Looking forward to support for tuning boomerang easily.The model itself is marginally interesting I guess. In most cases we need something open source and on our VPC for customers though.

Hi HN!Vectara is a &quot;batteries included&quot; retrieval augmented generation platform. You can upload your rich text documents like PDFs, HTML pages, word docs, etc, or semi-structured JSON and Vectara handles the text and metadata extraction, segmentation, vector embedding, and vector storage, and keyword storage. You can ask a question or perform a search in the UI or via our APIs and Vectara will automatically handle the vectorization, structured metadata filtering, vector+keyword retrieval, hybrid blending, and generative summarization of the results. We&#x27;re focusing on building and operationalizing the complex infrastructure for vector storage, hybrid retrieval, and generative summarization so you can use fairly high-level APIs and focus on building your own applications.We know that retrieval accuracy is incredibly important for RAG: garbage in, garbage out. We&#x27;ve seen a lot of projects not spend enough time on really getting the retrieval model right and wasting a lot of time&#x2F;money with poor outcomes. We&#x27;ve spent about the past 6 months working on a new embedding model named Boomerang and just released it on the Vectara platform. We&#x27;ve run it through standard evaluations like BEIR (though we know many models over-fit against BEIR) as well as multi-domain evaluations. We&#x27;ve published the details of our tests for those that really want to dive in, but the TL;DR is that Boomerang beats most&#x2F;all publicly available models in many&#x2F;most situations and is particularly strong at cross-lingual and multi-domain tests.We&#x27;d love any and all feedback!

Show HN: Launching a new home-grown embedding LLM for RAG