Structured data extraction

To begin using the structured data extractor right away, skip ahead to the how-to procedures.

Overview

When Unstructured partitions your source documents, the default result is a list of Unstructured document elements. These document elements are expressed in Unstructured’s format, which includes elements such as Title, NarrativeText, UncategorizedText, Table, Image, List, and so on. For example, you could have Unstructured ingest a stack of customer order forms in PDF format, where the PDF files’ layout is identical, but the content differs per individual PDF by customer order number. For each PDF, Unstructured might output elements such as a List element that contains details about the customer who placed the order, a Table element that contains the customer’s order details, NarrativeText or UncategorizedText elements that contains special instructions for the order, and so on. You might then use custom logic that you write yourself to parse those elements further in an attempt to extract information that you’re particularly interested in, such as customer IDs, item quantities, order totals, and so on. Unstructured’s structured data extractor simplifies this kind of scenario by allowing Unstructured to automatically extract the data from your source documents into a format that you define up front. For example, you could have Unstructured ingest that same stack of customer order form PDFs and then output a series of customer records, one record per order form. Each record could include data, with associated field labels, such as the customer’s ID; a series of order line items with descriptions, quantities, and prices; the order’s total amount; and any other available details that matter to you. This information is extracted in a consistent JSON format that is already fine-tuned for you to use in your own applications. The following diagram provides a conceptual representation of structured data extraction, showing a flow of data from a patient information form into JSON output that is saved as a JSON file in some remote cloud file storage location. From there, you could for example run your own script or similar to insert the JSON as a series of records into a database.

Conceptual flow of structured data extraction

To show how the structured data extractor works from a technical perspective, take a look at the following real estate listing PDF. This file is one of the sample files that is available directly from the Start page and the workflow editor’s Source node in the Unstructured use interface (UI). The file’s content is as follows:

Without the structured data extractor, if you run a workflow that references this file, Unstructured extracts the listing’s data in a default format similar to the following (note that the ellipses in this output indicate omitted fields for brevity):

[
  {
    "type": "Title",
    "element_id": "3f1ad705648037cf65e4d029d834a0de",
    "text": "HOME FOR FUTURE",
    "metadata": {
      "...": "..."
    }
  },
  {
    "type": "NarrativeText",
    "element_id": "320ca4f48e63d8bcfba56ec54c9be9af",
    "text": "221 Queen Street, Melbourne VIC 3000",
    "metadata": {
      "...": "..."
    }
  },
  {
    "type": "NarrativeText",
    "element_id": "05f648e815e73fe5140f203a62d8a3cc",
    "text": "2,800 sq. ft living space",
    "metadata": {
      "...": "..."
    }
  },
  {
    "type": "NarrativeText",
    "element_id": "27a9ded56b42f559999e48d1dcd76c9e",
    "text": "Recently renovated kitchen",
    "metadata": {
      "...": "..."
    }
  },
  {
    "...": "..."
  }
]

In the preceding output, the text fields contain information about the listing, such as the street address, the square footage, one of the listing’s features, and so on. However, you might want the information presented as street_address, square_footage, features, and so on. By using the structured data extractor in your Unstructured workflows, you could have Unstructured extract the listing’s data in a custom-defined output format similar to the following (ellipses indicate omitted fields for brevity):

[
  {
    "type": "DocumentData",
    "element_id": "f2ee7334-c00a-4fc0-babc-2fcea28c1fb6",
    "text": "",
    "metadata": {
      "...": "...",
      "extracted_data": {
        "street_address": "221 Queen Street, Melbourne VIC 3000",
        "square_footage": 2800,
        "price": 1000000,
        "features": [
          "Recently renovated kitchen",
          "Smart home automation system",
          "2-car garage with storage space",
          "Spacious open-plan layout with natural lighting",
          "Designer kitchen with quartz countertops and built-in appliances",
          "Master suite with walk-in closet and en-suite bath",
          "Covered patio and landscaped backyard garden"
        ],
        "agent_contact": {
          "phone": "+01 555 123456"
        }
      }
    }
  },
  {
    "type": "Title",
    "element_id": "3f1ad705648037cf65e4d029d834a0de",
    "text": "HOME FOR FUTURE",
    "metadata": {
      "...": "..."
    }
  },
  {
    "...": "..."
  }
]

In the preceding output, the first document element, of type DocumentData, has an extracted_data field within metadata that contains a representation of the document’s data in the custom output format that you specify. Beginning with the second document element and continuing until the end of the document, Unstructured also outputs the document’s data as a series of Unstructured’s document elements and metadata as it normally would. To use the structured data extractor, you can provide Unstructured with an extraction schema, which defines the structure of the data for Unstructured to extract. Or you can specify an extraction prompt that guides Unstructured on how to extract the data from the source documents, in the format that you want. An extraction prompt is like a prompt that you would give to a chatbot or AI agent. This prompt guides Unstructured on how to extract the data from the source documents. For this real estate listing example, the prompt might look like the following:

Extract the following information from the listing, and present it in the following format:

- street_address: The full street address of the property including street number, street name, city, state, and postal code.
- square_footage: The total living space area of the property, in square feet.
- price: The listed selling price of the property, in local currency.
- features: A list of property features and highlights.
- agent_contact: Contact information for the real estate agent.

  - phone: The agent's contact phone number.

An extraction schema is a JSON-formatted schema that defines the structure of the data that Unstructured extracts. The schema must conform to the OpenAI Structured Outputs guidelines, which are a subset of the JSON Schema language. For this real estate listing example, the schema might look like the following:

{
  "type": "object",
  "properties": {
    "property_listing": {
      "type": "object",
      "properties": {
        "street_address": {
          "type": "string",
          "description": "The full street address of the property including street number, street name, city, state, and postal code"
        },
        "square_footage": {
          "type": "integer",
          "description": "The total living space area of the property, in square feet"
        },
        "price": {
          "type": "number",
          "description": "The listed selling price of the property, in local currency"
        },
        "features": {
          "type": "array",
          "description": "A list of property features and highlights",
          "items": {
            "type": "string",
            "description": "A single property feature or highlight"
          }
        },
        "agent_contact": {
          "type": "object",
          "description": "Contact information for the real estate agent",
          "properties": {
            "phone": {
              "type": "string",
              "description": "The agent's contact phone number"
            }
          },
          "required": ["phone"],
          "additionalProperties": false
        }
      },
      "required": ["street_address", "square_footage", "price", "features", "agent_contact"],
      "additionalProperties": false
    }
  },
  "required": ["property_listing"],
  "additionalProperties": false
}

You can also use a visual schema builder to define the schema, like this:

Using the structured data extractor

There are two ways to use the structured data extractor in your Unstructured workflows:

From the Start page of your Unstructured account. This approach works only with a single file that is stored on your local machine. Learn how.
From the Unstructured workflow editor. This approach works with a single file that is stored on your local machine, or with any number of files that are stored in remote locations. Learn how.

Use the structured data extractor from the Start page

To have Unstructured extract the data in a custom-defined format for a single file that is stored on your local machine, do the following from the Start page:

Sign in to your Unstructured account, if you are not already signed in.
On the sidebar, click Start, if the Start page is not already showing.
In the Welcome, get started right away! tile, do one of the following:
- To use a file on your local machine, click Browse files and then select the file, or drag and drop the file onto Drop file to test.
  If you use a local file, the file must be 10 MB or less in size.
- To use a sample file provided by Unstructured, click one of the the sample files that are shown, such as realestate.pdf.
After Unstructured partitions the selected file into Unstructured’s document element format, click Update results to have Unstructured apply generative enrichments, such as image descriptions and generative OCR, to those document elements.
In the title bar, next to Transform, click Extract.
If the Define Schema pane, do one of the following to extract the data from the selected file by using a custom-defined format:
- To use the schema based on one that Unstructured suggests after analyzing the selected file, click Run Schema.
- To use a custom schema that conforms to the OpenAI Structured Outputs guidelines, click Upload JSON; enter your own custom schema or upload a JSON file that contains your custom schema; click Use this Schema; and then click Run Schema. Learn about the OpenAI Structured Outputs format.
- To use a visual editor to define the schema, click the ellipses (three dots) icon; click Reset form, enter your own custom schema objects and their properties, and then click Run Schema. Learn about OpenAI Structured Outputs data types.
- To use a plain language prompt to guide Unstructured on how to extract the data, click Suggest; enter your propmpt in the dialog; click Generate schema; make any changes to the suggested schema as needed; and then click Run Schema.
The extracted data appears in the Extract results pane. You can do one of the following:
- To view a human-viewable representation of the extracted data, click Formatted.
- To view the JSON representation of the extracted data, click JSON.
- To download the JSON representation of the extracted data as a local JSON file, click the download icon next to Formatted and JSON.
- To change the schema and then re-run the extraction, click the back arrow next to Extract Results, and then skip back to step 6 in this procedure.

Use the structured data extractor from the workflow editor

To have Unstructured extract the data in a custom-defined format for a single file that is stored on your local machine, or with any number of files that are stored in remote locations, do the following from the workflow editor:

If you already have an Unstructured workflow that you want to use, open it to show the workflow editor. Otherwise, create a new workflow as follows: a. Sign in to your Unstructured account, if you are not already signed in.
b. On the sidebar, click Workflows.
c. Click New Workflow +.
d. With Build it Myself already selected, click Continue. The workflow editor appears.
Add an Extract node to your existing Unstructured workflow. This node must be added right before the workflow’s Destination node. To add this node, in the workflow designer, click the + (add node) button immediately before the Destination node, and then click Enrich > Extract.
Click the newly added Extract node to select it.
In the node’s settings pane, on the Details tab, under Provider, select the provider for the model that you want Unstructured to use to do the extraction. Then, under Model, select the model.
To specify the custom schema for Unstructured to use to do the extraction, do one of the following:
- To use a custom schema that conforms to the OpenAI Structured Outputs guidelines, click Upload JSON; enter your own custom schema or upload a JSON file that contains your custom schema; and then click Use this Schema. Learn about the OpenAI Structured Outputs format.
- To use a visual editor to define the schema, enter your own custom schema objects and their properties. To clear the current schema and start over, click the ellipses (three dots) icon, and then click Reset form. Learn about OpenAI Structured Outputs data types.
Continue building your workflow as desired.
To see the results of the structured data extractor, do one of the following:
- If you have already selected a local file as input to your workflow, click Test immediately above the Source node. The results will be displayed on-screen in the Test output pane.
- If you are using source and destination connectors for your workflow, run the workflow as a job, monitor the job, and then examine the job’s results in your destination location.

Limitations

The structured data extractor is not guaranteed to work with the Pinecone destination connector. This is because Pinecone has strict limits on the amount of metadata that it can manage. These limits are below the threshold of what the structured data extractor typically needs for the amount of metadata that it manages.

Saving the extracted data separately

Unstructured does not recommend that you save DocumentData elements as rows or entries within a traditional SQL-style destination database or vector store, for the following reasons:

Saving a mixture of DocumentData elements and default Unstructured elements such as Title, NarrativeText, and Table elements and so on in the same table, collection, or index might cause unexpected performance issues or might return less useful search and query results.
The DocumentData elements’ extracted_data contents can get quite large and complex, exceeding the column or field limits of some SQL-style databases or vector stores.

Instead, you should save the JSON containing the DocumentData elements that Unstructured outputs into a blob storage, file storage, or No-SQL database destination location. You could then use the following approach to extract and save the extracted_data contents from the JSON into a SQL-style destination database or vector store from there. To save the contents of the extracted_data field separately from the rest of Unstructured’s JSON output, you could for example use a Python script such as the following. This script works with one or more Unstructured JSON output files that you already have stored on the same machine as this script. Before you run this script, do the following:

To process all Unstructured JSON files within a directory, change None for input_dir to a string that contains the path to the directory. This can be a relative or absolute path.
To process specific Unstructured JSON files within a directory or across multiple directories, change None for input_file to a string that contains a comma-separated list of filepaths on your local machine, for example "./input/2507.13305v1.pdf.json,./input2/table-multi-row-column-cells.pdf.json". These filepaths can be relative or absolute.
If input_dir and input_file are both set to something other than None, then the input_dir setting takes precedence, and the input_file setting is ignored.
For the output_dir parameter, specify a string that contains the path to the directory on your local machine that you want to send the extracted_data JSON. If the specified directory does not exist at that location, the code will create the missing directory for you. This path can be relative or absolute.

import asyncio
import os
import json

async def process_file_and_save_result(input_filename, output_dir):
    with open(input_filename, "r") as f:
        input_data = json.load(f)

    if input_data[0].get("type") == "DocumentData":
        if "extracted_data" in input_data[0]["metadata"]:
            extracted_data = input_data[0]["metadata"]["extracted_data"]

            results_name = f"{os.path.basename(input_filename)}"
            output_filename = os.path.join(output_dir, results_name)

            try:
                with open(output_filename, "w") as f:
                    json.dump(extracted_data, f)
                print(f"Successfully wrote 'metadata.extracted_data' to '{output_filename}'.")
            except Exception as e:
                print(f"Error: Failed to write 'metadata.extracted_data' to '{output_filename}'.")
        else:
            print(f"Error: Cannot find 'metadata.extracted_data' field in '{input_filename}'.")
    else: 
        print(f"Error: The first element in '{input_filename}' does not have 'type' set to 'DocumentData'.")
        

def load_filenames_in_directory(input_dir):
    filenames = []
    for root, _, files in os.walk(input_dir):
        for file in files:
            if file.endswith('.json'):
                filenames.append(os.path.join(root, file))
                print(f"Found JSON file '{file}'.")
            else:
                print(f"Error: '{file}' is not a JSON file.")

    return filenames

async def process_files():
    # Initialize with either a directory name, to process everything in the dir,
    # or a comma-separated list of filepaths.
    input_dir   = None # "path/to/input/directory"
    input_files = None # "path/to/file,path/to/file,path/to/file"

    # Set to the directory for output json files. This dir 
    # will be created if needed.
    output_dir = "./extracted_data/"

    if input_dir:
        filenames = load_filenames_in_directory(input_dir)
    else:
        filenames = input_files.split(",")

    os.makedirs(output_dir, exist_ok=True)

    tasks = []
    for filename in filenames:
        tasks.append(
            process_file_and_save_result(filename, output_dir)
        )

    await asyncio.gather(*tasks)

if __name__ == "__main__":
    asyncio.run(process_files())

Additional examples

In addition to the preceding real estate listing example, here are some more examples that you can adapt for your own use.

Caring for houseplants

Using the following image file (download this file):

An extraction schema for this file might look like the following:

{
  "type": "object",
  "properties": {
    "plants": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "The name of the plant"
          },
          "sunlight": {
            "type": "string",
            "description": "The sunlight requirements for the plant (for example: 'Direct', 'Bright Indirect - Some direct')."
          },
          "water": {
            "type": "string",
            "description": "The watering instructions for the plant (for example: 'Let dry between thorough watering', 'Water when 50-60% dry')."
          },
          "humidity": {
            "type": "string",
            "description": "The humidity requirements for the plant (for example:'Low', 'Medium', 'High')"
          }
        },
        "required": ["name", "sunlight", "water", "humidity"],
        "additionalProperties": false
      }
    }
  },
  "required": ["plants"],
  "additionalProperties": false
}

An extraction guidance prompt for this file might look like the following:

Providing an extraction guidance prompt is available only from the Start page. The workflow editor does not offer an extraction guidance prompt—you must provide an extraction schema instead.

Extract the plant information for each of the plants in this document, and present it in the following format:

- plants: A list of plants.

  - name: The name of the plant.
  - sunlight: The sunlight requirements for the plant (for example: 'Direct', 'Bright Indirect - Some direct').
  - water: The watering instructions for the plant (for example: 'Let dry between thorough watering', 'Water when 50-60% dry').
  - humidity: The humidity requirements for the plant (for example: 'Low', 'Medium', 'High').

And Unstructured’s output would look like the following:

[
  {
    "type": "DocumentData",
    "element_id": "3be179f1-e1e5-4dde-a66b-9c370b6d23e8",
    "text": "",
    "metadata": {
      "...": "...",
      "extracted_data": {
        "plants": [
          {
            "name": "Krimson Queen",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Let dry between thorough watering",
            "humidity": "Low"
          },
          {
            "name": "Chinese Money Plant",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Let dry between thorough watering",
            "humidity": "Low - Medium"
          },
          {
            "name": "String of Hearts",
            "sunlight": "Direct - Bright Indirect",
            "water": "Let dry between thorough watering",
            "humidity": "Low"
          },
          {
            "name": "Marble Queen",
            "sunlight": "Low- High Indirect",
            "water": "Water when 50 - 80% dry",
            "humidity": "Low - Medium"
          },
          {
            "name": "Sansevieria Whitney",
            "sunlight": "Direct - Low Direct",
            "water": "Let dry between thorough watering",
            "humidity": "Low"
          },
          {
            "name": "Prayer Plant",
            "sunlight": "Medium - Bright Indirect",
            "water": "Keep soil moist",
            "humidity": "Medium - High"
          },
          {
            "name": "Aloe Vera",
            "sunlight": "Direct - Bright Indirect",
            "water": "Water when dry",
            "humidity": "Low"
          },
          {
            "name": "Philodendron Brasil",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Water when 80% dry",
            "humidity": "Low - Medium"
          },
          {
            "name": "Pink Princess",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Water when 50 - 80% dry",
            "humidity": "Medium"
          },
          {
            "name": "Stromanthe Triostar",
            "sunlight": "Bright Indirect",
            "water": "Keep soil moist",
            "humidity": "Medium - High"
          },
          {
            "name": "Rubber Plant",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Let dry between thorough watering",
            "humidity": "Low - Medium"
          },
          {
            "name": "Monstera Deliciosa",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Water when 80% dry",
            "humidity": "Low - Medium"
          }
        ]
      }
    }
  },
  {
    "...": "..."
  }
]

Medical invoicing

Using the following PDF file (download this file):

An extraction schema for this file might look like the following:

{
  "type": "object",
  "properties": {
    "patient": {
      "type": "object",
      "properties": {
        "name": {
          "type": "string",
          "description": "Full name of the patient."
        },
        "birth_date": {
          "type": "string",
          "description": "Patient's date of birth."
        },
        "sex": {
          "type": "string",
          "enum": ["M", "F", "Other"],
          "description": "Patient's biological sex."
        }
      },
      "required": ["name", "birth_date", "sex"],
      "additionalProperties": false
    },
    "medical_summary": {
      "type": "object",
      "properties": {
        "prior_procedures": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "procedure": {
                "type": "string",
                "description": "Name or type of the medical procedure."
              },
              "date": {
                "type": "string",
                "description": "Date when the procedure was performed."
              },
              "levels": {
                "type": "string",
                "description": "Anatomical levels or location of the procedure."
              }
            },
            "required": ["procedure", "date", "levels"],
            "additionalProperties": false
          },
          "description": "List of prior medical procedures."
        },
        "diagnoses": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "List of medical diagnoses."
        },
        "comorbidities": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "List of comorbid conditions."
        }
      },
      "required": ["prior_procedures", "diagnoses", "comorbidities"],
      "additionalProperties": false
    }
  },
  "required": ["patient", "medical_summary"],
  "additionalProperties": false
}

An extraction guidance prompt for this file might look like the following:

Providing an extraction guidance prompt is available only from the Start page. The workflow editor does not offer an extraction guidance prompt—you must provide an extraction schema instead.

Extract the medical information from this record, and present it in the following format:

- patient

  - name: Full name of the patient.
  - birth_date: Patient's date of birth.
  - sex: Patient's biological sex.

- medical_summary

  - prior_procedures

    - procedure: Name or type of the medical procedure.
    - date: Date when the procedure was performed.
    - levels: Anatomical levels or location of the procedure.

  - diagnoses: List of medical diagnoses.
  - comorbidities: List of comorbid conditions.

Additional extraction guidance:

- name: Extract the full legal name as it appears in the document. Use proper capitalization (for example: "Marissa K. Donovan").
- birth_date: Convert to format "MM/DD/YYYY" (for example: "03/28/1976"),

  - Accept variations: MM/DD/YYYY, MM-DD-YYYY, YYYY-MM-DD, Month DD, YYYY,
  - If only age is given, do not infer birth date - mark as null,

- sex: Extract biological sex as single letter: "M" (Male), "F" (Female), or "X" (Other)

  - Map variations: Male/Man → "M", Female/Woman → "F", Others → "X"

- prior_procedures:

  Extract all surgical and major medical procedures, including:

  - procedure: Use standard medical terminology when possible.
  - date: Format as "MM/DD/YYYY". If only year/month available, use "01" for missing day.
  - levels: Include anatomical locations, vertebral levels, or affected areas.

    - For spine procedures: Use format like "L4 to L5" or "L4-L5".
    - Include laterality when specified (left, right, bilateral).

  - diagnoses:

    Extract all current and historical diagnoses:

    - Include both primary and secondary diagnoses.
    - Preserve medical terminology and ICD-10 descriptions if provided.
    - Include location/region specifications (for example: "radiculopathy — lumbar region").
    - Do not include procedure names unless they represent a diagnostic condition.

  - comorbidities

    Extract all coexisting medical conditions that may impact treatment:

    - Include chronic conditions (for example: "diabetes", "hypertension").
    - Include relevant surgical history that affects current state (for example: Failed Fusion, Multi-Level Fusion).
    - Include structural abnormalities (for example: Spondylolisthesis, Stenosis).
    - Do not duplicate items already listed in primary diagnoses.

Data quality rules:

1. Completeness: Only include fields where data is explicitly stated or clearly indicated.
2. No inference: Do not infer or assume information not present in the source.
3. Preserve specificity: Maintain medical terminology and specificity from source.
4. Handle missing data: Return empty arrays [] for sections with no data, never null.
5. Date validation: Ensure all dates are realistic and properly formatted.
6. Deduplication: Avoid listing the same condition in multiple sections.

Common variations to handle:

- Operative reports: Focus on procedure details, dates, and levels.
- H&P (history & physical): Rich source for all sections.
- Progress notes: May contain updates to diagnoses and new procedures.
- Discharge summaries: Comprehensive source for all data points.
- Consultation notes: Often contain detailed comorbidity lists.
- Spinal levels: C1-C7 (Cervical), T1-T12 (Thoracic), L1-L5 (Lumbar), S1-S5 (Sacral).
- Use "fusion surgery" not "fusion" alone when referring to procedures.
- Preserve specificity: "Type 2 Diabetes" not just "Diabetes" when specified.
- Multiple procedures same date**: List as separate objects in the array.
- Revised procedures: Include both original and revision as separate entries.
- Bilateral procedures: Note as single procedure with "bilateral" in levels.
- Uncertain dates: If date is approximate (for example, "Spring 2023"), use "01/04/2023" for Spring, "01/07/2023" for Summer, and so on.
- Name variations: Use the most complete version found in the document.
- Conflicting information**: Use the most recent or most authoritative source.

Output validation:

Before returning the extraction:

1. Verify all required fields are present.
2. Check date formats are consistent.
3. Ensure no duplicate entries within arrays.
4. Confirm sex field contains only "M", "F", or "Other".
5. Validate that procedures have all three required fields.
6. Ensure diagnoses and comorbidities are non-overlapping.

And Unstructured’s output would look like the following:

[
  {
    "type": "DocumentData",
    "element_id": "e8f09cb1-1439-4e89-af18-b6285aef5d37",
    "text": "",
    "metadata": {
      "...": "...",
      "extracted_data": {
        "patient": {
          "name": "Ms. Daovan",
          "birth_date": "01/01/1974",
          "sex": "F"
        },
        "medical_summary": {
          "prior_procedures": [],
          "diagnoses": [
            "Radiculopathy — lumbar region"
          ],
          "comorbidities": [
            "Diabetes",
            "Multi-Level Fusion",
            "Failed Fusion",
            "Spondylolisthesis"
          ]
        }
      }
    }
  },
  {
    "...": "..."
  }
]

Unstructured UI

Getting started with the UI

Using the UI

Concepts

Structured data extraction

Overview

Using the structured data extractor

Use the structured data extractor from the Start page

Use the structured data extractor from the workflow editor

Limitations

Saving the extracted data separately

Additional examples

Caring for houseplants

Medical invoicing

Unstructured UI

Getting started with the UI

Using the UI

Concepts

​Overview

​Using the structured data extractor

​Use the structured data extractor from the Start page

​Use the structured data extractor from the workflow editor

​Limitations

​Saving the extracted data separately

​Additional examples

​Caring for houseplants

​Medical invoicing

Overview

Using the structured data extractor

Use the structured data extractor from the Start page

Use the structured data extractor from the workflow editor

Limitations

Saving the extracted data separately

Additional examples

Caring for houseplants

Medical invoicing