feat: AI 서비스 및 AI 어시스턴트 전용 페이지 추가
- ai-service: Ollama 기반 AI 서비스 (분류, 시맨틱 검색, RAG Q&A, 패턴 분석) - AI 어시스턴트 페이지: 채팅형 Q&A, 시맨틱 검색, 패턴 분석, 분류 테스트 - 권한 시스템에 ai_assistant 페이지 등록 (기본 비활성) - 기존 페이지에 AI 기능 통합 (대시보드, 수신함, 관리함) - docker-compose, gateway, nginx 설정 업데이트 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Hyungi Ahn
0
ai-service/services/__init__.py
Normal file
0
ai-service/services/__init__.py
Normal file
60
ai-service/services/classification_service.py
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60
ai-service/services/classification_service.py
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@@ -0,0 +1,60 @@
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import json
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from services.ollama_client import ollama_client
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from config import settings
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CLASSIFY_PROMPT_PATH = "prompts/classify_issue.txt"
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SUMMARIZE_PROMPT_PATH = "prompts/summarize_issue.txt"
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def _load_prompt(path: str) -> str:
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with open(path, "r", encoding="utf-8") as f:
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return f.read()
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async def classify_issue(description: str, detail_notes: str = "") -> dict:
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template = _load_prompt(CLASSIFY_PROMPT_PATH)
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prompt = template.format(
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description=description or "",
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detail_notes=detail_notes or "",
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)
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raw = await ollama_client.generate_text(prompt)
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try:
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start = raw.find("{")
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end = raw.rfind("}") + 1
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if start >= 0 and end > start:
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return json.loads(raw[start:end])
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except json.JSONDecodeError:
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pass
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return {"raw_response": raw, "parse_error": True}
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async def summarize_issue(
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description: str, detail_notes: str = "", solution: str = ""
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) -> dict:
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template = _load_prompt(SUMMARIZE_PROMPT_PATH)
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prompt = template.format(
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description=description or "",
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detail_notes=detail_notes or "",
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solution=solution or "",
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)
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raw = await ollama_client.generate_text(prompt)
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try:
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start = raw.find("{")
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end = raw.rfind("}") + 1
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if start >= 0 and end > start:
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return json.loads(raw[start:end])
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except json.JSONDecodeError:
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pass
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return {"summary": raw.strip()}
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async def classify_and_summarize(
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description: str, detail_notes: str = ""
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) -> dict:
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classification = await classify_issue(description, detail_notes)
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summary_result = await summarize_issue(description, detail_notes)
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return {
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"classification": classification,
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"summary": summary_result.get("summary", ""),
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}
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97
ai-service/services/db_client.py
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97
ai-service/services/db_client.py
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@@ -0,0 +1,97 @@
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from urllib.parse import quote_plus
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from sqlalchemy import create_engine, text
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from config import settings
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def get_engine():
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password = quote_plus(settings.DB_PASSWORD)
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url = (
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f"mysql+pymysql://{settings.DB_USER}:{password}"
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f"@{settings.DB_HOST}:{settings.DB_PORT}/{settings.DB_NAME}"
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)
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return create_engine(url, pool_pre_ping=True, pool_size=5)
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engine = get_engine()
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def get_all_issues() -> list[dict]:
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with engine.connect() as conn:
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result = conn.execute(
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text(
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"SELECT id, category, description, detail_notes, "
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"final_description, final_category, solution, "
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"management_comment, cause_detail, project_id, "
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"review_status, report_date, responsible_department, "
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"location_info "
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"FROM qc_issues ORDER BY id"
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)
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)
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return [dict(row._mapping) for row in result]
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def get_issue_by_id(issue_id: int) -> dict | None:
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with engine.connect() as conn:
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result = conn.execute(
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text(
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"SELECT id, category, description, detail_notes, "
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"final_description, final_category, solution, "
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"management_comment, cause_detail, project_id, "
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"review_status, report_date, responsible_department, "
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"location_info "
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"FROM qc_issues WHERE id = :id"
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),
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{"id": issue_id},
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)
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row = result.fetchone()
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return dict(row._mapping) if row else None
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def get_issues_since(last_id: int) -> list[dict]:
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with engine.connect() as conn:
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result = conn.execute(
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text(
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"SELECT id, category, description, detail_notes, "
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"final_description, final_category, solution, "
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"management_comment, cause_detail, project_id, "
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"review_status, report_date, responsible_department, "
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"location_info "
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"FROM qc_issues WHERE id > :last_id ORDER BY id"
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),
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{"last_id": last_id},
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)
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return [dict(row._mapping) for row in result]
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def get_daily_qc_stats(date_str: str) -> dict:
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with engine.connect() as conn:
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result = conn.execute(
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text(
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"SELECT "
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" COUNT(*) as total, "
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" SUM(CASE WHEN DATE(report_date) = :d THEN 1 ELSE 0 END) as new_today, "
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" SUM(CASE WHEN review_status = 'in_progress' THEN 1 ELSE 0 END) as in_progress, "
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" SUM(CASE WHEN review_status = 'completed' THEN 1 ELSE 0 END) as completed, "
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" SUM(CASE WHEN review_status = 'pending_review' THEN 1 ELSE 0 END) as pending "
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"FROM qc_issues"
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),
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{"d": date_str},
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)
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row = result.fetchone()
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return dict(row._mapping) if row else {}
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def get_issues_for_date(date_str: str) -> list[dict]:
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with engine.connect() as conn:
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result = conn.execute(
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text(
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"SELECT id, category, description, detail_notes, "
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"review_status, responsible_department, solution "
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"FROM qc_issues "
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"WHERE DATE(report_date) = :d "
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"ORDER BY id"
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),
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{"d": date_str},
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)
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return [dict(row._mapping) for row in result]
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144
ai-service/services/embedding_service.py
Normal file
144
ai-service/services/embedding_service.py
Normal file
@@ -0,0 +1,144 @@
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from services.ollama_client import ollama_client
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from db.vector_store import vector_store
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from db.metadata_store import metadata_store
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from services.db_client import get_all_issues, get_issue_by_id, get_issues_since
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def build_document_text(issue: dict) -> str:
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parts = []
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if issue.get("description"):
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parts.append(issue["description"])
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if issue.get("final_description"):
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parts.append(issue["final_description"])
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if issue.get("detail_notes"):
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parts.append(issue["detail_notes"])
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if issue.get("solution"):
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parts.append(f"해결: {issue['solution']}")
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if issue.get("management_comment"):
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parts.append(f"의견: {issue['management_comment']}")
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if issue.get("cause_detail"):
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parts.append(f"원인: {issue['cause_detail']}")
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return " ".join(parts)
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def build_metadata(issue: dict) -> dict:
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meta = {"issue_id": issue["id"]}
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for key in [
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"category", "project_id", "review_status",
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"responsible_department", "location_info",
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]:
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val = issue.get(key)
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if val is not None:
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meta[key] = str(val)
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rd = issue.get("report_date")
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if rd:
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meta["report_date"] = str(rd)[:10]
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meta["has_solution"] = "true" if issue.get("solution") else "false"
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return meta
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async def sync_all_issues() -> dict:
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issues = get_all_issues()
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synced = 0
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skipped = 0
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for issue in issues:
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doc_text = build_document_text(issue)
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if not doc_text.strip():
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skipped += 1
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continue
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try:
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embedding = await ollama_client.generate_embedding(doc_text)
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vector_store.upsert(
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doc_id=f"issue_{issue['id']}",
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document=doc_text,
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embedding=embedding,
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metadata=build_metadata(issue),
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)
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synced += 1
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except Exception as e:
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skipped += 1
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if issues:
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max_id = max(i["id"] for i in issues)
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metadata_store.set_last_synced_id(max_id)
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return {"synced": synced, "skipped": skipped, "total": len(issues)}
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async def sync_single_issue(issue_id: int) -> dict:
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issue = get_issue_by_id(issue_id)
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if not issue:
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return {"status": "not_found"}
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doc_text = build_document_text(issue)
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if not doc_text.strip():
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return {"status": "empty_text"}
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embedding = await ollama_client.generate_embedding(doc_text)
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vector_store.upsert(
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doc_id=f"issue_{issue['id']}",
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document=doc_text,
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embedding=embedding,
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metadata=build_metadata(issue),
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)
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return {"status": "synced", "issue_id": issue_id}
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async def sync_incremental() -> dict:
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last_id = metadata_store.get_last_synced_id()
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issues = get_issues_since(last_id)
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synced = 0
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for issue in issues:
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doc_text = build_document_text(issue)
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if not doc_text.strip():
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continue
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try:
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embedding = await ollama_client.generate_embedding(doc_text)
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vector_store.upsert(
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doc_id=f"issue_{issue['id']}",
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document=doc_text,
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embedding=embedding,
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metadata=build_metadata(issue),
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)
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synced += 1
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except Exception:
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pass
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if issues:
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max_id = max(i["id"] for i in issues)
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metadata_store.set_last_synced_id(max_id)
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return {"synced": synced, "new_issues": len(issues)}
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async def search_similar_by_id(issue_id: int, n_results: int = 5) -> list[dict]:
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issue = get_issue_by_id(issue_id)
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if not issue:
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return []
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doc_text = build_document_text(issue)
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if not doc_text.strip():
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return []
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embedding = await ollama_client.generate_embedding(doc_text)
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results = vector_store.query(
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embedding=embedding,
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n_results=n_results + 1,
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)
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# exclude self
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filtered = []
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for r in results:
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if r["id"] != f"issue_{issue_id}":
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filtered.append(r)
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return filtered[:n_results]
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async def search_similar_by_text(query: str, n_results: int = 5, filters: dict = None) -> list[dict]:
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embedding = await ollama_client.generate_embedding(query)
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where = None
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if filters:
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conditions = []
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for k, v in filters.items():
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if v is not None:
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conditions.append({k: str(v)})
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if len(conditions) == 1:
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where = conditions[0]
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elif len(conditions) > 1:
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where = {"$and": conditions}
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return vector_store.query(
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embedding=embedding,
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n_results=n_results,
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where=where,
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)
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57
ai-service/services/ollama_client.py
Normal file
57
ai-service/services/ollama_client.py
Normal file
@@ -0,0 +1,57 @@
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import httpx
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from config import settings
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class OllamaClient:
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def __init__(self):
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self.base_url = settings.OLLAMA_BASE_URL
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self.timeout = httpx.Timeout(float(settings.OLLAMA_TIMEOUT), connect=10.0)
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async def generate_embedding(self, text: str) -> list[float]:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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response = await client.post(
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f"{self.base_url}/api/embeddings",
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json={"model": settings.OLLAMA_EMBED_MODEL, "prompt": text},
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)
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response.raise_for_status()
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return response.json()["embedding"]
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async def batch_embeddings(self, texts: list[str]) -> list[list[float]]:
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results = []
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for text in texts:
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emb = await self.generate_embedding(text)
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results.append(emb)
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return results
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async def generate_text(self, prompt: str, system: str = None) -> str:
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messages = []
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if system:
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messages.append({"role": "system", "content": system})
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messages.append({"role": "user", "content": prompt})
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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response = await client.post(
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f"{self.base_url}/api/chat",
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json={
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"model": settings.OLLAMA_TEXT_MODEL,
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"messages": messages,
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"stream": False,
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"options": {"temperature": 0.3, "num_predict": 2048},
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},
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)
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response.raise_for_status()
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return response.json()["message"]["content"]
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async def check_health(self) -> dict:
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try:
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async with httpx.AsyncClient(timeout=httpx.Timeout(5.0)) as client:
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response = await client.get(f"{self.base_url}/api/tags")
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models = response.json().get("models", [])
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return {
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"status": "connected",
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"models": [m["name"] for m in models],
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}
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except Exception:
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return {"status": "disconnected"}
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ollama_client = OllamaClient()
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164
ai-service/services/rag_service.py
Normal file
164
ai-service/services/rag_service.py
Normal file
@@ -0,0 +1,164 @@
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from services.ollama_client import ollama_client
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from services.embedding_service import search_similar_by_text, build_document_text
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from services.db_client import get_issue_by_id
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def _load_prompt(path: str) -> str:
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with open(path, "r", encoding="utf-8") as f:
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return f.read()
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def _format_retrieved_issues(results: list[dict]) -> str:
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if not results:
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return "관련 과거 사례가 없습니다."
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lines = []
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for i, r in enumerate(results, 1):
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meta = r.get("metadata", {})
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similarity = round(r.get("similarity", 0) * 100)
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doc = (r.get("document", ""))[:500]
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cat = meta.get("category", "")
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dept = meta.get("responsible_department", "")
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status = meta.get("review_status", "")
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has_sol = meta.get("has_solution", "false")
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date = meta.get("report_date", "")
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issue_id = meta.get("issue_id", r["id"])
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lines.append(
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f"[사례 {i}] No.{issue_id} (유사도 {similarity}%)\n"
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f" 분류: {cat} | 부서: {dept} | 상태: {status} | 날짜: {date} | 해결여부: {'O' if has_sol == 'true' else 'X'}\n"
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f" 내용: {doc}"
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)
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return "\n\n".join(lines)
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|
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async def rag_suggest_solution(issue_id: int) -> dict:
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"""과거 유사 이슈의 해결 사례를 참고하여 해결방안을 제안"""
|
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issue = get_issue_by_id(issue_id)
|
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if not issue:
|
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return {"available": False, "error": "이슈를 찾을 수 없습니다"}
|
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|
||||
doc_text = build_document_text(issue)
|
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if not doc_text.strip():
|
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return {"available": False, "error": "이슈 내용이 비어있습니다"}
|
||||
|
||||
# 해결 완료된 유사 이슈 검색
|
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similar = await search_similar_by_text(
|
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doc_text, n_results=5, filters={"has_solution": "true"}
|
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)
|
||||
# 해결 안 된 것도 포함 (참고용)
|
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if len(similar) < 3:
|
||||
all_similar = await search_similar_by_text(doc_text, n_results=5)
|
||||
seen = {r["id"] for r in similar}
|
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for r in all_similar:
|
||||
if r["id"] not in seen:
|
||||
similar.append(r)
|
||||
if len(similar) >= 5:
|
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break
|
||||
|
||||
context = _format_retrieved_issues(similar)
|
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template = _load_prompt("prompts/rag_suggest_solution.txt")
|
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prompt = template.format(
|
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description=issue.get("description", ""),
|
||||
detail_notes=issue.get("detail_notes", ""),
|
||||
category=issue.get("category", ""),
|
||||
retrieved_cases=context,
|
||||
)
|
||||
|
||||
response = await ollama_client.generate_text(prompt)
|
||||
return {
|
||||
"available": True,
|
||||
"issue_id": issue_id,
|
||||
"suggestion": response,
|
||||
"referenced_issues": [
|
||||
{
|
||||
"id": r.get("metadata", {}).get("issue_id", r["id"]),
|
||||
"similarity": round(r.get("similarity", 0) * 100),
|
||||
"has_solution": r.get("metadata", {}).get("has_solution", "false") == "true",
|
||||
}
|
||||
for r in similar
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
async def rag_ask(question: str, project_id: int = None) -> dict:
|
||||
"""부적합 데이터를 기반으로 자연어 질문에 답변"""
|
||||
# 프로젝트 필터 없이 전체 데이터에서 검색 (과거 미지정 데이터 포함)
|
||||
results = await search_similar_by_text(
|
||||
question, n_results=15, filters=None
|
||||
)
|
||||
context = _format_retrieved_issues(results)
|
||||
|
||||
template = _load_prompt("prompts/rag_qa.txt")
|
||||
prompt = template.format(
|
||||
question=question,
|
||||
retrieved_cases=context,
|
||||
)
|
||||
|
||||
response = await ollama_client.generate_text(prompt)
|
||||
return {
|
||||
"available": True,
|
||||
"answer": response,
|
||||
"sources": [
|
||||
{
|
||||
"id": r.get("metadata", {}).get("issue_id", r["id"]),
|
||||
"similarity": round(r.get("similarity", 0) * 100),
|
||||
"snippet": (r.get("document", ""))[:100],
|
||||
}
|
||||
for r in results
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
async def rag_analyze_pattern(description: str, n_results: int = 10) -> dict:
|
||||
"""유사 부적합 패턴 분석 — 반복되는 문제인지, 근본 원인은 무엇인지"""
|
||||
results = await search_similar_by_text(description, n_results=n_results)
|
||||
context = _format_retrieved_issues(results)
|
||||
|
||||
template = _load_prompt("prompts/rag_pattern.txt")
|
||||
prompt = template.format(
|
||||
description=description,
|
||||
retrieved_cases=context,
|
||||
total_similar=len(results),
|
||||
)
|
||||
|
||||
response = await ollama_client.generate_text(prompt)
|
||||
return {
|
||||
"available": True,
|
||||
"analysis": response,
|
||||
"similar_count": len(results),
|
||||
"sources": [
|
||||
{
|
||||
"id": r.get("metadata", {}).get("issue_id", r["id"]),
|
||||
"similarity": round(r.get("similarity", 0) * 100),
|
||||
"category": r.get("metadata", {}).get("category", ""),
|
||||
}
|
||||
for r in results
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
async def rag_classify_with_context(description: str, detail_notes: str = "") -> dict:
|
||||
"""과거 사례를 참고하여 더 정확한 분류 수행 (기존 classify 강화)"""
|
||||
query = f"{description} {detail_notes}".strip()
|
||||
similar = await search_similar_by_text(query, n_results=5)
|
||||
context = _format_retrieved_issues(similar)
|
||||
|
||||
template = _load_prompt("prompts/rag_classify.txt")
|
||||
prompt = template.format(
|
||||
description=description,
|
||||
detail_notes=detail_notes,
|
||||
retrieved_cases=context,
|
||||
)
|
||||
|
||||
raw = await ollama_client.generate_text(prompt)
|
||||
import json
|
||||
try:
|
||||
start = raw.find("{")
|
||||
end = raw.rfind("}") + 1
|
||||
if start >= 0 and end > start:
|
||||
result = json.loads(raw[start:end])
|
||||
result["rag_enhanced"] = True
|
||||
result["referenced_count"] = len(similar)
|
||||
return {"available": True, **result}
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
return {"available": True, "raw_response": raw, "rag_enhanced": True}
|
||||
122
ai-service/services/report_service.py
Normal file
122
ai-service/services/report_service.py
Normal file
@@ -0,0 +1,122 @@
|
||||
import httpx
|
||||
from services.ollama_client import ollama_client
|
||||
from services.db_client import get_daily_qc_stats, get_issues_for_date
|
||||
from config import settings
|
||||
|
||||
|
||||
REPORT_PROMPT_PATH = "prompts/daily_report.txt"
|
||||
|
||||
|
||||
def _load_prompt(path: str) -> str:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
return f.read()
|
||||
|
||||
|
||||
async def _fetch_system1_data(date_str: str, token: str) -> dict:
|
||||
headers = {"Authorization": f"Bearer {token}"}
|
||||
data = {"attendance": None, "work_reports": None, "patrol": None}
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=15.0) as client:
|
||||
# 근태
|
||||
try:
|
||||
r = await client.get(
|
||||
f"{settings.SYSTEM1_API_URL}/api/attendance/daily-status",
|
||||
params={"date": date_str},
|
||||
headers=headers,
|
||||
)
|
||||
if r.status_code == 200:
|
||||
data["attendance"] = r.json()
|
||||
except Exception:
|
||||
pass
|
||||
# 작업보고
|
||||
try:
|
||||
r = await client.get(
|
||||
f"{settings.SYSTEM1_API_URL}/api/daily-work-reports/summary",
|
||||
params={"date": date_str},
|
||||
headers=headers,
|
||||
)
|
||||
if r.status_code == 200:
|
||||
data["work_reports"] = r.json()
|
||||
except Exception:
|
||||
pass
|
||||
# 순회점검
|
||||
try:
|
||||
r = await client.get(
|
||||
f"{settings.SYSTEM1_API_URL}/api/patrol/today-status",
|
||||
params={"date": date_str},
|
||||
headers=headers,
|
||||
)
|
||||
if r.status_code == 200:
|
||||
data["patrol"] = r.json()
|
||||
except Exception:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
return data
|
||||
|
||||
|
||||
def _format_attendance(data) -> str:
|
||||
if not data:
|
||||
return "데이터 없음"
|
||||
if isinstance(data, dict):
|
||||
parts = []
|
||||
for k, v in data.items():
|
||||
parts.append(f" {k}: {v}")
|
||||
return "\n".join(parts)
|
||||
return str(data)
|
||||
|
||||
|
||||
def _format_work_reports(data) -> str:
|
||||
if not data:
|
||||
return "데이터 없음"
|
||||
return str(data)
|
||||
|
||||
|
||||
def _format_qc_issues(issues: list[dict], stats: dict) -> str:
|
||||
lines = []
|
||||
lines.append(f"전체: {stats.get('total', 0)}건")
|
||||
lines.append(f"금일 신규: {stats.get('new_today', 0)}건")
|
||||
lines.append(f"진행중: {stats.get('in_progress', 0)}건")
|
||||
lines.append(f"완료: {stats.get('completed', 0)}건")
|
||||
lines.append(f"미검토: {stats.get('pending', 0)}건")
|
||||
if issues:
|
||||
lines.append("\n금일 신규 이슈:")
|
||||
for iss in issues[:10]:
|
||||
cat = iss.get("category", "")
|
||||
desc = (iss.get("description") or "")[:50]
|
||||
status = iss.get("review_status", "")
|
||||
lines.append(f" - [{cat}] {desc} (상태: {status})")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _format_patrol(data) -> str:
|
||||
if not data:
|
||||
return "데이터 없음"
|
||||
return str(data)
|
||||
|
||||
|
||||
async def generate_daily_report(
|
||||
date_str: str, project_id: int = None, token: str = ""
|
||||
) -> dict:
|
||||
system1_data = await _fetch_system1_data(date_str, token)
|
||||
qc_stats = get_daily_qc_stats(date_str)
|
||||
qc_issues = get_issues_for_date(date_str)
|
||||
|
||||
template = _load_prompt(REPORT_PROMPT_PATH)
|
||||
prompt = template.format(
|
||||
date=date_str,
|
||||
attendance_data=_format_attendance(system1_data["attendance"]),
|
||||
work_report_data=_format_work_reports(system1_data["work_reports"]),
|
||||
qc_issue_data=_format_qc_issues(qc_issues, qc_stats),
|
||||
patrol_data=_format_patrol(system1_data["patrol"]),
|
||||
)
|
||||
|
||||
report_text = await ollama_client.generate_text(prompt)
|
||||
return {
|
||||
"date": date_str,
|
||||
"report": report_text,
|
||||
"stats": {
|
||||
"qc": qc_stats,
|
||||
"new_issues_count": len(qc_issues),
|
||||
},
|
||||
}
|
||||
Reference in New Issue
Block a user