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

# Implement vision and multimodal features

SambaNova vision models support multimodal inputs, allowing users to process both text and images. These models analyze images and generate context-aware text responses. Learn how to query SambaNova vision models using either the SambaNova or OpenAI Python client.

## Make a query with an image

On SambaNova, the vision model request follows OpenAI’s multimodal input format which accepts both text and image inputs in a structured payload. While the call is similar to [Text Generation](/en/features/text-generation), it differs by including an encoded image file, referenced via the `image_path` variable. A helper function is used to convert this image into a base64 string, allowing it to be passed alongside the text in the request.

<Steps>
  <Step title="Step 1">
    Make a new Python file and copy the code below.;

    <Note>
      This example uses the Gemma-4-31B-it model.
    </Note>

    <CodeGroup>
      ```python Python (SambaNova) theme={null}
      from sambanova import SambaNova
      import base64

      client = SambaNova(
          base_url="your-sambanova-base-url",
          api_key="your-sambanova-api-key",
      )

      # Helper function to encode the image
      def encode_image(image_path):
        with open(image_path, "rb") as image_file:
          return base64.b64encode(image_file.read()).decode('utf-8')

      # The path to your image
      image_path = "sample.JPEG"

      # The base64 string of the image
      image_base64 = encode_image(image_path)

      print(image_base64)

      response = client.chat.completions.create(
          model="gemma-4-31B-it",
          messages=[
              {
                  "role": "user",
                  "content": [
                      {"type": "text", "text": "What is happening in this image?"},
                      {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}
                  ]
              }
          ]
      )

      print(response.choices[0].message.content)
      ```

      ```python Python (OpenAI) theme={null}
      from openai import OpenAI
      import base64

      client = OpenAI(
          base_url="your-sambanova-base-url",
          api_key="your-sambanova-api-key",
      )

      # Helper function to encode the image
      def encode_image(image_path):
        with open(image_path, "rb") as image_file:
          return base64.b64encode(image_file.read()).decode('utf-8')

      # The path to your image
      image_path = "sample.JPEG"

      # The base64 string of the image
      image_base64 = encode_image(image_path)

      print(image_base64)

      response = client.chat.completions.create(
          model="gemma-4-31B-it",
          messages=[
              {
                  "role": "user",
                  "content": [
                      {"type": "text", "text": "What is happening in this image?"},
                      {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}
                  ]
              }
          ]
      )

      print(response.choices[0].message.content)
      ```
    </CodeGroup>
  </Step>

  <Step title="Step 2">
    Use your SambaNova API key and base URL from the [API keys and URLs](/en/get-started/api-keys-urls) page to replace the string fields `"your-sambanova-api-key"` and `"your-sambanova-base-url"`in the construction of the client.
  </Step>

  <Step title="Step 3">
    Select an image and move it to a suitable path that you can specify in the lines.

    ```python theme={null}
    # The path to your image
    image_path = "sample.JPEG"
    ```
  </Step>

  <Step title="Step 4">
    Verify the prompt to pair with the image in the `content` portion of the `user` prompt.
  </Step>

  <Step title="Step 5">
    Run the Python file to receive the text output.
  </Step>
</Steps>