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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`.
141 lines
4.7 KiB
JavaScript
141 lines
4.7 KiB
JavaScript
// errorHandler.js
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const { logger } = require('@librechat/data-schemas');
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const { CacheKeys, ViolationTypes } = require('librechat-data-provider');
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const { sendResponse } = require('~/server/middleware/error');
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const { recordUsage } = require('~/server/services/Threads');
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const getLogStores = require('~/cache/getLogStores');
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const { getConvo } = require('~/models');
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/**
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* @typedef {Object} ErrorHandlerContext
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* @property {OpenAIClient} openai - The OpenAI client
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* @property {string} run_id - The run ID
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* @property {boolean} completedRun - Whether the run has completed
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* @property {string} assistant_id - The assistant ID
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* @property {string} conversationId - The conversation ID
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* @property {string} parentMessageId - The parent message ID
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* @property {string} responseMessageId - The response message ID
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* @property {string} endpoint - The endpoint being used
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* @property {string} cacheKey - The cache key for the current request
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*/
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/**
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* @typedef {Object} ErrorHandlerDependencies
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* @property {ServerRequest} req - The Express request object
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* @property {Express.Response} res - The Express response object
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* @property {() => ErrorHandlerContext} getContext - Function to get the current context
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* @property {string} [originPath] - The origin path for the error handler
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*/
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/**
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* Creates an error handler function with the given dependencies
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* @param {ErrorHandlerDependencies} dependencies - The dependencies for the error handler
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* @returns {(error: Error) => Promise<void>} The error handler function
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*/
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const createErrorHandler = ({ req, res, getContext, originPath = '/assistants/chat/' }) => {
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const cache = getLogStores(CacheKeys.ABORT_KEYS);
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/**
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* Handles errors that occur during the chat process
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* @param {Error} error - The error that occurred
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* @returns {Promise<void>}
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*/
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return async (error) => {
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const {
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openai,
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run_id,
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endpoint,
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cacheKey,
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completedRun,
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assistant_id,
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conversationId,
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parentMessageId,
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responseMessageId,
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} = getContext();
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const defaultErrorMessage =
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'The Assistant run failed to initialize. Try sending a message in a new conversation.';
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const messageData = {
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assistant_id,
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conversationId,
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parentMessageId,
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sender: 'System',
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user: req.user.id,
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shouldSaveMessage: false,
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messageId: responseMessageId,
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endpoint,
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};
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if (error.message === 'Run cancelled') {
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return res.end();
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} else if (error.message === 'Request closed' && completedRun) {
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return;
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} else if (error.message === 'Request closed') {
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logger.debug(`[${originPath}] Request aborted on close`);
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} else if (/Files.*are invalid/.test(error.message)) {
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const errorMessage = `Files are invalid, or may not have uploaded yet.${
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endpoint === 'azureAssistants'
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? " If using Azure OpenAI, files are only available in the region of the assistant's model at the time of upload."
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: ''
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}`;
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return sendResponse(req, res, messageData, errorMessage);
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} else if (error?.message?.includes('string too long')) {
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return sendResponse(
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req,
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res,
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messageData,
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'Message too long. The Assistants API has a limit of 32,768 characters per message. Please shorten it and try again.',
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);
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} else if (error?.message?.includes(ViolationTypes.TOKEN_BALANCE)) {
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return sendResponse(req, res, messageData, error.message);
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} else {
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logger.error(`[${originPath}]`, error);
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}
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if (!openai || !run_id) {
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return sendResponse(req, res, messageData, defaultErrorMessage);
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}
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await new Promise((resolve) => setTimeout(resolve, 2000));
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try {
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const status = await cache.get(cacheKey);
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if (status === 'cancelled') {
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logger.debug(`[${originPath}] Run already cancelled`);
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return res.end();
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}
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await cache.delete(cacheKey);
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} catch (error) {
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logger.error(`[${originPath}] Error cancelling run`, error);
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}
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await new Promise((resolve) => setTimeout(resolve, 2000));
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let run;
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try {
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await recordUsage({
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...run.usage,
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model: run.model,
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user: req.user.id,
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conversationId,
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});
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} catch (error) {
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logger.error(`[${originPath}] Error fetching or processing run`, error);
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}
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let finalEvent;
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try {
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finalEvent = {
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final: true,
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conversation: await getConvo(req.user.id, conversationId),
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};
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} catch (error) {
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logger.error(`[${originPath}] Error finalizing error process`, error);
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return sendResponse(req, res, messageData, 'The Assistant run failed');
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}
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return sendResponse(req, res, finalEvent);
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};
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};
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module.exports = { createErrorHandler };
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