RASA FLASK Chinese Chatbot

screenshot of RASA FLASK Chinese Chatbot

基于rasa构建的中文任务型对话机器人,并用flask实现ui对话界面

Overview

The Chinese task-oriented chatbot built on the RASA + Flask framework presents an engaging solution for anyone looking to explore travel information. Utilizing the newer RASA framework (version 1.0.3), this chatbot is designed for users seeking insights into 5A-rated tourist attractions across provinces, alongside recommendations for local delicacies. With a simple yet functional UI interface built with Flask, it promises easy interaction and accessibility for users.

As the world of AI and chatbots continues to evolve, this particular implementation is a practical introduction to developing a customized chatbot with interactive capabilities. Capturing the essence of personal travel inquiries, it thrives on providing accurate and relevant information, making planning a journey smoother and more enjoyable.

Features

  • Tourist Attraction Queries: Easily inquire about 5A-rated tourist spots in specified provinces for comprehensive information about each location.
  • Local Cuisine Recommendations: Discover unique local cuisines from various provinces, enhancing your travel experience through culinary exploration.
  • Flask UI Integration: A basic yet effective user interface allows for straightforward interaction with the chatbot, enhancing accessibility.
  • Training Data Management: Uses structured data files such as nlu.md and story.md for training, ensuring robust natural language understanding.
  • Batch Model Training: The new RASA version supports simultaneous training of both core and NLU models, streamlining the development process.
  • Custom Actions: The ability to define personalized actions through actions.py allows for flexible and diverse user interactions.
  • Error Handling Strategies: The documentation provides insights into handling common errors encountered during development, enhancing user experience.
  • Open Source Resources: Access to extensive official documentation and community contributions enables continuous learning and improvement.