> ## Documentation Index
> Fetch the complete documentation index at: https://docs.experio.cloud/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Configurations

> Configure and manage AI language models

## Overview

Model configurations define which AI language models are available in your Experio deployment. You can configure multiple models from different providers and control which ones are available to end users.

Navigate to **Admin > Settings > Model Configurations**.

## Viewing Models

The model configurations page lists all configured models with:

* Name (internal identifier)
* Display Name (shown to users)
* Type (LLM, Embedding, Reranking)
* Provider
* Active status
* User-Facing status (whether end users can select this model)

## Creating a Model Configuration

Click **Create New** and fill in:

| Field                 | Description                                                                                         |
| --------------------- | --------------------------------------------------------------------------------------------------- |
| **Name**              | Internal identifier (no spaces, used in API calls)                                                  |
| **Display Name**      | Human-readable name shown in the UI                                                                 |
| **Type**              | The model type: LLM, Embedding, or Reranking                                                        |
| **Provider**          | The model provider (OpenAI, Anthropic, etc.)                                                        |
| **API Configuration** | JSON configuration with API keys, endpoints, model parameters, and other provider-specific settings |
| **Active**            | Whether this model is available for use                                                             |
| **User-Facing**       | Whether end users can select this model in the chat interface                                       |

## Model Types

| Type                   | Purpose                                                                                   |
| ---------------------- | ----------------------------------------------------------------------------------------- |
| **LLM**                | Language model for generating chat responses                                              |
| **Embedding**          | Model for creating vector embeddings of documents (used in semantic search)               |
| **Reranking**          | Model for re-ranking search results by relevance                                          |
| **Classification**     | Document classification during ingestion                                                  |
| **Ingestion - Large**  | Primary entity extraction for large or complex documents                                  |
| **Ingestion - Medium** | Optional mid-tier model for primary extraction when set on a content type                 |
| **Ingestion - Small**  | Lightweight model for secondary ingestion steps (validation, JSON repair, disambiguation) |

Assign ingestion models in [System Settings](/admin-guide/system-settings):

* `CLASSIFICATION_MODEL_CONFIG`
* `INGESTION_LARGE_MODEL_CONFIG`
* `INGESTION_MEDIUM_MODEL_CONFIG` (optional; used when a content type sets `model_tier: medium`)
* `INGESTION_SMALL_MODEL_CONFIG`

Content types can override the primary tier per type. See [Extraction Policy](/admin-guide/extraction-policy).

## Testing Connections

Before activating a model, test its connection:

1. Click **Test Connection** on any model
2. The system sends a real API request to the provider
3. Results show:
   * Response time
   * Response preview
   * Embedding dimensions (for embedding models)
   * Success or error status

<Tip>
  Always test a model connection after creation or after changing API configuration. This validates credentials, endpoints, and model availability before end users encounter issues.
</Tip>

## Managing Models

### Editing

Click any model to edit its configuration. Changes to API configuration or active status take effect immediately.

### Activating/Deactivating

Toggle the **Active** status to enable or disable a model. Deactivated models are not available for any operation.

Toggle the **User-Facing** status to control whether end users see the model in the model selector dropdown. Non-user-facing models can still be used by assistants as their default model.

### Deleting

Remove a model configuration permanently. Ensure no assistants are configured to use the model before deleting.