Using Boomerang can significantly improve your end-to-end RAG performance: retrieving the most relevant facts (or chunks) matters, a lot!
Show HN: Boomerang, a new embedding model for RAG and semantic search
11–14 of 14 posts
Re: Show HN: Boomerang, a new embedding model for RAG and semantic search
#12how good is it for code RAG applications?
So far, we haven't really focused on code ingestion. We've had a few users try it out for that use case, but we code ingestion and generation is a bit different. We've found a lot of users have success in the natural language areas (ingesting enterprise content, ecommerce content, etc) and then building chatbots on top of the all-in-one API
true, code is very different than natural language. any plans for incorporating it?
Re: Show HN: Boomerang, a new embedding model for RAG and semantic search
#13I work at Vectara and I'm curious -- are folks here using Retrieval-Augmented Generation (RAG)? What's your stack and what kind of improvements have you seen in answer quality?
Clickhouse + Custom Reranker
Re: Show HN: Boomerang, a new embedding model for RAG and semantic search
#14> Note that while Boomerang is optimized for low-latency performance, models like GTR-XXL, which weighs in at 4.8 billion parameters, are very challenging to productionize.
So what is the size of your model than, or did i miss something?