PHOTON1233

I think the link was spammed folks! Someone ate through my openai credits! So it'll no longer work. If you want to see what Im building, add me on github! @Photon48

Graph DBs (KGs) add a much needed structure to unstructured data fed into traditional RAG. They are more holistic (interconnectedness of all things) than traditional relational DBs. That comes with 3 advantages;

1. They find insights you didn’t know you needed. Like discovering both Bob and Alice like to play basketball & paint the wilderness, so they could be a good match!

2. KGs are simple to read by humans, so are perfect for natural language processing.

3. KGs are dead simple and scalable. No complex tables and referencing. Just dots and lines. So LLMs have an easy time understanding.

I really think graph data structures is the missing piece to fixing RAG, but it hasn’t been mass adopted because, building quality graphs from unstructured data is HARD. But we’re getting there!

Ive got some amazing articles saved on my computer! Will get back to you. Until then, its basically a csv with (head,relation,tail)—> converted to a KG (networkx)-> nodes embedded and vector stored ->queried similarity nodes on conversation-> n-neighbours connected to KG extracted and fed into llm relevant context.

Im nearly done with a version 2. Ill showcase that, gauge the interest and start work on open sourcing it!

Thats a fun use case! I might try that out after flashing out this conversational-personal assistant usecase. Thanks for the idea!

Hi Guys! This is a quick demo of AffinityRAG. Ask it a question, and it will fetch sub-graphs from a larger knowledge graph (Alex's medical history) and answer your queries BETTER than just RAG. I've built a lot more since then including remembering you're specific info. I will open-source it if you guys want it.