Botframework-example: An intent-aware AI powered bot

This repository implements a clothing e-commerce chatbot assistant using Botframework NodeJS API and two approaches: a cloud-based machine learning framework LUIS.ai, and a custom Naive Bayes classifier. Examples are inspired on the book Build Better Chatbots written by Rashid Khan. Source code was corrected on certain scenarios where features are deprecated in current API versions.

Botframework provides a simple-to-use interface through a NodeJS SDK, and REST APIs to be integrated by your chatbot application. In order to reduce some of the hassle of building intelligent bots. this example uses LUIS.ai, which is already integrated with Botframework, however, a custom ML model was also implemented to showcase diffenent solution approaches.

To run one of the examples, you will need a Microsoft Live ID to sign up for Botframework services. Botframework services are free to use, and you do not need to enter your credit card information.

Chatbot features

Intent-aware chatbot.
Connection with external APIs for product information retrieval.
Omnichannel communication framework, so you can plug-in your chatbot implementation on any messaging platform.
Messaging logging storage for analytics.
Chatbot implementation examples using a both a cloud-based ML service and a custom model.
Support for english and spanish (under construction) audiences.

Installation requirements

NodeJS
MongoDB
ngrok: public URL for testing your chatbot locally. You need to create an account

Configuring ngrok

In case you did not configured yet, open a terminal & run the following commands:

~ $ npm install -g ngrok
~ $ ngrok authtoken <YOUR_NGROK_TOKEN>

Project installation

Clone this repo
Open a terminal and run the following commands:

~ $ cd <REPO_DIR>
~ $ npm install

Project Setup

Environment configuration must be set by modifying the LuisChatbot/.env & NaiveBayesChatbot/.env files. Here you can configure your bot based on a specific language (English or Spanish (under construction)).
Go to LUIS.ai and create an account.
Create a new App and configure the corresponding intents & entities for your bot. Examples can be found in files Bot_en.md & Bot_es.md.
Train & Test your bot app under LUIS web environment.
Publish your app and copy its ${ENDPOINT_URL}. It is defined as /luis/v2.0/apps/${LUIS_APPLICATION_ID}?subscription-key=${LUIS_SUBSCRIPTION_KEY}&timezoneOffset=0&verbose=true&q=. You should copy the application values and set the corresponding environment variables on the LuisChatbot/.env file.
Go to Microsoft's BotFramework and create your account.
Create a new bot in the My bots section. It will redirect you to the Azure cloud Platform.
Select Bot Channels Registration option.
When Configuring your bot, you need to set the Messaging endpoint property to your chatbot public URL assigned by ngrok (see instructions below)
Create and ${APPLICATION_iD} & ${APPLICATION_PASSWORD} in the Microsoft Application Registration Portal and set this properties both on your new bot configuration in Azure, and on the LuisChatbot/.env & NaiveBayesChatbot/.env files.

Chatbot examples

In order to test your chatbot locally, you need to open a tunnel using ngrok. To do so, run the follwing command in another terminal:

~ $ ngrok http 3978

Then, enter to the web inspector localhost:4040 and copy the assigned HTTPS URL to the ${MESSAGING_ENDPOINT} property in the dev.botframework app you created.

Additionally, you need to deploy a mongodb instance, for example, in another terminal you can run the following:

~ $ mongod --dbpath ./db/

LUIS.ai powered chatbot

Intent-aware chatbot definition can be replicated using intents & entities definitons in Bot_en.md. To run the example, run the following on a terminal:

~ $ cd LuisChatbot
~ $ node app.js

Now You can test some interactions using the Test in Web Chat option in your Microsoft Botframework instance.

Chatbot with custom Naive Bayes intent classifier

To run the example, you first need to train your model by running the following command:

~ $ cd NaiveBayesChatbot
~ $ node app.js

Model will be saved on the classifier.json file. To deploy your model, just re-run the node app.js command.

santteegt/botframework-example

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