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 Information may be out of date and UI elements may have changed.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

Enhancing Grooper by integrating with modern AI technology.

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. Media:2023_Wiki_GPT-Integration_Batch.zip Media:2023_Wiki_GPT-Integration_Project.zip

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.

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

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

5. 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 Specification)

PLEASE NOTE: GPT Lookup is obsolete as of version 2025. Much of its functionality was replaced by newer and better LLM-based extraction methods, such as AI Extract. If absolutely necessary, its functionality could also be replicated with a Web Service Lookup implementation. GPT Lookup is a Lookup Specification that performs a lookup using an OpenAI GPT model. Please visit the GPT Lookup article for more information.

Glossary

Batch: inventory_2 Batch nodes are fundamental in Grooper's architecture. They are containers of documents that are moved through workflow mechanisms called settings Batch Processes. Documents and their pages are represented in Batches by a hierarchy of folder Batch Folders and contract Batch Pages.

Classification Method:

Classification: Classification is the process of identifying and organizing documents into categorical types based on their content or layout. Classification is key for efficient document management and data extraction workflows. Grooper has different methods for classifying documents. These include methods that use machine learning and text pattern recognition. In a Grooper Batch Process, the Classify Activity will assign a Content Type to a folder Batch Folder.

Data Extraction: Data Extraction involves identifying and capturing specific information from documents (represented by folder Batch Folders in Grooper). Extraction is performed by configurable Data Extractors, which transform unstructured or semi-structured data into a structured, usable format for processing and analysis.

Extract: export_notes Extract is an Activity that retrieves information from folder Batch Folder documents, as defined by Data Elements in a data_table Data Model. This is how Grooper locates unstructured data on your documents and collects it in a structured, usable format.

Extractor Type:

GPT Complete: 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.

GPT Embeddings: 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.

GPT Integration: 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.

GPT Lookup: PLEASE NOTE: GPT Lookup is obsolete as of version 2025. Much of its functionality was replaced by newer and better LLM-based extraction methods, such as AI Extract. If absolutely necessary, its functionality could also be replicated with a Web Service Lookup implementation. GPT Lookup is a Lookup Specification that performs a lookup using an OpenAI GPT model.

Lookup: A Lookup Specification defines a "lookup operation", where existing Grooper fields (called "lookup fields") are used to query an external data source, such as a database. The results of the lookup can be used to validate or populate field values (called "target fields") in Grooper. Lookup Specifications are created on "container elements" (data_table Data Models, insert_page_break Data Sections and table Data Tables) using their Lookups property. Lookups may query using all single-instance fields relative to the container element (including those defined on parent elements up to the root Data Model), but cannot be used to populate a field value on a parent of the container element.

Project: package_2 Projects are the primary containers for configuration nodes within Grooper. The Project is where various processing objects such as stacks Content Models, settings Batch Processes, profile objects are stored. This makes resources easier to manage, easier to save, and simplifies how node references are made in a Grooper Repository.