Search: join-data-table

at: [email protected] for more assistant. Step 4: Choose one or more users to join this pipeline for reviewing documents (if need). Step 5: Click "OK" button to complete creating a new pipeline. Table

:1704877579310-775.png||height="377" width="566"]] **Step 4**: Choose one or more users to join this pipeline

" %) ((( Table of Content ))) {{toc depth="4" start="2"/}} ))) )))

and choose data type is "Table". Step 3: (This step is optional) Turn on button "Required" to set require

and data type for each column Step 6: Click "Save" button Table of Content

and Table Field 1. Add Form Field Step 1: On Add Learning Instance screen, click "Add Field" button

the table name on "Label" field and choose data type is "Table". ))) [[image:image-20221028164141-11.png

types of fields: Form Field and Table Field == **1. Add Form Field** == (% class="box infomessage

the field name on "Label" field and choose data type for field on "Data Type" field ))) [[image:image

based upon the value entered in the ROI data set table, as well as the days until your defined goal

the Manual Time value entered in the ROI data set table. Goal Input Static display of your defined

to date, based upon the Manual Time value entered in the ROI data set table. Displays the Top 10

 value entered in the **ROI data set table**. ))) |((( **Goal ** )))|((( Input )))|((( Static display

in the **ROI data set table**. ))) |((( **Cumulative Time Saved** )))|((( Column Chart )))|((( Total time saved

** )))|((( Input )))|((( Displays the current total of money saved based upon the value entered in the **ROI data

documents to extract data. Staff can create a new learning model by following these below steps: Step 1

choose base model, staff will have to create form fields and table from scratch Step 5: Click "Save

. With the Form Fields, staff should label both "Label" and "Value" for each field if having enough data

, send the learning models to production and use them to run on actual documents to extract data. Staff

||cursorshover="true"]] * If staff doesn't choose base model, staff will have to create form fields and table

" for each field if having enough data in documents. This helps the model will extract data more exactly

Table Tabs Well Data Include components used to collect data Hidden Container Data map Data grid Edit

the ability to collect user data UI components are divided into 5 main groups, based on their functions

to fill in. Then click Save when you are done. The below table describes frequently used configure

|((( * HTML element * Content * Columns * Field set * Panel * Table * Tabs * Well ))) |Data|Include components used to collect data|((( * Hidden * Container * Data map * Data grid * Edit grid * Tree ))) |Premium

provide users the ability to collect user data [[image:1695034818758-663.png||height="379" width="1014

1. Create an Account Note: Although akaBot Vision currently supports Pre-trained data fields only for Invoice processing, the technology is documented agnostic and can extract data from any

customizable, so you can add/group/remove pipelines as needed. Table of Content

currently supports Pre-trained data fields only for Invoice processing, the technology is documented agnostic and can extract data from any structured document including receipts, purchase orders, shipping

-20220420182302-1.png||alt="image-20220420183141-4.png" data-xwiki-image-style-alignment="center"]] **Step 2

1. Introduction This document will guide you on how to install Data Service on Linux (Ubuntu

needed to run on your system: akabot_dataservice, akabot_userdata. Data Service web service

Table of Content

Data Service

[3] Data Service Installation

** === This document will guide you on how to install Data Service on Linux (Ubuntu) server. === **2. System

))) There are two database schemas needed to run on your system: akabot_dataservice, akabot_userdata. Data Service

: [email protected] ))) (% class="akb-toc" %) ((( (% class="akb-toc-title" %) ((( Table of Content

: Choose Automation Type and set conditions for required fields and data formats The Automation Type

will be bypassed Bypass wrong data formats: All the documents with wrong data formats inside will be moved

. If you turn this mode on, all the wrong data formats will be bypassed Step 3: Click [Save] to save

Customizing Data Extract

Automation Type and set conditions for required fields and data formats ))) * The Automation Type will have

fields will be bypassed * Bypass wrong data formats: All the documents with wrong data formats inside

these later. If you turn this mode on, all the wrong data formats will be bypassed [[image:image

As a manager or administrator, users may want to know about, for instance, all the documents imported to a specific queue or which data fields required the most corrections. The dashboard’s reports

by a selected granularity. Table Of Content

to a specific queue or which data fields required the most corrections. The dashboard’s reports could also help

-663.png]] ))) (% class="akb-toc" %) ((( (% class="akb-toc-title" %) ((( Table Of Content

organization's data and account information. In this document, you will find an introduction to the API usage

is processed successfully and the user can export data via calling API Export Document with input parameters

to review documents then change it to "Confirmed" status by API Update Document Status before exporting data

** == The akaBot Vision API allows you to programmatically access and manage your organization's data and account

and the user can export data via calling API (%%)[[Export Document>>https://docs.akabot.com/bin/view/akaBot

%20with%20RPA/API%20Automation/#H4.UpdateDocumentStatus]](% style="color:#000000" %) before exporting data