Chatbots, even simple ones, are an increasingly popular way for companies to manage some communications with customers. Even without human-sounding language, a chatbot can provide an easy, interactive way to sort through a user’s needs, resolve common problems, and free up time for some customer-facing roles.
In this guide, you’ll learn about the basic process for creating chatbots using the Python programming language. You’ll also learn a little about the benefits of Python web scraping, which is a helpful way to train chatbots.
Step One: Choose And Install A Library
The first part of creating a chatbot in Python is installing a library for your software. Many people use ChatterBot, an existing collection of machine learning algorithms that help provide realistic and natural-sounding responses to queries.
It is possible to code your own library and algorithms to create a chatbot in Python, but it’s usually better to build on an existing system. That drastically reduces the time and complexity necessary for creating a chatbot
Step Two: Import Classes To Your Chatbot
Once you have a library, the next step is adding classes to the chatbot. For any readers unfamiliar with these, classes in Python are essentially templates that help create objects. These provide the form and structure for the responses your chatbot will have.
The most important classes depend on the library you’re using, but in most cases, you’ll need at least two classes to make a viable chatbot.
The first class you need will be the basic chatbot class from your library. Chatterbot, perhaps unsurprisingly, calls their ChatBot. This class is the main structure that will help you communicate with others.
The second class you need is a trainer. This class will help you give inputs to your chatbot and help it learn how to communicate.
Aim for as few classes as possible, depending on what information your library needs. You can always add more and retrain your chatbot later, but trying to put in too many pieces now could lead to strange results.
Step Three: Train Your Chatbot
Once you finish installing the libraries, it’s time to begin training your chatbot. Remember to turn its learning off once finished, though, or it could move past what you want it to do.
There are two good ways to train a chatbot using Python.
The first is training it directly. This means talking to the bot, creating responses, and helping it learn which responses make the most sense. Training can feel slow at first, but once the algorithm has enough data to kick in, it starts advancing quickly.
The second method is using Python web scraping. This is a supplement for the first technique, not a replacement, so don’t rely exclusively on this. However, using tools to let a chatbot scour the web and come up with information can rapidly accelerate its understanding of language and how to respond to questions.
Make sure to create regular backups of your chatbot while you’re training it. This makes it easy to revert to a previous version and try another tactic if the algorithm ends up going in a strange direction.
Step Four: Refine The Chatbot
Once you’re done with the Python web scraping, it’s time to refine your chatbot and check for errors. Chatbots are always at their best when you simplify things and have them focus on a small number of subjects. It’s possible to create a good chatbot that can talk about any subject, but consider waiting on that until you have more experience making them.
It’s That Easy
Creating a chatbot using Python can sound intimidating at first, but the truth is that over 95% of the work is already done. There are plenty of libraries and tools that are freely available, so you can create a chatbot with just a little documentation and practice. There’s no substitute for experience, so try making one today and you’ll be experienced soon enough.
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