Building a rule-based chatbot in Python

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Building a rule-based chatbot in Python

Polyglot is a natural language pipeline that supports massive multilingual applications. The features include tokenization, language detection, named entity recognition, part of speech tagging, sentiment analysis, word embeddings, etc. Polyglot depends on Numpy and libicu-dev, on Ubuntu/Debian Linux distribution that you can use over those OS.

  • You can add as many keywords/phrases/sentences and intents as you want to make sure your chatbot is robust when talking to an actual human.
  • Another way to extend the chatbot is to make it capable of responding to more user requests.
  • For now, it only contains one string, but if you wanted to remove other content as well, you could quickly add more strings to this tuple as items.
  • You have successfully created an intelligent chatbot capable of responding to dynamic user requests.
  • With OpenDialog you can deploy, integrate and train efficiently.
  • It uses a number of machine learning algorithms to produce a variety of responses.

The AI alpython chatbot libraryy has a knowledge of linguistics understanding, common to all human languages. The configuration only consists of describing the format of the expected elements and providing the specific business vocabulary. This technology has been developed after many years of experimentation, to find the easiest and most efficient way to configure an NLU AI. These customer service chats are parsed, organized, classified and eventually used to train the NLU engine. Botpress has a visual conversation builder and an emulator to test your conversations. The built-in JavaScript code editor allows you to code actions that can be used to perform specific tasks.

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This model was pre-trained on a dataset with 147 million Reddit conversations. These libraries contain almost all necessary functionality for building a chatbot. All you need to do is define functionality with special parameters (depending on the chatbot’s library).

Python has been around for a while, so there’s plenty of documentation, guides, tutorials, and more. That means any time someone has a question, they can get an answer in a little to no delay. When ever you want to perform a set of operations based on a condition IF-ELSE is used.

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When a user is given the chat moderator rights in chat, anything here will be performed. When a user is banned from chat, anything here will be performed. When a user is kicked from chat, anything here will be performed. When a user comes back from being away in chat, anything here will be performed.

The boundaries of a chatbot

If it’s set to False, the bot will learn from the current conversation. If we set it to True, then it will not learn during the conversation. The DialoGPT model is pre-trained for generating text in chatbots, so it won’t work well with response generation. However, you can fine-tune the model with your dataset to achieve better performance.

simple chatbot

ChatterBot is a Python library that is developed to provide automated responses to user inputs. It makes utilization of a combination of Machine Learning algorithms in order to generate multiple types of responses. This feature enables developers to construct chatbots using Python that can communicate with humans and provide relevant and appropriate responses. Moreover, the ML algorithms support the bot to improve its performance with experience. In the past few years, chatbots in the Python programming language have become enthusiastically admired in the sectors of technology and business. These intelligent bots are so adept at imitating natural human languages and chatting with humans that companies across different industrial sectors are accepting them.

Building a Simple Chatbot from Scratch in Python (using NLTK)

Some of its built-in developer tools include content management, analytics, and operational mechanisms. You can learn how your visitors use the bots and who the users are. It offers extensive documentation and a great community you can consult if you have any issues while using the framework.

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But while you’re developing the script, it’s helpful to inspect intermediate outputs, for example with a print() call, as shown in line 18. Constructing multiple patterns helps you keep track of what you’re matching and gives you the flexibility to use the separate capturing groups to apply further preprocessing later on. For example, with access to username, you could chunk conversations by merging messages sent consecutively by the same user. In lines 9 to 12, you set up the first training round, where you pass a list of two strings to trainer.train(). Using .train() injects entries into your database to build upon the graph structure that ChatterBot uses to choose possible replies. The call to .get_response() in the final line of the short script is the only interaction with your chatbot.

How to Develop Smart Chatbots Using Python: Examples of Developing AI- and ML-Driven Chatbots

These were the advantages of using a bot framework instead of coding the chatbots from the ground up. If you want to get bots on your website but don’t have much coding experience, you can use a chatbot platform. These usually provide a builder that doesn’t require any coding knowledge. Simply put, bot frameworks offer a set of tools that help developers create chatbots better and faster.


This open-source platform gives you actionable chatbot analytics, so you can keep an eye on your results and make better business decisions. It lets you define intents, entities, and slots with the help of NLU modules. You can also use advanced permissions to control who gets to edit the bot.

  • If you have any queries please post them in the comment section below.
  • You can classify text into custom categories from multiple languages.
  • You can store data in customer databases to grow your understanding of your clients.
  • Ensure thorough testing of your product’s security and performance at different stages of the software development lifecycle.
  • If it’s set to 0, it will choose the sequence from all given sequences despite the probability value.
  • We will compare the user input with the base sentence stored in the variable weather and we will also extract the city name from the sentence given by the user.

You have created a chatbot that is intelligent enough to respond to a user’s statement—even when the user phrases their statement in different ways. The chatbot uses the OpenWeather API to get the current weather in a city specified by the user. This tutorial assumes you are already familiar with Python—if you would like to improve your knowledge of Python, check out our How To Code in Python 3 series. This tutorial does not require foreknowledge of natural language processing. TheChatterBot Corpus contains data that can be used to train chatbots to communicate.


Here are a few tips not to miss when combining a chatbot with a Python API. Ask any Python developer — or anyone that has ever used the language — and they’ll agree it’s strong, reliable, and efficient. You can work with and deploy Python applications in nearly any environment, and there’s little to no performance loss no matter what platform you work with.

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