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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Antigravity Agent
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---
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id: wiki-2026-0508-semantic-search
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title: Semantic Search
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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: [Vector Search, Dense Retrieval, Neural Search, Semantic Search with AI]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.93
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verification_status: applied
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tags: [search, retrieval, embeddings, vector-db, rag]
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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: faiss
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---
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# Semantic Search
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## 매 한 줄
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> **"매 query → embedding → ANN nearest neighbors in vector space"**. 매 BM25 매 lexical 한계를 dense retrieval (DPR, ColBERT) 매 극복. 매 2026 production: hybrid (BM25 + dense + reranker), 매 모범: OpenAI text-embedding-3-large, Cohere v3, Voyage-3, BGE-M3, Jina-v3.
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## 매 핵심
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### 매 Pipeline
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1. **Index time**: doc → chunk → embed → vector DB.
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2. **Query time**: query → embed → ANN search → (rerank) → results.
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3. **Hybrid**: BM25 score + dense score → RRF or weighted.
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4. **Rerank**: cross-encoder on top-100 → top-10 (Cohere Rerank, BGE-Reranker).
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### 매 Embedding models (2026)
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- **OpenAI text-embedding-3-large** (3072d, MRL truncatable).
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- **Cohere embed-v3** (multilingual, dot-product).
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- **Voyage-3** (state-of-art retrieval).
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- **BGE-M3** (open, multi-vector, sparse+dense).
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- **Jina-v3** (8k context, MRL).
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- **NV-Embed-v2** (NVIDIA, MTEB top).
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### 매 ANN algorithms
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- **HNSW** (graph): 매 default, fast, high recall.
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- **IVF-PQ** (Faiss): 매 huge scale, compressed.
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- **DiskANN**: 매 on-disk billion-scale.
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- **ScaNN** (Google): 매 best at fixed memory.
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### 매 Vector DBs
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- **Pinecone** (managed).
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- **Weaviate** (open + hybrid built-in).
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- **Qdrant** (Rust, fast).
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- **Milvus** (large-scale).
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- **pgvector** (Postgres).
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- **LanceDB** (embedded, columnar).
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- **Turbopuffer** (serverless 2024+).
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### 매 응용
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1. RAG knowledge retrieval.
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2. Code search (Cursor, Sourcegraph).
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3. E-commerce / product search.
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4. Multimodal (CLIP image+text).
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## 💻 패턴
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### Basic dense retrieval
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```python
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from openai import OpenAI
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import numpy as np
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import faiss
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client = OpenAI()
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def embed(texts):
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r = client.embeddings.create(model="text-embedding-3-large", input=texts)
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return np.array([d.embedding for d in r.data], dtype="float32")
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docs = ["Doc 1 text...", "Doc 2 text...", "..."]
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doc_vecs = embed(docs)
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index = faiss.IndexHNSWFlat(3072, 32)
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faiss.normalize_L2(doc_vecs)
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index.add(doc_vecs)
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q_vec = embed(["What is X?"])
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faiss.normalize_L2(q_vec)
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D, I = index.search(q_vec, 10)
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print([docs[i] for i in I[0]])
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```
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### Hybrid (BM25 + dense) with RRF
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```python
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from rank_bm25 import BM25Okapi
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bm25 = BM25Okapi([d.split() for d in docs])
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def rrf(rankings, k=60):
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scores = {}
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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)
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return sorted(scores.items(), key=lambda x: -x[1])
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def hybrid_search(query, k=10):
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bm25_top = np.argsort(-bm25.get_scores(query.split()))[:50]
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q_vec = embed([query]); faiss.normalize_L2(q_vec)
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_, dense_top = index.search(q_vec, 50)
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fused = rrf([bm25_top.tolist(), dense_top[0].tolist()])
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return [docs[i] for i, _ in fused[:k]]
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```
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### Cross-encoder reranking
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```python
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import cohere
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co = cohere.Client()
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def rerank(query, candidates, top_n=10):
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r = co.rerank(query=query, documents=candidates,
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model="rerank-english-v3.0", top_n=top_n)
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return [candidates[res.index] for res in r.results]
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```
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### Chunking with overlap
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```python
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def chunk_text(text, size=500, overlap=50):
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words = text.split()
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chunks = []
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for i in range(0, len(words), size - overlap):
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chunk = " ".join(words[i:i+size])
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chunks.append(chunk)
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return chunks
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# 매 better: 매 semantic chunker (매 paragraph + heading aware)
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50,
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separators=["\n\n", "\n", ". ", " "])
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```
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### MRL truncation (Matryoshka)
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```python
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# text-embedding-3-large: 3072d, truncatable to 256/512/1024
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def embed_mrl(text, dim=512):
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full = embed([text])[0]
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truncated = full[:dim]
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return truncated / np.linalg.norm(truncated)
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# 매 6× memory savings, 매 ~95% recall.
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```
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### ColBERT (multi-vector late interaction)
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```python
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from colbert.modeling.colbert import ColBERT
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# 매 token-level vectors per query+doc; 매 max-sim per query token then sum.
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def colbert_score(query_vecs, doc_vecs):
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# query_vecs: [Q, d], doc_vecs: [D, d]
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sim = query_vecs @ doc_vecs.T # [Q, D]
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return sim.max(axis=1).sum() # 매 sum of per-token max
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```
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### pgvector hybrid (production)
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```sql
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CREATE TABLE docs (id bigserial, content text, embedding vector(1536),
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tsv tsvector GENERATED ALWAYS AS (to_tsvector('english', content)) STORED);
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CREATE INDEX ON docs USING hnsw (embedding vector_cosine_ops);
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CREATE INDEX ON docs USING gin (tsv);
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-- Hybrid query
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WITH dense AS (
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SELECT id, 1 - (embedding <=> $1) AS score FROM docs ORDER BY embedding <=> $1 LIMIT 50
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), sparse AS (
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SELECT id, ts_rank_cd(tsv, websearch_to_tsquery($2)) AS score
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FROM docs WHERE tsv @@ websearch_to_tsquery($2) LIMIT 50
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)
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SELECT id, COALESCE(d.score, 0) * 0.7 + COALESCE(s.score, 0) * 0.3 AS score
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FROM dense d FULL OUTER JOIN sparse s USING (id)
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ORDER BY score DESC LIMIT 10;
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```
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### Multimodal CLIP search
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```python
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import torch
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from transformers import CLIPModel, CLIPProcessor
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model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
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proc = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
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def embed_image(img):
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with torch.no_grad():
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return model.get_image_features(**proc(images=img, return_tensors="pt"))
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def embed_text(t):
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with torch.no_grad():
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return model.get_text_features(**proc(text=t, return_tensors="pt"))
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# 매 same vector space → cross-modal search.
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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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| Quick prototype | 매 OpenAI embeddings + Faiss/LanceDB |
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| Production RAG | 매 hybrid (BM25 + dense) + Cohere rerank |
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| Self-host open | 매 BGE-M3 + Qdrant + BGE-reranker |
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| Multilingual | 매 BGE-M3, Cohere multilingual, embed-v4 |
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| Code search | 매 Voyage-code-3 또는 jina-code-v2 |
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| Multimodal | 매 CLIP / SigLIP / Jina-CLIP |
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**기본값**: 매 production RAG → hybrid (BM25 + dense) + cross-encoder rerank.
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## 🔗 Graph
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- 부모: [[Information Retrieval]] · [[Embeddings]]
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- 변형: [[Dense Retrieval]] · [[Sparse Retrieval]] · [[Information-Retrieval-IR|Hybrid Search]] · [[ColBERT]]
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- 응용: [[RAG]] · [[Recommender Systems]]
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- Adjacent: [[BM25]] · [[Cross-Encoder Reranking]] · [[CLIP]]
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## 🤖 LLM 활용
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**언제**: 매 RAG retrieval, 매 semantic deduplication, 매 cross-lingual search, 매 recommendation.
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**언제 X**: 매 exact-match (use BM25), 매 small corpus (<1k docs — 매 LLM-direct 가 simpler), 매 high-precision regex needs.
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## ❌ 안티패턴
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- **Dense-only**: 매 BM25 매 still wins on rare terms / proper nouns — 매 hybrid.
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- **No reranker**: 매 top-10 quality 매 leaves 30% on table.
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- **Bad chunking**: 매 fixed-size mid-sentence — 매 use semantic / heading-aware.
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- **No metadata filter**: 매 hybrid filter (date/source) before vector search.
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- **Cosine without normalize**: 매 silent bug — 매 always normalize L2.
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## 🧪 검증 / 중복
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- Verified (Karpukhin DPR 2020, Khattab ColBERT 2020, MTEB benchmark, Cohere Rerank docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — hybrid, MRL, ColBERT, pgvector, multimodal |
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## 🛠️ 적용 사례 (Applied in summary)
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<!-- CODE-GROUNDING:START -->
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### 🔎 코드베이스 근거 (자동 추출 — E:\Wiki 레포)
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**실제 구현/사용 위치:**
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- `connectai/src/features/projectChronicle/guardPrompt.ts:57` — [Omitted long matching line]
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_자동 생성: code_grounding.mjs · 재실행 시 갱신됨_
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<!-- CODE-GROUNDING:END -->
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