SenseML reference introduction

Use SenseML to write "configs" (collection of queries) to extract structured data from documents, for example, auto insurance quotes, home inspection reports, or your custom documents.

See the following topics for reference documentation for the SenseML query language:

Or, for a getting started tutorial, see:

Examples

For an overview, see the following example of a short config:

Click to enlarge

Try out this example in the Sensible app using the following document and config:

Example documentDownload link

This example uses the following config:

{
  "fingerprint": { // preferentially run this config if doc contains the test strings
    "tests": [
      "anyco",
      "quoted coverage changes"
    ]
  },
  "preprocessors": [
    {
      "type": "pageRange", // cuts out irrelevant doc pages before extraction
      "startPage": 0,
      "endPage": 2
    }
  ],
  "fields": [
    /* NATURAL-LANGUAGE, LLM-based EXAMPLE */
    {
      /* ID for target data */
      "id": "policy_period",
      /* unlike layout-based methods, no anchor required */
      "method": {
        "id": "query",
        /* ask a free-text question.
          best suited to simple questions
          that have one label and one answer
          in the document. */
        "description": "what's the policy period date range"
      }
    },
    /* LAYOUT-BASED EXAMPLE */
    {
      "id": "_driver_name_raw", // ID for extracted target data
      "anchor": "name of driver", // search for target data near text "name of driver" in doc
      "method": {
        "id": "label", // target to extract is a single line near anchor line
        "position": "below" // target is below anchor line ("name of driver")
      }
    }
  ],
  "computed_fields": [
    { // target data is a transformation of already extracted data
      "id": "driver_name_last", // ID for transformed target data
      "method": {
        "source_id": "_driver_name_raw", // extracted data to transform
        "id": "split", // target data is substring in extracted data
        "separator": ", ", // use commas to split the extracted data into substring array
        "index": 1 // target is 2nd element in substring array
      }
    }
  ]
}

The output of this example config is as follows:

{
	"policy_period": {
		"type": "string",
		"value": "April 14, 2021 - Oct 14, 2021"
	},
	"_driver_name_raw": {
		"type": "string",
		"value": "Petrov, Petar"
	},
	"driver_name_last": {
		"type": "string",
		"value": "Petrov"
	}
}

This example config has the following elements:

  • The field _driver_name_raw is a query that extracts a driver's name by searching below some matched text ("position": "below"). Its ID is the key for the extracted data. For more information, see Field query object.

  • (not applicable for LLM methods) an anchor is matched text that helps narrow down a location in the document from which to extract data. In the "_driver_name_raw" field, Sensible matches a string ("name of driver"). For information about more complex anchors, see Anchor object.

  • A method defines how to extract data after the anchor narrows down the data's location. In this example field, the Label method tells Sensible to extract data that's below and close to the anchor.

    There are two broad categories of methods:

    Natural language methodsLayout-based methods
    NotesAsk questions about info in the document, as you'd ask a human. For example, "what's the policy period"? Powered by large-language models (LLMs).Find the information in the document using anchoring text and layout data. For example, write instructions to grab the second cell in a column headed by "premium".
    Deterministicnoyes
    Handles complex layoutsnoyes
  • The preprocessor, pageRange, cuts out irrelevant pages of the document. For more information about using preprocessors to clean up documents before extracting data, see Preprocessors.

  • The fingerprint tells Sensible to preferentially run this config if the document contains the terms "anyco" or "quoted coverage changes." For more information about using fingerprints to improve performance, and other configuration settings, see Configuration Settings.

  • The computed field "driver_name_last" extracts the last name from the raw output of the _driver_name_raw field. For more information about transforming field output, see Computed field methods. You can also capture the full name as typed output. See types.

Using SenseML, you can extract just about any text, as well as image coordinates, from a document. Happy extracting!