Introducing support for sparse-dense embeddings for better search results

Advancements in AI continue to up the ante for what end-users expect out of their search experience. To keep up with these growing expectations, engineers are turning to the latest advancements in Large Language Models (LLMs) to deliver the best results possible.

While semantic search aims to meet these expectations, there are still use cases (e.g. searching for names or industry-specific jargon) that rely on keyword-search. Hybrid search combines the power of both semantic and keyword search to provide more relevant results than either one alone.

This is a companion discussion topic for the original entry at