---
id: wiki-2026-0508-reranking
title: Reranking
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Cross-Encoder-Reranking, Re-Ranker, RAG-Reranking]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [rag, retrieval, reranking, search]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: sentence-transformers
---

# Reranking

## 매 한 줄
> **"매 retrieval은 recall, 매 rerank는 precision"**. Reranking은 매 first-stage retrieval (BM25/dense) 에서 매 top-k candidates를 매 expensive cross-encoder/LLM으로 매 re-score — RAG quality 의 매 single biggest lever in 2026 (Cohere Rerank 4, BGE-Reranker-v2.5, Voyage rerank-3).

## 매 핵심

### 매 왜 필요
- Bi-encoder (dense retrieval): query, doc를 매 separately encode → cosine. Fast (cached doc embeddings) but 매 shallow interaction.
- Cross-encoder: `[query, doc]` 의 매 jointly encode → scalar score. 매 deep token-level attention → +10–30% NDCG.
- Trade-off: O(N) cross-encoder 의 매 too slow → first-stage retrieve top-100, rerank to top-5.

### 매 Architectures
- **Cross-encoder** (BERT-based): `[CLS] q [SEP] d [SEP]` → linear → score. BGE-Reranker-v2.5, Cohere Rerank 4, Voyage rerank-3.
- **ColBERT / late interaction**: doc의 매 token-level embeddings 매 미리 계산 → query token이 매 max-sim로 score. Cross-encoder의 매 ~80% quality at retrieval-speed.
- **LLM-as-reranker**: prompt 의 GPT-5/Claude 매 listwise rank. RankGPT, RankZephyr 매 paradigm — 매 quality 최고지만 매 가장 비쌈.
- **RRF (Reciprocal Rank Fusion)**: cheap fusion of multiple rankers — `score(d) = Σ 1/(k+rank_i(d))`.

### 매 Hybrid Search Stack (2026 standard)
1. BM25 (sparse) + Dense (e.g., BGE-M3) → parallel.
2. RRF fuse → top-100.
3. Cross-encoder rerank → top-10.
4. (Optional) LLM rerank → top-3 for high-stakes.

### 매 응용
1. RAG 의 매 답변 정확도 ↑.
2. E-commerce search relevance.
3. Legal/medical document discovery (precision-critical).
4. Code search (semantic + lexical hybrid).

## 💻 패턴

### Cross-encoder rerank (sentence-transformers)
```python
from sentence_transformers import CrossEncoder

reranker = CrossEncoder("BAAI/bge-reranker-v2.5-gemma2-lightweight")

def rerank(query: str, candidates: list[str], top_k: int = 5):
    pairs = [[query, doc] for doc in candidates]
    scores = reranker.predict(pairs)  # numpy array
    ranked = sorted(zip(candidates, scores), key=lambda x: -x[1])
    return ranked[:top_k]
```

### Cohere Rerank API
```python
import cohere
co = cohere.Client()

def cohere_rerank(query: str, docs: list[str], top_n: int = 5):
    resp = co.rerank(
        model="rerank-v4.0",
        query=query, documents=docs, top_n=top_n,
    )
    return [(docs[r.index], r.relevance_score) for r in resp.results]
```

### Reciprocal Rank Fusion
```python
def rrf(rankings: list[list[str]], k: int = 60) -> list[str]:
    """rankings: list of ranked doc-id lists from different retrievers."""
    scores: dict[str, float] = {}
    for ranking in rankings:
        for rank, doc_id in enumerate(ranking):
            scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank + 1)
    return sorted(scores, key=scores.get, reverse=True)
```

### Hybrid retrieve + rerank pipeline
```python
def hybrid_rag(query: str, k_first=100, k_final=5):
    bm25_hits = bm25.search(query, top_k=k_first)
    dense_hits = dense_index.search(query, top_k=k_first)
    fused = rrf([bm25_hits, dense_hits])[:k_first]
    docs = [load_doc(d) for d in fused]
    return rerank(query, docs, top_k=k_final)
```

### LLM-as-reranker (listwise)
```python
def llm_rerank(query: str, docs: list[str]) -> list[int]:
    numbered = "\n".join(f"[{i}] {d[:300]}" for i, d in enumerate(docs))
    resp = client.messages.create(
        model="claude-opus-4-7", max_tokens=200,
        messages=[{"role": "user", "content":
            f"Query: {query}\nDocs:\n{numbered}\nReturn comma-separated indices best→worst."}],
    ).content[0].text
    return [int(x) for x in resp.strip().split(",")]
```

### ColBERT late-interaction (RAGatouille)
```python
from ragatouille import RAGPretrainedModel

rag = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.5")
rag.index(collection=docs, index_name="my-index")
results = rag.search(query="foo", k=10)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Cost-sensitive RAG | BM25 + dense → RRF (no rerank) |
| Quality > latency | Hybrid + cross-encoder rerank |
| Highest quality | + LLM rerank top-20 → top-3 |
| 거대 corpus (>10M docs) | ColBERT for second stage |
| Multilingual | BGE-Reranker-v2.5 / Cohere rerank-v4 |

**기본값**: BM25 + BGE-M3 dense → RRF top-100 → BGE-Reranker-v2.5 top-5.

## 🔗 Graph
- 부모: [[Information Retrieval]] · [[RAG]]
- 변형: [[ColBERT]] · [[RRF]]
- 응용: [[Semantic Search|Semantic-Search]] · [[Hybrid Search]]
- Adjacent: [[BM25]] · [[Dense-Retrieval]] · [[Embeddings]]

## 🤖 LLM 활용
**언제**: high-stakes RAG (legal/medical/finance), small candidate set, listwise.
**언제 X**: 매 latency budget < 100ms, 매 large k (cost), 매 simple FAQ chat (overkill).

## ❌ 안티패턴
- **Rerank without first-stage filter**: O(N) on full corpus → cost explosion.
- **Cross-encoder for indexing**: 매 doc embeddings 의 매 cache 의 X — 매 query마다 recompute.
- **Pointwise LLM rerank**: 매 doc 별 separate call → listwise보다 매 비싸고 inconsistent.
- **Ignoring score calibration**: cross-encoder score는 매 not probability — threshold 매 dataset-specific tuning 필요.

## 🧪 검증 / 중복
- Verified (Cohere docs, BGE paper, ColBERT v2.5, RankGPT/RankZephyr).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — full rewrite as canonical for cross-encoder/ColBERT/RRF/LLM rerank |
