Appreciate that!

Thanks Brock 🙏🏻

Thanks Clarke!

I think this is currently the best approach for RAG.

we are working on a solution for this that can be white-labeled on release! does your client has an API endpoint or some kind o bucket containing all the files ? it really depends in what formats the data comes. If it its just text than you can use a hybrid-approach with semantic chunking, parent-document retrieval or other meta-data filtering techniques. The main point of importance is to make sure the data is pre-processed and cleaned before being chunked an embedded. Entity extraction is expensive but can be very helpful. A second best option is to extract meta-data. One is used for semantic extraction (entity) and the other for additional filtering. GraphRAG is the best solution, using entities, but it costs a massive amount of resources & development time making it only accessible to enterprise clients (10-50k +).

Yes!

GraphRAG is a more powerful solution than this baseline RAG. In GraphRAG, the data is stored in the graph with entities and relationships and also doing community summaries in detail which excels in retrieval flow. For eg: questions like "Did company underperform in Q4 vs Q3?" This kind of question would be difficult to answer using Baseline-RAG which can be answered easily using GraphRAG

@@sahiljain9376 you can enhance RAG with agentic frameworks to allow these questions e.g. an SQL Agent with meta-data filtering. I love graphRAG but its a.) super expensive since entity extraction requires a ton of LLM calls b.) takes allot of time to set-up the graph, c.) has additional challenges to be overcome before it can really be used for non-enterprise.

@@sahiljain9376 I was unaware of GraphRAG, and it looks really interesting thanks. It looks like it's beyond my skill level now, but hopefully MS integrates it into Azure soon