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I built a RAG implementation for 35k books/articles/wiki pages/web pages i collected over the years(it took about 6 weeks on 3070ti 100% constant usage). I quer
by sturza 3y ago
I built a RAG implementation for 35k books/articles/wiki pages/web pages i collected over the years(it took about 6 weeks on 3070ti 100% constant usage). I query it with various steps of data extraction/narrative building/refining etc, over LLMs. Almost daily i figure out new steps to add to the pipeline and honestly, i could not imagine learning about niche topic x from so many perspectives/periods in such a short time(including the original source). I did not yet figure out how to package this, but i spend at least 2h of my free time daily with it. Ideas and feedback is welcome.
- aej11 3y agocan you share more about this? when you say "it took about 6 weeks on 3070ti 100% constant usage" is that 6 weeks generating embeddings?
- sturza 3y agoyes, generating embeddings. i can share more: bsturza@duck.com
- RVRC 3y agoI’m also building a RAG app and I’m finding so many different ways to do it. I’m curious: was there one method that improved the accuracy/relevance of the answers the most? Also, are you using Langchain, Llamaindex, or something else?
- sturza 3y agoused langchain but for this implementation i used llamaindex. bsturza@duck.com
- whiddershins 3y agoI am doing something very similar I would love to trade notes.
- sturza 3y agobsturza@duck.com