Integrate a Custom Embedding Model

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Supported Vector Dimensions

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Integration Steps

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

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

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

1. Click Test to verify the response, then Save

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

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

{
  "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

{
  "data": [
    {
      "embedding": [-0.022822052, 0.01614314, 0.008042404, ...]
    }
  ]
}

6. Click Save

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

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Fine-Tuning Embedding Models