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* 🗑️ chore: Remove unused Legacy Provider clients and related helpers * Deleted OpenAIClient and GoogleClient files along with their associated tests. * Removed references to these clients in the clients index file. * Cleaned up typedefs by removing the OpenAISpecClient export. * Updated chat controllers to use the OpenAI SDK directly instead of the removed client classes. * chore/remove-openapi-specs * 🗑️ chore: Remove unused mergeSort and misc utility functions * Deleted mergeSort.js and misc.js files as they are no longer needed. * Removed references to cleanUpPrimaryKeyValue in messages.js and adjusted related logic. * Updated mongoMeili.ts to eliminate local implementations of removed functions. * chore: remove legacy endpoints * chore: remove all plugins endpoint related code * chore: remove unused prompt handling code and clean up imports * Deleted handleInputs.js and instructions.js files as they are no longer needed. * Removed references to these files in the prompts index.js. * Updated docker-compose.yml to simplify reverse proxy configuration. * chore: remove unused LightningIcon import from Icons.tsx * chore: clean up translation.json by removing deprecated and unused keys * chore: update Jest configuration and remove unused mock file * Simplified the setupFiles array in jest.config.js by removing the fetchEventSource mock. * Deleted the fetchEventSource.js mock file as it is no longer needed. * fix: simplify endpoint type check in Landing and ConversationStarters components * Updated the endpoint type check to use strict equality for better clarity and performance. * Ensured consistency in the handling of the azureOpenAI endpoint across both components. * chore: remove unused dependencies from package.json and package-lock.json * chore: remove legacy EditController, associated routes and imports * chore: update banResponse logic to refine request handling for banned users * chore: remove unused validateEndpoint middleware and its references * chore: remove unused 'res' parameter from initializeClient in multiple endpoint files * chore: remove unused 'isSmallScreen' prop from BookmarkNav and NewChat components; clean up imports in ArchivedChatsTable and useSetIndexOptions hooks; enhance localization in PromptVersions * chore: remove unused import of Constants and TMessage from MobileNav; retain only necessary QueryKeys import * chore: remove unused TResPlugin type and related references; clean up imports in types and schemas |
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LibreChat Helm Chart
This Librechat Helm Chart provides an easy, light weight template to deploy LibreChat on Kubernetes
Variables
In this Chart, LibreChat will only work with environment Variables. You can Specify Vars and Secret using an existing Secret (This can be generated by creating an Env File and converting it to a Kubernetes Secret --from-env-file)
Setup
- Generate Variables
Generate
CREDS_KEY,JWT_SECRET,JWT_REFRESH_SECRETandMEILI_MASTER_KEYusingopenssl rand -hex 32andCREDS_IVusing openssl rand -hex 16. place them in a secret like this (If you want to change the secret name, remember to change it in your helm values):
apiVersion: v1
kind: Secret
metadata:
name: librechat-credentials-env
namespace: <librechat-chart-namespace>
type: Opaque
stringData:
CREDS_KEY: <generated value>
JWT_SECRET: <generated value>
JWT_REFRESH_SECRET: <generated value>
MEILI_MASTER_KEY: <generated value>
- Add Credentials to the Secret Dependant of the Model you want to use, create Credentials in your provider and add them to the Secret:
apiVersion: v1
kind: Secret
. . . .
OPENAI_API_KEY: <your secret value>
-
Apply the Secret to the Cluster
-
Fill out values.yaml and apply the Chart to the Cluster