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✨ Autocomplete

Open WebUI offers an AI-powered Autocomplete feature that suggests text completions in real-time as you type your prompt. It acts like a "Copilot" for your chat input, helping you craft prompts faster using your configured task model.

How It Works​

When enabled, Open WebUI monitors your input in the chat box. When you pause typing, it sends your current text to a lightweight Task Model. This model predicts the likely next words or sentences, which appear as "ghost text" overlaying your input.

  • Accept Suggestion: Press Tab (or the Right Arrow key) to accept the suggestion.
  • Reject/Ignore: Simply keep typing to overwrite the suggestion.
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Performance Recommendation

Autocomplete functionality relies heavily on the response speed of your Task Model. We recommend using a small, fast, non-reasoning model to ensure suggestions appear instantly.

Recommended Models:

  • Llama 3.2 (1B or 3B)
  • Qwen 3 (0.6B or 3B)
  • Gemma 3 (1B or 4B)
  • GPT-5 Nano (Optimized for low latency)

Avoid using "Reasoning" models (e.g., o1, o3) or heavy Chain-of-Thought models for this feature, as the latency will make the autocomplete experience sluggish.

Configuration​

The Autocomplete feature is controlled by a two-layer system: Global availability and User preference.

1. Global Configuration (Admin)​

Admins control whether the autocomplete feature is available on the server.

1. Configuring Autocomplete (Global)​

Admin Panel Settings: Go to Admin Settings > Interface > Task Model and toggle Autocomplete Generation.

2. User Configuration (Personal)​

Even if enabled globally, individual users can turn it off for themselves if they find it distracting.

  • Go to Settings > Interface.
  • Toggle Autocomplete Generation.
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If the Admin has disabled Autocomplete globally, users will not be able to enable it in their personal settings.

Performance & Troubleshooting​

Why aren't suggestions appearing?​

  1. Check Settings: Ensure it is enabled in both Admin and User settings.
  2. Task Model: Go to Admin Settings > Interface and verify a Task Model is selected. If no model is selected, the feature cannot generate predictions.
  3. Latency: If your Task Model is large or running on slow hardware, predictions might arrive too late to be useful. Switch to a smaller model.
  4. Reasoning Models: Ensure you are not using a "Reasoning" model (like o1 or o3), as their internal thought process creates excessive latency that breaks real-time autocomplete.

Performance Impact​

Autocomplete sends a request to your LLM essentially every time you pause typing (debounced).

  • Local Models: This can consume significant GPU/CPU resources on the host machine.
  • API Providers: This will generate a high volume of API calls (though usually with very short token counts). Be mindful of your provider's Rate Limits (Requests Per Minute/RPM and Tokens Per Minute/TPM) to avoid interruptions.
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For multi-user instances running on limited local hardware, we recommend disabling Autocomplete to prioritize resources for actual chat generation.