---
id: wiki-2026-0508-information-retrieval-ir
title: Information Retrieval (IR)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [IR, information retrieval, search engine, BM25, dense retrieval, hybrid search, RAG]
duplicate_of: none
source_trust_level: A
confidence_score: 0.97
verification_status: applied
tags: [search, ir, bm25, dense-retrieval, vector-search, rag, elasticsearch]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python / TypeScript
  framework: Elasticsearch / Vespa / FAISS / Pinecone
---

# Information Retrieval (IR)

## 매 한 줄
> **"매 query 의 의 의 매 relevant document 의 의 의 의 retrieve"**. 매 BM25 (sparse), 매 dense (vector), 매 hybrid. 매 modern: 매 dense + cross-encoder rerank, 매 RAG 의 backbone.

## 매 핵심

### 매 method
- **Sparse**: TF-IDF, BM25 (Okapi).
- **Dense**: embedding cosine.
- **Hybrid**: BM25 + dense.
- **Cross-encoder**: 매 rerank.
- **Learned sparse**: SPLADE.
- **ColBERT**: 매 late interaction.

### 매 metric
- **Precision@k, Recall@k**.
- **MRR** (Mean Reciprocal Rank).
- **NDCG** (graded relevance).
- **MAP** (Mean Average Precision).

### 매 응용
1. **Search engine**.
2. **RAG**.
3. **E-commerce search**.
4. **Q&A**.
5. **Code search**.

## 💻 패턴

### BM25 (rank_bm25)
```python
from rank_bm25 import BM25Okapi
docs = [d.split() for d in corpus]
bm25 = BM25Okapi(docs)

scores = bm25.get_scores('search query'.split())
top = sorted(zip(corpus, scores), key=lambda x: -x[1])[:5]
```

### Dense retrieval (FAISS)
```python
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer

m = SentenceTransformer('all-mpnet-base-v2')
corpus_emb = m.encode(corpus)
index = faiss.IndexFlatIP(corpus_emb.shape[1])
faiss.normalize_L2(corpus_emb)
index.add(corpus_emb)

query_emb = m.encode(['my query'])
faiss.normalize_L2(query_emb)
D, I = index.search(query_emb, k=5)
```

### Hybrid (RRF — Reciprocal Rank Fusion)
```python
def reciprocal_rank_fusion(rankings, k=60):
    """매 매 ranking source 의 fuse."""
    scores = {}
    for ranking in rankings:
        for rank, doc_id in enumerate(ranking, 1):
            scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank)
    return sorted(scores.items(), key=lambda x: -x[1])
```

### Cross-encoder rerank
```python
from sentence_transformers import CrossEncoder
reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

def rerank(query, candidates, k=5):
    pairs = [[query, c] for c in candidates]
    scores = reranker.predict(pairs)
    return [c for _, c in sorted(zip(scores, candidates), reverse=True)][:k]
```

### Elasticsearch (production)
```python
from elasticsearch import Elasticsearch
es = Elasticsearch()

# 매 hybrid search (BM25 + kNN)
res = es.search(index='docs', body={
    'query': {
        'bool': {
            'should': [
                {'multi_match': {'query': 'my query', 'fields': ['title', 'body']}},
                {'knn': {'field': 'embedding', 'query_vector': query_emb, 'k': 10, 'num_candidates': 100}},
            ]
        }
    },
    'size': 10,
})
```

### Vespa (streaming + ML)
```yaml
schema doc {
  document doc {
    field title type string { indexing: index | summary }
    field embedding type tensor<float>(x[768]) { indexing: attribute | index }
  }
  rank-profile hybrid {
    first-phase {
      expression: 0.5 * bm25(title) + 0.5 * closeness(field, embedding)
    }
    second-phase {
      expression: cross_encoder_score
    }
  }
}
```

### ColBERT (late interaction)
```python
# 매 매 token-level interaction
from colbert.infra import ColBERTConfig
from colbert import Searcher
config = ColBERTConfig(nbits=2, root='./experiments')
searcher = Searcher(index='index_name', config=config)
results = searcher.search(query='my query', k=10)
```

### MMR (diversity)
```python
def mmr(query_emb, candidates_emb, k=5, lam=0.5):
    selected = []
    selected_emb = []
    while len(selected) < k and candidates_emb.size > 0:
        scores = []
        for i, c_emb in enumerate(candidates_emb):
            rel = cosine(query_emb, c_emb)
            div = max((cosine(c_emb, s) for s in selected_emb), default=0)
            scores.append(lam * rel - (1 - lam) * div)
        best = np.argmax(scores)
        selected.append(best)
        selected_emb.append(candidates_emb[best])
        candidates_emb = np.delete(candidates_emb, best, axis=0)
    return selected
```

### Eval (MRR)
```python
def mrr(predictions, gold_doc_ids):
    """매 매 query 의 first relevant rank."""
    reciprocals = []
    for pred, gold in zip(predictions, gold_doc_ids):
        for rank, doc_id in enumerate(pred, 1):
            if doc_id in gold:
                reciprocals.append(1 / rank)
                break
        else:
            reciprocals.append(0)
    return np.mean(reciprocals)
```

### NDCG
```python
from sklearn.metrics import ndcg_score
def ndcg_at_k(predictions, relevance, k=10):
    return ndcg_score(relevance, predictions, k=k)
```

### Negative mining
```python
def hard_negative_mining(model, query, gold_doc, candidates):
    """매 매 hard negatives 의 의 train pair."""
    scores = model.predict([[query, c] for c in candidates])
    # 매 high-scoring 의 의 negatives
    return [c for s, c in sorted(zip(scores, candidates), reverse=True) if c != gold_doc][:5]
```

### Index update (incremental)
```python
def upsert_doc(index, doc_id, doc, model):
    emb = model.encode(doc)
    index.upsert(doc_id, doc, emb)
```

### LLM-as-judge for relevance
```python
def llm_judge_relevance(query, doc, llm):
    prompt = f"""Rate relevance 0-3.
Query: {query}
Doc: {doc}
Output: integer."""
    return int(llm.generate(prompt).strip())
```

### Query expansion
```python
def query_expand(query, llm):
    """매 LLM 의 의 query 의 expand."""
    return llm.generate(f"Generate 3 alternative phrasings: {query}").split('\n')
```

### RAG-fit chunking
```python
def chunk_for_rag(text, chunk_size=500, overlap=100):
    chunks = []
    i = 0
    while i < len(text):
        chunks.append(text[i:i + chunk_size])
        i += chunk_size - overlap
    return chunks
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Keyword | BM25 |
| Semantic | Dense |
| Best quality | Hybrid + cross-encoder |
| Web-scale | Vespa / Elasticsearch |
| Serverless | Pinecone / Weaviate |
| Open-source | FAISS + ES |
| RAG | Hybrid + chunk + rerank |

**기본값**: 매 hybrid (BM25 + dense) + 매 cross-encoder rerank + 매 MMR diversity + 매 NDCG eval.

## 🔗 Graph
- 부모: [[Search]] · [[NLP]]
- 변형: [[BM25]] · [[Dense-Retrieval]] · [[Hybrid-Search]]
- 응용: [[RAG]] · [[Search-Engine]]
- Adjacent: [[Elasticsearch]] · [[FAISS]] · [[ColBERT]]

## 🤖 LLM 활용
**언제**: 매 search. 매 RAG. 매 Q&A.
**언제 X**: 매 small / static dataset.

## ❌ 안티패턴
- **Dense-only**: 매 keyword 의 lose.
- **No rerank**: 매 final quality 의 ↓.
- **Expensive cross-encoder on full corpus**: 매 latency.
- **No diversity**: 매 echo.
- **Fixed chunk regardless content**: 매 break sentence.

## 🧪 검증 / 중복
- Verified (Robertson BM25, Karpukhin DPR 2020, Khattab ColBERT 2020).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — methods + 매 BM25 / FAISS / RRF / rerank / MMR / ColBERT code |
