LibreChat/api/server/controllers/agents/__tests__/openai.spec.js
Danny Avila a6fb257bcf
📦 refactor: Consolidate DB models, encapsulating Mongoose usage in data-schemas (#11830)
* chore: move database model methods to /packages/data-schemas

* chore: add TypeScript ESLint rule to warn on unused variables

* refactor: model imports to streamline access

- Consolidated model imports across various files to improve code organization and reduce redundancy.
- Updated imports for models such as Assistant, Message, Conversation, and others to a unified import path.
- Adjusted middleware and service files to reflect the new import structure, ensuring functionality remains intact.
- Enhanced test files to align with the new import paths, maintaining test coverage and integrity.

* chore: migrate database models to packages/data-schemas and refactor all direct Mongoose Model usage outside of data-schemas

* test: update agent model mocks in unit tests

- Added `getAgent` mock to `client.test.js` to enhance test coverage for agent-related functionality.
- Removed redundant `getAgent` and `getAgents` mocks from `openai.spec.js` and `responses.unit.spec.js` to streamline test setup and reduce duplication.
- Ensured consistency in agent mock implementations across test files.

* fix: update types in data-schemas

* refactor: enhance type definitions in transaction and spending methods

- Updated type definitions in `checkBalance.ts` to use specific request and response types.
- Refined `spendTokens.ts` to utilize a new `SpendTxData` interface for better clarity and type safety.
- Improved transaction handling in `transaction.ts` by introducing `TransactionResult` and `TxData` interfaces, ensuring consistent data structures across methods.
- Adjusted unit tests in `transaction.spec.ts` to accommodate new type definitions and enhance robustness.

* refactor: streamline model imports and enhance code organization

- Consolidated model imports across various controllers and services to a unified import path, improving code clarity and reducing redundancy.
- Updated multiple files to reflect the new import structure, ensuring all functionalities remain intact.
- Enhanced overall code organization by removing duplicate import statements and optimizing the usage of model methods.

* feat: implement loadAddedAgent and refactor agent loading logic

- Introduced `loadAddedAgent` function to handle loading agents from added conversations, supporting multi-convo parallel execution.
- Created a new `load.ts` file to encapsulate agent loading functionalities, including `loadEphemeralAgent` and `loadAgent`.
- Updated the `index.ts` file to export the new `load` module instead of the deprecated `loadAgent`.
- Enhanced type definitions and improved error handling in the agent loading process.
- Adjusted unit tests to reflect changes in the agent loading structure and ensure comprehensive coverage.

* refactor: enhance balance handling with new update interface

- Introduced `IBalanceUpdate` interface to streamline balance update operations across the codebase.
- Updated `upsertBalanceFields` method signatures in `balance.ts`, `transaction.ts`, and related tests to utilize the new interface for improved type safety.
- Adjusted type imports in `balance.spec.ts` to include `IBalanceUpdate`, ensuring consistency in balance management functionalities.
- Enhanced overall code clarity and maintainability by refining type definitions related to balance operations.

* feat: add unit tests for loadAgent functionality and enhance agent loading logic

- Introduced comprehensive unit tests for the `loadAgent` function, covering various scenarios including null and empty agent IDs, loading of ephemeral agents, and permission checks.
- Enhanced the `initializeClient` function by moving `getConvoFiles` to the correct position in the database method exports, ensuring proper functionality.
- Improved test coverage for agent loading, including handling of non-existent agents and user permissions.

* chore: reorder memory method exports for consistency

- Moved `deleteAllUserMemories` to the correct position in the exported memory methods, ensuring a consistent and logical order of method exports in `memory.ts`.
2026-02-18 00:31:36 -05:00

190 lines
6.1 KiB
JavaScript

/**
* Unit tests for OpenAI-compatible API controller
* Tests that recordCollectedUsage is called correctly for token spending
*/
const mockSpendTokens = jest.fn().mockResolvedValue({});
const mockSpendStructuredTokens = jest.fn().mockResolvedValue({});
const mockRecordCollectedUsage = jest
.fn()
.mockResolvedValue({ input_tokens: 100, output_tokens: 50 });
const mockGetBalanceConfig = jest.fn().mockReturnValue({ enabled: true });
const mockGetTransactionsConfig = jest.fn().mockReturnValue({ enabled: true });
jest.mock('nanoid', () => ({
nanoid: jest.fn(() => 'mock-nanoid-123'),
}));
jest.mock('@librechat/data-schemas', () => ({
logger: {
debug: jest.fn(),
error: jest.fn(),
warn: jest.fn(),
},
}));
jest.mock('@librechat/agents', () => ({
Callback: { TOOL_ERROR: 'TOOL_ERROR' },
ToolEndHandler: jest.fn(),
formatAgentMessages: jest.fn().mockReturnValue({
messages: [],
indexTokenCountMap: {},
}),
}));
jest.mock('@librechat/api', () => ({
writeSSE: jest.fn(),
createRun: jest.fn().mockResolvedValue({
processStream: jest.fn().mockResolvedValue(undefined),
}),
createChunk: jest.fn().mockReturnValue({}),
buildToolSet: jest.fn().mockReturnValue(new Set()),
sendFinalChunk: jest.fn(),
createSafeUser: jest.fn().mockReturnValue({ id: 'user-123' }),
validateRequest: jest
.fn()
.mockReturnValue({ request: { model: 'agent-123', messages: [], stream: false } }),
initializeAgent: jest.fn().mockResolvedValue({
model: 'gpt-4',
model_parameters: {},
toolRegistry: {},
}),
getBalanceConfig: mockGetBalanceConfig,
createErrorResponse: jest.fn(),
getTransactionsConfig: mockGetTransactionsConfig,
recordCollectedUsage: mockRecordCollectedUsage,
buildNonStreamingResponse: jest.fn().mockReturnValue({ id: 'resp-123' }),
createOpenAIStreamTracker: jest.fn().mockReturnValue({
addText: jest.fn(),
addReasoning: jest.fn(),
toolCalls: new Map(),
usage: { promptTokens: 0, completionTokens: 0, reasoningTokens: 0 },
}),
createOpenAIContentAggregator: jest.fn().mockReturnValue({
addText: jest.fn(),
addReasoning: jest.fn(),
getText: jest.fn().mockReturnValue(''),
getReasoning: jest.fn().mockReturnValue(''),
toolCalls: new Map(),
usage: { promptTokens: 100, completionTokens: 50, reasoningTokens: 0 },
}),
createToolExecuteHandler: jest.fn().mockReturnValue({ handle: jest.fn() }),
isChatCompletionValidationFailure: jest.fn().mockReturnValue(false),
}));
jest.mock('~/server/services/ToolService', () => ({
loadAgentTools: jest.fn().mockResolvedValue([]),
loadToolsForExecution: jest.fn().mockResolvedValue([]),
}));
jest.mock('~/server/controllers/agents/callbacks', () => ({
createToolEndCallback: jest.fn().mockReturnValue(jest.fn()),
}));
jest.mock('~/server/services/PermissionService', () => ({
findAccessibleResources: jest.fn().mockResolvedValue([]),
}));
jest.mock('~/models', () => ({
getAgent: jest.fn().mockResolvedValue({ id: 'agent-123', name: 'Test Agent' }),
getFiles: jest.fn(),
getUserKey: jest.fn(),
getMessages: jest.fn(),
updateFilesUsage: jest.fn(),
getUserKeyValues: jest.fn(),
getUserCodeFiles: jest.fn(),
getToolFilesByIds: jest.fn(),
getCodeGeneratedFiles: jest.fn(),
spendTokens: mockSpendTokens,
spendStructuredTokens: mockSpendStructuredTokens,
getConvoFiles: jest.fn().mockResolvedValue([]),
}));
describe('OpenAIChatCompletionController', () => {
let OpenAIChatCompletionController;
let req, res;
beforeEach(() => {
jest.clearAllMocks();
const controller = require('../openai');
OpenAIChatCompletionController = controller.OpenAIChatCompletionController;
req = {
body: {
model: 'agent-123',
messages: [{ role: 'user', content: 'Hello' }],
stream: false,
},
user: { id: 'user-123' },
config: {
endpoints: {
agents: { allowedProviders: ['openAI'] },
},
},
on: jest.fn(),
};
res = {
status: jest.fn().mockReturnThis(),
json: jest.fn(),
setHeader: jest.fn(),
flushHeaders: jest.fn(),
end: jest.fn(),
write: jest.fn(),
};
});
describe('token usage recording', () => {
it('should call recordCollectedUsage after successful non-streaming completion', async () => {
await OpenAIChatCompletionController(req, res);
expect(mockRecordCollectedUsage).toHaveBeenCalledTimes(1);
expect(mockRecordCollectedUsage).toHaveBeenCalledWith(
{ spendTokens: mockSpendTokens, spendStructuredTokens: mockSpendStructuredTokens },
expect.objectContaining({
user: 'user-123',
conversationId: expect.any(String),
collectedUsage: expect.any(Array),
context: 'message',
balance: { enabled: true },
transactions: { enabled: true },
}),
);
});
it('should pass balance and transactions config to recordCollectedUsage', async () => {
mockGetBalanceConfig.mockReturnValue({ enabled: true, startBalance: 1000 });
mockGetTransactionsConfig.mockReturnValue({ enabled: true, rateLimit: 100 });
await OpenAIChatCompletionController(req, res);
expect(mockRecordCollectedUsage).toHaveBeenCalledWith(
expect.any(Object),
expect.objectContaining({
balance: { enabled: true, startBalance: 1000 },
transactions: { enabled: true, rateLimit: 100 },
}),
);
});
it('should pass spendTokens and spendStructuredTokens as dependencies', async () => {
await OpenAIChatCompletionController(req, res);
const [deps] = mockRecordCollectedUsage.mock.calls[0];
expect(deps).toHaveProperty('spendTokens', mockSpendTokens);
expect(deps).toHaveProperty('spendStructuredTokens', mockSpendStructuredTokens);
});
it('should include model from primaryConfig in recordCollectedUsage params', async () => {
await OpenAIChatCompletionController(req, res);
expect(mockRecordCollectedUsage).toHaveBeenCalledWith(
expect.any(Object),
expect.objectContaining({
model: 'gpt-4',
}),
);
});
});
});