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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

to a pipeline that has Automation Type is Confident, then export them and change the status. Table

. (% style="text-align:center" %) [[image:image-20220420200751-2.png||cursorshover="true" data-xwiki-image

||cursorshover="true"]] ))) (% class="akb-toc" %) ((( (% class="akb-toc-title" %) ((( Table of Content

on: Built-in checks – we perform Data Integrity checks based on values found on the document. Such checks

the Extraction schema you should be seeing the message for the required fields with no captured value. Table

Customizing Data Extract

– we perform Data Integrity checks based on values found on the document. Such checks could

value. ))) ))) ))) (% class="akb-toc" %) ((( (% class="akb-toc-title" %) ((( Table of Content

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

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

When you send a document to the pipeline, akaBot Vision will immediately start to extract the data from it. Description During this stage, the document has an importing status. In case something

splitting suggestions available in the app. Table Of Content

the data from it. == **Description** == (% class="wikigeneratedid" %) During this stage, the document has an importing status. (% style="text-align:center" %) [[[[image:image-20220420191058-1.png||data-xwiki-image

" %) [[[[image:image-20220420191111-2.png||data-xwiki-image-style-alignment="center"]]>>attach:image-20220420191111-2

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

: 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

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

After importing the document successfully and the data extraction process is successfully finished

, akaBot Vision provides users with the capability to add or remove rows in a table To insert a row, you can click "+" icon To delete a row, you can click "x" icon To add a new row at the end of the table

="wikigeneratedid" %) After importing the document successfully and the data extraction process is successfully

with this status in the “To review” tab in the user interface. [[image:image-20220420193327-1.png||data-xwiki

in each field that has been detected incorrectly [[image:image-20220420193327-2.png||data-xwiki-image