Hey guys, in this video, I am going to show you an entirely different approach to open-ended question answering with chatbots. Traditional chatbots have limitations, where you have to ask a proper question to get a proper answer. However, with this new approach, we will cover scenarios where the query is incomplete or lacks specific information.
Let’s start by understanding the problem. Imagine you have uploaded information about 10 different YouTube channels, and you ask the chatbot a question about a specific channel without mentioning its name. The chatbot won’t be able to answer your query because it doesn’t know which channel you are referring to. To address this issue, we will combine the traditional approach with OpenAI function calling.
I have created a built-in project called ‘Answer.py’ that uses OpenAI and a knowledge base stored in ‘knowledgebase.json’. The ‘VectorGenerator.py’ is responsible for generating the knowledge base from PDF files.
To demonstrate the functionality, I have provided example code for the function calling approach. This approach allows the chatbot to reference previous conversations and generate proper queries to fetch the relevant information. It enhances the chatbot’s ability to understand context and provide accurate answers.
In the video, I test the chatbot with various queries and show how it generates queries based on the conversation history. The chatbot is able to provide detailed answers by referencing the previous conversation and fetching the correct information.
I hope you find this advanced approach to open-ended question answering with chatbots interesting. Feel free to explore the code and experiment with it. If you have any questions, leave them in the comments section. Don’t forget to subscribe to my channel for more exciting content. Thank you for watching!
