Image store sample skills for cognitive search

These custom skills store or retrieve a base64-encoded image to or from blob storage. This is useful to make images extracted from a cognitive search pipeline's data source available downstream as both blob URIs or raw base64 data, and to feed those into other skills.

Requirements

In addition to the common requirements described in the root README.md file, this function requires access to Azure blob storage.

Settings

This function requires a BLOB_STORAGE_CONNECTION_STRING setting set to a valid Azure blob storage connection string, and a BLOB_STORAGE_CONTAINER_NAME setting set to the name of the blob storage container under which to save the new images. If running locally, this can be set in your project's debug environment variables (go to project properties, in the debug tab). This ensures your key won't be accidentally checked in with your code. If running in an Azure function, this can be set in the application settings.

Deployment

Deploy to Azure

image-store function

Sample Input:

{
    "values": [
        {
            "recordId": "logo",
            "data":
            {
                "imageName": "azure-logo.png",
                "mimeType": "image/png",
                "imageData": "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"
            }
        }
    ]
}

If unspecified or empty, mimeType defaults to "image/jpeg". If imageName is unspecified or empty, a new GUID is generated and used as the blob name.

Sample Output:

{
    "values": [
        {
            "recordId": "logo",
            "data": {
                "imageStoreUri": "https://[your storage account].blob.core.windows.net/pics/azure-logo.png"
            },
            "errors": [],
            "warnings": []
        }
    ]
}

The returned imageStoreUri points to the image and can be used in its stead.

Sample Skillset Integration

In order to use this skill in a cognitive search pipeline, you'll need to add a skill definition to your skillset. Here's a sample skill definition for this example (inputs and outputs should be updated to reflect your particular scenario and skillset environment):

{
    "@odata.type": "#Microsoft.Skills.Custom.WebApiSkill",
    "description": "Upload image data to the annotation store",
    "uri": "[AzureFunctionEndpointUrl]/api/image-store?code=[AzureFunctionDefaultHostKey]",
    "batchSize": 1,
    "context": "/document/normalized_images/*",
    "httpHeaders": {
        "BlobContainerName": "[BlobContainerName]"
    },
    "inputs": [
        {
            "name": "imageData",
            "source": "/document/normalized_images/*/data"
        }
    ],
    "outputs": [
        {
            "name": "imageStoreUri",
            "targetName": "imageStoreUri"
        }
    ]
}

image-fetch function

This function is the reverse of image-store, and the inputs and outputs are identical, just reversed, if one excludes the errors and warnings sections.

Sample Input:

{
    "values": [
        {
            "recordId": "logo",
            "data": {
                "imageStoreUri": "https://[your storage account].blob.core.windows.net/pics/azure-logo.png"
            }        }
    ]
}

Sample Output:

{
    "values": [
        {
            "recordId": "logo",
            "data":
            {
                "imageName": "azure-logo.png",
                "mimeType": "image/png",
                "imageData": "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"
            },
            "errors": [],
            "warnings": []
        }
    ]
}

Sample Skillset Integration

In order to use this skill in a cognitive search pipeline, you'll need to add a skill definition to your skillset. Here's a sample skill definition for this example (inputs and outputs should be updated to reflect your particular scenario and skillset environment):

{
    "@odata.type": "#Microsoft.Skills.Custom.WebApiSkill",
    "description": "Upload image data to the annotation store",
    "uri": "[AzureFunctionEndpointUrl]/api/image-fetch?code=[AzureFunctionDefaultHostKey]",
    "batchSize": 1,
    "context": "/document/normalized_images/*",
    "httpHeaders": {
        "BlobContainerName": "[BlobContainerName]"
    },
    "inputs": [
        {
            "name": "imageStoreUri",
            "source": "/document/normalized_images/*/uri"
        }
    ],
    "outputs": [
        {
            "name": "imageData",
            "targetName": "data"
        }
    ]
}