Building a rule-based chatbot in Python

Then you can improve your chatbot’s results by feeding the bot with your own conversations. A chatbot is a computer program that holds an automated conversation with a human via text or speech. In other words, a chatbot simulates a human-like conversation in order to perform a specific task for an end user.

The updated and formatted dictionary is stored in keywords_dict. The intent is the key and the string of keywords is the value of the dictionary. Here, we first defined a list of words list_words that we will be using as our keywords.

The best part? The chatbot will remember your chat history

A corpus is a collection of authentic text or audio that has been organised into datasets. There are numerous sources of data that can be used to create a corpus, including novels, newspapers, television shows, radio broadcasts, and even tweets. Building a ChatBot with Python is easier than you may initially think. Chatbots are extremely popular right now, as they bring many benefits to companies in terms of user experience.

Inside the function, we construct the URL for the OpenWeather API. The URL returns the weather information of the city in JSON format. After this, we make a GET request using requests.get() function to the API endpoint and we store the result in the response variable. After this, the result of the GET request is converted to a Python dictionary using response.json(). And that is how you build your own AI chatbot with the ChatGPT API.

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With the rise in the use of machine learning in recent years, a new approach to building chatbots has emerged. Using artificial intelligence, it has become possible to create extremely intuitive and precise chatbots tailored to specific purposes. After all of the functions that we have added to our chatbot, it can now use speech recognition techniques to respond to speech cues and reply with predetermined responses. However, our chatbot is still not very intelligent in terms of responding to anything that is not predetermined or preset. Scripted chatbots are chatbots that operate based on pre-determined scripts stored in their library. When a user inputs a query, or in the case of chatbots with speech-to-text conversion modules, speaks a query, the chatbot replies according to the predefined script within its library.

RNNs process data sequentially, one word for input and one word for the output. In the case of processing long sentences, RNNs work too slowly and can fail at handling long texts. As you can see the chatbot responded to ‘My name is Akshay’ because we have trained it. It returned None when we used the sentence or rule on which it is not trained. So we need to train our chatbot on each and everything we need it to answer.

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