Skip to main content

Creating a Chatbot with RiveScript in Java

Motivation

"Artificial Intelligence (AI) is considered a major innovation that could disrupt many things. Some people even compare it to the Internet. A large investor firm predicted that some AI startups could become the next Apple, Google or Amazon within five years" 
- Prof. John Vu, Carnegie Mellon University.

Using chatbots to support our daily tasks is super useful and interesting. In fact, "Jenkins CI, Jira Cloud, and Bitbucket" have been becoming must-have apps in Slack of my team these days.

There are some existing approaches for chatbots including pattern matching, algorithms, and neutral networks. RiveScript is a scripting language using "pattern matching" as a simple and powerful approach for building up a Chabot.

Architecture

Actually, it was flexible to choose a programming language for the used Rivescript interpreter like Java, Go, Javascript, Python, and Perl. I went with Java.


Used Technologies and Tools

  • Oracle JDK 1.8.0_151
  • Apache Maven 3.5.2
  • Apache Tomcat 7.0.85
  • RiveScript-Java
  • Jersey sever/client
  • MyFaces

Module ChatBot Backend

I had a backend for chatbot's brain which provided APIs responding to received messages from users via a GUI.

1. Generate a web app project via Maven

mvn archetype:generate \
-DgroupId=vn.nvanhuong \
-DartifactId=chatbot_rivescript_backend \
-DarchetypeArtifactId=maven-archetype-webapp \
-DinteractiveMode=false;

Tips: When importing the project into Eclipse, I encountered an error "The superclass "javax.servlet.http.HttpServlet" was not found on the Java Build Path". I solved it by "Right click on the project/Properties/Project Facets/Runtimes/Check Apache Tomcat v.7.0"

2. Add dependencies needed in `pom.xml`

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
 xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
 <modelVersion>4.0.0</modelVersion>
 <groupId>vn.nvanhuong</groupId>
 <artifactId>chatbot_rivescript_backend</artifactId>
 <packaging>war</packaging>
 <version>1.0-SNAPSHOT</version>
 <name>chatbot_rivescript_backend Maven Webapp</name>
 <url>http://maven.apache.org</url>

 <dependencies>
  <!-- ChatBot Brain -->
  <dependency>
   <groupId>com.rivescript</groupId>
   <artifactId>rivescript-core</artifactId>
   <version>0.10.0</version>
  </dependency>

  <!-- RESTful APIs -->
  <dependency>
   <groupId>com.sun.jersey</groupId>
   <artifactId>jersey-server</artifactId>
   <version>1.8</version>
  </dependency>

  <!-- JSON -->
  <dependency>
   <groupId>org.json</groupId>
   <artifactId>json</artifactId>
   <version>20160810</version>
  </dependency>

  <!-- Unit tests -->
  <dependency>
   <groupId>junit</groupId>
   <artifactId>junit</artifactId>
   <version>4.12</version>
   <scope>test</scope>
  </dependency>
 </dependencies>

 <build>
  <finalName>chatbot_rivescript_backend</finalName>
 </build>
</project>

3. Create chatbot's brain with RiveScript

I created a file "chatbot_brain.rive" under the folder "src/main/resources/rivescript". I copied the content of template file "rs_standard.rive" at https://www.rivescript.com/try
+ hello bot
- Hello human!

4. Create RESTful APIs

package vn.nvanhuong.chatbot.rivescript.backend;

import javax.ws.rs.POST;
import javax.ws.rs.Path;

import com.rivescript.Config;
import com.rivescript.RiveScript;
import com.sun.jersey.spi.resource.Singleton;

@Path("/bot")
@Singleton
public class ChatBot {
 private RiveScript bot;
 
 public ChatBot() {
  String rivescriptFilePath = ChatBot.class.getClassLoader().getResource("rivescript").getFile();
  bot = new RiveScript(Config.utf8());
  
  bot.loadDirectory(rivescriptFilePath);
        bot.sortReplies();
 }
 
 @POST
 public String getMsg(String msg) {
  return bot.reply("user", msg);
 }

}

5. Configure RESTful at `web.xml`

<web-app id="WebApp_ID" version="2.4"
 xmlns="http://java.sun.com/xml/ns/j2ee"
 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
 xsi:schemaLocation="http://java.sun.com/xml/ns/j2ee
 http://java.sun.com/xml/ns/j2ee/web-app_2_4.xsd">
 <display-name>Restful Web Application</display-name>

 <servlet>
  <servlet-name>jersey-serlvet</servlet-name>
  <servlet-class>
                     com.sun.jersey.spi.container.servlet.ServletContainer
                </servlet-class>
  <init-param>
       <param-name>com.sun.jersey.config.property.packages</param-name>
       <param-value>vn.nvanhuong.chatbot.rivescript.backend</param-value>
  </init-param>
  <load-on-startup>1</load-on-startup>
 </servlet>

 <servlet-mapping>
  <servlet-name>jersey-serlvet</servlet-name>
  <url-pattern>/rest/*</url-pattern>
 </servlet-mapping>

</web-app>

6. Write a test case

package vn.nvanhuong.chatbot.rivescript.backend.test;

import static org.junit.Assert.assertEquals;

import org.junit.Test;

import vn.nvanhuong.chatbot.rivescript.backend.ChatBot;

public class ChatBotTest {
 
 @Test
 public void should_say_hello() {
  ChatBot bot = new ChatBot();
  
  assertEquals("Hello Human!", bot.getMsg("Hello Bot"));
 }
}

7. Test the API with Postman

URL: http://localhost:8080/chatbot_rivescript_backend/rest/bot

Module ChatBot GUI

1. Generate a web app project via Maven

mvn archetype:generate \
-DgroupId=vn.nvanhuong \
-DartifactId=chatbot_rivescript_gui \
-DarchetypeArtifactId=maven-archetype-webapp \
-DinteractiveMode=false

2. Add dependencies needed in pom.xml

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
 xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
 <modelVersion>4.0.0</modelVersion>
 <groupId>vn.nvanhuong</groupId>
 <artifactId>chatbot_rivescript_gui</artifactId>
 <packaging>war</packaging>
 <version>1.0-SNAPSHOT</version>
 <name>chatbot_rivescript_gui Maven Webapp</name>
 <url>http://maven.apache.org</url>

 <dependencies>
  <!-- JAX-RS Client -->
  <dependency>
   <groupId>org.glassfish.jersey.core</groupId>
   <artifactId>jersey-client</artifactId>
   <version>2.25.1</version>
  </dependency>

  <!-- JSF Pages -->
  <dependency>
   <groupId>org.apache.myfaces.core</groupId>
   <artifactId>myfaces-api</artifactId>
   <version>2.2.0</version>
  </dependency>
  <dependency>
   <groupId>org.apache.myfaces.core</groupId>
   <artifactId>myfaces-impl</artifactId>
   <version>2.2.0</version>
  </dependency>

  <!-- Unit test -->
  <dependency>
   <groupId>junit</groupId>
   <artifactId>junit</artifactId>
   <version>4.12</version>
   <scope>test</scope>
  </dependency>
 </dependencies>

 <build>
  <finalName>chatbot_rivescript_gui</finalName>
 </build>
</project>

3. Configure JSF at web.xml

<?xml version="1.0" encoding="UTF-8"?>
<web-app xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" 
 xmlns="http://java.sun.com/xml/ns/javaee"
 xmlns:web="http://java.sun.com/xml/ns/javaee/web-app_2_5.xsd"
 xsi:schemaLocation="http://java.sun.com/xml/ns/javaee http://java.sun.com/xml/ns/javaee/web-app_2_5.xsd"
 version="2.5">
  
 <!-- JSF mapping -->
 <servlet>
  <servlet-name>Faces Servlet</servlet-name>
  <servlet-class>javax.faces.webapp.FacesServlet</servlet-class>
  <load-on-startup>1</load-on-startup>
 </servlet>
 <servlet-mapping>
  <servlet-name>Faces Servlet</servlet-name>
  <url-pattern>*.xhtml</url-pattern>
 </servlet-mapping>
   
  <!-- welcome page -->
  <welcome-file-list>
    <welcome-file>index.xhtml</welcome-file>
  </welcome-file-list>
</web-app>

4. Create a GUI

Rename index.jsp to index.xthml

<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml"
 xmlns:f="http://java.sun.com/jsf/core"
 xmlns:h="http://java.sun.com/jsf/html"
 xmlns:p="http://xmlns.jcp.org/jsf/passthrough">
 <h:head>
  <title>RiveScript</title>
  <style>
   
   .container {
    display: block;
     margin: 50px auto;
     width: 90%;
   }
   
   .chatbox {
    height: 600px;
     border: solid 1px #039;
     background-image: url(bot_logo.png);
     background-repeat: no-repeat;
     background-position: center;
     background-size: contain;
     display: flex;
     justify-content: center;
     align-items: center;
   }
   
   .chatbox .bot-dialog {
    width: 90%;
     border: dashed 1px purple;
     text-align: center;
     background-color: orange;
   }
   
   .chatbox .bot-dialog > span{
    font-size: larger;
   }
   
   .message {
    display: flex;
    justify-content: space-between;
    
   }
   .message > input.message-input {
    width: 90%;
    margin-top: 10px;
    line-height: 2.3;
   }
   
   .message > input.submit {
    width: 9%;
     background-color: #039;
     color: white;
     font-size: 15px;
     margin-top: 10px;
   }
   
   .message-display > span {
     font-style: italic;
 }
 .message-display > label {
     font-weight: bold;
 }
 .message-display {
     margin-top: 5px;
 }
   
  </style>
 </h:head>
 <h:body>
 <h:form>
    <h:panelGroup layout="block" styleClass="container">
      <h:panelGroup layout="block" styleClass="chatbox">
       <h:panelGroup layout="block" styleClass="bot-dialog">
        <h:outputText id="botMessage" value="#{controller.botMessage}" escape="false"/>
       </h:panelGroup>
      </h:panelGroup>
      
      <h:panelGroup layout="block" styleClass="message">
       <h:inputText id="input" value="#{controller.humanMessage}" styleClass="message-input" 
        p:placeholder="Send a message to the bot"
        p:autofocus="true"
        onblur="this.focus()"/>
       <h:commandButton id="button" value="Send" actionListener="#{controller.onSend}" styleClass="submit"/>
      </h:panelGroup>
      
      <h:panelGroup layout="block" styleClass="message-display" rendered="#{not empty controller.humanMessageDisplay}">
       <h:outputLabel for="messageDisplay" value="You just said: "/>
       <h:outputText id="messageDisplay" value="#{controller.humanMessageDisplay}"/>
      </h:panelGroup>
    </h:panelGroup>
 </h:form>
 </h:body>
</html>

5. Create a Controller to call the RESTful APIs

package vn.vanhuong.chatbot.rivescript.gui;

import javax.faces.bean.ManagedBean;
import javax.faces.event.ActionEvent;
import javax.ws.rs.client.ClientBuilder;
import javax.ws.rs.client.Entity;
import javax.ws.rs.core.MediaType;
import javax.ws.rs.core.Response;

@ManagedBean(name = "controller")
public class Controller {
 
 private String humanMessage;
 private String botMessage;
 private String humanMessageDisplay;

 public void onSend(ActionEvent event) {
  Response response = ClientBuilder.newClient().target("http://localhost:8080/chatbot_rivescript_backend/rest/bot")
    .request(MediaType.APPLICATION_FORM_URLENCODED)
    .post(Entity.entity(humanMessage, MediaType.APPLICATION_FORM_URLENCODED));
  this.botMessage = response.readEntity(String.class);
  this.humanMessageDisplay = humanMessage;
  this.humanMessage = null;
 }

 public String getHumanMessage() {
  return humanMessage;
 }

 public void setHumanMessage(String humanMessage) {
  this.humanMessage = humanMessage;
 }

 public String getBotMessage() {
  return botMessage;
 }

 public void setBotMessage(String botMessage) {
  this.botMessage = botMessage;
 }

 public String getHumanMessageDisplay() {
  return humanMessageDisplay;
 }

 public void setHumanMessageDisplay(String humanMessageDisplay) {
  this.humanMessageDisplay = humanMessageDisplay;
 }
}

6. Enjoy playing with your ChatBot

Check out my source code as below

- Backend: https://github.com/vnnvanhuong/chatbot_rivescript_backend.git
- GUI: https://github.com/vnnvanhuong/chatbot_rivescript_gui.git

References:
[1]. http://science-technology.vn/?p=5761
[2]. https://www.rivescript.com/interpreters
[3]. https://github.com/aichaos/rivescript-java
[4]. https://youtu.be/wf8w1BJb9Xc
Labels:

Comments

Post a Comment

Popular posts from this blog

Only allow input number value with autoNumeric.js

autoNumeric is a jQuery plugin that automatically formats currency and numbers as you type on form inputs. I used autoNumeric 1.9.21 for demo code. 1. Dowload autoNumeric.js file from  https://github.com/BobKnothe/autoNumeric 2. Import to project <script src="http://ajax.googleapis.com/ajax/libs/jquery/1.11.0/jquery.min.js"></script> <script type="text/javascript" src="js/autoNumeric.js"></script> 3. Define a function to use it <script type="text/javascript"> /* only number is accepted */ function txtNumberOnly_Mask() { var inputOrgNumber = $("#numberTxt"); inputOrgNumber.each(function() { $(this).autoNumeric({ aSep : '', aDec: '.', vMin : '0.00' }); }); } </script> 4. Call the function by event <form> <input type="text" value="" id="numberTxt"/>(only number) </form> <script ty...
Post a Comment
Read more

Why Business Rules Engine Matter

"A good DSL minimizes the 'communication gap' between a domain concept and the code that implements it" - Robert C. Martin A Use Case It looks like adopting a new technology is always driven by a business need. The organizations, such as banks, have their own business processes (such as data gathering and document management). These processes are different from others but usually the differences are not big. How do we build a product which can be reused the similar features but still be adaptable with the specific requirements of each bank? Then, the answer is we should have a library/framework. There is an idea that we can implement this library/framework by using a business rules engine. Which has the following benefits: Be able to modify implementation of business domain at runtime (such as XML, CSV) S ource code is more readable for both developers and domain experts. Therefore, source code can be consider it as a document Since Business Rules Engine...
Post a Comment
Read more

The Evolution of Team Collaboration

I n my point of view, if a team has a good collaboration, team members will achieve the following: To be more effective To make work more enjoyable I have been working for a company for nearly four years as an software developer. Working on various projects from maintaining existing systems to developing a substantial product resulted in me moving to new teams three times. Actually, my most stable team lasted only around three years.  Every time I've moved to a new team, I have a chance to work with new members and a new team culture again. Indeed, I realize that there is a process of developing the team collaboration which gets better time by time. I think it is an evolution ! Phase 1: Poorly collaborate For example, that is when the team members have the following issues: Only work on his/her area of expertise Don't communicate to others Be not confident to take on new challages Don't listen to other members. Subsequently, the team ha...
Post a Comment
Read more

Building Axon.ivy Projects on Bitbucket Pipelines

Read me  if you don't know what Axon.ivy (Ivy) is. Motivation -  Ivy projects are designed to be built on a continuous integration (CI) server like Jenkins - Today, Bitbucket supports for CI with Bitbucket Pipelines - We're using Bitbucket. Then, why not? It must be very cool and convenient for us if we can centralize our CI and VCS (version control system) tools in one place. Here is an approach We have to use a maven plugin called project-build-plugin  to build ivy projects. This plugin requires an instance of Ivy engine during building time. Bitbucket Pipelines allows us to specify our own docker image as a build environment. What we need to do  is to prepare our docker image with needed stuffs such as JDK, Maven, Ivy engine, etc. Step 1. Prepare Docker images For testing purpose, I already created two docker images: Maven and Axon.ivy engine. They are now available on Docker Hub This image for Maven using Oracle JDK 8 This image for Axon.iv...
Post a Comment
Read more

Applying pipeline “tensorflow_embedding” of Rasa NLU

According to this nice article , there was a new pipeline released using a different approach from the standard one ( spacy_sklearn ). I wanted to give it a try to see whether it can help with improving bot’s accuracy. After applying done, I gave an evaluation of “tensorflow_embedding”. It seemed to work better a bit. For example, I defined intents “greet” and “goodbye” with some following messages in my training data. ## intent:greet - Hey! How are you? - Hi! How can I help you? - Good to see you! - Nice to see you! - Hi - Hello - Hi there ## intent:goodbye - Bye - Bye Bye - See you later - Take care - Peace In order to play around with Rasa NLU, I created a project here . You can have a look at this change from this pull request . Yay! When I entered message “hi bot”, then bot with “tensorflow_embedding” could detect intent “greet” with better confidence scores rather than bot with “spacy_sklearn”. The following are responses after executing curl -X POST loc...
Post a Comment
Read more