Perspectives on R in RAG | Vespa Blog

Retrieval-augmented generation (RAG) has led to a surge in the number of developers interested in working on retrieval. In this blog post, I share perspectives providing insights and perspectives on the R in RAG.

Hybrid retrieval and ranking pipelines allow you to combine signals from unsupervised methods (such as BM25) with supervised methods (such as neural rankers). By combining unsupervised and supervised techniques, we have shown that ranking accuracy increases compared to using either method independently. The rise in popularity of hybrid models can be attributed to the lack of the necessary tools, data, time and resources to fine-tune text embedding models specifically for their retrieval tasks. Extensive research and experimentation have shown that hybrid ranking outperforms either method when used alone in a new setting or a new domain with slightly different texts than what the model was trained on.

What is often overlooked in this hybrid search discussion is the ability to perform standard full-text-search (FTS) functionality like exact and phrase matching. Text embedding models are limited by their fixed vocabulary, leading to poor search results for unseen words not in the vocabulary. This is particularly evident in cases such as searching for a product identifier, a phone number, a zip code, or a code snippet, where text embedding models with fixed vocabularies fail. For example, if we look at BERT, one of the most popular language models, its default vocabulary does not include the word 2024.

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