docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동
최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치. 콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서 전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한 업데이트0615/무제 3.canvas 뿐).
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Antigravity Agent
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id: wiki-2026-0508-reranking
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title: Reranking
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Cross-Encoder-Reranking, Re-Ranker, RAG-Reranking]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [rag, retrieval, reranking, search]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: sentence-transformers
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---
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# Reranking
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## 매 한 줄
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> **"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).
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## 매 핵심
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### 매 왜 필요
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- Bi-encoder (dense retrieval): query, doc를 매 separately encode → cosine. Fast (cached doc embeddings) but 매 shallow interaction.
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- Cross-encoder: `[query, doc]` 의 매 jointly encode → scalar score. 매 deep token-level attention → +10–30% NDCG.
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- Trade-off: O(N) cross-encoder 의 매 too slow → first-stage retrieve top-100, rerank to top-5.
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### 매 Architectures
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- **Cross-encoder** (BERT-based): `[CLS] q [SEP] d [SEP]` → linear → score. BGE-Reranker-v2.5, Cohere Rerank 4, Voyage rerank-3.
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- **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.
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- **LLM-as-reranker**: prompt 의 GPT-5/Claude 매 listwise rank. RankGPT, RankZephyr 매 paradigm — 매 quality 최고지만 매 가장 비쌈.
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- **RRF (Reciprocal Rank Fusion)**: cheap fusion of multiple rankers — `score(d) = Σ 1/(k+rank_i(d))`.
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### 매 Hybrid Search Stack (2026 standard)
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1. BM25 (sparse) + Dense (e.g., BGE-M3) → parallel.
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2. RRF fuse → top-100.
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3. Cross-encoder rerank → top-10.
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4. (Optional) LLM rerank → top-3 for high-stakes.
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### 매 응용
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1. RAG 의 매 답변 정확도 ↑.
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2. E-commerce search relevance.
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3. Legal/medical document discovery (precision-critical).
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4. Code search (semantic + lexical hybrid).
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## 💻 패턴
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### Cross-encoder rerank (sentence-transformers)
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```python
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from sentence_transformers import CrossEncoder
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reranker = CrossEncoder("BAAI/bge-reranker-v2.5-gemma2-lightweight")
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def rerank(query: str, candidates: list[str], top_k: int = 5):
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pairs = [[query, doc] for doc in candidates]
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scores = reranker.predict(pairs) # numpy array
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ranked = sorted(zip(candidates, scores), key=lambda x: -x[1])
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return ranked[:top_k]
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```
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### Cohere Rerank API
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```python
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import cohere
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co = cohere.Client()
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def cohere_rerank(query: str, docs: list[str], top_n: int = 5):
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resp = co.rerank(
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model="rerank-v4.0",
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query=query, documents=docs, top_n=top_n,
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)
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return [(docs[r.index], r.relevance_score) for r in resp.results]
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```
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### Reciprocal Rank Fusion
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```python
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def rrf(rankings: list[list[str]], k: int = 60) -> list[str]:
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"""rankings: list of ranked doc-id lists from different retrievers."""
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scores: dict[str, float] = {}
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for ranking in rankings:
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for rank, doc_id in enumerate(ranking):
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scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank + 1)
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return sorted(scores, key=scores.get, reverse=True)
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```
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### Hybrid retrieve + rerank pipeline
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```python
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def hybrid_rag(query: str, k_first=100, k_final=5):
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bm25_hits = bm25.search(query, top_k=k_first)
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dense_hits = dense_index.search(query, top_k=k_first)
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fused = rrf([bm25_hits, dense_hits])[:k_first]
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docs = [load_doc(d) for d in fused]
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return rerank(query, docs, top_k=k_final)
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```
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### LLM-as-reranker (listwise)
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```python
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def llm_rerank(query: str, docs: list[str]) -> list[int]:
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numbered = "\n".join(f"[{i}] {d[:300]}" for i, d in enumerate(docs))
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resp = client.messages.create(
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model="claude-opus-4-7", max_tokens=200,
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messages=[{"role": "user", "content":
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f"Query: {query}\nDocs:\n{numbered}\nReturn comma-separated indices best→worst."}],
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).content[0].text
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return [int(x) for x in resp.strip().split(",")]
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```
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### ColBERT late-interaction (RAGatouille)
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```python
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from ragatouille import RAGPretrainedModel
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rag = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.5")
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rag.index(collection=docs, index_name="my-index")
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results = rag.search(query="foo", k=10)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Cost-sensitive RAG | BM25 + dense → RRF (no rerank) |
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| Quality > latency | Hybrid + cross-encoder rerank |
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| Highest quality | + LLM rerank top-20 → top-3 |
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| 거대 corpus (>10M docs) | ColBERT for second stage |
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| Multilingual | BGE-Reranker-v2.5 / Cohere rerank-v4 |
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**기본값**: BM25 + BGE-M3 dense → RRF top-100 → BGE-Reranker-v2.5 top-5.
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## 🔗 Graph
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- 부모: [[Information Retrieval]] · [[RAG]]
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- 변형: [[ColBERT]] · [[RRF]]
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- 응용: [[Semantic Search|Semantic-Search]] · [[Hybrid Search]]
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- Adjacent: [[BM25]] · [[Dense-Retrieval]] · [[Embeddings]]
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## 🤖 LLM 활용
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**언제**: high-stakes RAG (legal/medical/finance), small candidate set, listwise.
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**언제 X**: 매 latency budget < 100ms, 매 large k (cost), 매 simple FAQ chat (overkill).
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## ❌ 안티패턴
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- **Rerank without first-stage filter**: O(N) on full corpus → cost explosion.
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- **Cross-encoder for indexing**: 매 doc embeddings 의 매 cache 의 X — 매 query마다 recompute.
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- **Pointwise LLM rerank**: 매 doc 별 separate call → listwise보다 매 비싸고 inconsistent.
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- **Ignoring score calibration**: cross-encoder score는 매 not probability — threshold 매 dataset-specific tuning 필요.
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## 🧪 검증 / 중복
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- Verified (Cohere docs, BGE paper, ColBERT v2.5, RankGPT/RankZephyr).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full rewrite as canonical for cross-encoder/ColBERT/RRF/LLM rerank |
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