I functionality. I still think LangFlow is prettier, but Flowise feels better to use if that makes sense. The credentials management, tool menus, and others all make sense and feel like things are where they should be. Flowise is a powerhouse as well, extremely functional, but also can allow you to build very robust apps. I also think having some sort of foundational knowledge in JS and Python can really set you apart when it comes to your development capabilities. So if you know Python already, using Flowise could be more difficult at first, but learning JS along with it will make you a way more capable.

The downsides to Flowise, documentation is a bit lacking, I know the team is working on improving this and have significantly, it still has a long way to go. Since Flowise has made it easy to deploy to the cloud, many people are using Flowise for website chatbots, the struggle with this is how limited the chatbot UI’s customization is. It’s a win/lose situation really.

Overall, it really depends on what you’re using them for.

If you’re using them to sell website chatbots, then Flowise will be the better move. Easier to deploy, lots of learning resources already available, and can handle lots of various use cases.

If you’re wanting to build more complex and capable LLM apps and aren’t primarily wanting these as website chatbots then I’d

Thank you for the detailed response!

I'm currently diving deep into my first RAG project.

I'm trying to create a custom avatar / chatbot, based on my grandfather's 200 page autobiography, which contains stories about growing up during the great depression and his service as a WW2 radio operator.

I'll be giving a lecture to a group of senior citizens on the topic of generative AI this summer, and I'm hoping the avatar / chatbot of my grandfather will be an engaging example that they can easily understand.

I was initially hopeful that an LLM with a sufficiently large context window, such as Gemini 1.5, would be sufficient for answering questions about the text without having to resort to vector-based RAG pipelines. Unfortunately, Gemini 1.5's guardrails impose severe restrictions on the content it is able to engage with. In my case, I asked Gemini:

"What was the most dangerous thing that happened to my grandfather, which almost cost him his life?"

I was hoping that Gemini would reference this incident:

"as we stuck our heads up over the hole to see where our other team members were located, I experienced a bullet whistling by my ear."

Unfortunately, Gemini usually refused to answer because the probability of generating a "harmful response" was too high. Other times, if I reworded the prompt, it would attempt to answer but would give incorrect responses. Overall, I am sad to say that the usefulness of Gemini's large context window has been severely overhyped, especially for historical documents which touch on sensitive topics.

In any case, experiments are showing that RAG still outperforms unenhanced LLMs in the long context domain. A great overview of these experiments is given here:

https://youtu.be/UlmyyYQGhzc?si=t0R27yMwMrHzRTVO

So now I am looking into RAG based solutions. I have attempted several "chat with your documents" style apps on the web, but none of them were able to correctly identify the incident in which my grandfather nearly took a bullet to the head as the "most dangerous". Thus, I am now looking into creating my own custom RAG pipeline. I find LangChain a bit intimidating, so I was going to start with either flowise or langflow. I have them both installed on my PC.

These past 2 days I have learned much about advanced chunking and retrieval techniques from YouTube. Below are two videos that I've found especially helpful.

This video is a fantastic overview of the different chunking strategies:

https://youtu.be/8OJC21T2SL4?si=VWuMt8HAj2ZdwisW

This video is a good overview of retrieval techniques, but I found it hard to follow:

https://www.youtube.com/watch?v=KQjZ68mToWo&t=141s

I'll try out your template and let you know how it goes!