Easily build AI-based chatbots in Python

Chatterbot storage adapters

Chatterbot stores its knowledge graph and user conversation data in an SQLite database. Developers can interface with this database using Chatterbot’s Storage Adapters.

Storage Adapters allow developers to change the default database from SQLite to MongoDB or any other database supported by the SQLAlchemy ORM. Developers can also use these Adapters to add, remove, search, and modify user statements and responses in the Knowledge Graph as well as create, modify and query other databases that Chatterbot might use.

Building an AI-based chatbot

In this tutorial, we will be using the Chatterbot Python library to build an AI-based Chatbot.

We will be following the steps below to build our chatbot

1. Importing Dependencies
2. Instantiating a ChatBot Instance
3. Training on Chatbot-Corpus Data
4. Training on Custom Data
5. Building a front end

Importing dependencies

The first thing we’ll need to do is import the modules we’ll be using. The ChatBot module contains the fundamental Chatbot class that will be used to instantiate our chatbot object. The ListTrainer module allows us to train our chatbot on a custom list of statements that we will define. The ChatterBotCorpusTrainer module contains code to download and train our chatbot on datasets part of the ChatterBot Corpus Project.

#Importing modules
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
from chatterbot.trainers import ChatterBotCorpusTrainer

Instantiating chatbots instance

A chatbot instance can be created by creating a Chatbot object. The Chatbot object needs to have the name of the chatbot and must reference any logic or storage adapters you might want to use.

In the case you don’t want your chatbot to learn from user inputs after it has been trained, you can set the read-only parameter to True.

BankBot = ChatBot(name = 'BankBot',
                  read_only = False,                  
                  logic_adapters = ["chatterbot.logic.BestMatch"],                 
                  storage_adapter = "chatterbot.storage.SQLStorageAdapter")

Training on chatterbot-corpus data

Training your chatbot agent on data from the Chatterbot-Corpus project is relatively simple. To do that, you need to instantiate a ChatterBotCorpusTrainer object and call the train() method. The ChatterBotCorpusTrainer takes in the name of your ChatBot object as an argument. The train() method takes in the name of the dataset you want to use for training as an argument.

Detailed information about ChatterBot-Corpus Datasets is available on the project’s Github repository.

corpus_trainer = ChatterBotCorpusTrainer(BankBot)
corpus_trainer.train("chatterbot.corpus.English")

Training on custom list data

You can also train ChatterBot on custom conversations. This can be done by using the module’s ListTrainer class.

In this case, you will need to pass in a list of statements where the order of each statement is based on its placement in a given conversation. Each statement in the list is a possible response to its predecessor in the list.

The training can be undertaken by instantiating a ListTrainer object and calling the train() method. It is important to note that the train() method must be individually called for each list to be used.

greet_conversation = [
    "Hello",
    "Hi there!",
    "How are you doing?",
    "I'm doing great.",
    "That is good to hear",
    "Thank you.",
    "You're welcome."
]
open_timings_conversation = [
    "What time does the Bank open?",
    "The Bank opens at 9AM",
]
close_timings_conversation = [
    "What time does the Bank close?",
    "The Bank closes at 5PM",
]
#Initializing Trainer Object
trainer = ListTrainer(BankBot)

#Training BankBot
trainer.train(greet_conversation)
trainer.train(open_timings_conversation)
trainer.train(close_timings_conversation)

Building a front end

Once the chatbot has been trained, it can be used by calling Chatterbot’s get response() method. The method takes a user string as an input and returns a response string.

while (True):
    user_input = input()
    if (user_input == 'quit'):
        break
    response = BankBot.get_response(user_input)
    print (response)

Conclusion

This blog was hands-on to building a simple AI-based chatbot in Python. The functionality of this bot can easily be increased by adding more training examples. You could, for example, add more lists of custom responses related to your application.

As we saw, building an AI-based chatbot is easy compared to building and maintaining a Rule-based Chatbot. Despite this ease, chatbots such as this are very prone to mistakes and usually give robotic responses because of a lack of good training data.

A better way of building robust AI-based Chatbots is to use Conversational AI Tools offered by companies like Google and Amazon. These tools are based on complex machine learning models with AI that has been trained on millions of datasets. This makes them extremely intelligent and, in most cases, are almost indistinguishable from human operators.

In the next blog to learn data science, we’ll be looking at how to create a Dialog Flow Chatbot using Google’s Conversational AI Platform.

Want to upgrade your Python abilities? Check out Data Science Dojo’s Introduction to Python for Data Science.