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https://github.com/danny-avila/LibreChat.git
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* fix: Token Spending Logic for Multi-Agents on Abort Scenarios * Implemented logic to skip token spending if a conversation is aborted, preventing double-spending. * Introduced `spendCollectedUsage` function to handle token spending for multiple models during aborts, ensuring accurate accounting for parallel agents. * Updated `GenerationJobManager` to store and retrieve collected usage data for improved abort handling. * Added comprehensive tests for the new functionality, covering various scenarios including cache token handling and parallel agent usage. * fix: Memory Context Handling for Multi-Agents * Refactored `buildMessages` method to pass memory context to parallel agents, ensuring they share the same user context. * Improved handling of memory context when no existing instructions are present for parallel agents. * Added comprehensive tests to verify memory context propagation and behavior under various scenarios, including cases with no memory available and empty agent configurations. * Enhanced logging for better traceability of memory context additions to agents. * chore: Memory Context Documentation for Parallel Agents * Updated documentation in the `AgentClient` class to clarify the in-place mutation of agentConfig objects when passing memory context to parallel agents. * Added notes on the implications of mutating objects directly to ensure all parallel agents receive the correct memory context before execution. * chore: UsageMetadata Interface docs for Token Spending * Expanded the UsageMetadata interface to support both OpenAI and Anthropic cache token formats. * Added detailed documentation for cache token properties, including mutually exclusive fields for different model types. * Improved clarity on how to access cache token details for accurate token spending tracking. * fix: Enhance Token Spending Logic in Abort Middleware * Refactored `spendCollectedUsage` function to utilize Promise.all for concurrent token spending, improving performance and ensuring all operations complete before clearing the collectedUsage array. * Added documentation to clarify the importance of clearing the collectedUsage array to prevent double-spending in abort scenarios. * Updated tests to verify the correct behavior of the spending logic and the clearing of the array after spending operations.
428 lines
13 KiB
JavaScript
428 lines
13 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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const mockSpendTokens = jest.fn().mockResolvedValue();
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const mockSpendStructuredTokens = jest.fn().mockResolvedValue();
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jest.mock('~/models/spendTokens', () => ({
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spendTokens: (...args) => mockSpendTokens(...args),
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spendStructuredTokens: (...args) => mockSpendStructuredTokens(...args),
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}));
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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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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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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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}));
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jest.mock('./abortRun', () => ({
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abortRun: jest.fn(),
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}));
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// Import the module after mocks are set up
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// We need to extract the spendCollectedUsage function for testing
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// Since it's not exported, we'll test it through the handleAbort flow
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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 logic', () => {
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// Since spendCollectedUsage is not exported, we test the logic directly
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// by replicating the function here for unit testing
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const spendCollectedUsage = async ({
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userId,
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conversationId,
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collectedUsage,
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fallbackModel,
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}) => {
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if (!collectedUsage || collectedUsage.length === 0) {
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return;
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}
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const spendPromises = [];
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for (const usage of collectedUsage) {
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if (!usage) {
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continue;
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}
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const cache_creation =
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Number(usage.input_token_details?.cache_creation) ||
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Number(usage.cache_creation_input_tokens) ||
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0;
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const cache_read =
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Number(usage.input_token_details?.cache_read) ||
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Number(usage.cache_read_input_tokens) ||
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0;
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const txMetadata = {
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context: 'abort',
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conversationId,
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user: userId,
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model: usage.model ?? fallbackModel,
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};
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if (cache_creation > 0 || cache_read > 0) {
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spendPromises.push(
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mockSpendStructuredTokens(txMetadata, {
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promptTokens: {
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input: usage.input_tokens,
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write: cache_creation,
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read: cache_read,
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},
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completionTokens: usage.output_tokens,
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}).catch(() => {
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// Log error but don't throw
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}),
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);
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continue;
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}
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spendPromises.push(
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mockSpendTokens(txMetadata, {
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promptTokens: usage.input_tokens,
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completionTokens: usage.output_tokens,
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}).catch(() => {
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// Log error but don't throw
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}),
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);
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}
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// Wait for all token spending to complete
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await Promise.all(spendPromises);
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// Clear the array to prevent double-spending
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collectedUsage.length = 0;
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};
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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(mockSpendTokens).not.toHaveBeenCalled();
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expect(mockSpendStructuredTokens).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(mockSpendTokens).not.toHaveBeenCalled();
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expect(mockSpendStructuredTokens).not.toHaveBeenCalled();
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});
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it('should skip null entries in collectedUsage', async () => {
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const collectedUsage = [
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{ input_tokens: 100, output_tokens: 50, model: 'gpt-4' },
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null,
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{ input_tokens: 200, output_tokens: 60, model: 'gpt-4' },
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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(mockSpendTokens).toHaveBeenCalledTimes(2);
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});
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it('should spend tokens for single model', 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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});
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expect(mockSpendTokens).toHaveBeenCalledTimes(1);
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expect(mockSpendTokens).toHaveBeenCalledWith(
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expect.objectContaining({
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context: 'abort',
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conversationId: 'convo-123',
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user: 'user-123',
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model: 'gpt-4',
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}),
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{ promptTokens: 100, completionTokens: 50 },
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);
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});
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it('should spend tokens 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(mockSpendTokens).toHaveBeenCalledTimes(3);
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// Verify each model was called
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expect(mockSpendTokens).toHaveBeenNthCalledWith(
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1,
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expect.objectContaining({ model: 'gpt-4' }),
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{ promptTokens: 100, completionTokens: 50 },
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);
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expect(mockSpendTokens).toHaveBeenNthCalledWith(
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2,
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expect.objectContaining({ model: 'claude-3' }),
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{ promptTokens: 80, completionTokens: 40 },
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);
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expect(mockSpendTokens).toHaveBeenNthCalledWith(
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3,
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expect.objectContaining({ model: 'gemini-pro' }),
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{ promptTokens: 120, completionTokens: 60 },
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);
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});
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it('should use fallbackModel when usage.model is missing', async () => {
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const collectedUsage = [{ input_tokens: 100, output_tokens: 50 }];
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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: 'fallback-model',
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});
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expect(mockSpendTokens).toHaveBeenCalledWith(
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expect.objectContaining({ model: 'fallback-model' }),
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expect.any(Object),
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);
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});
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it('should use spendStructuredTokens for OpenAI format cache tokens', async () => {
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const collectedUsage = [
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{
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input_tokens: 100,
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output_tokens: 50,
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model: 'gpt-4',
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input_token_details: {
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cache_creation: 20,
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cache_read: 10,
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},
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},
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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(mockSpendStructuredTokens).toHaveBeenCalledTimes(1);
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expect(mockSpendTokens).not.toHaveBeenCalled();
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expect(mockSpendStructuredTokens).toHaveBeenCalledWith(
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expect.objectContaining({ model: 'gpt-4', context: 'abort' }),
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{
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promptTokens: {
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input: 100,
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write: 20,
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read: 10,
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},
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completionTokens: 50,
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},
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);
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});
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it('should use spendStructuredTokens for Anthropic format cache tokens', async () => {
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const collectedUsage = [
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{
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input_tokens: 100,
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output_tokens: 50,
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model: 'claude-3',
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cache_creation_input_tokens: 25,
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cache_read_input_tokens: 15,
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},
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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: 'claude-3',
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});
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expect(mockSpendStructuredTokens).toHaveBeenCalledTimes(1);
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expect(mockSpendTokens).not.toHaveBeenCalled();
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expect(mockSpendStructuredTokens).toHaveBeenCalledWith(
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expect.objectContaining({ model: 'claude-3' }),
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{
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promptTokens: {
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input: 100,
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write: 25,
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read: 15,
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},
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completionTokens: 50,
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},
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);
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});
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it('should handle mixed cache and non-cache entries', async () => {
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const collectedUsage = [
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{ input_tokens: 100, output_tokens: 50, model: 'gpt-4' },
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{
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input_tokens: 150,
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output_tokens: 30,
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model: 'claude-3',
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cache_creation_input_tokens: 20,
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cache_read_input_tokens: 10,
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},
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{ input_tokens: 200, output_tokens: 20, 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(mockSpendTokens).toHaveBeenCalledTimes(2);
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expect(mockSpendStructuredTokens).toHaveBeenCalledTimes(1);
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});
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it('should handle real-world parallel agent abort scenario', async () => {
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// Simulates: Primary agent (gemini) + addedConvo agent (gpt-5) aborted mid-stream
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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(mockSpendTokens).toHaveBeenCalledTimes(2);
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// Primary model
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expect(mockSpendTokens).toHaveBeenNthCalledWith(
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1,
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expect.objectContaining({ model: 'gemini-3-flash-preview' }),
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{ promptTokens: 31596, completionTokens: 151 },
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);
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// Parallel model (addedConvo)
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expect(mockSpendTokens).toHaveBeenNthCalledWith(
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2,
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expect.objectContaining({ model: 'gpt-5.2' }),
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{ promptTokens: 28000, completionTokens: 120 },
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);
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});
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it('should clear collectedUsage array after spending to prevent double-spending', async () => {
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// This tests the race condition fix: 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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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(mockSpendTokens).toHaveBeenCalledTimes(2);
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// The array should be cleared after spending
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expect(collectedUsage.length).toBe(0);
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});
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it('should await all token spending operations before clearing array', async () => {
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// Ensure we don't clear the array before spending completes
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let spendCallCount = 0;
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mockSpendTokens.mockImplementation(async () => {
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spendCallCount++;
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// Simulate async delay
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await new Promise((resolve) => setTimeout(resolve, 10));
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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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// Both spend calls should have completed
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expect(spendCallCount).toBe(2);
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// Array should be cleared after awaiting
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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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