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DOCS/skills/companyx-rag/SKILL.md
happybell80 0a967ce7c1 fix: SKILL.md를 3중 검색 + CompanyXRAGOutput 기준으로 정합화 
- 검색 경로에 벡터+키워드+그래프 RRF 합산 명시
- Storage에 AGE 추가
- Question Types 4종 섹션 추가
- Response Shape를 Pydantic 모델 기준으로 갱신

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-21 13:04:53 +09:00

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name, description
name description
companyx-rag Use this skill when a Company X user asks for answers grounded in Company X internal documents, especially for evidence checks, internal-material summaries, program explanations, or document-based fact verification.

Company X RAG

Use this skill only for Company X users and only when the answer should be grounded in Company X internal documents.

Trigger

  • The user is authenticated as a Company X member.
  • The question asks for 근거, 내부 문서, 자료 기준, 문서 기준, or similar evidence-first phrasing.
  • The topic is a Company X internal program, partner, MOU, proposal, or operational fact.

Do

  • Search Company X internal documents before using general web knowledge.
  • Answer in this order:
    1. direct answer
    2. evidence documents
    3. short evidence summary
  • If documents conflict, say they conflict.
  • If documents are missing, say the evidence is insufficient.

Do Not

  • Do not guess missing internal facts.
  • Do not answer from general web knowledge first when the user expects internal grounding.
  • Do not dump long raw excerpts without a short explanation.
  • Do not expose Company X internal grounding to non-Company X users.

Current Operating Path

  • Team boundary: 79441171-3951-4870-beb8-916d07fe8be5
  • Retrieval service: skill-rag-file (/api/search, search_mode=hybrid)
  • 검색 경로: 벡터(PGVector cosine) + 키워드(TSVECTOR + GIN) + 그래프(Apache AGE) → RRF 합산
  • Embedding: Gemini Embedding 2 (gemini-embedding-2-preview), 768d, skill-embedding 게이트웨이 경유
  • Storage: PostgreSQL (pgvector + AGE) 단일 운영. ChromaDB는 레거시(운영 비사용).
  • 청킹: 텍스트 추출 후 문자 단위 분할 (chunk_size=1000, overlap=200)
  • 인덱싱 대상: 200개 파일 (latest_200_companyx.txt), DB 기준 team_document 1,172건 / team_document_chunk 3,095건
  • NAS 원본: /mnt/nas/workspace/6.Company X (53,249 파일)

Question Types

  • fact_check: 사실 확인 (기본값) — "근거 있어?", "협력 관계야?"
  • explanatory: 설명 요청 — "뭐야?", "설명해줘"
  • quantitative: 수치 질문 — "몇 개야?", "얼마나?"
  • recap: 재정리 — "다시 정리해줘", "문서명만"

Response Shape

  • Pydantic 모델: CompanyXRAGOutput
    • direct_answer: 1~3문장 답변 (근거 부족 시 빈 문자열)
    • evidence_docs: 근거 문서 파일명 목록
    • failure_reason: 답변 불가 사유 (null 또는 "문서 없음", "단정 불가" 등)
  • 사용자 응답 구조:
    1. Direct answer: 네/아니요/현재는 단정 불가
    2. Evidence section: 문서명, snippet 요약, chunk/page, 점수(score, vec, kw)
    3. Limitation section (필요 시): 문서 없음, 문서 간 불일치, 최신 집계 미확인