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With Box AI API, you can extract metadata from the provided file and get the result in the form of key-value pairs. As input, you can either create a structure using the fields parameter, or use an already defined metadata template. To learn more about creating templates, see Creating metadata templates in the Admin Console or use the . You can also autofill metadata in templates using our Standard or Enhanced Extraction Agent.

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Supported file formats

The endpoint supports the following file formats:
  • PDF
  • TIFF
  • PNG
  • JPEG
Box AI automatically applies optical character recognition (OCR) when processing image files (TIFF, PNG, JPEG) and scanned documents. This eliminates the need to convert images to PDF before extraction, saving time and simplifying your integration.

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Supported languages

Box AI can extract metadata from documents in the following languages:
  • English
  • Japanese
  • Chinese
  • Korean
  • Cyrillic-based languages (such as Russian, Ukrainian, Bulgarian, and Serbian)
No additional configuration is required to use different languages or image formats. Box AI automatically detects the language and applies OCR when needed.

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Before you start

Make sure you followed the steps listed in to create a platform app and authenticate.

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Send a request

To send a request, use the POST /2.0/ai/extract_structured endpoint. 
curl -i -L 'https://api.box.com/2.0/ai/extract_structured' \
     -H 'content-type: application/json' \
     -H 'authorization: Bearer <ACCESS_TOKEN>' \
     -d '{
        "items": [
          {
            "id": "12345678",
            "type": "file",
            "content": "This is file content."
          }
        ],
        "metadata_template": {
            "template_key": "",
            "type": "metadata_template",
            "scope": ""
        },
        "fields": [
            {
              "key": "name",
              "description": "The name of the person.",
              "displayName": "Name",
              "prompt": "The name is the first and last name from the email address.",
              "type": "string",
              "options": [
                {
                  "key": "First Name"
                },
                {
                  "key": "Last Name"
                }
              ]
            }
        ],
        "ai_agent": {
          "type": "ai_agent_extract_structured",
          "long_text": {
            "model": "azure__openai__gpt_4o_mini"
            },
          "basic_text": {
            "model": "azure__openai__gpt_4o_mini"
         }
      }
   }'

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Parameters

To make a call, you must pass the following parameters. Mandatory parameters are in bold.
The items array can have exactly one element.
ParameterDescriptionExample
metadata_templateThe metadata template containing the fields to extract. For your request to work, you must provide either metadata_template or fields, but not both.
metadata_template.typeThe type of metadata template.metadata_template
metadata_template.scopeThe scope of the metadata template that can either be global or enterprise. Global templates are those available to any Box enterprise, whereas enterprise templates are bound to a specific enterprise.metadata_template
metadata_template.template_keyThe name of your metadata template.invoice
items.idBox file ID of the document. The ID must reference an actual file with an extension.1233039227512
items.typeThe type of the supplied input.file
items.contentThe content of the item, often the text representation.This article is about Box AI.
fields.typeThe type of the field. It include but is not limited to string, float, date, enum, and multiSelect.string
fields.descriptionA description of the field.The person's name.
fields.displayNameThe display name of the field.Name
fields.keyA unique identifier for the field.name
fields.optionsA list of options for this field. This is most often used in combination with the enum and multiSelect field types.[{"key":"First Name"},{"key":"Last Name"}]
fields.options.keyA unique identifier for the field.First Name
fields.promptAdditional context about the key (identifier) that may include how to find and format it.Name is the first and last name from the email address
ai_agentThe AI agent used to override the default agent configuration. This parameter allows you to, for example, replace the default LLM with a custom one using the parameter, tweak the base to allow for a more customized user experience, or change an LLM parameter, such as temperature, to make the results more or less creative. Before you use the ai_agent parameter, you can get the default configuration using the request. For specific use cases, see the .

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Use cases

This example shows you how to extract metadata from a sample invoice in a structured way. Let’s assume you want to extract the vendor name, invoice number, and a few more details.
sample invoice

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Create the request

To get the response from Box AI, call POST /2.0/ai/extract_structured endpoint with the following parameters:
  • items.type and items.id to specify the file to extract the data from.
  • fields to specify the data that you want to extract from the given file.
  • metadata_template to supply an already existing metadata template.
You can use either fields or metadata_template to specify your structure, but not both.

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Use fields parameter

The fields parameter allows you to specify the data you want to extract. Each fields object has a subset of parameters you can use to add more information about the searched data. For example, you can add the field type, description, or even a prompt with some additional context. 
curl --location 'https://api.box.com/2.0/ai/extract_structured' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <ACCESS_TOKEN>'' \
--data '{
    "items": [
        {
            "id": "1517628697289",
            "type": "file"
        }
    ],
    "fields": [
        {
            "key": "document_type",
            "type": "enum",
            "prompt": "what type of document is this?",
            "options": [
                {
                    "key": "Invoice"
                },
                {
                    "key": "Purchase Order"
                },
                {
                    "key": "Unknown"
                }
            ]
        },
        {
            "key": "document_date",
            "type": "date"
        },
        {
            "key": "vendor",
            "description": "The name of the entity.",
            "prompt": "Which vendor is sending this document.",
            "type": "string"
        },
        {
            "key": "document_total",
            "type": "float"
        }
    ]
  }'
The response lists the specified fields and their values: 
{
    "document_date": "2024-02-13",
    "vendor": "Quasar Innovations",
    "document_total": $1050,
    "document_type": "Purchase Order"
}

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Use metadata template

If you prefer to use a metadata template, you can provide its template_key, type, and scope. 
curl --location 'https://api.box.com/2.0/ai/extract_structured' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <ACCESS_TOKEN>' \
--data '{
    "items": [
        {
            "id": "1517628697289",
            "type": "file"
        }
    ],
    "metadata_template": {
        "template_key": "rbInvoicePO",
        "type": "metadata_template",
        "scope": "enterprise_1134207681"
    }
}'
The response lists the fields included in the metadata template and their values: 
{
  "documentDate": "February 13, 2024",
  "total": "$1050",
  "documentType": "Purchase Order",
  "vendor": "Quasar Innovations",
  "purchaseOrderNumber": "003"
}

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Enhanced Extract Agent

To use the Enhanced Extract Agent, specify the ai_agent object as follows: 
{
  "ai_agent": {
    "type": "ai_agent_id", 
    "id": "enhanced_extract_agent"
  }
}
To extract data using the Enhanced Extract Agent you need one of the following: See the sample code snippet using Box Python SDK: 
from box_sdk_gen import (
    AiAgentReference,
    AiAgentReferenceTypeField,
    AiItemBase,
    AiItemBaseTypeField,
    BoxClient,
    BoxCCGAuth,
    CCGConfig,
    CreateAiExtractStructuredMetadataTemplate
)

# Create your client credentials grant config from the developer console
ccg_config = CCGConfig(
    client_id="my_box_client_id", # replace with your client id
    client_secret="my_box_client_secret", # replace with your client secret
    user_id="my_box_user_id", # replace with the box user id that has access
                              # to the file you are referencing
)
auth = BoxCCGAuth(config=ccg_config)
client = BoxClient(auth=auth)
# Create the agent config referencing the enhanced extract agent
enhanced_extract_agent_config = AiAgentReference(
    id="enhanced_extract_agent",
    type=AiAgentReferenceTypeField.AI_AGENT_ID
)
# Use the Box SDK to call the extract_structured endpoint
box_ai_response = client.ai.create_ai_extract_structured(
    # Create the items array containing the file information to extract from
    items=[
        AiItemBase(
            id="my_box_file_id", # replace with the file id
            type=AiItemBaseTypeField.FILE
        )
    ],
    # Reference the Box Metadata template 
    metadata_template=CreateAiExtractStructuredMetadataTemplate(
        template_key="InvoicePO",
        scope="enterprise"
    ),
    # Attach the agent config you created earlier
    ai_agent=enhanced_extract_agent_config,
)
print(f"box_ai_response: {box_ai_response.answer}")