fix: per-user model list support with resolved headers

[bensi94]

Summary

When using an upstream model provider like LiteLLM with JWT/OIDC-based authentication, different users may have access to different models based on their role and permissions. The previous implementation cached the models config globally (MODELS_CONFIG key), causing all users to see the same model list regardless of their identity or authorization level.

This PR fixes two issues:

1. Removes global model config caching in ModelController.js — models are now fetched fresh on each request, ensuring per-user model lists are correctly returned. This applies to both master-key and user-token scenarios, but is especially important when tokens vary per user.

2. Resolves custom headers through resolveHeaders() in fetchModels() — template placeholders like {{LIBRECHAT_OPENID_ID_TOKEN}} in config headers are now properly expanded per-user before the model fetch request. Custom headers are merged after default auth headers, so config-level authorization headers (e.g. forwarding an OIDC token) take precedence over the default Bearer <apiKey>.

Example config that now works correctly:

headers:
  authorization: "Bearer {{LIBRECHAT_OPENID_ID_TOKEN}}"

Change Type

• Bug fix (non-breaking change which fixes an issue)

Testing

• Updated existing tests in models.spec.ts to verify:
  • Custom headers are resolved via resolveHeaders() before being sent
  • Custom authorization header overrides the default Bearer token
  • Anthropic endpoint merges custom headers on top of its defaults
• Verified that ModelController.js no longer references CacheKeys or getLogStores

Test Configuration:

• LibreChat with LiteLLM backend using OIDC JWT authentication
• Config with headers.authorization: "Bearer {{LIBRECHAT_OPENID_ID_TOKEN}}"
• Multiple users with different role-based model access

Checklist

• My code adheres to this project's style guidelines
• I have performed a self-review of my own code
• I have commented in any complex areas of my code
• My changes do not introduce new warnings
• I have written tests demonstrating that my changes are effective or that my feature works
• Local unit tests pass with my changes

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