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You are glossing over the fact that for RAG you need to search over those 500GB+ which will be painfully slow and CPU-intensive. The goal is fast retrieval to a
by ricardobeat 1y ago
You are glossing over the fact that for RAG you need to search over those 500GB+ which will be painfully slow and CPU-intensive. The goal is fast retrieval to add data to the LLM context. Storage space is not the sole reason to minimize the DB size.
- deleted 1y ago[deleted]
- brookst 1y agoYou’re not searching over 500GB, you’re searching an index of the vectors. That’s the magic of embeddings and vector databases. Same way you might have a 50TB relational database but “select id, name from people where country=‘uk’ and name like ‘benj%’ might only touch a few MB of storage at most.
- ricardobeat 1y agoThat’s precisely the point I tried to clear up in the previous comment. The LEANN author proposes to create a 9GB index for a 500GB archive, and the other poster argued that it is not helpful because “storage is cheap”.