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

# Integrate a Custom Embedding Model

Connect your own embedding model to control how Search AI vectorizes text, enabling domain-specific embeddings or compliance with data privacy requirements.

## Supported Vector Dimensions

Your embedding model must output one of these vector sizes:

| Supported Sizes                                       |
| ----------------------------------------------------- |
| 128, 256, 384, 512, 768, 1024, 1028, 1536, 2048, 3072 |

## Integration Steps

**Step 1: Configure the Model**

1. Go to **Generative AI Tools > Model Library**
2. Click **+New Model** and select **Custom Integration**
3. In the **Configurations** tab, provide:

| Field            | Description                                      |
| ---------------- | ------------------------------------------------ |
| Integration Name | Unique identifier for this integration           |
| Model Name       | Your model name (e.g., `text-embedding-ada-002`) |
| Endpoint         | API endpoint that returns embeddings             |
| Auth             | Authorization profile (if required)              |
| Headers          | Any required request headers                     |

4. Click **Next** and enter your **Request Prompt** (the payload sent to the model):

```json theme={null}
{
  "input": "The food was delicious and the waiter...",
  "model": "text-embedding-ada-002",
  "encoding_format": "float"
}
```

5. Click **Test** to verify the response, then **Save**

**Step 2: Create a Custom Prompt**

1. Go to **Generative AI Tools > Prompt Library**
2. Click **+New Prompt** and configure:

| Field   | Description                          |
| ------- | ------------------------------------ |
| Name    | Unique identifier for this prompt    |
| Feature | Select **Vector Generation**         |
| Model   | Select your custom model from Step 1 |

3. Define the **Request** using the `{{embedding_input}}` variable:

```json theme={null}
{
  "input": "{{embedding_input}}",
  "model": "text-embedding-ada-002",
  "encoding_format": "float"
}
```

4. Enter sample values and click **Test**
5. In the **Response**, double-click the field containing the embeddings array to set the **Text Response Path**

The selected field must contain an array of numbers:

```json theme={null}
{
  "data": [
    {
      "embedding": [-0.022822052, 0.01614314, 0.008042404, ...]
    }
  ]
}
```

6. Click **Save**

<Note>If the response format doesn't match, use a post-processor script to transform it.</Note>

**Step 3: Enable the Model**

1. Go to **GenAI Features**
2. For **Vector Generation**:
   * Select the model from Step 1
   * Select the prompt from Step 2
3. Enable the feature

Search AI now uses your custom embedding model.

## Fine-Tuning Embedding Models

For improved relevance, fine-tune embedding models with your domain-specific data using the [Fine-Tune Embedding Utility]() from the Search AI Toolkit.