What's New in Grooper 2024

WIP

WORK IN PROGRESS!!

Please excuse our mess. This article is under construction.

Grooper version 2024 is here!

Below you will find brief descriptions on new and/or changed features.
When available, follow any links to extended articles on a topic.

Moving Grooper fully into the web

Deploying Grooper over a web server is a more distributable, more secure, and more modern experience. Version 2022 started Grooper's foray into web development with a web client for user operated Review tasks. Versions 2023 and 2023.1 expanded our web client to incorporate all aspects of Grooper in the web client. Version 2024 fully cements our commitment to moving Grooper to a web-based application.

Thick client removal

In 2024, there is no longer a Grooper thick client (aka "Windows client"). There is only the Grooper web client. This opens Grooper up to several advantages for cloud-based app development and cloud-based deployments.

All thick client Grooper applications have an equivalent in the Grooper web client. Most of these are now pages you will navigate to from the web client. For those unfamiliar with the Grooper web client, refer to the table below for the web client equivalent versions of thick client apps in version 2024.

Former thick client application	Current web client equivalent
Grooper Design Studio	The Design page
Grooper Dashboard	The Batches page
Grooper Attended Client	The Tasks page
Grooper Kiosk	The Stats page (displaying stats queries in a browser window)
Grooper Config	Grooper Command Console (GCC) See below for more information
Grooper Unattended Client	Replaced by "gcc services host" command in GCC

Grooper Command Console

Grooper Command Console (or GCC) is a replacement for the thick client administrative application, Grooper Config. Previous functions performed by Grooper Config can be accomplished in Grooper Command Console. This includes:

Connecting to Grooper Repositories
Installing and managing Grooper Services
Managing licensing for self hosted licensing installations

Grooper Command Console is a command line utility. All functions are performed using command line commands rather than a "point and click" user interface. Users of previous versions will find the difference somewhat shocking, but the command line interface has several advantages:

Most administrative functions are accomplished with a single command. In Grooper Config, to accomplish the same function you would perform several clicks to do the same thing. Once you are familiar with the GCC commands, Grooper Command Console ends up saving you time.
Commands can be easily scripted. There was not an easy way to procedurally execute the functions of Grooper Config like creating a Grooper Repository or spinning up new Grooper services. GCC commands allow you to do this.
Scaling services is much easier. In previous versions of Grooper, we have done proof-of-concept tests to ensure Grooper can scale in cloud deployments (such as using auto-scaling in Amazon AWS instances). However, in older Grooper versions scaling Activity Processing services was somewhat clunky. Using GCC commands to spin up services makes this process much simpler. Grooper Command Console also has specific commands to make scaling with Docker containers simpler.

For more information about Grooper Command Console, visit the Grooper Command Console article.

Improved web UI: New icons!

Improved integrations with Large Language Models (LLMs)

Innovations in Large Language Models (or LLMs) have changed the landscape of artificial intelligence. Companies like OpenAI and their GPT models have developed LLM-based technologies, like ChatGPT, that are highly effective at natural language processing. Being fully committed to advancing our capabilities through new AI integrations, Grooper has vastly improved what we can do with LLM providers such as OpenAI.

New and improved LLM-based extraction techniques

First and foremost, in 2024 you will see new and improved ways to extract data from your documents using LLMs. Because LLMs are so good at processing natural language, set up for these new extraction techniques is done in a fraction of the time of traditional extractors in Grooper.

New in 2024 you will find:

AI Extract: A "Fill Method" designed to extract a full Data Model with little configuration necessary.
Clause Detection: A new Data Section extract method designed to find clauses of a certain type in a contract.
Ask AI: This extractor type replaces the deprecated "GPT Complete" extractor, with new functionality that allows table extraction using responses from LLMs.

AI Extract

AI Extract introduces the concept of a Fill Method in Grooper. Fill Methods are configured on "container elements", like Data Models, Data Sections and Data Tables. The Fill Method runs after data extraction. It will fill in the Data Model using whatever method is configured (Fill Methods can be configured to overwrite initial extraction results or only supplement them).

AI Extract is the first Fill Method in Grooper. It uses chat responses from Large Language Models (like OpenAI's GPT models), to fill in a Data Model. We have designed this fill method to be as simple as possible to get data back from a chat response and into fields in the Data Model. In many cases, all you need to do is add Data Elements to a Data Model to return results.

AI Extract uses the Data Elements' names, data types (string, datetime, etc.) and (optionally) descriptions to craft a prompt sent to an LLM chatbot. Then, it parses out the response, populating fields, sections and even table cells. As long as the Data Elements' names are descriptive ("Invoice Number" for an invoice number located on an invoice), that's all you need to locate the value in many cases. With no further configuration necessary, this is the fastest to deploy method of extracting data in Grooper to date.

Clause Detection

Detecting contract clauses of a certain type has always been doable in Grooper using Field Classes. However, training the Field Class is a laborious and tedious process. This can be particularly taxing when attempting to locate several different clauses throughout a contract.

Large Language Models make this process so much simpler. LLMs are well suited to find examples of clauses in contracts. Natural language processing, after all, is their bread and butter. Clause Detection is a new Data Section extract method that uses chat responses to locate clauses in a contract. All you have to do is provide one or more written examples of the clause and Clause Detection does the rest. It parses the clause's location from the chatbot's response, which then forms the Data Section's data instance. This can be used to return the full text of a clause, extract information in the clause to Data Fields or both.

Ask AI

Ask AI is a new Grooper Extractor Type in Grooper 2024. It was created as a replacement for the "GPT Complete" extractor, which uses a deprecated method to call OpenAI GPT models. Ask AI works much like GPT Complete. It is an extractor configured with a prompt sent to a LLM chatbot and returns the chatbot's response.

Ask AI is more robust than its predecessor in that:

Ask AI has access to more LLM models, including those accessed via the OpenAI API, privately hosted GPT clones, and compatible LLMs from Azure's machine learning model catalog.
Ask AI can more easily parse JSON responses.
Ask AI has a mechanism to decompose chat responses into extraction instances (or "sub-elements"). This means Ask AI can potentially be used for a Row Match Data Table extractor.

Chat with your documents

Publicly accessible LLM chatbots like ChatGPT are always limited by what content they were trained on. The documents you're processing are probably not part of their training set. If they were, the LLM would be able to process it more effectively. You could even "chat" with your documents. You could ask more specific questions and get more accurate responses.

Now you can do just that! Using OpenAI's Assistants API, we've created a mechanism to quickly generate custom AI chatbot assistants in Grooper that can answer questions directly about one or more selected documents.

Build AI assistants with Grooper AI Analysts

AI Analysts are a new node type in Grooper that facilitate chatting with a document set. Creating an AI Analyst requires an OpenAI API account. AI Analysts create task-specific OpenAI "assistants" that answer questions based on a "knowledge base" of supplied information. Selecting one or more documents, users can chat with the assistant in Grooper about the documents. The text data from these documents form the assistant's knowledge base.

Using this mechanism, users can have a conversation with a single document or a Batch with hundreds of documents. Each conversion is logged as a "Chat Session" and stored as a child of the AI Analyst. These Chat Sessions can be accessed again (either in the Design Page's Node Tree or the Chat Page), allowing users to continue previous conversions.

The process of creating an AI Analyst and starting a Chat Session is fairly straightforward:

Add an LLM Connector to the Grooper Repository's Options (more on "Repository Options" below).
Create an AI Analyst.
Select the documents you want to chat with. This can be done in multiple ways.
- From a Batch Viewer or Folder Viewer.
- From a Search Page query (more on the Search Page below).
- From the Chat Viewer in Review (effectively selects each document in the Batch)
Start a Chat Session. This can also be done in multiple ways.
- Using the Discuss command
- Using the AI Dialogue activity. This is a way of automating chat questions.
- Using the Chat Viewer in Review

Chat Page

The Chat Page is a brand new UI page that allows users to continue previous Chat Sessions. Chat Sessions are archived as children of an AI Analyst . Each Chat Session is organized into subfolders by user name. The Chat Page allows users to access their previous Chat Sessions stored in these folders. Furthermore, since Chat Sessions are archived by user name, users will only have access to Chat Sessions created by their user session.

Chat in Review

The Chat View is a new Review View that can be added to a Review step in a Batch Process. This allows human operators a mechanism to chat with a document during Review. The Chat View facilitates a chat with an AI Analyst. Users may select one document or multiple documents and enter questions into the chat console. The human reviewer can ask questions to better understand the document or help locate information to complete their review.

Furthermore, if there are "stock questions" any Review user should be asking, the new AI Dialogue activity can automate a scripted set of questions with an AI Analyst. Any "Predefined Messages" configured for the AI Analyst will be asked by the AI Dialogue activity. These answers to these questions can be reviewed by a user during Review with a Chat View. This also allows users to continue the conversation with Predefined Messages getting the conversation started.

Grooper as a document repository

Moving away from "Batch oriented processing". Moving away from "delete Batches as soon as data is out of Grooper".

Batch redesign

Search Page & AI Search

Job oriented processing Indexing Behavior (and related indexing object commands to add and update search indexes) Indexing Service Generators