2023:GPT Integration (Concept)

This article is about an older version of Grooper.

Information may be out of date and UI elements may have changed.

2025

2023

Grooper's GPT Integration is refers to the usage of OpenAI's GPT models within Grooper to enhance the capabilities of data extractors, classification, and lookups.

OpenAI's GPT model has made waves in the world of computing. Our Grooper developers recognized the potential for this to grow Grooper's capabilities. Adding its functionality will allow for users to explore and find creative solutions for processing their documents using this advanced technology.

You may download the ZIP(s) below and upload it into your own Grooper environment (version 2023). The first contains one or more Batches of sample documents. The second contains one or more Projects with resources used in examples throughout this article.

ABOUT

GPT (Generative Pre-trained Transformer) integration can be used for three things in Grooper:

Extraction - Prompt the GPT model to return information it finds in a document.
Classification - GPT has been trained against a massive corpus of information, which allows for a lot of potential when it comes to classifying documents. The idea here is that because it's seen so much, the amount of training required in Grooper should be less.
Lookup - With a GPT lookup you can provide information collected from a model in Grooper as @ variables in a prompt to have GPT generate data.

In this article you will be shown how Grooper leverages GPT for the aforementioned methods. Some example use cases will be given to demonstrate a basic approach. Given the nature of the way this technology works, it will be up to the user to get creative about how this can be used for their needs.

Things to Consider

Before moving forward it would be prudent to mention a few things about GPT and how to use it.

Prompt Engineering

This first thing to consider is how to structure a good prompt so that you get the results you are expecting. There is a bit of an art to knowing how to do this. GPT can tell bad jokes and write accidentally hilarious poems about your life, but it can also help you do your job better. The catch: you need to help it do its job better, too. At its most basic level, OpenAI's GPT-3 and GPT-4 predict text based on an input called a prompt. But to get the best results, you need to write a clear prompt with ample context. Further on in this article when the GPT Complete Value Extractor is being demonstrated you will see an example of prompt engineering.

Follow this link, or perhaps even this one, for more information on prompt engineering.

Tokens and Pricing

Another consideration is the way GPT pricing works. You are going to be charged for the "tokens" used when interacting with GPT. To that end, the prompt that you write, the text that you leverage to get a result, and the result that is returned to you are all considered part of the token consumption. You will need to be considerate of this as you build and use GPT in your models.

Follow this link for more information on what tokens are.

Follow this link for more information on GPT pricing.

Location Data for Data Extraction

The final thing to consider is in regards to the GPT Complete Value Extractor type (more on this soon.) If you have used Grooper before then you are probably familiar with how a returned value is highlighted with a green box in the document viewer. One of the main strengths of Grooper's text synthesis is that it collects location information for each character which allows this highlighting to occur. The GPT model does not consider location information when generating its results which means there will be no highlighting on the document for values collected with this method. The main impact this will have is on your ability to validate information returned by the GPT model.

How To

With the discussion of concepts out of the way, it is time to get into Grooper and see how and where to use the GPT integration.

Obtain an API Key

Grooper is able to integrate with OpenAI's GPT model because they have provided a web API. All we need in order use the Grooper GPT functionality is an API key. Here you will learn how to obtain an API key for yourself so you can start using GPT with Grooper.

The first thing you should do is visit OpenAI API site and login or create an account.
Once logged in, click the "Personal" menu in the top right.
Within in this menu click the "View API Keys" option, which will take you to the "API keys" page.

On the "API keys" page, click the "+ Create new secret key" button, which will make an "API key generated" pop-up.

Highlight and copy, or click the copy button to copy the key string to your clipboard.
- A word of warning here. You WILL NOT get another chance to copy this string. You can always create a new one, but once you close this pop-up, you will not have another chance to copy the key string out.

GPT Complete (Extractor Type)

GPT Complete is a Value Extractor that leverages Open AI's GPT models to generate chat completions for inputs, returning one hit for each result choice provided by the model's response.

PLEASE NOTE: GPT Complete is a deprecated Value Extractor. It uses an outdated method to call the OpenAI API. Please use the Ask AI extractor going forward.

Please visit the GPT Complete article for more information.

GPT Embeddings (Classification Method)

BE AWARE: GPT Embeddings is obsolete as of version 2025. The LLM Classifier and Search Classifier methods are the new and improved AI-enabled classification methods. GPT Embeddings is a Classify Method that uses an OpenAI embeddings model and trained document samples to tell one document from another. Please visit the GPT Embeddings article for more information.

GPT Lookup (Lookup)

Following is a simple of example that will demonstrate how to use the GPT Lookup functionality. As with everything else regarding GPT Integration in Grooper 2023, this is fairly untested and needs more experimentation to see its full potential. If nothing else, this example is intended to give you a basic understanding of how to establish the lookup so you can try things out on your own.

Start by deleting all other fields in the example Data Model other than "Lessor" and "Lessee".
- This is meant to reduce the number of calls you will be making to OpenAI for GPT results as "Lessor" and "Lessee" are the only Data Fields that will be leveraged in the following lookup example.

Right-click the Data Model.
Add a Data Field.

Name it "Letter of Thanks".
Click the "Execute" button.

With the newly created Data Field object selected, set the Display Width property to 500.
Set the Multi-line property to Enabled.
Expand the sub-properties of the Multi Line property and set the Display Lines property to 15.
Set the Word Wrap property to True.

Select the Data Model.
Click the ellipsis button on the Lookups property.

In the "Lookups" window, click the "Add new lookups specification" button.
Select the "GPT Lookup" option.

With the "GPT Lookup" added to the "List of Lookup Specification" and selected, paste in your API key to the API Key property.
Click the ellipsis button for the Prompt property.

In the "Prompt" editor, type the following string:
- Write a letter of thanks regarding the ease of purchase and clean state of the property from @Lessor to @Lessee.
- As you type this out (if you do instead of copy pasting) you will notice intellisense pop-up for when you use the @ symbol. Using the @ symbol allows you to leverage elements from your Data Model when creating your lookup.
When you have completed writing your prompt, click the "OK" button.

Click the ellipsis button for the Value Selectors property.

In the "Value Selectors" window click the "Add new value selector" button.

With "Value Selector" added to the "list of Value Selector" and selected, click the drop-down button for the Target Field property.
Select the "Letter_of_Thanks" field.
- Based on this configuration, the value generated by our prompt from our lookup will populate this field with the information generated by GPT.

Back in the "Lookups" menu, scroll down in the property grid, and in the "Lookup Options" area click the drop-down button for the Trigger Mode property.
Because @ symbols are being used in the prompt to leverage elements from the Data Model the Conditional setting should be selected.

At the bottom of the property grid notice the Lookup Fields and Target Fields are populated because elements were targeted in the prompt, and a field was targeted with the Value Selectors property.
Click "OK" to close this menu.

With the lookup configured it's time to test. Click the "Tester" tab.
Select "Folder (1)" from the "GPT Complete Examples" batch.
Click the "Test the data element" button.
Notice the "Lessee" value is successfully returned ...
... and that it is being leveraged as the salutation in the value created for the "Letter of Thanks" field.

Also notice the "Lessor" value being returned ...
... and that it is being leveraged as the complementary close in the value created for the "Letter of Thanks" field.
Feel free to take a look at the text created for the letter from the GPT AI.

Lookup Properties

Following are brief descriptions of properties that are unique to GPT Lookup. Properties that overlap with previously explained properties, or are self explanatory, will be skipped.

Response Format

This specifies the format in which data will be exchanged with the web service. Can be one of the following values:

Text - The response will be plain text. Record and value selectors should be specified using regular expressions.
JSON - The response will be in JSON format. Record and value selectors should be specified using JSONPath syntax.
XML - The request and response body will be in XML format. Record and value selectors should be specified using XPath syntax.

The format selected here will be used both for sending POST data and interpreting responses. It is currently not possible to send an XML request then interpret the response as JSON, or vice-versa.

Record Selector

This is a JSONPath or XPath expression which selects records in the response.

The record selector is used to specify which JSON or XML entities represent records in the result set.

JSON Notes

In a JSON response, the Record Selector may be used as follows:

If the selector matches an array, one record will be generated for each element of the array.
If the selector matches one or more objects, one record will be generated for each object.
Leave the property empty to select an array or object at the root of the JSON document.

XML Notes

In an XML response, the Record Selector may be used as follows:

One record will be generated for each XML element matched by the selector.
Leave the property empty to select a singleton record at the root of the XML.