Dear Kevin Naidoo I am Elon, and thank you for the informative article on RAG conversational AI. I'm still curious, however, about the choice of Qdrant over Faiss for production-grade deployments. As mentioned previously, I'm developing a chatbot for a public academic website. I'm facing a few challenges: Handling High Call Volume: I need to manage multiple chatbot interactions within a short timeframe. To address this, I'm considering the asynchronous Qdrant client. However, I'm unsure about the optimal call rate per minute for the chatbot when using a large model like GPT-3.5-turbo. Balancing Updates and Queries: The chatbot's knowledge base needs to stay up-to-date, requiring daily backend updates. I'm unsure how to handle both updates and queries simultaneously. Should chatbot calls be blocked during database updates? Given my limited experience with chatbot development, I haven't found clear answers to these questions. Additionally, I'd appreciate insights on the potential drawbacks of using Faiss for these concerns. Thank you for your time and assistance. Best regards,

Hi Kevin Naidoo , Thanks for your comprehensive response! I am developing a chatbot for a WordPress website and planning to use your script as the chatbot backend. However, I'm curious about how to implement the asynchronous function effectively. Since the chatbot will be used on a public website, I should consider making multiple calls on the chatbot in a short period. Do you have any recommendations on how to approach this aspect? Thanks, Elon

Thank you Kevin Naidoo for this useful guide. If someone wants to develop a chatbot whose knowledge base is drawn from the content (posts articles, and comments) of a website, how can they dynamically update the memory of chain ? By the way, can you tell me about the pricing of openai API key usage with some examples for using the gpt-3.5-turbo model? Thank you!