Craft Your Own Python AI ChatBot: A Comprehensive Guide to Harnessing NLP
This process will show you some tools you can use for data cleaning, which may help you prepare other input data to feed to your chatbot. You can build an industry-specific chatbot by training it with relevant data. Additionally, the chatbot will remember user responses and continue building its internal graph structure to improve the responses that it can give. You’ll achieve that by preparing WhatsApp chat data and using it to train the chatbot. Beyond learning from your automated training, the chatbot will improve over time as it gets more exposure to questions and replies from user interactions.
In this tutorial, you’ll start with an untrained chatbot that’ll showcase how quickly you can create an interactive chatbot using Python’s ChatterBot. You’ll also notice how small the vocabulary of an untrained chatbot is. The CEO noted that DataGPT’s lightning cache database is 90 times faster than traditional databases. It can run queries 600 times faster than standard business intelligence tools while reducing the analysis cost by 15 times at the same time. The results from the analysis are then delivered in a conversational format to the user.
If you scroll further down the conversation file, you’ll find lines that aren’t real messages. Because you didn’t include media files in the chat export, WhatsApp replaced these files with the text . To avoid this problem, you’ll clean the chat export data before using it to train your chatbot. In this example, you saved the chat export file to a Google Drive folder named Chat exports.
The ChatterBot library combines language corpora, text processing, machine learning algorithms, and data storage and retrieval to allow you to build flexible chatbots. Chatbots can provide real-time customer support and are therefore a valuable asset in many industries. When you understand the basics of the ChatterBot library, you can build and train a self-learning chatbot with just a few lines of Python code.
Step-by-Step Guide: Build AI Chatbot Using Python
The choice ultimately depends on your chatbot’s purpose, the complexity of tasks it needs to perform, and the resources at your disposal. To extract the city name, you get all the named entities in the user’s statement and check which of them is a geopolitical entity (country, state, city). If it is, then you save the name of the entity (its text) in a variable called city. Here the weather and statement variables contain spaCy tokens as a result of passing each corresponding string to the nlp() function. First, you import the requests library, so you are able to work with and make HTTP requests. The next line begins the definition of the function get_weather() to retrieve the weather of the specified city.
- There are a lot of undertones dialects and complicated wording that makes it difficult to create a perfect chatbot or virtual assistant that can understand and respond to every human.
- Eventually, you’ll use cleaner as a module and import the functionality directly into bot.py.
- There’s also a GitHub cookbook repository with over a dozen more projects.
- You want to extract the name of the city from the user’s statement.
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