refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-term-frequency-inverse-document-
+title: Term Frequency-Inverse Document Frequency
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [TF-IDF, tfidf, classic IR baseline]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.95
+verification_status: applied
+tags: [ir, nlp, retrieval, baseline, sklearn]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: scikit-learn
+---
+
+# Term Frequency-Inverse Document Frequency
+
+## 매 한 줄
+> **"매 term frequency × inverse document frequency — 매 word's importance 의 corpus context 매 weight"**. Karen Spärck Jones (1972) 의 IDF formalization. 2026 매 dense retrieval (BGE, E5) 매 default 매도 매 baseline + hybrid (BM25 + dense) 의 second stage 매 still ubiquitous.
+
+## 매 핵심
+
+### 매 Formula
+- **TF**: term의 doc 매 count (raw / log-normalized / frequency).
+- **IDF**: `log(N / df_t)` — 매 N corpus size, df_t = doc 매 t의 contains 의 count.
+- **TF-IDF**: TF(t,d) × IDF(t).
+- **L2 norm**: 매 cosine 의 prepare.
+
+### 매 Variants
+- Raw TF / log(1+TF) / sublinear.
+- IDF smoothing: `log((1+N)/(1+df)) + 1`.
+- BM25: 매 TF saturation + length normalization 의 add.
+
+### 매 응용
+1. Search baseline (sklearn + scikit-learn).
+2. Hybrid retrieval — 매 BM25 + dense embedding의 reciprocal-rank fuse.
+3. Feature extraction 매 classical ML (logistic regression, SVM).
+4. Keyword extraction (top-k tfidf).
+
+## 💻 패턴
+
+### sklearn TF-IDF
+```python
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+corpus = [
+    "the cat sat on the mat",
+    "the dog ate the bone",
+    "cats and dogs are pets",
+]
+vec = TfidfVectorizer(stop_words="english", sublinear_tf=True, ngram_range=(1, 2))
+X = vec.fit_transform(corpus)  # sparse (n_docs, n_features)
+print(vec.get_feature_names_out())
+```
+
+### Cosine search
+```python
+from sklearn.metrics.pairwise import cosine_similarity
+
+q = vec.transform(["pet animals"])
+sims = cosine_similarity(q, X).flatten()
+ranking = sims.argsort()[::-1]
+```
+
+### Manual IDF (educational)
+```python
+import math
+from collections import Counter
+
+def compute_idf(corpus_tokens):
+    N = len(corpus_tokens)
+    df = Counter()
+    for tokens in corpus_tokens:
+        for t in set(tokens):
+            df[t] += 1
+    return {t: math.log((N + 1) / (df_t + 1)) + 1 for t, df_t in df.items()}
+```
+
+### BM25 (preferred over plain TF-IDF for IR)
+```python
+from rank_bm25 import BM25Okapi
+
+tokenized = [doc.lower().split() for doc in corpus]
+bm25 = BM25Okapi(tokenized, k1=1.5, b=0.75)
+scores = bm25.get_scores("pet animals".split())
+```
+
+### Hybrid search (2026 standard)
+```python
+import numpy as np
+
+def rrf(rankings, k=60):
+    scores = {}
+    for ranking in rankings:
+        for rank, doc_id in enumerate(ranking):
+            scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank)
+    return sorted(scores, key=scores.get, reverse=True)
+
+bm25_top = bm25.get_top_n("query".split(), corpus, n=100)
+dense_top = dense_index.search("query", k=100)
+final = rrf([bm25_top, dense_top])[:10]
+```
+
+### Top keyword extraction
+```python
+def top_keywords(doc_idx, vec, X, k=10):
+    row = X[doc_idx].toarray().flatten()
+    feats = vec.get_feature_names_out()
+    top = np.argsort(-row)[:k]
+    return [(feats[i], row[i]) for i in top]
+```
+
+### Persistence
+```python
+import joblib
+joblib.dump((vec, X), "tfidf_index.joblib")
+vec, X = joblib.load("tfidf_index.joblib")
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 small corpus + interpretability | TF-IDF (sklearn) |
+| 매 medium corpus + better recall | BM25 |
+| 매 semantic / paraphrase | Dense (BGE-M3, E5) |
+| 매 production search | Hybrid (BM25 + dense + RRF) |
+| 매 keyword extraction / explanation | Plain TF-IDF top-k |
+
+**기본값**: 매 BM25 baseline → 매 hybrid + reranker (cross-encoder) for 2026 production.
+
+## 🔗 Graph
+- 부모: [[Information Retrieval]]
+- 변형: [[BM25]]
+- 응용: [[Search Engine]] · [[RAG]]
+- Adjacent: [[Dense Retrieval]]
+
+## 🤖 LLM 활용
+**언제**: 매 small corpus 매 lookup, 매 RAG 의 sparse channel, 매 explainability ("matched on 'mat', 'cat'").
+**언제 X**: 매 paraphrase / multilingual 매 weak — 매 dense 의 prefer.
+
+## ❌ 안티패턴
+- **TF-IDF 만으로 production search**: 매 paraphrase miss.
+- **No stopword / lowercasing**: 매 noisy features.
+- **Same vectorizer not pickled**: 매 train/serve mismatch.
+- **No length normalization**: 매 long docs 의 unfair advantage (use BM25 또는 normalize).
+
+## 🧪 검증 / 중복
+- Verified (Spärck Jones 1972; Manning IR Book Ch.6; sklearn TfidfVectorizer 2026).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — TF-IDF formula + sklearn + BM25 + hybrid RRF |