Try infranodus.com to get a high-level overview of your AI knowledge base. Timecodes: 0:00 Quick summary of what we're going to build 2:10 How RAG works (easy explanation) 4:16 The problem with retrieval augmented generation 5:11 Our approach: knowledge graph data for your prompt 8:25 Using Dify to create a chatbot for a website 9:30 Importing a knowledge base with Firecrawl into Dify 11:15 Importing a knowledge base into Open-WebUI 11:48 Building a chatbot from a knowledge base 13:39 InfraNodus extracts the main elements from our knowledge base 15:28 Verifying the quality of your knowledge base 16:58 Transferring insights from InfraNodus to your AI chatbot 18:28 Building complex AI flows and injecting InfraNodus insights before RAG 20:08 Augmenting your prompt in Open-WebUI
Sure! InfraNodus is specifically designed for representing text as a graph. Neo4J is a multi-purpose graph database, so it is much less adapted to analyzing text. For instance, in InfraNodus you have much more detailed meta-data about each relation in plain text form, in addition to the relation description, so the graph is much more precise. It also provides multiple metrics from network science to give you a better information about the underlying structure. Rag-Fusion is a completely different thing. It's a framework that uses LLM to generate more general queries from your original one hoping to get better results. You can use the data from InfraNodus to do the same, but the precision of InfraNodus is much higher, because it rephrases those queries in a way that is pertinent to the context you're working with. So it's going to be much more domain-specific. I hope that explains it!
![[1hr Talk] Intro to Large Language Models](http://i.ytimg.com/vi/zjkBMFhNj_g/mqdefault.jpg)
Try infranodus.com to get a high-level overview of your AI knowledge base.
Timecodes:
0:00 Quick summary of what we're going to build
2:10 How RAG works (easy explanation)
4:16 The problem with retrieval augmented generation
5:11 Our approach: knowledge graph data for your prompt
8:25 Using Dify to create a chatbot for a website
9:30 Importing a knowledge base with Firecrawl into Dify
11:15 Importing a knowledge base into Open-WebUI
11:48 Building a chatbot from a knowledge base
13:39 InfraNodus extracts the main elements from our knowledge base
15:28 Verifying the quality of your knowledge base
16:58 Transferring insights from InfraNodus to your AI chatbot
18:28 Building complex AI flows and injecting InfraNodus insights before RAG
20:08 Augmenting your prompt in Open-WebUI
Brillant Dmitry....brilliant!
this man is living in 2160
Haha that’s my problem though. 🙏🏼
Great video. How does Infranodus compare with Neo4j or Rag-Fusion?
Sure! InfraNodus is specifically designed for representing text as a graph. Neo4J is a multi-purpose graph database, so it is much less adapted to analyzing text. For instance, in InfraNodus you have much more detailed meta-data about each relation in plain text form, in addition to the relation description, so the graph is much more precise. It also provides multiple metrics from network science to give you a better information about the underlying structure.
Rag-Fusion is a completely different thing. It's a framework that uses LLM to generate more general queries from your original one hoping to get better results. You can use the data from InfraNodus to do the same, but the precision of InfraNodus is much higher, because it rephrases those queries in a way that is pertinent to the context you're working with. So it's going to be much more domain-specific.
I hope that explains it!
Yes, GNN + RL = next AI
Yep. Making more content on that soon.
GraphRAG is gonna catch fire Dima
Check HybridRAG :)