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Use GPT-4 Turbo with Vision

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GPT-4 Turbo with Vision is a large multimodal model (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. It incorporates both natural language processing and visual understanding.

The GPT-4 Turbo with Vision model answers general questions about what's present in images.

Tip

To use GPT-4 Turbo with Vision, you call the Chat Completion API on a GPT-4 Turbo with Vision model that you have deployed. If you're not familiar with the Chat Completion API, see the GPT-4 Turbo & GPT-4 how-to guide.

GPT-4 Turbo model upgrade

The latest GA release of GPT-4 Turbo is:

  • gpt-4 Version: turbo-2024-04-09

This is the replacement for the following preview models:

  • gpt-4 Version: 1106-Preview
  • gpt-4 Version: 0125-Preview
  • gpt-4 Version: vision-preview

Differences between OpenAI and Azure OpenAI GPT-4 Turbo GA Models

  • OpenAI's version of the latest 0409 turbo model supports JSON mode and function calling for all inference requests.
  • Azure OpenAI's version of the latest turbo-2024-04-09 currently doesn't support the use of JSON mode and function calling when making inference requests with image (vision) input. Text based input requests (requests without image_url and inline images) do support JSON mode and function calling.

Differences from gpt-4 vision-preview

  • Azure AI specific Vision enhancements integration with GPT-4 Turbo with Vision isn't supported for gpt-4 Version: turbo-2024-04-09. This includes Optical Character Recognition (OCR), object grounding, video prompts, and improved handling of your data with images.

GPT-4 Turbo provisioned managed availability

  • gpt-4 Version: turbo-2024-04-09 is available for both standard and provisioned deployments. Currently the provisioned version of this model doesn't support image/vision inference requests. Provisioned deployments of this model only accept text input. Standard model deployments accept both text and image/vision inference requests.

Deploying GPT-4 Turbo with Vision GA

To deploy the GA model from the Studio UI, select GPT-4 and then choose the turbo-2024-04-09 version from the dropdown menu. The default quota for the gpt-4-turbo-2024-04-09 model will be the same as current quota for GPT-4-Turbo. See the regional quota limits.

Call the Chat Completion APIs

The following command shows the most basic way to use the GPT-4 Turbo with Vision model with code. If this is your first time using these models programmatically, we recommend starting with our GPT-4 Turbo with Vision quickstart.

Send a POST request to https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview where

  • RESOURCE_NAME is the name of your Azure OpenAI resource
  • DEPLOYMENT_NAME is the name of your GPT-4 Turbo with Vision model deployment

Required headers:

  • Content-Type: application/json
  • api-key: {API_KEY}

Body: The following is a sample request body. The format is the same as the chat completions API for GPT-4, except that the message content can be an array containing text and images (either a valid HTTP or HTTPS URL to an image, or a base-64-encoded image).

Important

Remember to set a "max_tokens" value, or the return output will be cut off.

Important

When uploading images, there is a limit of 10 images per chat request.

{
    "messages": [ 
        {
            "role": "system", 
            "content": "You are a helpful assistant." 
        },
        {
            "role": "user", 
            "content": [
	            {
	                "type": "text",
	                "text": "Describe this picture:"
	            },
	            {
	                "type": "image_url",
	                "image_url": {
                        "url": "<image URL>"
                    }
                } 
           ] 
        }
    ],
    "max_tokens": 100, 
    "stream": false 
}

Tip

Use a local image

If you want to use a local image, you can use the following Python code to convert it to base64 so it can be passed to the API. Alternative file conversion tools are available online.

import base64
from mimetypes import guess_type

# Function to encode a local image into data URL 
def local_image_to_data_url(image_path):
    # Guess the MIME type of the image based on the file extension
    mime_type, _ = guess_type(image_path)
    if mime_type is None:
        mime_type = 'application/octet-stream'  # Default MIME type if none is found

    # Read and encode the image file
    with open(image_path, "rb") as image_file:
        base64_encoded_data = base64.b64encode(image_file.read()).decode('utf-8')

    # Construct the data URL
    return f"data:{mime_type};base64,{base64_encoded_data}"

# Example usage
image_path = '<path_to_image>'
data_url = local_image_to_data_url(image_path)
print("Data URL:", data_url)

When your base64 image data is ready, you can pass it to the API in the request body like this:

...
"type": "image_url",
"image_url": {
   "url": "data:image/jpeg;base64,<your_image_data>"
}
...

Output

The API response should look like the following.

{
    "id": "chatcmpl-8VAVx58veW9RCm5K1ttmxU6Cm4XDX",
    "object": "chat.completion",
    "created": 1702439277,
    "model": "gpt-4",
    "prompt_filter_results": [
        {
            "prompt_index": 0,
            "content_filter_results": {
                "hate": {
                    "filtered": false,
                    "severity": "safe"
                },
                "self_harm": {
                    "filtered": false,
                    "severity": "safe"
                },
                "sexual": {
                    "filtered": false,
                    "severity": "safe"
                },
                "violence": {
                    "filtered": false,
                    "severity": "safe"
                }
            }
        }
    ],
    "choices": [
        {
            "finish_reason":"stop",
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "The picture shows an individual dressed in formal attire, which includes a black tuxedo with a black bow tie. There is an American flag on the left lapel of the individual's jacket. The background is predominantly blue with white text that reads \"THE KENNEDY PROFILE IN COURAGE AWARD\" and there are also visible elements of the flag of the United States placed behind the individual."
            },
            "content_filter_results": {
                "hate": {
                    "filtered": false,
                    "severity": "safe"
                },
                "self_harm": {
                    "filtered": false,
                    "severity": "safe"
                },
                "sexual": {
                    "filtered": false,
                    "severity": "safe"
                },
                "violence": {
                    "filtered": false,
                    "severity": "safe"
                }
            }
        }
    ],
    "usage": {
        "prompt_tokens": 1156,
        "completion_tokens": 80,
        "total_tokens": 1236
    }
}

Every response includes a "finish_reason" field. It has the following possible values:

  • stop: API returned complete model output.
  • length: Incomplete model output due to the max_tokens input parameter or model's token limit.
  • content_filter: Omitted content due to a flag from our content filters.

Detail parameter settings in image processing: Low, High, Auto

The detail parameter in the model offers three choices: low, high, or auto, to adjust the way the model interprets and processes images. The default setting is auto, where the model decides between low or high based on the size of the image input.

  • low setting: the model does not activate the "high res" mode, instead processes a lower resolution 512x512 version, resulting in quicker responses and reduced token consumption for scenarios where fine detail isn't crucial.
  • high setting: the model activates "high res" mode. Here, the model initially views the low-resolution image and then generates detailed 512x512 segments from the input image. Each segment uses double the token budget, allowing for a more detailed interpretation of the image.''

For details on how the image parameters impact tokens used and pricing please see - What is Azure OpenAI? Image Tokens

Output

The chat responses you receive from the model should now include enhanced information about the image, such as object labels and bounding boxes, and OCR results. The API response should look like the following.

{
    "id": "chatcmpl-8UyuhLfzwTj34zpevT3tWlVIgCpPg",
    "object": "chat.completion",
    "created": 1702394683,
    "model": "gpt-4",
    "choices":
    [
        {
            "finish_reason": {
                "type": "stop",
                "stop": "<|fim_suffix|>"
            },
            "index": 0,
            "message":
            {
                "role": "assistant",
                "content": "The image shows a close-up of an individual with dark hair and what appears to be a short haircut. The person has visible ears and a bit of their neckline. The background is a neutral light color, providing a contrast to the dark hair."
            }
        }
    ],
    "usage":
    {
        "prompt_tokens": 816,
        "completion_tokens": 49,
        "total_tokens": 865
    }
}

Every response includes a "finish_reason" field. It has the following possible values:

  • stop: API returned complete model output.
  • length: Incomplete model output due to the max_tokens input parameter or model's token limit.
  • content_filter: Omitted content due to a flag from our content filters.

Create a video retrieval index

  1. Get an Azure AI Vision resource in the same region as the Azure OpenAI resource you're using.

  2. Create an index to store and organize the video files and their metadata. The example command below demonstrates how to create an index named my-video-index using the Create Index API. Save the index name to a temporary location; you'll need it in later steps.

    Tip

    For more detailed instructions on creating a video index, see Do video retrieval using vectorization.

    Important

    A video index name can be up to 24 characters long, unless it's a GUID, which can be 36 characters.

    curl.exe -v -X PUT "https://<YOUR_ENDPOINT_URL>/computervision/retrieval/indexes/my-video-index?api-version=2023-05-01-preview" -H "Ocp-Apim-Subscription-Key: <YOUR_SUBSCRIPTION_KEY>" -H "Content-Type: application/json" --data-ascii "
{
  'metadataSchema': {
    'fields': [
      {
        'name': 'cameraId',
        'searchable': false,
        'filterable': true,
        'type': 'string'
      },
      {
        'name': 'timestamp',
        'searchable': false,
        'filterable': true,
        'type': 'datetime'
      }
    ]
  },
  'features': [
    {
      'name': 'vision',
      'domain': 'surveillance'
    },
    {
      'name': 'speech'
    }
  ]
}"

  3. Add video files to the index with their associated metadata. The example below demonstrates how to add two video files to the index using SAS URLs with the Create Ingestion API. Save the SAS URLs and documentId values to a temporary location; you'll need them in later steps.

    curl.exe -v -X PUT "https://<YOUR_ENDPOINT_URL>/computervision/retrieval/indexes/my-video-index/ingestions/my-ingestion?api-version=2023-05-01-preview" -H "Ocp-Apim-Subscription-Key: <YOUR_SUBSCRIPTION_KEY>" -H "Content-Type: application/json" --data-ascii "
{
  'videos': [
    {
      'mode': 'add',
      'documentId': '02a504c9cd28296a8b74394ed7488045',
      'documentUrl': 'https://example.blob.core.windows.net/videos/02a504c9cd28296a8b74394ed7488045.mp4?sas_token_here',
      'metadata': {
        'cameraId': 'camera1',
        'timestamp': '2023-06-30 17:40:33'
      }
    },
    {
      'mode': 'add',
      'documentId': '043ad56daad86cdaa6e493aa11ebdab3',
      'documentUrl': '[https://example.blob.core.windows.net/videos/043ad56daad86cdaa6e493aa11ebdab3.mp4?sas_token_here',
      'metadata': {
        'cameraId': 'camera2'
      }
    }
  ]
}"

  4. After you add video files to the index, the ingestion process starts. It might take some time depending on the size and number of files. To ensure the ingestion is complete before performing searches, you can use the Get Ingestion API to check the status. Wait for this call to return "state" = "Completed" before proceeding to the next step.

    curl.exe -v -X GET "https://<YOUR_ENDPOINT_URL>/computervision/retrieval/indexes/my-video-index/ingestions?api-version=2023-05-01-preview&$top=20" -H "ocp-apim-subscription-key: <YOUR_SUBSCRIPTION_KEY>"

Next steps