Introducing Conversational Chat bots using Rule Based Approach by Saif Ali Kheraj

rule based chatbot python

The conversations generated will help in identifying gaps or dead-ends in the communication flow. We have used the speech recognition function to enable the computer to listen to what the chatbot user replies in the form of speech. These time limits are baselined to ensure no delay caused in breaking if nothing is spoken.

rule based chatbot python

Rasa is a great open-source tool to build chatbots fast and easy. We can also implement advanced machine learning models on our own, instead of using the default one. NLTK is a leading platform for building Python programs to work with human language data. Moreover, from the last statement, we can observe that the ChatterBot library provides this functionality in multiple languages. Thus, we can also specify a subset of a corpus in a language we would prefer.

What is Natural Language Understanding?

Such bots operate on the base of predefined commands or scripts. They are programmed to recognize some words and answer basic questions. Nowadays, this is the most popular type of e-commerce chatbots. Some online shops integrate bots with Natural Language Processing (NLP) technology to make interactions with customers more natural. 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.

If you have any questions or suggestions about Chatbot Project in Python with Source Code, please feel free to leave a comment below. The data file is in the JSON format, so we used the json package to read the JSON file into Python. All these facilities are provided by the python’s NLTK libraries. Let us consider the following snippet of code to understand the same. We will follow a step-by-step approach and break down the procedure of creating a Python chat.

Building a dictionary of intents

Intentions are usually terms, which are the actual subject or motive behind a sentence given. For example, here there are intentions like ‘introductions’, ‘thanks’, ‘greetings’, and ‘goodbye’ which are basically motives. Secondly, we have a ‘patterns’ field which actually depicts the patterns or type of sentences that can have the corresponding motive. Then we have the responses field which contains some responses which may be the bot’s response if the corresponding motive is detected. For example, for the ‘tag’ or intention of ‘greeting’ patterns detected can be ‘Hi’, ‘Hello’, and the corresponding responses can be ‘Hello’, ‘Hi There’.

  • The platform includes a basic plan (from $9 per month) and a pro plan with more advanced features (from $209 per month).
  • The NLP (natural language processing) technology allows your future chatbot to recognize and understand what online shoppers request.
  • At the same time, because of their conversational nature, chatbots generally provide more information and try to help users.
  • Lemmatizing is the process of changing a word into its lemma form and then making a pickle file to store the Python objects we will use when predicting.
  • The first thing we’ll need to do is import the packages/libraries we’ll be using.
  • They differ by the complexity, features, and cost of development.

We will be using NLTK or Python’s Natural Language Toolkit Library. Over time, as the chatbot indulges in more communications, the precision of reply progresses. It is imperative to choose topics that are related to and are close to the purpose served by the chatbot. Interpreting user answers and attending to both open-ended and close-ended conversations are other important aspects of developing the conversation script.

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But with conversational AI, there are very few unmanageable drawbacks. And compared to rule-based chatbots, conversation AI can better implement a customer-focused approach. Online business owners can become overwhelmed by the variety of chatbots on the market and their specifications. Let us look into the advantages and disadvantages of both conversational AI and rule-based chatbots. An Artificial Intelligence bot will converse with the customers by linking one question to another. The Artificial Intelligence and Machine Learning technologies behind a conversational AI bot will predict the users’ questions and give accurate answers.

How to Train an AI Chatbot With Custom Knowledge Base Using ChatGPT API – Beebom

How to Train an AI Chatbot With Custom Knowledge Base Using ChatGPT API.

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Natural Language Understanding (NLU) is an art of extracting the purpose or intent of the text, which in our case would be question. Also, we need to pull out right piece of information from the text. Algorithms reduce the number of classifiers and create a more manageable structure.

Everything You Need To Know About Matrix In Python

Conversing with the rule-based chatbots might be frustrating for customers since rule-based bots don’t have Artificial intelligence behind them to understand every question. Rule-based chatbots don’t jump from one question to another, they don’t link new questions to the previous conversation. Online business owners build AI chatbots using advanced technologies such as machine learning, artificial intelligence, and sentiment analysis.

  • Thus, we can also specify a subset of a corpus in a language we would prefer.
  • Let us look into the advantages and disadvantages of both conversational AI and rule-based chatbots.
  • Now you are going to discover how chatbots learn and what chatbot training data is.
  • Most of the companies today engage with their end users to provide customer support, flight details, product inquiries, etc.
  • On the other hand, if the input text is not equal to “bye”, it is checked if the input contains words like “thanks”, “thank you”, etc. or not.
  • These intelligent bots are so adept at imitating natural human languages and chatting with humans that companies across different industrial sectors are accepting them.

In chatbots design, an intent is the purpose or category of the user query. In rule-based chatbots, you can use regular expressions to match a user’s statement to a chatbot intent. When creating a conversational interface for your online store, it is essential to write a script that interprets user answers. Their primary goal is to receive commands from a user in the form of conversations and carry the interaction forward in a human manner.


One of the advantages of rule-based chatbots is that they always give accurate results. In such a situation, rule-based chatbots become very impractical as maintaining a rule base would become extremely complex. This is a fail-safe response in case the chatbot is unable to extract any relevant keywords from the user input. The list of keywords the bot will be searching for and the dictionary of responses will be built up manually based on the specific use case for the chatbot. The cost of such platform-based chatbots depends on the platform fee since most chatbot platforms operate on a subscription basis. Since we have finished with the different components of e-commerce chatbot development, let us look at what matters to our clients on a more complex level.

rule based chatbot python

How do you make a custom chatbot in Python?

  1. Demo.
  2. Project Overview.
  3. Prerequisites.
  4. Step 1: Create a Chatbot Using Python ChatterBot.
  5. Step 2: Begin Training Your Chatbot.
  6. Step 3: Export a WhatsApp Chat.
  7. Step 4: Clean Your Chat Export.
  8. Step 5: Train Your Chatbot on Custom Data and Start Chatting.


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