Show HN: Boomerang, a new embedding model for RAG and semantic search (opens in new tab)

pinecone + custom built ingestion & retrieval for codebase RAG (for the purpose of code search and understanding)

Clickhouse + Custom Reranker

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

The metrics presented in the blog post are those of our production model. When designing Boomerang, we tried to balance latency and search relevance in a manner that strikes the right balance for most use cases.

On the other hand, GTR-XXL is an example of a research model that biases in favor of search relevance, at the expense of latency. It's not really practical to deploy in production environments as a result.