Extract metadata from file (structured)

Guides Box AI Extract metadata from file (structured)

Extract metadata from file (structured)

Box AI API is currently a beta feature offered subject to Box’s Main Beta Agreement, and the available capabilities may change. Box AI API is available to all Enterprise Plus customers.

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 metadata template API.

To send a request, use the POST /2.0/ai/extract_structured endpoint.

Make sure you have generated the developer token to authorize your app. See prerequisites for using Box AI for details.

cURL

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",
          "long_text": {
            "model": "azure__openai__gpt_3_5_turbo_16k",
            "system_message": "You are a helpful travel assistant specialized in budget travel",
            "prompt_template": "It is `{current_date}`, and I have $8000 and want to spend a week in the Azores. What should I see?",
            "num_tokens_for_completion": 8400,
            "llm_endpoint_params": {
              "type": "openai_params",
              "temperature": 0,
              "top_p": 1,
              "frequency_penalty": 1.5,
              "presence_penalty": 1.5,
              "stop": "<|im_end|>"
            },
            "embeddings": {
              "model": "openai__text_embedding_ada_002",
              "strategy": {
                "id": "basic",
                "num_tokens_per_chunk": 64
              }
            }
          },
          "basic_text": {
            "model": "azure__openai__gpt_3_5_turbo_16k",
            "system_message": "You are a helpful travel assistant specialized in budget travel",
            "prompt_template": "It is `{current_date}`, and I have $8000 and want to spend a week in the Azores. What should I see?",
            "num_tokens_for_completion": 8400,
            "llm_endpoint_params": {
              "type": "openai_params",
              "temperature": 0,
              "top_p": 1,
              "frequency_penalty": 1.5,
              "presence_penalty": 1.5,
              "stop": "<|im_end|>"
            }
          }
        }
      }'

Python Gen

client.ai.create_ai_extract_structured(
    [AiItemBase(id=file.id)],
    metadata_template=CreateAiExtractStructuredMetadataTemplate(
        template_key=template_key, scope="enterprise"
    ),
)

.NET Gen

await client.Ai.CreateAiExtractStructuredAsync(requestBody: new AiExtractStructured(items: Array.AsReadOnly(new [] {new AiItemBase(id: file.Id)})) { MetadataTemplate = new AiExtractStructuredMetadataTemplateField() { TemplateKey = templateKey, Scope = "enterprise" } });

Java

BoxAIExtractMetadataTemplate template = new BoxAIExtractMetadataTemplate("templateKey", "enterprise");
BoxAIExtractStructuredResponse result = BoxAI.extractMetadataStructured(
    api,
    Collections.singletonList(new BoxAIItem("123456", BoxAIItem.Type.FILE)),
    template
);
JsonObject sourceJson = result.getSourceJson();

To make a call, you must pass the following parameters. Mandatory parameters are in bold.

The items array can have exactly one element.

Parameter	Description	Example
`metadata_template`	The 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.type`	The type of metadata template.	`metadata_template`
`metadata_template.scope`	The 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_key`	The name of your metadata template.	`invoice`
`items.id`	Box file ID of the document. The ID must reference an actual file with an extension.	`1233039227512`
`items.type`	The type of the supplied input.	`file`
`items.content`	The content of the item, often the text representation.	`This article is about Box AI`.
`fields.type`	The type of the field. It include but is not limited to `string`, `float`, `date`, `enum`, and `multiSelect`.	`string`
`fields.description`	A description of the field.	`The person's name.`
`fields.displayName`	The display name of the field.	`Name`
`fields.key`	A unique identifier for the field.	`name`
`fields.options`	A 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.key`	A unique identifier for the field.	`First Name`
`fields.prompt`	Additional 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_agent`	The 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 `model` parameter, tweak the base `prompt` 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 `GET 2.0/ai_agent_default` request. For specific use cases, see the AI model overrides tutorial.

Let's assume you want to extract the vendor name, invoice number, and a few more details from the following sample invoice:

To get the response from Box AI, call POST /2.0/ai/extract 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.

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"
}

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"
}

Extract metadata from file (structured)

Extract metadata from file (structured)

Send a request

Parameters

Use case

Create the request

Using `fields` parameter

Using metadata template

Related Guides

Extract metadata from file (structured)

Extract metadata from file (structured)

Send a request

Parameters

Use case

Create the request

Using fields parameter

Using metadata template

Related Guides

Using `fields` parameter