Building Chatbots with Python: Using Natural Language Processing and Machine Learning by Sumit Raj

Building Chatbots with Python: Using Natural Language Processing and Machine Learning by Sumit Raj

December 21, 2022

Now that we have a token being generated and stored, this is a good time to update the get_token dependency in our /chat WebSocket. We do this to check for a valid token before starting the chat session. In order to use Redis JSON’s ability to store our chat history, we need to install rejson provided by Redis labs. Next, we test the Redis connection in by running the code below.

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Code challenges

We building a chatbot in python a Redis object and initialize the required parameters from the environment variables. Then we create an asynchronous method create_connection to create a Redis connection and return the connection pool obtained from the aioredis method from_url. In the .env file, add the following code – and make sure you update the fields with the credentials provided in your Redis Cluster.


Increase sales of business by offering promo codes or gifts. Finding details about business such as hours of operation, phone number and address. Improve business branding thereby achieving great customer satisfaction. There are a few things I needed to get set up first before I started coding. They have 24/7 Availability – they are available all hours of the day for customers to get their questions answered.

How to Model the Chat Data

Chatbots relying on logic adapters work best for simple applications where there are not so many dialog variations and the conversation flow is easy to control. Preprocessors are simple functions for input preprocessing, such as for removing consecutive whitespace characters from statement text. As we can see, our bot can generate a few logical responses, but it actually can’t keep up the conversation.


Then we can pick some random responses from the list of responses. Here eachintent contains a tag, patterns, responses, and context. Patterns are the data that the user is more likely to type and responses are the results from the chatbot. This data file above only contains a very little amount of data.

SAP Conversational AI

We have also included another parameter named ‘logic_adapters’ that specifies the adapters utilized to train the chatbot. It then delivers us either a written response or a verbal one. Since these bots can learn from experiences and behavior, they can respond to a large variety of queries and commands.

  • ChatBot — An Artificial Intelligence programme that communicates with users through app, message, or phone.
  • In this article, we are going to use the transformer model to generate answers to users’ questions when developing an AI chatbot in Python.
  • They are widely used for text searching and matching in UNIX.
  • You should have a full conversation input and output with the model.
  • A chatbot is a computer program that holds an automated conversation with a human via text or speech.
  • Now copy the token generated when you sent the post request to the /token endpoint and paste it as the value to the token query parameter required by the /chat WebSocket.

This breaks up cleaned_corpus into a list where each line represents a separate item. Then, you convert this list into a tuple and return it from remove_chat_metadata(). If you’re comfortable with these concepts, then you’ll probably be comfortable writing the code for this tutorial.

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We do a quick check to ensure that the name field is not empty, then generate a token using uuid4. To generate a user token we will use uuid4 to create dynamic routes for our chat endpoint. Since this is a publicly available endpoint, we won’t need to go into details about JWTs and authentication.


ChatterBot 1.0.4 comes with a couple of dependencies that you won’t need for this project. However, you’ll quickly run into more problems if you try to use a newer version of ChatterBot or remove some of the dependencies. Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4 … This should about a minute, with a lot of output in the command screen. Once finished, you should now have the application deployed.