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이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
123 lines
3.7 KiB
Markdown
123 lines
3.7 KiB
Markdown
---
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id: wiki-2026-0508-connect-ai-documentation
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title: Connect AI Documentation
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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: [ConnectAI documentation, AI agent documentation, system docs, SOP, API spec]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.83
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verification_status: applied
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tags: [documentation, ai-agent, sop, api-spec, knowledge-base, project-internal]
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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: documentation
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applicable_to: [Internal AI Agent Project, SOP, Onboarding]
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---
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# Connect AI Documentation
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## 매 한 줄
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> **"매 complex AI 의 readable 의 manual"**. 매 system architecture + SOP + API spec 의 unified. 매 modern: 매 living doc + 매 RAG 의 query-able + 매 AI 의 self-update. 매 [[Codebase_Onboarding_Guide]] 와 의 align.
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## 매 핵심 component
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1. **System Architecture**: 매 C4 model.
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2. **SOP**: 매 step-by-step operation.
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3. **API Spec**: 매 OpenAPI / GraphQL schema.
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4. **ADR**: 매 decision record.
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5. **Glossary**: 매 term.
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6. **Runbook**: 매 incident response.
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7. **Onboarding**: 매 new dev.
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## 매 living doc 의 element
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- **CI 의 sync**: 매 stale 의 detect.
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- **RAG-queryable**: 매 LLM 의 answer.
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- **Code → doc**: 매 generate from source.
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- **Multi-format**: markdown + diagram + interactive.
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## 매 best practice
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- 매 README first.
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- 매 Diátaxis (tutorial / how-to / reference / explanation).
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- 매 minimum viable doc.
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- 매 example > prose.
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- 매 versioning.
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- 매 link > duplicate.
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## 💻 패턴
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### Doc-as-code (Mintlify / Docusaurus / Astro)
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```
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docs/
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├── index.mdx
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├── architecture.mdx (C4)
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├── runbook/
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│ ├── incident-database.mdx
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│ └── incident-deploy.mdx
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├── api/
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│ └── (OpenAPI generated)
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└── onboarding.mdx
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```
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### Living doc (CI sync check)
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```yaml
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- name: Doc drift check
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run: |
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npm run extract-types
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git diff --exit-code docs/api-types.json || \
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(echo "Doc out of sync — regenerate" && exit 1)
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```
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### RAG over docs
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```python
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from langchain.vectorstores import Chroma
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
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chunks = splitter.split_documents(load_md_files('docs/'))
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vectordb = Chroma.from_documents(chunks, embeddings, persist_directory='./doc-rag')
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def ask_doc(question):
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relevant = vectordb.similarity_search(question, k=5)
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return llm.generate(f"Use these docs:\n{relevant}\n\nQuestion: {question}")
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```
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### OpenAPI generation
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```ts
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// 매 zod-to-openapi
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import { extendZodWithOpenApi } from '@asteasolutions/zod-to-openapi';
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extendZodWithOpenApi(z);
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const userSchema = z.object({
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id: z.string().openapi({ example: 'uuid-...' }),
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email: z.string().email().openapi({ example: 'user@example.com' }),
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}).openapi('User');
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```
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## 🔗 Graph
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- 응용: [[Codebase_Onboarding_Guide]] · [[Codebase_Maps_and_Interactive_Tours]] · [[ADR]] · [[C4_Model]]
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- Adjacent: [[Asset-Specific-Knowledge]] · [[Software Architecture Styles]]
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## 🤖 LLM 활용
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**언제**: 매 internal AI project 의 doc design.
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**언제 X**: 매 single-file script.
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## ❌ 안티패턴
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- **Stale doc**: 매 CI 의 sync 의 X.
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- **Single huge file**: 매 navigate 의 X.
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- **No examples**: 매 pure prose.
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- **Code 의 doc 의 mismatch**: 매 source-of-truth 의 ambiguous.
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## 🧪 검증 / 중복
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- Verified (Diátaxis, doc-as-code best practice).
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- 신뢰도 B.
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- Related: [[Codebase_Onboarding_Guide]] · [[Codebase_Maps_and_Interactive_Tours]] · [[Asset-Specific-Knowledge]].
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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 — components + 매 doc-as-code / RAG / OpenAPI code |
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