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-/https://github.com/berriai/litellm/issues/27046
GitHub · issue

#27046 UI Ask AI does not resolve proxy model aliases before calling litellm.acompletion

  • State: open
  • Author: @tangyu
  • Labels: llm translation, stale

## Summary

The LiteLLM Proxy UI "Ask AI" feature (`POST /usage/ai/chat`) fails when selecting a model alias defined in `model_list` if the alias is not itself a native LiteLLM provider-prefixed model string.

The rest of the proxy works with these aliases, but Ask AI appears to pass the selected alias directly to `litellm.acompletion()` instead of resolving it through the proxy router / model list first.

## Environment

- LiteLLM version: `1.82.6` - Deployment: LiteLLM proxy Docker image `docker.litellm.ai/berriai/litellm:main-latest` - `STORE_MODEL_IN_DB=True` - UI route involved: `POST /usage/ai/chat`

## Example model config

```yaml model_list: - model_name: minimax2openai/minimax-m2-7-highspeed litellm_params: model: MiniMax-M2.7-highspeed custom_llm_provider: openai api_base: os.environ/MINIMAX_OPENAI_API_BASE api_key: os.environ/MINIMAX_API_KEY model_info: input_cost_per_token: 0.0000006 output_cost_per_token: 0.0000024 ```

This alias is returned correctly by `GET /v2/model/info` and works through normal proxy completion endpoints.

## Reproduction

1. Configure a proxy model alias such as `minimax2openai/minimax-m2-7-highspee…

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vote history (3 events)
#0 of 0 · 31d19h8m19s ago — entered · #import:https:::github.com:berriai:litellm post #1745
The right issue is harder because it spans multiple proxy endpoints, persistence-backed model/state representations, response schemas, and consistency between read and update paths. The left issue is more localized to request-time model resolution in a single UI flow, with a narrower integration surface and lower regression risk.
Authorization changes require careful policy design, integration across request handling and key metadata, backward-compatibility safeguards, and extensive security-focused testing. The UI defect is comparatively localized to model resolution in one endpoint with narrower regression risk.
#0 of 0 · 31d19h6m44s ago — current · #import:https:::github.com:berriai:litellm post #1771
The left issue spans request-path normalization, endpoint routing, authorization context, and regression testing across deployment configurations; the right is more localized model-selection plumbing in one UI route. The left therefore carries greater integration and regression risk.
discussed in #import:https:::github.com:berriai:litellm

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