AI Predictor


To install the plug-in just proceed with  this tutorial Installation procedure


This powerful extension depends of these three main systems:

  • OpenAI API. You need to subscribe to their API service from their signup page at
  • Hugging Face Transformers Library(js). We use public models for NLP tasks like spam and toxic content classification.
  • Google TensorFlow Library(js). The extension uses their JS version to train and use models in the browser.

Please use the data generated by the models exclusively as a component of your marketing and financial decision-making toolkit. However, do not rely solely on predictions when making investment decisions.

Privacy of data

We recommend including a disclaimer page for the Chatbot to inform your customers that the provided information may be imprecise, incorrect, or incomplete.

Advise customers to avoid entering sensitive information into the Chat or Prompt system, including credit card details, financial data, passwords, or personal private information. Please note that this data passes through OpenAI servers, and we cannot guarantee its use for improving their systems.


Important : After signup please get your API key and limit your account as instructions below.

Browse Limits link

Set a low limit, like $5-10-15 a month.
This prevents abusing your credit.
Set this to 50% of the month's limit set before.
So you will be warned when your credit reaches half.

API key generation

Generate and write down the KEY in a secure place, like your PC.

Browse API keys link

Click on "Create new secret key".

Write down the KEY generated and store anywhere secure.
It´s not able to see anymore and you need to create a new one if you loss it.

Extension settings

Check the instructions below.

Enables all the features in the extension.
Enables our Cloud predictions service.
Needs a valid subscription and API Key.

Fill your API Key here.

Enables OpenAI service related features like Chatbot and Prompts.

Fill your openAI Key here.

Shows the Chatbot in your store.

The title in the header of Chatbot widget.
The main color for the Chatbot widget.

Describe to the Chatbot the context, the "personallity".

Enable so the Chatbot knows the current content.
Like product page, category, or Cms page.
Use with precaution, long content increases the price of API calls.
The history or "memory" of items the Chatbot reminds.
Around 15 is standard for short conversations.
Increase if you want reminds older conversations.
Rate limit by IP.
This limits the messages in a single day for the same user/IP.
Set to 0 for disabling the limitation.
Limits the total amount of conversations in a single day for all users.
Set 0 to disabling this limit.
Enable to allow Chatbot to search orders and other features.
Experimental feature.

Fill with the text to show users when Chatbot is unaivalable.

Select the privacy policy link where the users can read conditions for Chatbot.

Machine Learning

This feature allow train models using external csv files, or your E-commerce entities like sales, reviews, to make predictions.

Model setup

For start with the utility, almost 1 model should be created.
Please refer to this video as tutorial

Create a new model

Edit settings

The model needs settings to define the architecture, the source and type.

Create a new model

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Train a model

The model needs to train usually once a month, this is a manual feature, then the predictions include the most recent data.
In this video you see the entire process of train. If you need an automated solution, please read the following section.

Automated predictions, (PRO) feature

Embedded models with the provided javascript libraries in the pack, are enough for general predictions, but with the help of a data scientist you can receive custom models for your business.
Those models are developed using python or online modeling, using services like Amazon Sagemaker, Microsoft Power BI, and the predictions are imported by the developer back to the model.
Feel free to contact us to receive a quote for a dedicated data scientist for your project.


This system provides smart Chatbot for your site, providing advanced features like product answering about stock, description, translation and order status (Experimental feature).

Create a new model

Google Dialogflow and other NLP sytems.

This system provides multiple endpoints, currently supports Dialogflow, so users can get order status and other features. (Beta)
The endpoint you need to define in DF, is https://your-magento-domain/visualwebs_ml/callback/index

As example, the system awaits this call structure for the “order status” intent.


and in the “parameters” Index expects information provided by the user in the chat. This is the full $_POST request you need to pass.






and finally returns the Json response with the order status.

    "fulfillmentText":"This is order status is Shipped.","fulfillmentMessages":[{"text":{"text":["This is order status is Shipped."]}}]

Chatbot Tools

The Extension offers Stats and Chatbot History. Start searching the Links in the backend.

Chatbot History Link

Chatbot Analytics Link


Here you can find 3 big areas, Last conversations, Top terms and 30 days Stats.

Summary of last conversations.

Summary of terms.

Charts for Sessions, total messages, Bot responses and user questions for the last 30 days.

Conversation History

This grid allow check history, filter and clear completely.

Grid with full history.

Clears all the history.

Filters and pagination.


This section describes how create Prompt templates. This allow users to create templates for sending requests to chatGPT

Find menu link

Start clicking the Prompts link

Prompts Link


Please refer to this tutorial, hover flash points to see details.

Prompts List.
Just click to preview the content on the center area.
Add new Prompt to the list.
It opens a modal for fill the title and the content.
Preview of the template Title.
Editable when you use the Edit button.
Content of template.
This is what chatGPT receives for completion.
Editable when you click on Edit button.
Result of the chatGPT call.
This is the response opeanAI provides to your content.

Edit/Cancel button to change the title or the content of the template.

Delete button for remove the template completely.

Save button to save changes for the current template.

Notification area for display save success, and another warnings.

This button sends the content to openAI.
After few seconds the result it´s visible in the "Result" area.
Select the desired model.
GTP-3 is the most used and cheap model. GPT-4 are more expensive but better models.
Temperature means the level of creativity for the response, leave default value is not sure what to fill.
High levels can be better for artistic intent.
The "max_tokens" parameter is used to control the length of the response generated by the model.
It specifies the maximum number of tokens in the response text.
Tokens can be words or even smaller units like characters, depending on the language and text.
For example, if you set "max_tokens" to 50, the response generated by the model will be limited to 50 tokens in length.
If the model reaches this token limit, it will truncate the text accordingly.
"stop_sequence" could refer to a special token or sequence of characters that you use to signal the end of a response.
For example, you can use a stop sequence like "\n" to indicate the end of a paragraph or a conversation turn.
Reset the settings on the box above to default values.
Leave these values if you're not sure what to fill.
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