LibreChat/api/server/services/Config/loadAsyncEndpoints.js

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const path = require('path');
const { logger } = require('@librechat/data-schemas');
const { loadServiceKey, isUserProvided } = require('@librechat/api');
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * 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
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const { EModelEndpoint } = require('librechat-data-provider');
const { config } = require('./EndpointService');
const { openAIApiKey, azureOpenAIApiKey, useAzurePlugins, userProvidedOpenAI, googleKey } = config;
/**
* Load async endpoints and return a configuration object
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * 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
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* @param {Express.Request} req - The request object
*/
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * 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
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async function loadAsyncEndpoints(req) {
feat: Google Gemini ❇️ (#1355) * 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
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let serviceKey, googleUserProvides;
/** Check if GOOGLE_KEY is provided at all(including 'user_provided') */
const isGoogleKeyProvided = googleKey && googleKey.trim() !== '';
if (isGoogleKeyProvided) {
/** If GOOGLE_KEY is provided, check if it's user_provided */
googleUserProvides = isUserProvided(googleKey);
} else {
/** Only attempt to load service key if GOOGLE_KEY is not provided */
const serviceKeyPath =
process.env.GOOGLE_SERVICE_KEY_FILE || path.join(__dirname, '../../..', 'data', 'auth.json');
try {
serviceKey = await loadServiceKey(serviceKeyPath);
} catch (error) {
logger.error('Error loading service key', error);
serviceKey = null;
}
}
const google = serviceKey || isGoogleKeyProvided ? { userProvide: googleUserProvides } : false;
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * 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
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const useAzure = req.app.locals[EModelEndpoint.azureOpenAI]?.plugins;
const gptPlugins =
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * 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
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useAzure || openAIApiKey || azureOpenAIApiKey
? {
availableAgents: ['classic', 'functions'],
userProvide: useAzure ? false : userProvidedOpenAI,
userProvideURL: useAzure
? false
: config[EModelEndpoint.openAI]?.userProvideURL ||
config[EModelEndpoint.azureOpenAI]?.userProvideURL,
azure: useAzurePlugins || useAzure,
}
: false;
return { google, gptPlugins };
}
module.exports = loadAsyncEndpoints;