# Ranking

> Browse 8 items tagged with Ranking.

## Items

- [Reranking](https://vectordb.ever.works/items/reranking) — A two-stage retrieval process where initial candidates from vector search are reordered using more sophisticated models like cross-encoders. Reranking significantly improves result quality by applying computationally expensive models to a small set of candidates, commonly used in RAG systems and search applications.
- [Hybrid Search with Reciprocal Rank Fusion](https://vectordb.ever.works/items/hybrid-search-with-reciprocal-rank-fusion) — Search technique combining BM25 lexical search and semantic vector search using Reciprocal Rank Fusion (RRF) to merge results, balancing precision of keyword matching with contextual understanding of neural embeddings.
- [MaxSim Operator](https://vectordb.ever.works/items/maxsim-operator) — Scoring function used in late interaction models like ColBERT that computes query-document relevance by finding maximum similarity between each query token and document tokens, then summing.
- [Reciprocal Rank Fusion (RRF)](https://vectordb.ever.works/items/reciprocal-rank-fusion-rrf) — Hybrid search algorithm combining results from multiple ranking systems by computing reciprocal ranks, commonly used to merge dense vector search with sparse keyword search for improved retrieval.
- [BM25](https://vectordb.ever.works/items/bm25) — Best Matching 25 ranking function for information retrieval that ranks documents based on query term frequency with length normalization. Core component of hybrid search RAG systems combining keyword and semantic search.
- [MaxSim](https://vectordb.ever.works/items/maxsim) — Maximum Similarity late interaction function introduced by ColBERT for ranking. Calculates cosine similarity between query and document token embeddings, keeping maximum score per query token for highly effective long-document retrieval.
- [Reciprocal Rank Fusion](https://vectordb.ever.works/items/reciprocal-rank-fusion) — Method for combining ranked lists from multiple retrieval systems in hybrid search. Standard technique in RAG pipelines for fusing BM25 and dense vector results before reranking, creating diverse high-confidence candidate sets.
- [BM25 (Okapi BM25)](https://vectordb.ever.works/items/bm25-okapi-bm25) — Probabilistic ranking function for estimating document relevance to search queries. Industry standard for keyword search, combining term frequency, rarity, and length normalization into a single scoring model.

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