Hybrid Search and Learning-to-Rank with Metarank

As more advanced Large Language Models (LLMs) are released, the dream of an accurate semantic search comes closer to reality. But a classical term search is still hard to beat, even with the largest LLMs. So what if you don’t need to choose between two approaches and combine them within a single hybrid multi-retriever system?

In this article, we’re going to discuss a case when Elasticsearch, Opensearch, Solr, and Pinecone are used together to get the best from both words, with the final combined ranking produced with Metarank.

This is a companion discussion topic for the original entry at https://www.pinecone.io/learn/metarank/