LibreChat/api/server/middleware/abortMiddleware.spec.js
Danny Avila 8fafda47c2
📦 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`.
2026-03-05 13:56:40 -05:00

237 lines
7.3 KiB
JavaScript

/**
* Tests for abortMiddleware - spendCollectedUsage function
*
* This tests the token spending logic for abort scenarios,
* particularly for parallel agents (addedConvo) where multiple
* models need their tokens spent.
*
* spendCollectedUsage delegates to recordCollectedUsage from @librechat/api,
* passing pricing + bulkWriteOps deps, with context: 'abort'.
* After spending, it clears the collectedUsage array to prevent double-spending
* from the AgentClient finally block (which shares the same array reference).
*/
const mockSpendTokens = jest.fn().mockResolvedValue();
const mockSpendStructuredTokens = jest.fn().mockResolvedValue();
const mockRecordCollectedUsage = jest
.fn()
.mockResolvedValue({ input_tokens: 100, output_tokens: 50 });
const mockGetMultiplier = jest.fn().mockReturnValue(1);
const mockGetCacheMultiplier = jest.fn().mockReturnValue(null);
jest.mock('@librechat/data-schemas', () => ({
logger: {
debug: jest.fn(),
error: jest.fn(),
warn: jest.fn(),
info: jest.fn(),
},
}));
jest.mock('@librechat/api', () => ({
countTokens: jest.fn().mockResolvedValue(100),
isEnabled: jest.fn().mockReturnValue(false),
sendEvent: jest.fn(),
GenerationJobManager: {
abortJob: jest.fn(),
},
recordCollectedUsage: mockRecordCollectedUsage,
sanitizeMessageForTransmit: jest.fn((msg) => msg),
}));
jest.mock('librechat-data-provider', () => ({
isAssistantsEndpoint: jest.fn().mockReturnValue(false),
ErrorTypes: { INVALID_REQUEST: 'INVALID_REQUEST', NO_SYSTEM_MESSAGES: 'NO_SYSTEM_MESSAGES' },
}));
jest.mock('~/app/clients/prompts', () => ({
truncateText: jest.fn((text) => text),
smartTruncateText: jest.fn((text) => text),
}));
jest.mock('~/cache/clearPendingReq', () => jest.fn().mockResolvedValue());
jest.mock('~/server/middleware/error', () => ({
sendError: jest.fn(),
}));
const mockUpdateBalance = jest.fn().mockResolvedValue({});
const mockBulkInsertTransactions = jest.fn().mockResolvedValue(undefined);
jest.mock('~/models', () => ({
saveMessage: jest.fn().mockResolvedValue(),
getConvo: jest.fn().mockResolvedValue({ title: 'Test Chat' }),
updateBalance: mockUpdateBalance,
bulkInsertTransactions: mockBulkInsertTransactions,
}));
jest.mock('./abortRun', () => ({
abortRun: jest.fn(),
}));
const { spendCollectedUsage } = require('./abortMiddleware');
describe('abortMiddleware - spendCollectedUsage', () => {
beforeEach(() => {
jest.clearAllMocks();
});
describe('spendCollectedUsage delegation', () => {
it('should return early if collectedUsage is empty', async () => {
await spendCollectedUsage({
userId: 'user-123',
conversationId: 'convo-123',
collectedUsage: [],
fallbackModel: 'gpt-4',
});
expect(mockRecordCollectedUsage).not.toHaveBeenCalled();
});
it('should return early if collectedUsage is null', async () => {
await spendCollectedUsage({
userId: 'user-123',
conversationId: 'convo-123',
collectedUsage: null,
fallbackModel: 'gpt-4',
});
expect(mockRecordCollectedUsage).not.toHaveBeenCalled();
});
it('should call recordCollectedUsage with abort context and full deps', async () => {
const collectedUsage = [{ input_tokens: 100, output_tokens: 50, model: 'gpt-4' }];
await spendCollectedUsage({
userId: 'user-123',
conversationId: 'convo-123',
collectedUsage,
fallbackModel: 'gpt-4',
messageId: 'msg-123',
});
expect(mockRecordCollectedUsage).toHaveBeenCalledTimes(1);
expect(mockRecordCollectedUsage).toHaveBeenCalledWith(
{
spendTokens: expect.any(Function),
spendStructuredTokens: expect.any(Function),
pricing: {
getMultiplier: mockGetMultiplier,
getCacheMultiplier: mockGetCacheMultiplier,
},
bulkWriteOps: {
insertMany: mockBulkInsertTransactions,
updateBalance: mockUpdateBalance,
},
},
{
user: 'user-123',
conversationId: 'convo-123',
collectedUsage,
context: 'abort',
messageId: 'msg-123',
model: 'gpt-4',
},
);
});
it('should pass context abort for multiple models (parallel agents)', async () => {
const collectedUsage = [
{ input_tokens: 100, output_tokens: 50, model: 'gpt-4' },
{ input_tokens: 80, output_tokens: 40, model: 'claude-3' },
{ input_tokens: 120, output_tokens: 60, model: 'gemini-pro' },
];
await spendCollectedUsage({
userId: 'user-123',
conversationId: 'convo-123',
collectedUsage,
fallbackModel: 'gpt-4',
});
expect(mockRecordCollectedUsage).toHaveBeenCalledTimes(1);
expect(mockRecordCollectedUsage).toHaveBeenCalledWith(
expect.any(Object),
expect.objectContaining({
context: 'abort',
collectedUsage,
}),
);
});
it('should handle real-world parallel agent abort scenario', async () => {
const collectedUsage = [
{ input_tokens: 31596, output_tokens: 151, model: 'gemini-3-flash-preview' },
{ input_tokens: 28000, output_tokens: 120, model: 'gpt-5.2' },
];
await spendCollectedUsage({
userId: 'user-123',
conversationId: 'convo-123',
collectedUsage,
fallbackModel: 'gemini-3-flash-preview',
});
expect(mockRecordCollectedUsage).toHaveBeenCalledTimes(1);
expect(mockRecordCollectedUsage).toHaveBeenCalledWith(
expect.any(Object),
expect.objectContaining({
user: 'user-123',
conversationId: 'convo-123',
context: 'abort',
model: 'gemini-3-flash-preview',
}),
);
});
/**
* Race condition prevention: after abort middleware spends tokens,
* the collectedUsage array is cleared so AgentClient.recordCollectedUsage()
* (which shares the same array reference) sees an empty array and returns early.
*/
it('should clear collectedUsage array after spending to prevent double-spending', async () => {
const collectedUsage = [
{ input_tokens: 100, output_tokens: 50, model: 'gpt-4' },
{ input_tokens: 80, output_tokens: 40, model: 'claude-3' },
];
expect(collectedUsage.length).toBe(2);
await spendCollectedUsage({
userId: 'user-123',
conversationId: 'convo-123',
collectedUsage,
fallbackModel: 'gpt-4',
});
expect(mockRecordCollectedUsage).toHaveBeenCalledTimes(1);
expect(collectedUsage.length).toBe(0);
});
it('should await recordCollectedUsage before clearing array', async () => {
let resolved = false;
mockRecordCollectedUsage.mockImplementation(async () => {
await new Promise((resolve) => setTimeout(resolve, 10));
resolved = true;
return { input_tokens: 100, output_tokens: 50 };
});
const collectedUsage = [
{ input_tokens: 100, output_tokens: 50, model: 'gpt-4' },
{ input_tokens: 80, output_tokens: 40, model: 'claude-3' },
];
await spendCollectedUsage({
userId: 'user-123',
conversationId: 'convo-123',
collectedUsage,
fallbackModel: 'gpt-4',
});
expect(resolved).toBe(true);
expect(collectedUsage.length).toBe(0);
});
});
});