#Gpt chatbot code#

Image by the author.īut first, I had to construct a knowledge graph based on news articles. My idea was to develop a chatbot that could be used to explore, analyze, and understand news articles.

#Gpt chatbot code#

Additionally, Sixing has already written about and shared the code to implement a knowledge graph-based chatbot, which meant I could borrow some existing ideas and wouldn’t have to start from scratch.

Using a knowledge graph as a storage object for answers gives you explicit and complete control over the answers provided by the chatbot and allows you to avoid hallucinations. I was especially intrigued by the knowledge graph-based approach to chatbots, where the chatbot returns answers based on information and facts stored in the knowledge graph. So I wanted to learn more about chatbots, and luckily Sixing Huang gave me a crash course on different ways of implementing a chatbot. On the other hand, there is tremendous value in having the ability to interact with chatbots and use them as an interface for various applications. Consequently, it might not be a good idea to depend on answers from ChatGPT if mission-critical tasks or lives are at stake. The problem is that these large language models (LLM) are inherently black boxes, so it is hard to fix and retrain models to reduce hallucinations. However, one problem with ChatGPT and similar chatbots is that they can hallucinate and return great-sounding - yet wildly inaccurate - results.

We can use chatbots to help us find information, construct creative works, and more. ChatGPT has changed how I, and probably most of you, look at AI and chatbots.