7 Perfect ChatBot Framework for your AI-Driven Chatbots in Python
Is the future of customer service relying solely on chatbots powered by Python frameworks?
In the era of digitalization, businesses are looking for ways to simplify their processes and improve customer experience. Chatbots have become one of the most popular ways to achieve this goal. With the help of Artificial Intelligence, chatbots can interact with customers more naturally and human-likely. Python is a popular language for developing chatbots due to its simplicity, versatility, and robust community. Python has several chatbot frameworks, and this blog will discuss each framework separately.
Chatbots are a relatively new concept with numerous programs and NLP tools. Still, there are only two different categories of chatbots based on the NLP technology they use. Therefore, these two types of chatbots are mentioned here:
- Scripted Chatbots: Scripted chatbots are classified as chatbots that work with predefined scripts created and stored in libraries. When a user types a query or speaks a question (for chatbots with a speech-to-text module), the chatbot responds to that query according to a predefined script stored in the library.
- Artificial Intelligence Chatbots: Artificial intelligence chatbots, as the name suggests, are made to mimic human-like characteristics and responses. NLP, or natural language processing, plays a significant role in enabling such chatbots to understand the dialects and undertones of human conversation.
Let’s discuss the 7 perfect chatbot frameworks for developing AI-driven chatbots in python.
- RASA Framework: Rasa is an open-source framework for building text- and voice-based chatbots. This works at Level 3 of Conversational AI, where the bot can understand the context. Level 3 conversational agents can handle user second thoughts, context handling, unexpected questions, and more. Rasa is not the only tool available to build a chatbot, but it’s one of the best. There are a few others, such as Dialogflow. In addition, Rasa can be integrated with multiple platforms such as WhatsApp, Facebook, Slack, Telegram, and websites.
- Microsoft Bot Framework: The Microsoft Bot Framework platform helps you create, connect, publish, and manage intelligent, interactive chatbots to deliver the best user experience that comes with active learning. You can use pre-built models to interact with the users, such as Skype, Slack, Facebook Messenger, Websites, Cortana, Microsoft Teams, Kik, etc.
- Dialogflow : Build text- or voice-based conversational interfaces for Dialogflow bots and applications. Dialogflow is powered by Google’s machine learning and can connect with users on Google Assistant, Amazon Alexa, mobile apps, messengers, websites, Slack, Twitter, and more. It runs on the Google Cloud Platform and scales to serve hundreds of millions of users. In addition, you can use the Node.js SDK for fulfillment and detect intents and agent APIs using the following: PHP, Go, Java (Maven), Ruby (Gem), Python, C#, and Node.js. Dialogflow is easy to use, supports over 20 languages, and is probably the best framework for developing NLP-based applications.
- IBM Watson: Based on Wikipedia’s billion-word neural network, IBM Watson can communicate with bot users.
- Use machine learning to respond to natural language input on platforms such as mobiles, websites, robots, and messaging apps.
- Watson Assistant helps you quickly create chatbots for your business.
- Get started for free with 10,000 API calls per month.
- Some retail and banking sample bot code is available.
- Botpress: Botpress is an open-source platform based on a modular architecture. Some of the features are listed below:
- Editor – Flexible Flow Management System
- Natural Language Understanding
- Actionable Analytics
- Multi-Channel – Use bots on all significant platforms like Skype, SMS, and WeChat.
- ChatterBot: Node.js takes control of it, and the bot automates the entire process through machine learning. ChatterBot is powered by building a Python library and is language agnostic. This allows you to train your bot in any language. The working mechanisms of both are relatively straightforward. The more input ChatterBot receives, the more efficiently it processes output and is more accurate. In addition, the bot is easily adaptable, so it learns continuously.
- MobileMonkey: MobileMonkey is not a framework, but its marketing platform helps connect customers and audiences through multiple channels such as Live Chat, Facebook Messenger, and SMS. Write your code; alternatively, you can start with an existing template.
Conclusion
Chatbots are essential for businesses looking to improve their customer experience. With the help of AI, chatbots can provide a more human-like interaction with customers. Python offers several chatbot frameworks that make it easy for developers to create chatbots. Whether you are a seasoned developer or just starting out, these Chatbot frameworks will undoubtedly suit your needs.
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