Model Class

Reranker Models

A class of model that re-scores and re-orders retrieval results from vector search, improving precision by applying a more expensive cross-attention computation to the top-K candidates.

3 connections 2 resources

Summary

What it is

A class of model that re-scores and re-orders retrieval results from vector search, improving precision by applying a more expensive cross-attention computation to the top-K candidates.

Where it fits

Reranker models sit between vector retrieval and the final result set in RAG pipelines. When semantic search over S3-backed vector indexes returns approximate matches, a reranker applies a more accurate (but slower) relevance scoring to the top candidates — improving the quality of context fed to LLMs.

Misconceptions / Traps

Rerankers are not embedding models. They take a (query, document) pair and produce a relevance score — they do not generate reusable vectors. They are applied at query time, not at indexing time.
Reranking adds latency. The cross-attention computation is more expensive than vector similarity. Only apply reranking to a small top-K set (typically 20-100 candidates).

Key Connections

augments Semantic Search — improves retrieval precision
augments Hybrid S3 + Vector Index — refines vector search results
scoped_to LLM-Assisted Data Systems, Vector Indexing on Object Storage

Definition

What it is

A class of model that re-scores and re-orders an initial retrieval set (from vector search or keyword search) to improve precision, using cross-attention between the query and each candidate to produce more accurate relevance scores.

Why it exists

RAG systems retrieving from S3-backed vector indexes produce a ranked list that is fast but approximate. Reranker models refine this list, pushing truly relevant S3-stored documents to the top and filtering false positives.

Primary use cases

Improving RAG precision over S3-stored document corpora, refining semantic search results from S3-backed vector indexes, two-stage retrieval pipelines.

Connections 3

Outbound 3

scoped_to2

LLM-Assisted Data Systems Vector Indexing on Object Storage

augments1

Semantic Search

Resources 2

DocsHigh

sbert.net/examples/applications/cross-encoder/README.html

SBERT cross-encoder reranker documentation covering training, evaluation, and deployment of reranking models for retrieval pipelines.

DocsHigh

docs.cohere.com/docs/reranking

Cohere reranking API documentation for the leading commercial reranking service used in RAG applications.