A big thanks to Chatling.ai for making this video possible. You can support my channel by using my affiliate link: chatling.ai/?ref=leonvanzyl
@WayneBruton15 күн бұрын
Yet again, another awesome tutorial, thanks Leon
@leonvanzyl15 күн бұрын
Thank you Wayne!
@tomasdablju219914 күн бұрын
You are doing great content. Thanks a lot.
@leonvanzyl11 күн бұрын
Thank you 🙏
@plannedillusion16 күн бұрын
Well done brother 👍🏾
@leonvanzyl16 күн бұрын
Appreciated
@antonvoskoboynikov145716 күн бұрын
Hi Leon, thank you for great tutorials! Any chance you could build similar things on n8n some time soon? =)
@leonvanzyl16 күн бұрын
Will see what I can do 👍. N8n and Flowise won't be able to give you this level of interactivity though.
@pressplayg15 күн бұрын
@@leonvanzyl what if both combined ?
@moroccangamereviews882416 күн бұрын
First. Love you bro ❤
@leonvanzyl16 күн бұрын
Thanks 🙏
@pressplayg15 күн бұрын
Can you do the same Thing using Flowise And N8N Combined ?
@leonvanzyl15 күн бұрын
Not to this extent. I think Chatling is closer to Botpress and Voiceflow, as it is a chatbot builder like those platforms. If you really wanted, you could use Chatling to build a chatbot without any LLM / AI model at all, like the traditional way 😊.
@pressplayg15 күн бұрын
But which is more powerful isnt flowise and n8n combination powerful than this no code chtabot builders like chatling ? @@leonvanzyl
@k1r0vsiii16 күн бұрын
now u can do a flowise chatbot that does all this for free :)))) ok ok, give it a free weeks for the paid promotion to wear off :D u da goat
@SoloJetMan16 күн бұрын
Leon got me chasing another shiny toy 🙂 .... be great to add conversational voice capability
@leonvanzyl16 күн бұрын
Hehehe, different tools for different use-cases. I see Chatling as an alternative to something like botpress or Voiceflow.
@Bu3askoorDXB16 күн бұрын
next time you are not allowed to be away from us for this long 😅. thanks as always
@leonvanzyl16 күн бұрын
Aww, thanks!
@jntaca16 күн бұрын
¿ Is your document sent into to the context or stored in a vector database and queried via vectorizing the prompt ? It makes a lot of difference in the consumed tokens.
@leonvanzyl16 күн бұрын
From what I can tell the knowledge base seems to use RAG. The docs are chunked and loaded into a vector database.