mirror of
https://github.com/danny-avila/LibreChat.git
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65990a33e9
* 📥 fix: Use Endpoint-Aware Default Model on Imported Conversations Claude conversations imported from claude.ai's data export display "gpt-4o-mini" in the chat UI until the page is refreshed, and any attempt to send a message before refreshing fails with "The model 'gpt-4o-mini' is not available for Anthropic." Root cause: ImportBatchBuilder.finishConversation() unconditionally defaulted the saved conversation's `model` field to openAISettings.model.default, regardless of `this.endpoint`. Claude exports don't carry a model name, so every imported Claude conversation landed with endpoint=anthropic but model=gpt-4o-mini. Fix: pick the default based on `this.endpoint` via a small lookup (openAI -> gpt-4o-mini, anthropic -> claude-3-5-sonnet-latest), keeping the existing OpenAI default as the fallback for unknown endpoints. Fixes #12844 * 🪄 refactor: Resolve Import Default Model From `modelsConfig` Replace the hardcoded per-endpoint default lookup added in the previous commit with a runtime resolver that consults the same models config the chat UI uses (`getModelsConfig` in ModelController -> `loadDefaultModels` + `loadConfigModels`). This way an imported conversation defaults to a model the LibreChat instance has actually configured / discovered for the endpoint, instead of a hardcoded constant that may not exist on this deployment. Resolution order: 1. First non-empty model in `modelsConfig[endpoint]`. 2. Per-endpoint hardcoded fallback (anthropic/openAI settings) if the runtime config is empty for the endpoint or `getModelsConfig` throws. 3. `openAISettings.model.default` if even the per-endpoint fallback is missing (unknown endpoint). `importBatchBuilder.finishConversation` now accepts an optional `defaultModel` argument; each importer resolves it once at the top via `resolveImportDefaultModel({ endpoint, requestUserId, userRole })` and threads it through. ChatGPT message-level model selection also falls back to the resolved default before the hardcoded gpt-4o-mini.
67 lines
2.5 KiB
JavaScript
67 lines
2.5 KiB
JavaScript
const { logger, getTenantId } = require('@librechat/data-schemas');
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const { EModelEndpoint, openAISettings, anthropicSettings } = require('librechat-data-provider');
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const { getModelsConfig } = require('~/server/controllers/ModelController');
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/**
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* Last-resort hardcoded defaults used only when the runtime models config is
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* unavailable or returns no models for the endpoint.
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*/
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const FALLBACK_MODEL_BY_ENDPOINT = {
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[EModelEndpoint.openAI]: openAISettings.model.default,
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[EModelEndpoint.anthropic]: anthropicSettings.model.default,
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};
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/**
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* Picks the first available model for an endpoint from a runtime models config.
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*
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* @param {string} endpoint - The endpoint key (e.g. EModelEndpoint.anthropic).
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* @param {TModelsConfig} [modelsConfig] - Map of endpoint -> available model list.
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* @returns {string | undefined} The first model for the endpoint, or undefined.
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*/
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function pickFirstConfiguredModel(endpoint, modelsConfig) {
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const models = modelsConfig?.[endpoint];
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if (!Array.isArray(models)) {
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return undefined;
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}
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for (const model of models) {
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if (typeof model === 'string' && model.length > 0) {
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return model;
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}
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}
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return undefined;
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}
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/**
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* Resolves the default model that imported conversations should be saved with
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* for a given endpoint. Prefers the first model exposed by the runtime models
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* config (admin-configured / provider-discovered), and only falls back to the
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* hardcoded per-endpoint default if the runtime config is empty or fails.
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*
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* @param {object} args
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* @param {string} args.endpoint - The endpoint key the import is targeting.
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* @param {string} args.requestUserId - The id of the importing user.
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* @param {string} [args.userRole] - The role of the importing user.
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* @returns {Promise<string>} The default model name to persist on the conversation.
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*/
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async function resolveImportDefaultModel({ endpoint, requestUserId, userRole }) {
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try {
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const modelsConfig = await getModelsConfig({
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user: { id: requestUserId, role: userRole, tenantId: getTenantId() },
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});
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const configured = pickFirstConfiguredModel(endpoint, modelsConfig);
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if (configured) {
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return configured;
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}
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} catch (error) {
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logger.warn(
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`[import] Failed to resolve default model from modelsConfig for ${endpoint}: ${error.message}`,
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);
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}
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return FALLBACK_MODEL_BY_ENDPOINT[endpoint] ?? openAISettings.model.default;
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}
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module.exports = {
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FALLBACK_MODEL_BY_ENDPOINT,
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pickFirstConfiguredModel,
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resolveImportDefaultModel,
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};
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