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To better augment LLMs with context, it makes a lot of sense to organize context not just as a flat list of text chunks, but as a hierarchy of high-level to low-level details. RAPTOR is a super simple but neat idea towards this direction. Hierarchically cluster and summarize theโฆ https://t.co/LFRgYGLCfu https://t.co/fWzU3AsX2Z
Indexing is one of the important aspect for production ready RAG systems, especially if your data needs to be dynamic and real time. Its a very nice blog to read in your free time to get some ideas and inspiration ๐๐ผ๐๐ผ๐๐ผ๐๐ผ https://t.co/JUi6ZvSbMp
A lot of newer RAG techniques involve some form of query analysis - taking the raw user query and converting it into a more optimized version We've added a bunch of new docs on this, including implementations of a bunch of techniques as well as some how-to guides https://t.co/0oD1xlT3Rk
Recent discussions in the AI community have highlighted the importance of query analysis in enhancing RAG techniques. OpenAI's retrieval talk introduced various strategies, including RAPTOR, a tree-structured advanced RAG technique. RAPTOR aims to address the limitations of naive top-k RAG by focusing on higher-level context. Additionally, organizing context hierarchically is seen as beneficial for augmenting LLMs with better context.
