31–40 of 116 posts
Once Bedrock KB backed by S3 Vectors is released from Beta it'll eat everybody's lunch.
The point about synthetic query generation is good. We found users had very poor queries, so we initially had the LLM generate synthetic queries. But then we found that the results could vary widely based on the specific synthetic query it generated, so we had it create three variants (all in one LLM call, so that you can prompt it to generate a wide variety, instead of getting three very similar ones back), do paral…
The point about synthetic query generation is good. We found users had very poor queries, so we initially had the LLM generate synthetic queries. But then we found that the results could vary widely based on the specific synthetic query it generated, so we had it create three variants (all in one LLM call, so that you can prompt it to generate a wide variety, instead of getting three very similar ones back), do paral…
One thing I’m always curious about is if you could simplify this and get good/better results using SPLADE. The v3 models look really good and seem to provide a good balance of semantic and lexical retrieval.
Not here to schlep for AWS but S3 Vectors is hands down the SOTA here. That combined with a Bedrock Knowledge Base to handle Discovery/Rebalance tasks makes for the simplest implementation on the Market. Once Bedrock KB backed by S3 Vectors is released from Beta it'll eat everybody's lunch.
I'm correcting you less out of pedantry, and more because I find the correct term to be funny.
Similar writeup I did about 1.5 years ago for processing millions of (technical) pages for RAG. Lots has stayed the same it seems https://jakobs.dev/learnings-ingesting-millions-pages-rag-az...
I find it interesting that so many services and tools were investigated except for embedding models. I would have thought that's one of the biggest levers.
The point about synthetic query generation is good. We found users had very poor queries, so we initially had the LLM generate synthetic queries. But then we found that the results could vary widely based on the specific synthetic query it generated, so we had it create three variants (all in one LLM call, so that you can prompt it to generate a wide variety, instead of getting three very similar ones back), do paral…
Not here to schlep for AWS but S3 Vectors is hands down the SOTA here. That combined with a Bedrock Knowledge Base to handle Discovery/Rebalance tasks makes for the simplest implementation on the Market. Once Bedrock KB backed by S3 Vectors is released from Beta it'll eat everybody's lunch.
Shill, not schlep. I'm correcting you less out of pedantry, and more because I find the correct term to be funny.