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-knowledge-synthesis
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title: Knowledge Synthesis
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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: [지식 종합, Synthesis, Information Integration]
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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: [research, knowledge, synthesis, methodology, llm]
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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: rag
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---
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# Knowledge Synthesis
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## 매 한 줄
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> **"매 synthesis = 차이의 통합"**. 여러 source 의 fragmented knowledge 를 단일 coherent representation 으로 통합하는 process. 2026 LLM 시대에 RAG + structured extraction + cross-document reasoning 이 표준 toolchain.
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## 매 핵심
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### 매 5-step pipeline
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1. **Acquisition**: search / scrape / corpus collection.
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2. **Extraction**: structured fact / claim 추출.
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3. **Alignment**: same entity / concept matching across sources.
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4. **Integration**: conflict resolution + gap filling.
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5. **Presentation**: report / map / model.
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### 매 synthesis types
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- **Aggregative**: meta-analysis, systematic review.
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- **Interpretive**: thematic synthesis, narrative review.
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- **Generative**: 새 framework / theory 제시.
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### 매 응용
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1. Systematic literature review.
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2. Competitive intelligence.
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3. Wiki / knowledge graph build-out.
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4. Investigation reporting.
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## 💻 패턴
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### RAG synthesis pipeline
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```python
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from anthropic import Anthropic
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import chromadb
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client = Anthropic()
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db = chromadb.Client().create_collection("docs")
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def synthesize(question: str, k=8):
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docs = db.query(query_texts=[question], n_results=k)["documents"][0]
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context = "\n---\n".join(docs)
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return client.messages.create(
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model="claude-opus-4-7",
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max_tokens=4096,
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system="Synthesize the sources. Cite [N]. Note conflicts.",
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messages=[{"role": "user", "content": f"Q: {question}\nSources:\n{context}"}],
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).content[0].text
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```
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### Structured extraction
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```python
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from pydantic import BaseModel
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class Claim(BaseModel):
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statement: str
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source: str
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confidence: float
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contradicts: list[str] = []
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def extract_claims(text: str, source: str) -> list[Claim]:
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=2048,
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system="Extract atomic claims as JSON list of {statement, confidence}.",
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messages=[{"role": "user", "content": text}],
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)
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return [Claim(source=source, **c) for c in json.loads(resp.content[0].text)]
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```
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### Entity alignment
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```python
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from sentence_transformers import SentenceTransformer
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import numpy as np
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model = SentenceTransformer("all-MiniLM-L6-v2")
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def align(entities_a, entities_b, threshold=0.85):
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emb_a = model.encode(entities_a)
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emb_b = model.encode(entities_b)
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sim = emb_a @ emb_b.T / (np.linalg.norm(emb_a, axis=1)[:, None] *
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np.linalg.norm(emb_b, axis=1)[None, :])
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pairs = []
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for i, j in zip(*np.where(sim > threshold)):
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pairs.append((entities_a[i], entities_b[j], sim[i, j]))
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return pairs
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```
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### Conflict detection
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```python
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def detect_conflicts(claims: list[Claim]) -> list[tuple]:
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pairs = []
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for i, c1 in enumerate(claims):
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for c2 in claims[i+1:]:
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resp = client.messages.create(
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model="claude-haiku-4-5",
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max_tokens=10,
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messages=[{"role": "user",
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"content": f"Contradict?\nA: {c1.statement}\nB: {c2.statement}\nYes/No"}],
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)
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if "yes" in resp.content[0].text.lower():
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pairs.append((c1, c2))
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return pairs
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```
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### Hierarchical summarization
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```python
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def hier_summarize(docs: list[str], chunk=4) -> str:
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if len(docs) <= 1:
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return docs[0] if docs else ""
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summaries = []
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for i in range(0, len(docs), chunk):
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merged = "\n".join(docs[i:i+chunk])
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s = spawn_summarize(merged)
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summaries.append(s)
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return hier_summarize(summaries, chunk)
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```
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### Citation graph build
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```python
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import networkx as nx
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def build_graph(claims: list[Claim]) -> nx.DiGraph:
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g = nx.DiGraph()
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for c in claims:
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g.add_node(c.statement, source=c.source, conf=c.confidence)
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for contra in c.contradicts:
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g.add_edge(c.statement, contra, type="contradicts")
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return g
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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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| 수십 개 sources | RAG + hierarchical summarize |
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| Conflicting claims | Structured extraction + LLM judge |
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| Novel framework needed | Generative synthesis |
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| Quantitative pooling | Meta-analysis stats |
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**기본값**: RAG + structured extraction + claim graph.
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## 🔗 Graph
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- 부모: [[Research Methodology]] · [[Information Retrieval]]
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- 응용: [[RAG]] · [[Knowledge Graph]]
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## 🤖 LLM 활용
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**언제**: literature review, competitive analysis, contradictory source reconciliation.
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**언제 X**: domain-novel concept invention without grounding — hallucination 위험.
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## ❌ 안티패턴
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- **Cherry-picking**: 매 confirming source 만 select.
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- **Source-blind synthesis**: citation 없이 claim 통합 → traceability 상실.
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- **Premature consensus**: conflict 무시하고 단일 narrative.
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- **Hallucinated alignment**: LLM 이 entity 임의 동일시.
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## 🧪 검증 / 중복
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- Verified (PRISMA 2020, Cochrane Handbook 6.4, Anthropic RAG cookbook).
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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 — 5-step pipeline + RAG/extraction 패턴 |
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