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const { OpenAICompatibleChat } = require('../../../utils/chats/openaiCompatible');
const { WorkspaceChats } = require('../../../models/workspaceChats');
const { getVectorDbClass, getLLMProvider } = require('../../../utils/helpers');
const { extractTextContent, extractAttachments } = require('../../../endpoints/api/openai/helpers');
// Mock dependencies
jest.mock('../../../models/workspaceChats');
jest.mock('../../../utils/helpers');
jest.mock('../../../utils/DocumentManager', () => ({
DocumentManager: class {
constructor() {
this.pinnedDocs = jest.fn().mockResolvedValue([]);
}
}
}));
describe('OpenAICompatibleChat', () => {
let mockWorkspace;
let mockVectorDb;
let mockLLMConnector;
let mockResponse;
beforeEach(() => {
// Reset all mocks
jest.clearAllMocks();
// Setup mock workspace
mockWorkspace = {
id: 1,
slug: 'test-workspace',
chatMode: 'chat',
chatProvider: 'openai',
chatModel: 'gpt-4',
};
// Setup mock VectorDb
mockVectorDb = {
hasNamespace: jest.fn().mockResolvedValue(true),
namespaceCount: jest.fn().mockResolvedValue(1),
performSimilaritySearch: jest.fn().mockResolvedValue({
contextTexts: [],
sources: [],
message: null,
}),
};
getVectorDbClass.mockReturnValue(mockVectorDb);
// Setup mock LLM connector
mockLLMConnector = {
promptWindowLimit: jest.fn().mockReturnValue(4000),
compressMessages: jest.fn().mockResolvedValue([]),
getChatCompletion: jest.fn().mockResolvedValue({
textResponse: 'Mock response',
metrics: {},
}),
streamingEnabled: jest.fn().mockReturnValue(true),
streamGetChatCompletion: jest.fn().mockResolvedValue({
metrics: {},
}),
handleStream: jest.fn().mockResolvedValue('Mock streamed response'),
defaultTemp: 0.7,
};
getLLMProvider.mockReturnValue(mockLLMConnector);
// Setup WorkspaceChats mock
WorkspaceChats.new.mockResolvedValue({ chat: { id: 'mock-chat-id' } });
// Setup mock response object for streaming
mockResponse = {
write: jest.fn(),
};
});
describe('chatSync', () => {
test('should handle OpenAI vision multimodal messages', async () => {
const multiModalPrompt = [
{
type: 'text',
text: 'What do you see in this image?'
},
{
type: 'image_url',
image_url: {
url: 'data:image/png;base64,abc123',
detail: 'low'
}
}
];
const prompt = extractTextContent(multiModalPrompt);
const attachments = extractAttachments(multiModalPrompt);
const result = await OpenAICompatibleChat.chatSync({
workspace: mockWorkspace,
prompt,
attachments,
systemPrompt: 'You are a helpful assistant',
history: [
{ role: 'user', content: 'Previous message' },
{ role: 'assistant', content: 'Previous response' }
],
temperature: 0.7
});
// Verify chat was saved with correct format
expect(WorkspaceChats.new).toHaveBeenCalledWith(
expect.objectContaining({
workspaceId: mockWorkspace.id,
prompt: multiModalPrompt[0].text,
response: expect.objectContaining({
text: 'Mock response',
attachments: [{
name: 'uploaded_image_0',
mime: 'image/png',
contentString: multiModalPrompt[1].image_url.url
}]
})
})
);
// Verify response format
expect(result).toEqual(
expect.objectContaining({
object: 'chat.completion',
choices: expect.arrayContaining([
expect.objectContaining({
message: expect.objectContaining({
role: 'assistant',
content: 'Mock response',
}),
}),
]),
})
);
});
test('should handle regular text messages in OpenAI format', async () => {
const promptString = 'Hello world';
const result = await OpenAICompatibleChat.chatSync({
workspace: mockWorkspace,
prompt: promptString,
systemPrompt: 'You are a helpful assistant',
history: [
{ role: 'user', content: 'Previous message' },
{ role: 'assistant', content: 'Previous response' }
],
temperature: 0.7
});
// Verify chat was saved without attachments
expect(WorkspaceChats.new).toHaveBeenCalledWith(
expect.objectContaining({
workspaceId: mockWorkspace.id,
prompt: promptString,
response: expect.objectContaining({
text: 'Mock response',
attachments: []
})
})
);
expect(result).toBeTruthy();
});
});
describe('streamChat', () => {
test('should handle OpenAI vision multimodal messages in streaming mode', async () => {
const multiModalPrompt = [
{
type: 'text',
text: 'What do you see in this image?'
},
{
type: 'image_url',
image_url: {
url: 'data:image/png;base64,abc123',
detail: 'low'
}
}
];
const prompt = extractTextContent(multiModalPrompt);
const attachments = extractAttachments(multiModalPrompt);
await OpenAICompatibleChat.streamChat({
workspace: mockWorkspace,
response: mockResponse,
prompt,
attachments,
systemPrompt: 'You are a helpful assistant',
history: [
{ role: 'user', content: 'Previous message' },
{ role: 'assistant', content: 'Previous response' }
],
temperature: 0.7
});
// Verify streaming was handled
expect(mockLLMConnector.streamGetChatCompletion).toHaveBeenCalled();
expect(mockLLMConnector.handleStream).toHaveBeenCalled();
// Verify chat was saved with attachments
expect(WorkspaceChats.new).toHaveBeenCalledWith(
expect.objectContaining({
workspaceId: mockWorkspace.id,
prompt: multiModalPrompt[0].text,
response: expect.objectContaining({
text: 'Mock streamed response',
attachments: [{
name: 'uploaded_image_0',
mime: 'image/png',
contentString: multiModalPrompt[1].image_url.url
}]
})
})
);
});
test('should handle regular text messages in streaming mode', async () => {
const promptString = 'Hello world';
await OpenAICompatibleChat.streamChat({
workspace: mockWorkspace,
response: mockResponse,
prompt: promptString,
systemPrompt: 'You are a helpful assistant',
history: [
{ role: 'user', content: 'Previous message' },
{ role: 'assistant', content: 'Previous response' }
],
temperature: 0.7
});
// Verify streaming was handled
expect(mockLLMConnector.streamGetChatCompletion).toHaveBeenCalled();
expect(mockLLMConnector.handleStream).toHaveBeenCalled();
// Verify chat was saved without attachments
expect(WorkspaceChats.new).toHaveBeenCalledWith(
expect.objectContaining({
workspaceId: mockWorkspace.id,
prompt: promptString,
response: expect.objectContaining({
text: 'Mock streamed response',
attachments: []
})
})
);
});
});
}); |