Thank you for you answer! I’ve worked hard to improve my personal RAG implementation, searching (and asking here) ad nauseam to find ways to enhance the performance of the retrivial process…

i will study over this approach linked in the OP post, and your answer really helped me to take everything to a more “practical / tangibile” level.

I’ll try to integrate that on my experimental pipeline (currently I’m stable on RAG fusion using “query expansion” and hybrid search using transformer, SPLADE and bm25.

i already tried an approach that need a LLM to iterate over every chunk before generating embedding, mainly to solve pronouns and cross reference between chunks… Good results… But not good enough if analyzed in relation to the resource needed to iterate the llm over every item. Maybe the integration of this “knowledge nodes/edges generation” in my “llm” pre processing will change the pro/cons balance since, from a rapid test, the model seem able to do both text preprocessing and concept extraction in the same run.

Thanks again!

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Finally, a question on this sub that is not about an “AI girlfriend” (ahem RP)

I had many good discussions on this sub, and I really like that community… Anyway, i got your point Lol.