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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

Each Pipeline defines the structure of Data fields that akaBot Vision extracts. Description When editing this structure you have two options: Use pre-trained Data fields – AkaBot Vision’s Generic AI engine has been pre-trained to recognize specific Data fields and enables you to start extracting data

[3] Configure Fields for Data Extraction

Customizing Data Extract

Configuring Fields for Data Extraction

="wikigeneratedid" id="HParagraph1" %) Each Pipeline defines the structure of Data fields that akaBot Vision

options: * Use pre-trained Data fields – AkaBot Vision’s Generic AI engine has been pre-trained to recognize specific Data fields and enables you to start extracting data without any additional training

will be logged at Verbose level. Output Out Data Service Client: The DataService Scope activities following

RCA.Activities.DataService.DataServiceScope Description This scope helps to run Data Service activities Properties Input Authentication Type: Determines the method used to authenticate with the Data

[1] Data Service Scope

Data Service Activities

" %) ((( RCA.Activities.DataService.DataServiceScope == **Description** == (% class="wikigeneratedid" %) This scope helps to run Data Service

to authenticate with the Data Service. There are two options available: Agent and ManagedToken ** **Data Service Client: **Re-use token of previous Data Service Scope * **Manage Token**:  ** **Authentication Token

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

. Output Output Record: The record created from the Data Service, stored in an Entity type object

an existing record for the selected Entity from the Data Service. In the body of activity Entity: Select the entity you want to use with the activity. The property only supports entities imported from Data Service

Data Service Activities

and arguments will be logged at Verbose level. * **Output** ** Output Record: The record created from the Data

Entity from the Data Service. == **In the body of activity** == * **Entity: **Select the entity you want to use with the activity. The property only supports entities imported from Data Service

a specified record for the selected Entity from the Data Service. In the body of activity Entity: Select the entity you want to use with the activity. The property only supports entities imported from Data Service

entities imported from Data Service Record Id: the ID of the record you want to retrieve from the entity

Data Service Activities

at Verbose level. * **Output​​​​​​​** ** Output Record: The record created from the Data Service, stored

Entity from the Data Service. == **In the body of activity** == * **Entity: **Select the entity you want to use with the activity. The property only supports entities imported from Data Service

at Verbose level. Output Output Record: The record created from the Data Service, stored in an Entity type

record for the selected Entity in Data Service. In the body of activity Entity: Select the entity you want to use with the activity. The property only supports entities imported from Data Service Switch

Data Service Activities

and arguments will be logged at Verbose level. * **Output** ** Output Record: The record created from the Data

" %) ((( RCA.Activities.DataService.CreateEntityRecord == **Description** == This activity allow to create a new record for the selected Entity in Data

with the activity. The property only supports entities imported from Data Service * **Switch to record view:** Click

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

. Step 4 and 5: IDP system process the document and put the output in a ready queue. RPA bot, API, or human will download IDP output and then use that structured data to input to other enterprise systems

data to the user's server by API Output Note: Most of the APIs in akaBot Vision are synchronous APIs

process the document and put the output in a ready queue. RPA bot, API, or human will download IDP output and then use that structured data to input to other enterprise systems. **Step 3**: IDP system will inform

. == == == **Operation Model** == There are 3 modes to get output from the akaBot Vision system: 1. **Single Invoice

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