> ## Documentation Index > Fetch the complete documentation index at: https://docs.aryn.ai/llms.txt > Use this file to discover all available pages before exploring further. # Tutorial: Property Extraction with the Aryn SDK > Using the Aryn SDK to extract properties from a document ## Introduction In this tutorial, we will walk through an example of using DocParse to extract structured metadata from a document. This lets you combine document parsing and semantic field extraction with a single API call. We will be working with a [sample insurance document](https://aryn-public.s3.us-east-1.amazonaws.com/extraction-tutorial/gri.pdf) that contains information about a workers compensation reinsurance submission. By the end of this tutorial, you will be able to extract properties into structured JSON like the following using the Aryn SDK. ```json theme={null} { ... "properties": { "Submission Date": "2026-09-15T00:00:00", "Submission Contact Email": "jane.doe@globalre-demo.com", ... "Current Exposure Info": { "Rating Basis": "Payroll", "Exposure Premium": "Manual / Standard", "Exposure Amount": 215750000, "Exposure Amount Currency": "USD", "Exposure Modification": "Yes (NCCI Mod: 0.92)", "Current Exposure Information": [ { "State": "CA", "Class Code": "7219", "Payroll": 45000000, "Manual Premium": 1890000, "Modified Premium": 1738800 }, { "State": "TX", "Class Code": "7219", "Payroll": 32500000, "Manual Premium": 910000, "Modified Premium": 837200 }, { "State": "IL", "Class Code": "7380", "Payroll": 28000000, "Manual Premium": 812000, "Modified Premium": 747040 }, ... ] } } } ``` Let's start simple and build up to more complex use cases. ## Prerequisites 1. Get your free Aryn API key by signing up on the Aryn Console at [app.aryn.ai](https://app.aryn.ai). 2. Open up the [follow-along Colab notebook](https://colab.research.google.com/drive/18Pw_jHSpIv9vt6wMBZaIe561CPBAxaaK?usp=sharing) and copy in your API key by clicking the key icon on the leftmost side of the screen. Then, click the \`\`+ Add new secret'' button in blue on the left hand side of your screen. Finally, add your Aryn API key in the value field under the name "ARYN\_API\_KEY". Alternatively, you can set the API key as an environment variable and follow along locally instead of on Colab. 3. Download the [sample document](https://aryn-public.s3.us-east-1.amazonaws.com/extraction-tutorial/gri.pdf) or have your own handy. ## Schemas Property extraction is based on a *schema* that defines what to extract. A schema is a list of properties, each of which have a name, a type, and optional fields like a description, default value, and example values. These fields are used to guide the LLM on what to extract. While you can provide the Schema as a dictionary, the Aryn SDK also includes a Pydantic model that makes it easy to work with schemas. For example, the following code creates a schema with two fields: ```python theme={null} from aryn_sdk.types.schema import DataType, make_property, make_named_property, Schema schema = Schema( properties=[ make_named_property( name="Submission Date", type=DataType.DATE, description="The submission date of the claim." ), make_named_property( name="Submission Contact Email", type=DataType.STRING, description="The submission contact email." ) ] ) ``` ## Step 1: Simple Property Extraction You can extract all of the fields a schema by specifying the schema under `property_extraction_options`. ```python theme={null} import json from aryn_sdk.partition import partition_file data = partition_file( "gri.pdf", table_mode="standard", property_extraction_options={ "schema": schema }, ) print(json.dumps(data, indent=4)) ``` Note here that we set the `table_mode` to `standard`. This ensures that we can extract properties that are present in tables, such as the Submission Contact Email. In the output, you will see a separate top-level key \`\`properties'' that contains the extracted properties. For this document, you should see something like the following: ```json theme={null} { "elements": {...} "properties": { "Submission Date": "2025-09-15T00:00:00", "Submission Contact Email": "jane.doe@globalre-demo.com" } } ``` ## Step 2: Extracting Nested Data with Arrays It's common to want to extract multiple records from a single document, each of which has a similar schema. For example, in the sample document, the current exposure information is broken out by state in a table on page 2. You can extract all of these entries with DocParse by specifying a schema containing an array of objects, like the following: ```python theme={null} state_exposure = make_property( type=DataType.OBJECT, properties=[ make_named_property(name="state", type=DataType.STRING), make_named_property(name="class_code", type=DataType.STRING), make_named_property(name="payroll", type=DataType.INT), make_named_property(name="manual premium", type=DataType.INT), make_named_property(name="modified premium", type=DataType.INT), ] ) schema = Schema( properties = [ make_named_property(name="current_exposure", type=DataType.ARRAY, item_type=state_exposure) ] ) data = partition_file( "gri.pdf", table_mode="standard", property_extraction_options={ "schema": schema }, ) print(json.dumps(data, indent=4)) ``` The \`\`current\_exposure'' property is of type `ARRAY`, and you specify the type of each element in the Array as the `item_type`. In this case, the item type is an object with the fields you want to extract for the individual exposure records. The result of running this script is the following: ```json theme={null} { ... "properties": { "current_exposure": [ { "state": "CA", "class_code": "7219", "payroll": 45000000, "manual premium": 1890000, "modified premium": 1738800 }, { "state": "TX", "class_code": "7219", "payroll": 32500000, "manual premium": 910000, "modified premium": 837200 }, { "state": "IL", "class_code": "7380", "payroll": 28000000, "manual premium": 812000, "modified premium": 747040 }, { "state": "FL", "class_code": "7219", "payroll": 21000000, "manual premium": 546000, "modified premium": 502320 }, { "state": "PA", "class_code": "7380", "payroll": 18500000, "manual premium": 481000, "modified premium": 442520 }, { "state": "GA", "class_code": "8292", "payroll": 15000000, "manual premium": 330000, "modified premium": 303600 }, { "state": "Other", "class_code": "Various", "payroll": 55750000, "manual premium": 1505250, "modified premium": 1384830 } ] } } ``` Notice that each entry in the Current Exposures table is extracted as a separate array element in the returned JSON. {/* ### Validation */} {/* Here's an example of a property with a regex validator to check the format of the submission contact email is not unreasonable. Currently, only regex validators are supported. */} {/* import json */} {/* submission_date_property = { */} {/* "type": { */} {/* "description": "The submission date of the claim", */} {/* } */} {/* "name": "Submission Contact Email", */} {/* "type": "string", */} {/* "validators": [ */} {/* "type": "regex", */} {/* } */} {/* }, */} {/* data = partition_file( */} {/* table_mode="standard", */} {/* "schema": { */} {/* submission_date_property, */} {/* ] */} {/* }, */} {/* print(json.dumps(data, indent=4)) */} {/* The output should look the same as before. */} Congratulations! You are now able to extract properties from a document using the Aryn SDK. You can also do this via the UI on the DocParse page of the [Aryn Console](https://app.aryn.ai/docparse).