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Documentation Index

Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt

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

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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

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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:
FieldDescription
Integration NameUnique identifier for this integration
Model NameYour model name (e.g., text-embedding-ada-002)
EndpointAPI endpoint that returns embeddings
AuthAuthorization profile (if required)
HeadersAny required request headers
  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
Step 2: Create a Custom Prompt
  1. Go to Generative AI Tools > Prompt Library
  2. Click +New Prompt and configure:
FieldDescription
NameUnique identifier for this prompt
FeatureSelect Vector Generation
ModelSelect your custom model from Step 1
  1. Define the Request using the {{embedding_input}} variable:
{
  "input": "{{embedding_input}}",
  "model": "text-embedding-ada-002",
  "encoding_format": "float"
}
  1. Enter sample values and click Test
  2. 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: 
{
  "data": [
    {
      "embedding": [-0.022822052, 0.01614314, 0.008042404, ...]
    }
  ]
}
  1. Click Save
If the response format doesn’t match, use a post-processor script to transform it.
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.

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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.