Reranking

Hybrid Search achieves more precise search results through multiple simultaneous ANN searches. Multiple searches return several sets of results, which require a reranking strategy to help merge and reorder the results and return a single set of results. This guide will introduce the reranking strategies supported by Zilliz Cloud and provide tips for selecting the appropriate reranking strategy.

Weighted Ranker [READ MORE]

Weighted Ranker intelligently combines and prioritizes results from multiple search paths by assigning different importance weights to each. Similar to how a skilled chef balances multiple ingredients to create the perfect dish, Weighted Ranker balances different search results to deliver the most relevant combined outcomes. This approach is ideal when searching across multiple vector fields or modalities where certain fields should contribute more significantly to the final ranking than others.

RRF Ranker [READ MORE]

Reciprocal Rank Fusion (RRF) Ranker is a reranking strategy for Zilliz Cloud hybrid search that balances results from multiple vector search paths based on their ranking positions rather than their raw similarity scores. Like a sports tournament that considers players' rankings rather than individual statistics, RRF Ranker combines search results based on how highly each item ranks in different search paths, creating a fair and balanced final ranking.

Boost Ranker [READ MORE]

Instead of relying solely on semantic similarity calculated based on vector distances, Boost Rankers allow you to influence search results in a meaningful way. It is ideal for quickly adjusting search results using metadata filtering.

Decay Ranker [READ MORE]

This chapter discusses Decay Rankers, which use dynamic ranking methods based on the idea that similarity scores for certain entities should decrease according to values in specific numeric fields, helping others stand out.