* refactor(Login & Registration)
* fix(Registration) test errors
* refactor(LoginForm & ResetPassword)
* fix(LoginForm): display 'undefined' when loading page; style(SocialButton): match OpenAI's graphics
* some refactor and style update for social logins
* style: width like OpenAI; feat: custom social login order; refactor: alphabetical socials
* fix(Registration & Login) test
* Update .env.example
* Update .env.example
* Update dotenv.md
* refactor: remove `SOCIAL_LOGIN_ORDER` for `socialLogins` configured from `librechat.yaml`
- initialized by AppService, attached as app.locals property
- rename socialLoginOrder and loginOrder to socialLogins app-wide for consistency
- update types and docs
- initialize config variable as array and not singular string to parse
- bump data-provider to 0.3.9
---------
Co-authored-by: Danny Avila <messagedaniel@protonmail.com>
* refactor(custom): add all recognized models to maxTokensMap for custom endpoint
* feat(librechat.yaml): log the custom config file on initial load
* fix(OpenAIClient): pass endpointType/endpoint to `getModelMaxTokens` call
* WIP(backend/api): custom endpoint
* WIP(frontend/client): custom endpoint
* chore: adjust typedefs for configs
* refactor: use data-provider for cache keys and rename enums and custom endpoint for better clarity and compatibility
* feat: loadYaml utility
* refactor: rename back to from and proof-of-concept for creating schemas from user-defined defaults
* refactor: remove custom endpoint from default endpointsConfig as it will be exclusively managed by yaml config
* refactor(EndpointController): rename variables for clarity
* feat: initial load custom config
* feat(server/utils): add simple `isUserProvided` helper
* chore(types): update TConfig type
* refactor: remove custom endpoint handling from model services as will be handled by config, modularize fetching of models
* feat: loadCustomConfig, loadConfigEndpoints, loadConfigModels
* chore: reorganize server init imports, invoke loadCustomConfig
* refactor(loadConfigEndpoints/Models): return each custom endpoint as standalone endpoint
* refactor(Endpoint/ModelController): spread config values after default (temporary)
* chore(client): fix type issues
* WIP: first pass for multiple custom endpoints
- add endpointType to Conversation schema
- add update zod schemas for both convo/presets to allow non-EModelEndpoint value as endpoint (also using type assertion)
- use `endpointType` value as `endpoint` where mapping to type is necessary using this field
- use custom defined `endpoint` value and not type for mapping to modelsConfig
- misc: add return type to `getDefaultEndpoint`
- in `useNewConvo`, add the endpointType if it wasn't already added to conversation
- EndpointsMenu: use user-defined endpoint name as Title in menu
- TODO: custom icon via custom config, change unknown to robot icon
* refactor(parseConvo): pass args as an object and change where used accordingly; chore: comment out 'create schema' code
* chore: remove unused availableModels field in TConfig type
* refactor(parseCompactConvo): pass args as an object and change where used accordingly
* feat: chat through custom endpoint
* chore(message/convoSchemas): avoid saving empty arrays
* fix(BaseClient/saveMessageToDatabase): save endpointType
* refactor(ChatRoute): show Spinner if endpointsQuery or modelsQuery are still loading, which is apparent with slow fetching of models/remote config on first serve
* fix(useConversation): assign endpointType if it's missing
* fix(SaveAsPreset): pass real endpoint and endpointType when saving Preset)
* chore: recorganize types order for TConfig, add `iconURL`
* feat: custom endpoint icon support:
- use UnknownIcon in all icon contexts
- add mistral and openrouter as known endpoints, and add their icons
- iconURL support
* fix(presetSchema): move endpointType to default schema definitions shared between convoSchema and defaults
* refactor(Settings/OpenAI): remove legacy `isOpenAI` flag
* fix(OpenAIClient): do not invoke abortCompletion on completion error
* feat: add responseSender/label support for custom endpoints:
- use defaultModelLabel field in endpointOption
- add model defaults for custom endpoints in `getResponseSender`
- add `useGetSender` hook which uses EndpointsQuery to determine `defaultModelLabel`
- include defaultModelLabel from endpointConfig in custom endpoint client options
- pass `endpointType` to `getResponseSender`
* feat(OpenAIClient): use custom options from config file
* refactor: rename `defaultModelLabel` to `modelDisplayLabel`
* refactor(data-provider): separate concerns from `schemas` into `parsers`, `config`, and fix imports elsewhere
* feat: `iconURL` and extract environment variables from custom endpoint config values
* feat: custom config validation via zod schema, rename and move to `./projectRoot/librechat.yaml`
* docs: custom config docs and examples
* fix(OpenAIClient/mistral): mistral does not allow singular system message, also add `useChatCompletion` flag to use openai-node for title completions
* fix(custom/initializeClient): extract env var and use `isUserProvided` function
* Update librechat.example.yaml
* feat(InputWithLabel): add className props, and forwardRef
* fix(streamResponse): handle error edge case where either messages or convos query throws an error
* fix(useSSE): handle errorHandler edge cases where error response is and is not properly formatted from API, especially when a conversationId is not yet provided, which ensures stream is properly closed on error
* feat: user_provided keys for custom endpoints
* fix(config/endpointSchema): do not allow default endpoint values in custom endpoint `name`
* feat(loadConfigModels): extract env variables and optimize fetching models
* feat: support custom endpoint iconURL for messages and Nav
* feat(OpenAIClient): add/dropParams support
* docs: update docs with default params, add/dropParams, and notes to use config file instead of `OPENAI_REVERSE_PROXY`
* docs: update docs with additional notes
* feat(maxTokensMap): add mistral models (32k context)
* docs: update openrouter notes
* Update ai_setup.md
* docs(custom_config): add table of contents and fix note about custom name
* docs(custom_config): reorder ToC
* Update custom_config.md
* Add note about `max_tokens` field in custom_config.md
* feat: add GOOGLE_MODELS env var
* feat: add gemini vision support
* refactor(GoogleClient): adjust clientOptions handling depending on model
* fix(logger): fix redact logic and redact errors only
* fix(GoogleClient): do not allow non-multiModal messages when gemini-pro-vision is selected
* refactor(OpenAIClient): use `isVisionModel` client property to avoid calling validateVisionModel multiple times
* refactor: better debug logging by correctly traversing, redacting sensitive info, and logging condensed versions of long values
* refactor(GoogleClient): allow response errors to be thrown/caught above client handling so user receives meaningful error message
debug orderedMessages, parentMessageId, and buildMessages result
* refactor(AskController): use model from client.modelOptions.model when saving intermediate messages, which requires for the progress callback to be initialized after the client is initialized
* feat(useSSE): revert to previous model if the model was auto-switched by backend due to message attachments
* docs: update with google updates, notes about Gemini Pro Vision
* fix: redis should not be initialized without USE_REDIS and increase max listeners to 20
* refactor: add gemini-pro to google Models list; use defaultModels for central model listing
* refactor(SetKeyDialog): create useMultipleKeys hook to use for Azure, export `isJson` from utils, use EModelEndpoint
* refactor(useUserKey): change variable names to make keyName setting more clear
* refactor(FileUpload): allow passing container className string
* feat(GoogleClient): Gemini support
* refactor(GoogleClient): alternate stream speed for Gemini models
* feat(Gemini): styling/settings configuration for Gemini
* refactor(GoogleClient): substract max response tokens from max context tokens if context is above 32k (I/O max is combined between the two)
* refactor(tokens): correct google max token counts and subtract max response tokens when input/output count are combined towards max context count
* feat(google/initializeClient): handle both local and user_provided credentials and write tests
* fix(GoogleClient): catch if credentials are undefined, handle if serviceKey is string or object correctly, handle no examples passed, throw error if not a Generative Language model and no service account JSON key is provided, throw error if it is a Generative m
odel, but not google API key was provided
* refactor(loadAsyncEndpoints/google): activate Google endpoint if either the service key JSON file is provided in /api/data, or a GOOGLE_KEY is defined.
* docs: updated Google configuration
* fix(ci): Mock import of Service Account Key JSON file (auth.json)
* Update apis_and_tokens.md
* feat: increase max output tokens slider for gemini pro
* refactor(GoogleSettings): handle max and default maxOutputTokens on model change
* chore: add sensitive redact regex
* docs: add warning about data privacy
* Update apis_and_tokens.md
* chore: bump vite, vitejs/plugin-react, mark client package as esm, move react-query as a peer dep in data-provider
* chore: import changes due to new data-provider export strategy, also fix type imports where applicable
* chore: export react-query services as separate to avoid react dependencies in /api/
* chore: suppress sourcemap warnings and polyfill node:path which is used by filenamify
TODO: replace filenamify with an alternative and REMOVE polyfill
* chore: /api/ changes to support `librechat-data-provider`
* refactor: rewrite Dockerfile.multi in light of /api/ changes to support `librechat-data-provider`
* chore: remove volume mapping to node_modules directories in default compose file
* chore: remove schemas from /api/ as is no longer needed with use of `librechat-data-provider`
* fix(ci): jest `librechat-data-provider/react-query` module resolution
* feat: update PaLM icons
* feat: add additional google models
* POC: formatting inputs for Vertex AI streaming
* refactor: move endpoints services outside of /routes dir to /services/Endpoints
* refactor: shorten schemas import
* refactor: rename PALM to GOOGLE
* feat: make Google editable endpoint
* feat: reusable Ask and Edit controllers based off Anthropic
* chore: organize imports/logic
* fix(parseConvo): include examples in googleSchema
* fix: google only allows odd number of messages to be sent
* fix: pass proxy to AnthropicClient
* refactor: change `google` altName to `Google`
* refactor: update getModelMaxTokens and related functions to handle maxTokensMap with nested endpoint model key/values
* refactor: google Icon and response sender changes (Codey and Google logo instead of PaLM in all cases)
* feat: google support for maxTokensMap
* feat: google updated endpoints with Ask/Edit controllers, buildOptions, and initializeClient
* feat(GoogleClient): now builds prompt for text models and supports real streaming from Vertex AI through langchain
* chore(GoogleClient): remove comments, left before for reference in git history
* docs: update google instructions (WIP)
* docs(apis_and_tokens.md): add images to google instructions
* docs: remove typo apis_and_tokens.md
* Update apis_and_tokens.md
* feat(Google): use default settings map, fully support context for both text and chat models, fully support examples for chat models
* chore: update more PaLM references to Google
* chore: move playwright out of workflows to avoid failing tests
* refactor: move endpoint services to own directory
* refactor: make endpointconfig handling more concise, separate logic, and cache result for subsequent serving
* refactor: ModelController gets same treatment as EndpointController, draft OverrideController
* wip: flesh out override controller more to return real value
* refactor: client/api changes in anticipation of override
