Structured Data Extraction

Tensorlake can extract structured data from documents. This enables pulling out specific fields from documents. Some key features of structured extraction are -

• No limits on the number of fields you can extract.
• Extraction is guided by JSON Schema you provide(or Pydantic models with the Python SDK).
• Structured data can be extracted along with markdown representation of the document in a single API call, without having to parse the document twice.
• You can submit multiple schemas in a single API call, and the model will extract data from the document according to each schema.

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Structured Extraction Request

The same Parse Endpoint is used for structured extraction. You can specify the schema in the structured_extraction_options parameter in the parse endpoint.

curl --request POST \
  --url https://api.tensorlake.ai/documents/v2/parse \
  --header 'Authorization: Bearer ${TENSORLAKE_API_KEY}' \
  --header 'Content-Type: application/json' \
  --data '{
    "file_url": "https://pub-226479de18b2493f96b64c6674705dd8.r2.dev/california_id.jpg",
    "page_range": "1-3",
    "structured_extraction_options": [
      {
        "schema_name": "DriverLicense",
        "json_schema": {
          "title": "DriverLicense",
          "type": "object",
          "properties": {
            "first_name": { "type": "string", "description": "Name next to FN" },
            "last_name": { "type": "string", "description": "Name next to LN" },
            "id": { "type": "string", "description": "ID number" },
            "address": { "type": "string", "description": "Address of the ID holder" },
            "dob": { "type": "string", "description": "Date of birth of the ID holder" }
          }
        }
      }
    ]
  }'

The structured_extraction_options parameter is an array of objects, where each object contains the schema name and the JSON Schema to use for structured extraction.

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Response

Structured Data Extracted from the document is returned in the outputs field of the response.

Structured Data extracted from the document is returned in the structured_data field of the Get Parse Job endpoint response.

The structured_data field is a JSON object where each key is the schema name you provided in the structured_extraction_options parameter, making it easy to access the structured data for each schema.

It includes the extracted data and the pages from which the data was extracted.

{
  // ... other fields ...
  "structured_data": {
    "DriverLicense": {
      "data": {
        "first_name": "John",
        "last_name": "Doe",
        "id": "D1234567",
        "address": "123 Main St, Springfield, IL 62701",
        "dob": "1990-01-01"
      },
      "pages": [1, 2, 3],
    }
  }
}

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Chunking

You can extract structured data from the whole Document at once, or from every page of the document.

Specify the chunking_strategy parameter in the structured_extraction_options object to control how the document is chunked for structured data extraction.

• none(Default) - Extract structured data from the whole document at once.
• page - Extract structured data from every page of the document.
• fragment - Extract structured data from every fragment of the document. This is useful for documents with multiple sections or tables.

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Extracting from a Subset of Pages

By default, structured data extraction is performed on all pages of the document.

You can specify a subset of pages to extract structured data from by using the page_classes parameter in each structured data extraction request object.

curl --request POST \
  --url https://api.tensorlake.ai/documents/v2/parse \
  --header 'Authorization: Bearer ${TENSORLAKE_API_KEY}' \
  --header 'Content-Type: application/json' \
  --data '{
  "page_range": "1-3",
  "file_id": "tensorlake-XXX",  # Replace with your file ID or URL
  "page_classifications": [
    {
      "name": "front_of_dl",
      "description": "Pages that have a photo of a person."
    },
    {
      "name": "back_of_dl",
      "description": "Pages that have a barcode."
    }
  ]
  "structured_extraction_options": [
    {
      "schema_name": "DriverLicense",
      "json_schema": {
        "title": "DriverLicense",
        "type": "object",
        "page_classifications": [ "front_of_dl" ],
        "properties": {
          "name": { "type": "string", "description": "Name of the ID holder" },
          "age": { "type": "integer", "description": "Age of the ID holder" },
          "address": { "type": "string", "description": "Address of the ID holder" },
          "dob": { "type": "string", "description": "Date of birth of the ID holder" }
        }
      }
    }
  ]
}'

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Model

Structured Extraction is performed by using an LLM. At the moment, the following models are supported -

• tensorlake - This is our own model specifically trained for structured data extraction.
• gpt-4o-mini - In case you want to use OpenAI’s models for structured extraction.
• claude-3-5-sonnet-20240620 - In case you want to use Anthropic’s models for structured extraction.

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Tips

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Skip OCR

Some times document parsing doesn’t work well on certain documents, which can lead to poor structured data extraction. We recommend skipping the OCR step if you care about only structured data extraction. This will make use of a Vision Language Model trained to extract JSON from Document Images.

You should try this out in case you are seeing poor accuracy in structured data extraction.

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Describe the Fields

Adding descriptions to the fields in the schema always improves the accuracy of the structured data extraction. Help the model understand the context of the fields you are extracting, and if possible mention what text or visual cues to look for in the document for each field.

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Don’t compute new data in the schema

We don’t recommend make the LLM derive new information while performing structured extraction. For ex, if you ask the model to sum up all the rows in a table and return this in a new field, the model will likely hallucinate.

We recommend doing this in your application code in a downstream task.