Excellent Thomas, thank you. May I suggest a tutorial on how to chain 2 separate flowise chatflows. Why is it important? As you know, there are many AI apps which allow people to chat with different "experts", and each expert has its own tools, functions, prompts, memory and knowledge. This can come very handy for businesses as well because they can setup a robust chatflow for customer support, one for sales, one for issues, subscriptions, etc.. and developers can add individual features to each chatflow as they become available without breaking the rest of the chatflows. It is also easier to analyze each LLM and conversation on individual chatflows. Right now it looks like everyone makes this huge chatflows with so much stuff that it quickly feels as if Flowise is limited, and I think it has no limits. I guess the best way to chain 2 chatflows is using API or special tools with each chatflow. I imagine one main chatflow and satellie chaflows providing extra support when necessary. I think you have a wonderful way to explain AI and flowise and it will be great if you consider a tutorial on chatflow chain using some real case such as an small ecommerce business with 3 or 4 different departments (chatflows). I also think you have a pretty good collection to start an Udemy course. Plus yours will be up to date, unlike the ones there which are about 8 or 9 months old and AI has evolved so much in the last year. Thank you.
@RanjotSingh-y2h7 ай бұрын
How to buy course , I want to buy using paypal , there is no option over there
@tyessenov8 ай бұрын
How to utilise rag in prompt chaining and work with retrieved docs?
@OnyxStudiosInteractive6 ай бұрын
It really depends on the workflow and what you'd like to achieve. For example you could chain the output of an LLM Chain to the input of a JS Custom Function that calls a Conversational Retrieval QA Chain and then connect the output to another LLM Prompt Chain.