* 🔧 refactor: Improve Google Key Handling in `loadAsyncEndpoints`
- Enhanced logic to check if GOOGLE_KEY is provided, including user-provided checks.
- Updated service key loading mechanism to only attempt loading if GOOGLE_KEY is not provided.
- Added error logging for service key loading failures.
* 🔧 refactor: Enhance service key loading logic in `initializeClient`
* adding youtube tool
* refactor: use short `url` param instead of `videoUrl`
* refactor: move API key retrieval to a separate credentials module
* refactor: remove unnecessary `isEdited` message property
* refactor: remove unnecessary `isEdited` message property pt. 2
* refactor: YouTube Tool with new `tool()` generator, handle tools already created by new `tool` generator
* fix: only reset request data for multi-convo messages
* refactor: enhance YouTube tool by adding transcript parsing and returning structured JSON responses
* refactor: update transcript parsing to handle raw response and clean up text output
* feat: support toolkits and refactor YouTube tool as a toolkit for better LLM usage
* refactor: remove unused OpenAPI specs and streamline tools transformation in loadAsyncEndpoints
* refactor: implement manifestToolMap for better tool management and streamline authentication handling
* feat: support toolkits for assistants
* refactor: rename loadedTools to toolDefinitions for clarity in PluginController and assistant controllers
* feat: complete support of toolkits for assistants
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Co-authored-by: Danilo Pejakovic <danilo.pejakovic@leoninestudios.com>
* chore: bump browserslist-db@latest
* refactor(EndpointService): simplify with `generateConfig`, utilize optional baseURL for OpenAI-based endpoints, use `isUserProvided` helper fn wherever needed
* refactor(custom/initializeClient): use standardized naming for common variables
* feat: user provided baseURL for openAI-based endpoints
* refactor(custom/initializeClient): re-order operations
* fix: knownendpoints enum definition and add FetchTokenConfig, bump data-provider
* refactor(custom): use tokenKey dependent on userProvided conditions for caching and fetching endpointTokenConfig, anticipate token rates from custom config
* refactor(custom): assure endpointTokenConfig is only accessed from cache if qualifies for fetching
* fix(ci): update tests for initializeClient based on userProvideURL changes
* fix(EndpointService): correct baseURL env var for assistants: `ASSISTANTS_BASE_URL`
* fix: unnecessary run cancellation on res.close() when response.run is completed
* feat(assistants): user provided URL option
* ci: update tests and add test for `assistants` endpoint
* chore: leaner condition for request closing
* chore: more descriptive error message to provide keys again
* wip: first pass for azure endpoint schema
* refactor: azure config to return groupMap and modelConfigMap
* wip: naming and schema changes
* refactor(errorsToString): move to data-provider
* feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors
* feat(AppService): load Azure groups
* refactor(azure): use imported types, write `mapModelToAzureConfig`
* refactor: move `extractEnvVariable` to data-provider
* refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests
* refactor(AppService): ensure each model is properly configured on startup
* refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config
* feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file
* refactor: redefine types as well as load azureOpenAI models from config file
* chore(ci): fix test description naming
* feat(azureOpenAI): use validated model grouping for request authentication
* chore: bump data-provider following rebase
* chore: bump config file version noting significant changes
* feat: add title options and switch azure configs for titling and vision requests
* feat: enable azure plugins from config file
* fix(ci): pass tests
* chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated
* fix(fetchModels): early return if apiKey not passed
* chore: fix azure config typing
* refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions
* feat(createLLM): use `azureOpenAIBasePath`
* feat(parsers): resolveHeaders
* refactor(extractBaseURL): handle invalid input
* feat(OpenAIClient): handle headers and baseURL for azureConfig
* fix(ci): pass `OpenAIClient` tests
* chore: extract env var for azureOpenAI group config, baseURL
* docs: azureOpenAI config setup docs
* feat: safe check of potential conflicting env vars that map to unique placeholders
* fix: reset apiKey when model switches from originally requested model (vision or title)
* chore: linting
* docs: CONFIG_PATH notes in custom_config.md
* 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
* 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
