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