Make Chatbot Smarter with is an AI and Natural Language Processing engine provided by Facebook. Using or any Natural Language Processing engine helps your bot adapt much better to free-style inputs from users and provide the correct answers which wasn’t taken care by your flow-based design.

Enable or other NLP integrations to the following BotStar features:

Use in Training

Training is a much more adaptive version of Keyword Training. A well-trained bot can respond to diversified ranges of input by users.

For example, if you train your bot with the keyword "booking", it can (and should) respond to both of the following requests "Can I book a table" and "I need a table" instead of rejecting the later one.

Main flow as a best-case scenario for your bot but your users do not always have to follow a predefined path. They might want to exit the current flow and ask your bot other topics that flash through their minds. By using Keyword, you can train your bot to handle some of the expected topics or casual small talks, thus increasing end-users’ satisfaction with your bot.

Use in Flow Editor

In BotStar platform, NLP can be used to increase the flexibility and power of the connectors conditions in the Flow. However this requires that you already have an already-trained app with predefined Entities and Intents. (See for more information about Entity and Intent).

You can utilise NLP to increase the chance of:

  • Matching the right connector between blocks by analysing the semantics of the whole sentence instead of single words.
  • Reading and extracting the necessary information from the sentence. is an optimal choice to provide your bot this ability through the natural language processing engine.

Integrate to BotStar

Before activating in BotStar, you need to enable the integration with an app in your account.

Step 1: Set up Wit.Ai account

1. Access to your account at:

2. Select an app and open Settings, then copy the code in the Server Access Token.

Step 2: Connect BotStar to Wit.Ai

1. Access to your BotStar account and select a bot

2. Go to Bot Builder > Integrations

3. Enable's integration and fill the copied code above to the Server Access Token (see image).

4. Click on the Save button to save all settings

Congrats! Now you can start using in the Flow and Keyword.

Use Wit.AI for Keyword

After building a seamless flow in the Flow, you can make your bot more lively and sound more like humans by adding some randomness or funny questions. Training will support you to improve chat conversations and make your bot smarter.

In the example below, you can train your chatbot how to respond to customers.

Step 1: Define a Set

Set is a group of bot responses for a specific topic. Think of set like a group of related topics for example.

In this example, we will create a set "Small Talk", let's open Keyword and click on New set. Then you need to consider "Minimum Confidence" that provided by "Minimum Confidence" is the minimum ratio of confidence for analyzed results.

Note: In case your bot is multilingual, you can add more the similar sets through the supported language of

Step 2: Create a New dialogue

Dialogue is a group of related questions and answers. There are two ways to define the response for a dialog:

Method 1: Using a block and text that the bot will respond to users automatically

Select block

Use text

Method 2: Writing as many the different types of text responses as possible

Define sample questions


  • In a dialogue, keep in mind that the more sample phrases you give, the better your bot will understand your users.
  • In a set, dialogues should not have the similarity of sample phrases, otherwise this will reduce the accuracy of the responses.

Use in the Flow

(Before continuing with this section, we assume that you are already familiar with and have trained content in your app.)

You can start solving the problem: Matching the connector between blocks by analysing the semantics of the whole sentence instead of single words in the given example:

Sometimes, a part of the flow requires your bot to be able to understand user’s response, but the designed connector is only able to read a part of user response and can be misleading. Take a look at the example below:

In this scenario, the bot might trigger the wrong outgoing connector when a user responds with a phrase like "I don’t like any city" due to the condition of the block Smart Response containing the word "city", so the bot will respond incorrectly to a user.

What should we do to change this behavior?

Step 1: Train your chatbot

Firstly, we need to create an entity in the app associated with the bot.

For the example above, we will train entity, "favorite_city", with the value is "live" in response to "I want to live in Danang" phrase.

Step 2: Update the flow

Next you can apply the trained entity into the flow. Then you can change the connector condition between Question and Smart Response.

After changing the condition, chatbot will respond with Smart Response when the user's response is similar to the sample answer (the trained sample phrases), which is accompanied by the accurate rate greater than or equal to 50%, provided by

For more information, please visit the official documentation page: