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title: Configuring Vision Operations
collection: mulesoft
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content_type: documentation
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description: MuleSoft Documentation Site
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---
---
product: MuleSoft Inference Connector
version: 1.1
---

# Configuring Vision Operations

> For the full documentation index, see: https://docs.mulesoft.com/llms.txt

Configure the **\[Image\] Read by (Url or Base64)** operation.

## Configure the Image Read by (Url or Base64) Operation

The **\[Image\] Read by (Url or Base64)** operation reads and interprets an image based on a prompt.

Apply the **\[Image\] Read by (Url or Base64)** operation in various scenarios, such as for:

- **Image Analysis**

  Analyze images in business reports, presentations, or customer service scenarios.

- **Content Generation**

  Describe images for blog posts, articles, or social media.

- **Visual Insights**

  Extract insights from images in research or design projects.

To configure the **\[Image\] Read by (Url or Base64)** operation:

1.  Select the operation on the Anypoint Code Builder or Studio canvas.

2.  In the **General** properties tab for the operation, enter these values:

    - **Prompt**

      Enter the prompt for the operation.

    - **Image**

      Enter the URL or Base64 String of the image file that is to be read.

This is the XML for this operation:

```xml
<ms-inference:read-image
  doc:id="dfbd1a61-6e98-4b5b-b77a-bfe031e70d45"
  config-ref="OpenAIConfig"
  doc:name="Read image">
    <ms-inference:prompt>
      <![CDATA[Describe what you see in this image in detail]]>
    </ms-inference:prompt>
    <ms-inference:image-url>
      <![CDATA[https://example.com/image.png]]>
    </ms-inference:image-url>
</ms-inference:read-image>
```

### Output Configuration

This operation responds with a JSON payload containing the main LLM response. This is an example
response:

```json
{
  "payload": {
    "response": "The image depicts the Eiffel Tower in Paris during a snowy day. The tower is partially covered in snow, and the surrounding trees and ground are also blanketed in snow. There is a pathway leading towards the Eiffel Tower, with a lamppost and some fencing along the sides. The overall scene has a serene and picturesque winter atmosphere."
  }
}
```

The operation also returns attributes that aren’t within the main JSON payload, that include
information about token usage, for example:

```json
{
  "attributes": {
    "tokenUsage": {
      "inputCount": 267,
      "outputCount": 68,
      "totalCount": 335
    },
    "additionalAttributes": {
      "finish_reason": "stop",
      "model": "gpt-4o-mini",
      "id": "604ae573-8265-4dc0-b06e-457422f2fbd8"
    }
  }
}
```

- `tokenUsage`: Token usage metadata returned as attributes

  - `inputCount`: Number of tokens used to process the input

  - `outputCount`: Number of tokens used to generate the output

  - `totalCount`: Total number of tokens used for input and output

- `additionalAttributes`: Additional metadata from the LLM provider

  - `finish_reason`: The finish reason for the LLM response

  - `model`: The ID of the model used

  - `id`: The ID of the request

## See Also

- [MuleSoft Inference Connector Overview](https://docs.mulesoft.com/mulesoft-inference-connector/1.1/)

- [MuleSoft Inference Connector Reference](https://docs.mulesoft.com/mulesoft-inference-connector/1.1/mulesoft-inference-connector-reference)

- [Salesforce Help](https://help.salesforce.com)