
|
clickGetBackToCanvas();
|
|
|
|
addToolNodeToParent(AI_TOOL_CALCULATOR_NODE_NAME, AGENT_NODE_NAME);
|
|
clickGetBackToCanvas();
|
|
openNode(AGENT_NODE_NAME);
|
|
|
|
const inputMessage = 'Hello!';
|
|
const outputMessage = 'Hi there! How can I assist you today?';
|
|
|
|
clickExecuteNode();
|
|
|
|
runMockWorkflowExecution({
|
|
trigger: () => sendManualChatMessage(inputMessage),
|
|
runData: [
|
|
createMockNodeExecutionData(AGENT_NODE_NAME, {
|
|
jsonData: {
|
|
main: { output: outputMessage },
|
|
},
|
|
metadata: {
|
|
subRun: [{ node: AI_LANGUAGE_MODEL_OPENAI_CHAT_MODEL_NODE_NAME, runIndex: 0 }],
|
|
},
|
|
}),
|
|
createMockNodeExecutionData(AI_TOOL_CALCULATOR_NODE_NAME, {}),
|
|
],
|
|
lastNodeExecuted: AGENT_NODE_NAME,
|
|
});
|
|
|
|
closeManualChatModal();
|
|
openNode(AGENT_NODE_NAME);
|
|
// This waits to ensure the output panel is rendered
|
|
getOutputPanelTable();
|
|
|
|
getNoToolsUsedCallout().should('not.exist');
|
|
});
|
|
});
|