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

# Connect Source

> Learn how to connect and configure an AI/ML model as a source for use within the AI Squared platform.

Connecting an AI/ML source is the first step in activating AI within your business workflows. AI Squared allows you to seamlessly integrate your deployed model endpoints—from providers like SageMaker, Vertex AI, Databricks, or custom HTTP APIs.

This guide walks you through connecting a new model source.

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## Step 1: Select an AI/ML Source

1. Navigate to **AI Activation → AI Modeling → Connect Source**
2. Click on **Add Source**
3. Choose your desired connector from the list:
   * AWS SageMaker
   * Google Vertex AI
   * Databricks Model
   * OpenAI Model Endpoint
   * HTTP Model Source (Generic)

📸 *Placeholder for: Screenshot of “Add Source” screen*

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## Step 2: Enter Endpoint Details

Each connector requires some basic configuration for successful integration.

### Required Fields

* **Endpoint Name** – A meaningful name for this model source
* **Endpoint URL** – The endpoint where the model is hosted
* **Authentication Method** – e.g., OAuth, API Key, Bearer Token
* **Auth Header / Secret Key** – If applicable
* **Request Format** – Structure expected by the model (e.g., JSON payload)
* **Response Format** – Format returned by the model (e.g., structured JSON with keys)

📸 *Placeholder for: Screenshot of endpoint input form*

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## Step 3: Test Connection

Click **Test Connection** to validate that the model endpoint is reachable and returns a valid response.

* Ensure all fields are correct
* The system will validate the endpoint and return a success or error message

📸 *Placeholder for: Screenshot of test success/failure*

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## Step 4: Define Input Schema

The input schema specifies the fields your model expects during inference.

| Field     | Description                                |
| --------- | ------------------------------------------ |
| **Name**  | Key name expected by the model             |
| **Type**  | Data type: String, Integer, Float, Boolean |
| **Value** | Static or dynamic input value              |

📸 *Placeholder for: Input schema editor screenshot*

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## Step 5: Define Output Schema

The output schema ensures consistent mapping of the model’s response.

| Field          | Description                                |
| -------------- | ------------------------------------------ |
| **Field Name** | Key name from the model response           |
| **Type**       | Data type: String, Integer, Float, Boolean |

📸 *Placeholder for: Output schema editor screenshot*

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## Step 6: Save the Source

Click **Save** once configuration is complete. Your model source will now appear in the **AI Modeling** tab and can be used in downstream workflows such as Data Apps or visualizations.

📸 *Placeholder for: Final save and confirmation screen*

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Need help? Head over to our [Support & FAQs](/support) section for troubleshooting tips or reach out via the in-app help widget.