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183 lines
5.5 KiB
TypeScript
183 lines
5.5 KiB
TypeScript
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import type { AgentExecutor } from '@langchain/classic/agents';
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import type { IExecuteFunctions } from 'n8n-workflow';
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import { NodeConnectionTypes } from 'n8n-workflow';
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import { GuardrailError } from '../../actions/types';
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import { getChatModel, runLLMValidation } from '../../helpers/model';
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import { ChatPromptTemplate } from '@langchain/core/prompts';
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import { StructuredOutputParser } from '@langchain/core/output_parsers';
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jest.mock('@langchain/core/prompts', () => ({
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ChatPromptTemplate: {
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fromMessages: jest.fn(() => ({
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format: jest.fn(),
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pipe: jest.fn().mockReturnValue({
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pipe: jest.fn().mockReturnValue({
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invoke: jest.fn(),
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}),
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}),
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})),
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},
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}));
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jest.mock('@langchain/classic/agents', () => ({
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AgentExecutor: jest.fn().mockImplementation(() => ({
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invoke: jest.fn(),
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})),
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createToolCallingAgent: jest.fn(() => ({
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streamRunnable: false,
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})),
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}));
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jest.mock('@langchain/core/output_parsers', () => ({
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StructuredOutputParser: jest.fn().mockImplementation(() => ({
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invoke: jest.fn(),
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getFormatInstructions: jest.fn().mockReturnValue('Format instructions'),
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})),
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OutputParserException: jest.fn().mockImplementation((message) => ({
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message,
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name: 'OutputParserException',
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})),
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}));
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describe('model helper', () => {
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let mockExecuteFunctions: IExecuteFunctions;
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let mockModel: BaseChatModel;
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beforeEach(() => {
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mockModel = {
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invoke: jest.fn(),
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} as any;
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mockExecuteFunctions = {
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getInputConnectionData: jest.fn(),
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} as any;
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});
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afterEach(() => {
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jest.clearAllMocks();
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});
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describe('getChatModel', () => {
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it('should return model when getInputConnectionData returns a single model', async () => {
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(mockExecuteFunctions.getInputConnectionData as jest.Mock).mockResolvedValue(mockModel);
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const result = await getChatModel.call(mockExecuteFunctions);
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expect(mockExecuteFunctions.getInputConnectionData).toHaveBeenCalledWith(
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NodeConnectionTypes.AiLanguageModel,
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0,
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);
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expect(result).toBe(mockModel);
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});
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it('should return first model when getInputConnectionData returns an array', async () => {
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const models = [mockModel, {} as BaseChatModel];
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(mockExecuteFunctions.getInputConnectionData as jest.Mock).mockResolvedValue(models);
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const result = await getChatModel.call(mockExecuteFunctions);
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expect(mockExecuteFunctions.getInputConnectionData).toHaveBeenCalledWith(
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NodeConnectionTypes.AiLanguageModel,
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0,
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);
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expect(result).toBe(mockModel);
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});
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it('should handle empty array from getInputConnectionData', async () => {
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(mockExecuteFunctions.getInputConnectionData as jest.Mock).mockResolvedValue([]);
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const result = await getChatModel.call(mockExecuteFunctions);
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expect(result).toBeUndefined();
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});
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});
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describe('runLLMValidation', () => {
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it('should return failed GuardrailResult when agent execution fails', async () => {
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const mockAgentExecutor = {
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invoke: jest.fn().mockRejectedValue(new Error('Agent execution failed')),
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};
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jest
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.mocked((await import('@langchain/classic/agents')).AgentExecutor)
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.mockImplementation(() => mockAgentExecutor as unknown as AgentExecutor);
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const result = await runLLMValidation('test-guardrail', 'Test input', {
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model: mockModel,
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prompt: 'Test prompt',
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threshold: 0.5,
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});
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expect(result).toEqual({
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guardrailName: 'test-guardrail',
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tripwireTriggered: true,
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executionFailed: true,
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originalException: expect.any(GuardrailError),
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info: {},
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});
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expect(result.originalException).toBeInstanceOf(GuardrailError);
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expect((result.originalException as GuardrailError).guardrailName).toBe('test-guardrail');
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});
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it('should return failed GuardrailResult when agent does not call tool', async () => {
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const mockAgentExecutor = {
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invoke: jest.fn().mockResolvedValue({}), // No tool call
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};
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jest
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.mocked((await import('@langchain/classic/agents')).AgentExecutor)
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.mockImplementation(() => mockAgentExecutor as unknown as AgentExecutor);
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const result = await runLLMValidation('test-guardrail', 'Test input', {
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model: mockModel,
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prompt: 'Test prompt',
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threshold: 0.5,
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});
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expect(result).toEqual({
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guardrailName: 'test-guardrail',
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tripwireTriggered: true,
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executionFailed: true,
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originalException: expect.any(GuardrailError),
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info: {},
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});
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});
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it('should use provided systemMessage instead of default rules', async () => {
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const invokeMock = jest.fn().mockResolvedValue({
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content: [{ type: 'text', text: '{"confidenceScore":0.6,"flagged":true}' }],
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});
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jest.mocked(ChatPromptTemplate.fromMessages).mockImplementationOnce(
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() =>
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({
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pipe: jest.fn().mockReturnValue({ invoke: invokeMock }),
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}) as unknown as any,
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);
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jest.mocked(StructuredOutputParser).mockImplementationOnce(
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() =>
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({
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getFormatInstructions: jest.fn().mockReturnValue('Format instructions'),
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parse: jest.fn().mockResolvedValue({ confidenceScore: 0.6, flagged: true }),
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}) as unknown as any,
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);
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const model = { invoke: jest.fn() } as unknown as BaseChatModel;
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await runLLMValidation('test-guardrail', 'Input text', {
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model,
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prompt: 'System Prompt',
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threshold: 0.5,
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systemMessage: 'CUSTOM_RULES',
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});
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expect(invokeMock).toHaveBeenCalled();
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const callArg = invokeMock.mock.calls[0][0];
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expect(callArg.system_message).toContain('CUSTOM_RULES');
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expect(callArg.system_message).not.toContain('Only respond with the json object');
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});
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});
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});
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