The AI Chatbot Handbook How to Build an AI Chatbot with Redis, Python, and GPT

Chatbots can help you perform many tasks and increase your productivity. AI-based Chatbots are a much more practical solution for real-world scenarios. In the next blog in the series, we’ll be looking at how to build a simple AI-based Chatbot in Python.

In this article, we will discuss how Python plays a major role in the development of AI chatbots. NLP is a branch of artificial intelligence focusing on the interactions between computers and the human language. This enables the chatbot to generate responses similar to humans.

Additional References

You need to use a Python version below 3.8 to successfully work with the recommended version of ChatterBot in this tutorial.

For example, if one person tells the bot their name is Alice, and the other person tells the bot their name is Bob, the bot can differentiate the people. To specify which session you are using you pass it as a second parameter to respond(). To make sure your SaaS product will be in demand, it’s essential to listen to customers’ needs and focus on software security. It’s responsible for choosing a response from the fewest possible words whose cumulative probability exceeds the top_p parameter.

Basics of building an Artificial Intelligence Chatbot – 2023

This tutorial does not require foreknowledge of natural language processing. Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. The target audience is basically the natural language processing (NLP) and information retrieval (IR) community. After initializing the AI agent and setting up the tools, the next step is to create the user interface for our chatbot using Streamlit. In the above, we have created two functions, “greet_res()” to greet the user based on bot_greet and usr_greet lists and “send_msz()” to send the message to the user.

Python version 3.6 or higher is recommended for building AI applications, including chatbots. Next, you should opt for Natural Language Processing (NLP) libraries. Among python’s robust NLP libraries are NLTK, Gensim, and SpaCy.

What is retrieval-augmented generation, and why use it for chatbots?

I am a full-stack software, and machine learning solutions developer, with experience architecting solutions in complex data & event driven environments, for domain specific use cases. When it gets a response, the response is added to a response channel and the chat history is updated. The client listening to the response_channel immediately sends the response to the client once it receives a response with its token. We are sending a hard-coded message to the cache, and getting the chat history from the cache. When you run python main.py in the terminal within the worker directory, you should get something like this printed in the terminal, with the message added to the message array.

The chatbot or chatterbot is a software application used to conduct an online chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. A chatbot is a piece of software or a computer program that mimics human interaction via voice or text exchanges. More users are using chatbot virtual assistants to complete basic activities or get a solution addressed in business-to-business (B2B) and business-to-consumer (B2C) settings. Today, almost all companies have chatbots to engage their users and serve customers by catering to their queries. We practically will have chatbots everywhere, but this doesn’t necessarily mean that all will be well-functioning.

Step 4: Train Your Chatbot with a Predefined Corpus

This is just a basic example of a chatbot, and there are many ways to improve it. Artificially intelligent chatbots, as the name suggests, are designed to mimic human-like traits and responses. NLP (Natural Language Processing) plays a significant role in enabling these chatbots to understand the nuances and subtleties of human conversation. AI chatbots find applications in various platforms, including automated chat support and virtual assistants designed to assist with tasks like recommending songs or restaurants.

• Chatbot Python has gained widespread attention from both technology and business sectors in the last few years.
• Next, click on your profile in the top-right corner and select “View API keys” from the drop-down menu.
• Don’t forget to notice that we have used a Dropout layer which helps in preventing overfitting during training.
• Setting a minimum value that’s too high (like 0.9) will exclude some statements that are actually similar to statement 1, such as statement 2.
• Apart from the applications above, there are several other areas where natural language processing plays an important role.

I am using Windows Terminal on Windows, but you can also use Command Prompt. Once here, run the below command below, and it will output the Python version. On Linux or other platforms, you may have to use python3 –version instead of python –version. To create an AI chatbot, you don’t need a powerful computer with a beefy CPU or GPU.

Step 10: Choose a random goodbye when the user says “bye”.

They are represented in the form of a list of unique tokens and, thus, vocabulary is created. This is then converted into a sparse matrix where each row is a sentence, and the number of columns is equivalent to the number of words in the vocabulary. NLP helps translate text or speech from one language to another.

These libraries allow for advanced processing capabilities including linguistics annotation and entity recognition, crucial properties for an AI chatbot. Furthermore, you’ll need to install chatbot AI libraries and frameworks, such as Chatterbot. A toolkit like Chatterbot, built explicitly for creating conversational engines, allows developers to generate responses based on collected knowledge. The next hurdle is the designing of your AI chatbot and it’s criteria for conversation. You will want to utilize all in one messenger design.

Preprocess data

Next, we trim off the cache data and extract only the last 4 items. Then we consolidate the input data by extracting the msg in a list and join it to an empty string. Note that to access the message array, we need to provide .messages as an argument to the Path. If your message data has a different/nested structure, just provide the path to the array you want to append the new data to.

You need to specify a minimum value that the similarity must have in order to be confident the user wants to check the weather. You’ll write a chatbot() function that compares the user’s statement with a statement that represents checking the weather in a city. To make this comparison, you will use the spaCy similarity() method. This method computes the semantic similarity of two statements, that is, how similar they are in meaning. This will help you determine if the user is trying to check the weather or not. 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.

Once the training data is prepared in vector representation, it can be used to train the model. Model training involves creating a complete neural network where these vectors are given as inputs along with the query vector that the user has entered. The query vector is compared with all the vectors to find the best intent.

Let’s create a couple more lists of keywords and responses that your AI chatbot will know. Another example of an AI Chatbot is the chatbot used by Capital One, a bank. The chatbot can help users with account information, transactions, and other banking needs because it is integrated with the bank’s mobile app and website. The chatbot understands and responds to natural language client inquiries, and it can also deliver customized recommendations and guidance.

After importing ChatBot in line 3, you create an instance of ChatBot in line 5. The only required argument is a name, and you call this one “Chatpot”. No, that’s not a typo—you’ll actually build a chatty flowerpot chatbot in this tutorial! You’ll soon notice that pots may not be the best conversation partners after all. After data cleaning, you’ll retrain your chatbot and give it another spin to experience the improved performance. It is standard to create a startup file called std-startup.xml as

the main entry point for loading AIML files.

• A Python chatbot is an artificial intelligence-based program that mimics human speech.
• You can make it smarter by adding more keywords and responses, exploring some of the libraries and project ideas listed below, or taking our Python for AI class.
• The Langchain library is a frame work for incorporating tools with large language models.
• Joseph Weizenbaum created the first chatbot in 1966, named Eliza.

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