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>

.env.example

@@ -84,6 +84,14 @@ PMA_USER=root
 PMA_PASSWORD=change_this_root_password_min_12_chars
 UPLOAD_LIMIT=50M
 
+# -------------------------------------------------------------------
+# AI Service
+# -------------------------------------------------------------------
+OLLAMA_BASE_URL=http://your-ollama-server:11434
+OLLAMA_TEXT_MODEL=qwen2.5:14b-instruct-q4_K_M
+OLLAMA_EMBED_MODEL=bge-m3
+OLLAMA_TIMEOUT=120
+
 # -------------------------------------------------------------------
 # Cloudflare Tunnel
 # -------------------------------------------------------------------

ai-service/Dockerfile

@@ -0,0 +1,9 @@
+FROM python:3.11-slim
+WORKDIR /app
+RUN apt-get update && apt-get install -y gcc build-essential && rm -rf /var/lib/apt/lists/*
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+RUN mkdir -p /app/data/chroma
+EXPOSE 8000
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

ai-service/config.py

@@ -0,0 +1,24 @@
+import os
+
+
+class Settings:
+    OLLAMA_BASE_URL: str = os.getenv("OLLAMA_BASE_URL", "http://100.111.160.84:11434")
+    OLLAMA_TEXT_MODEL: str = os.getenv("OLLAMA_TEXT_MODEL", "qwen2.5:14b-instruct-q4_K_M")
+    OLLAMA_EMBED_MODEL: str = os.getenv("OLLAMA_EMBED_MODEL", "bge-m3")
+    OLLAMA_TIMEOUT: int = int(os.getenv("OLLAMA_TIMEOUT", "120"))
+
+    DB_HOST: str = os.getenv("DB_HOST", "mariadb")
+    DB_PORT: int = int(os.getenv("DB_PORT", "3306"))
+    DB_USER: str = os.getenv("DB_USER", "hyungi_user")
+    DB_PASSWORD: str = os.getenv("DB_PASSWORD", "")
+    DB_NAME: str = os.getenv("DB_NAME", "hyungi")
+
+    SECRET_KEY: str = os.getenv("SECRET_KEY", "")
+    ALGORITHM: str = "HS256"
+
+    SYSTEM1_API_URL: str = os.getenv("SYSTEM1_API_URL", "http://system1-api:3005")
+    CHROMA_PERSIST_DIR: str = os.getenv("CHROMA_PERSIST_DIR", "/app/data/chroma")
+    METADATA_DB_PATH: str = os.getenv("METADATA_DB_PATH", "/app/data/metadata.db")
+
+
+settings = Settings()

ai-service/db/metadata_store.py

@@ -0,0 +1,39 @@
+import sqlite3
+from config import settings
+
+
+class MetadataStore:
+    def __init__(self):
+        self.db_path = settings.METADATA_DB_PATH
+
+    def initialize(self):
+        conn = sqlite3.connect(self.db_path)
+        conn.execute(
+            "CREATE TABLE IF NOT EXISTS sync_state ("
+            "  key TEXT PRIMARY KEY,"
+            "  value TEXT"
+            ")"
+        )
+        conn.commit()
+        conn.close()
+
+    def get_last_synced_id(self) -> int:
+        conn = sqlite3.connect(self.db_path)
+        cur = conn.execute(
+            "SELECT value FROM sync_state WHERE key = 'last_synced_id'"
+        )
+        row = cur.fetchone()
+        conn.close()
+        return int(row[0]) if row else 0
+
+    def set_last_synced_id(self, issue_id: int):
+        conn = sqlite3.connect(self.db_path)
+        conn.execute(
+            "INSERT OR REPLACE INTO sync_state (key, value) VALUES ('last_synced_id', ?)",
+            (str(issue_id),),
+        )
+        conn.commit()
+        conn.close()
+
+
+metadata_store = MetadataStore()

ai-service/db/vector_store.py

@@ -0,0 +1,76 @@
+import chromadb
+from config import settings
+
+
+class VectorStore:
+    def __init__(self):
+        self.client = None
+        self.collection = None
+
+    def initialize(self):
+        self.client = chromadb.PersistentClient(path=settings.CHROMA_PERSIST_DIR)
+        self.collection = self.client.get_or_create_collection(
+            name="qc_issues",
+            metadata={"hnsw:space": "cosine"},
+        )
+
+    def upsert(
+        self,
+        doc_id: str,
+        document: str,
+        embedding: list[float],
+        metadata: dict = None,
+    ):
+        self.collection.upsert(
+            ids=[doc_id],
+            documents=[document],
+            embeddings=[embedding],
+            metadatas=[metadata] if metadata else None,
+        )
+
+    def query(
+        self,
+        embedding: list[float],
+        n_results: int = 5,
+        where: dict = None,
+    ) -> list[dict]:
+        kwargs = {
+            "query_embeddings": [embedding],
+            "n_results": n_results,
+            "include": ["documents", "metadatas", "distances"],
+        }
+        if where:
+            kwargs["where"] = where
+        try:
+            results = self.collection.query(**kwargs)
+        except Exception:
+            return []
+
+        items = []
+        if results and results["ids"] and results["ids"][0]:
+            for i, doc_id in enumerate(results["ids"][0]):
+                item = {
+                    "id": doc_id,
+                    "document": results["documents"][0][i] if results["documents"] else "",
+                    "distance": results["distances"][0][i] if results["distances"] else 0,
+                    "metadata": results["metadatas"][0][i] if results["metadatas"] else {},
+                }
+                # cosine distance → similarity
+                item["similarity"] = round(1 - item["distance"], 4)
+                items.append(item)
+        return items
+
+    def delete(self, doc_id: str):
+        self.collection.delete(ids=[doc_id])
+
+    def count(self) -> int:
+        return self.collection.count()
+
+    def stats(self) -> dict:
+        return {
+            "total_documents": self.count(),
+            "collection_name": "qc_issues",
+        }
+
+
+vector_store = VectorStore()

ai-service/main.py

@@ -0,0 +1,41 @@
+from contextlib import asynccontextmanager
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from routers import health, embeddings, classification, daily_report, rag
+from db.vector_store import vector_store
+from db.metadata_store import metadata_store
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    vector_store.initialize()
+    metadata_store.initialize()
+    yield
+
+
+app = FastAPI(
+    title="TK AI Service",
+    description="AI 서비스 (유사 검색, 분류, 보고서)",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=False,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(health.router, prefix="/api/ai")
+app.include_router(embeddings.router, prefix="/api/ai")
+app.include_router(classification.router, prefix="/api/ai")
+app.include_router(daily_report.router, prefix="/api/ai")
+app.include_router(rag.router, prefix="/api/ai")
+
+
+@app.get("/")
+async def root():
+    return {"message": "TK AI Service", "version": "1.0.0"}

ai-service/prompts/classify_issue.txt

@@ -0,0 +1,18 @@
+당신은 공장 품질관리(QC) 전문가입니다. 아래 부적합 신고 내용을 분석하여 판별하세요.
+
+부적합 내용:
+{description}
+
+상세 내용:
+{detail_notes}
+
+다음 JSON 형식으로만 응답하세요:
+{{
+  "category": "material_missing|design_error|incoming_defect|inspection_miss|기타",
+  "category_confidence": 0.0~1.0,
+  "responsible_department": "production|quality|purchasing|design|sales",
+  "department_confidence": 0.0~1.0,
+  "severity": "low|medium|high|critical",
+  "summary": "한줄 요약 (30자 이내)",
+  "reasoning": "판단 근거 (2-3문장)"
+}}

ai-service/prompts/daily_report.txt

@@ -0,0 +1,22 @@
+당신은 공장 관리 보고서 작성자입니다. 아래 데이터를 바탕으로 일일 브리핑을 작성하세요.
+
+날짜: {date}
+
+[근태 현황]
+{attendance_data}
+
+[작업 현황]
+{work_report_data}
+
+[부적합 현황]
+{qc_issue_data}
+
+[순회점검 현황]
+{patrol_data}
+
+다음 형식으로 작성하세요:
+
+1. 오늘의 요약 (2-3문장)
+2. 주요 이슈 및 관심사항
+3. 부적합 현황 (신규/진행/지연)
+4. 내일 주의사항

ai-service/prompts/rag_classify.txt

@@ -0,0 +1,23 @@
+당신은 공장 품질관리(QC) 전문가입니다. 아래 부적합 신고를 분류하세요.
+
+[신고 내용]
+{description}
+
+[상세 내용]
+{detail_notes}
+
+[참고: 과거 유사 사례]
+{retrieved_cases}
+
+위 과거 사례의 분류 패턴을 참고하여, 현재 부적합을 판별하세요.
+
+다음 JSON 형식으로만 응답하세요:
+{{
+  "category": "material_missing|design_error|incoming_defect|inspection_miss|기타",
+  "category_confidence": 0.0~1.0,
+  "responsible_department": "production|quality|purchasing|design|sales",
+  "department_confidence": 0.0~1.0,
+  "severity": "low|medium|high|critical",
+  "summary": "한줄 요약 (30자 이내)",
+  "reasoning": "판단 근거 — 과거 사례 참고 내용 포함 (2-3문장)"
+}}

ai-service/prompts/rag_pattern.txt

@@ -0,0 +1,16 @@
+당신은 공장 품질관리(QC) 데이터 분석가입니다. 아래 부적합에 대해 패턴을 분석하세요.
+
+[분석 대상]
+{description}
+
+[유사 부적합 {total_similar}건]
+{retrieved_cases}
+
+다음을 분석하세요:
+
+1. **반복 여부**: 이 문제가 과거에도 발생했는지, 반복 빈도는 어느 정도인지
+2. **공통 패턴**: 유사 사례들의 공통 원인, 공통 부서, 공통 시기 등
+3. **근본 원인 추정**: 반복되는 원인이 있다면 근본 원인은 무엇인지
+4. **개선 제안**: 재발 방지를 위한 구조적 개선 방안
+
+데이터 기반으로 객관적으로 분석하세요.

ai-service/prompts/rag_qa.txt

@@ -0,0 +1,14 @@
+당신은 공장 품질관리(QC) 데이터 분석가입니다. 아래 질문에 대해 과거 부적합 데이터를 기반으로 답변하세요.
+
+[질문]
+{question}
+
+[관련 부적합 데이터]
+{retrieved_cases}
+
+위 데이터를 근거로 질문에 답변하세요.
+- 제공된 데이터를 적극적으로 활용하여 답변하세요
+- 관련 사례를 구체적으로 인용하며 분석하세요
+- 패턴이나 공통점이 있다면 정리하세요
+- 숫자나 통계가 있다면 포함하세요
+- 간결하되 유용한 답변을 하세요

ai-service/prompts/rag_suggest_solution.txt

@@ -0,0 +1,18 @@
+당신은 공장 품질관리(QC) 전문가입니다. 아래 부적합 이슈에 대한 해결방안을 제안하세요.
+
+[현재 부적합]
+분류: {category}
+내용: {description}
+상세: {detail_notes}
+
+[과거 유사 사례]
+{retrieved_cases}
+
+위 과거 사례들을 참고하여 다음을 제안하세요:
+
+1. **권장 해결방안**: 과거 유사 사례에서 효과적이었던 해결 방법을 기반으로 구체적인 조치를 제안
+2. **예상 원인**: 유사 사례에서 확인된 원인 패턴을 바탕으로 가능한 원인 분석
+3. **담당 부서**: 어느 부서에서 처리해야 하는지
+4. **주의사항**: 과거 사례에서 배운 교훈이나 주의할 점
+
+간결하고 실용적으로 작성하세요. 과거 사례가 없는 부분은 일반적인 QC 지식으로 보완하세요.

ai-service/prompts/summarize_issue.txt

@@ -0,0 +1,17 @@
+당신은 공장 품질관리(QC) 전문가입니다. 아래 부적합 이슈를 간결하게 요약하세요.
+
+부적합 내용:
+{description}
+
+상세 내용:
+{detail_notes}
+
+해결 방법:
+{solution}
+
+다음 JSON 형식으로만 응답하세요:
+{{
+  "summary": "핵심 요약 (50자 이내)",
+  "key_points": ["요점1", "요점2", "요점3"],
+  "suggested_action": "권장 조치사항 (선택)"
+}}

ai-service/requirements.txt

@@ -0,0 +1,10 @@
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+httpx==0.27.0
+chromadb==0.4.22
+numpy==1.26.2
+pydantic==2.5.0
+pydantic-settings==2.1.0
+python-jose[cryptography]==3.3.0
+pymysql==1.1.0
+sqlalchemy==2.0.23

ai-service/routers/classification.py

@@ -0,0 +1,47 @@
+from fastapi import APIRouter
+from pydantic import BaseModel
+from services.classification_service import (
+    classify_issue,
+    summarize_issue,
+    classify_and_summarize,
+)
+
+router = APIRouter(tags=["classification"])
+
+
+class ClassifyRequest(BaseModel):
+    description: str
+    detail_notes: str = ""
+
+
+class SummarizeRequest(BaseModel):
+    description: str
+    detail_notes: str = ""
+    solution: str = ""
+
+
+@router.post("/classify")
+async def classify(req: ClassifyRequest):
+    try:
+        result = await classify_issue(req.description, req.detail_notes)
+        return {"available": True, **result}
+    except Exception as e:
+        return {"available": False, "error": str(e)}
+
+
+@router.post("/summarize")
+async def summarize(req: SummarizeRequest):
+    try:
+        result = await summarize_issue(req.description, req.detail_notes, req.solution)
+        return {"available": True, **result}
+    except Exception as e:
+        return {"available": False, "error": str(e)}
+
+
+@router.post("/classify-and-summarize")
+async def classify_and_summarize_endpoint(req: ClassifyRequest):
+    try:
+        result = await classify_and_summarize(req.description, req.detail_notes)
+        return {"available": True, **result}
+    except Exception as e:
+        return {"available": False, "error": str(e)}

ai-service/routers/daily_report.py

@@ -0,0 +1,33 @@
+from fastapi import APIRouter, Request
+from pydantic import BaseModel
+from services.report_service import generate_daily_report
+from datetime import date
+
+router = APIRouter(tags=["daily_report"])
+
+
+class DailyReportRequest(BaseModel):
+    date: str | None = None
+    project_id: int | None = None
+
+
+@router.post("/report/daily")
+async def daily_report(req: DailyReportRequest, request: Request):
+    report_date = req.date or date.today().isoformat()
+    token = request.headers.get("authorization", "").replace("Bearer ", "")
+    try:
+        result = await generate_daily_report(report_date, req.project_id, token)
+        return {"available": True, **result}
+    except Exception as e:
+        return {"available": False, "error": str(e)}
+
+
+@router.post("/report/preview")
+async def report_preview(req: DailyReportRequest, request: Request):
+    report_date = req.date or date.today().isoformat()
+    token = request.headers.get("authorization", "").replace("Bearer ", "")
+    try:
+        result = await generate_daily_report(report_date, req.project_id, token)
+        return {"available": True, "preview": True, **result}
+    except Exception as e:
+        return {"available": False, "error": str(e)}

ai-service/routers/embeddings.py

@@ -0,0 +1,77 @@
+from fastapi import APIRouter, BackgroundTasks, Query
+from pydantic import BaseModel
+from services.embedding_service import (
+    sync_all_issues,
+    sync_single_issue,
+    sync_incremental,
+    search_similar_by_id,
+    search_similar_by_text,
+)
+from db.vector_store import vector_store
+
+router = APIRouter(tags=["embeddings"])
+
+
+class SyncSingleRequest(BaseModel):
+    issue_id: int
+
+
+class SearchRequest(BaseModel):
+    query: str
+    n_results: int = 5
+    project_id: int | None = None
+    category: str | None = None
+
+
+@router.post("/embeddings/sync")
+async def sync_embeddings(background_tasks: BackgroundTasks):
+    background_tasks.add_task(sync_all_issues)
+    return {"status": "sync_started", "message": "전체 임베딩 동기화가 시작되었습니다"}
+
+
+@router.post("/embeddings/sync-full")
+async def sync_embeddings_full():
+    result = await sync_all_issues()
+    return {"status": "completed", **result}
+
+
+@router.post("/embeddings/sync-single")
+async def sync_single(req: SyncSingleRequest):
+    result = await sync_single_issue(req.issue_id)
+    return result
+
+
+@router.post("/embeddings/sync-incremental")
+async def sync_incr():
+    result = await sync_incremental()
+    return result
+
+
+@router.get("/similar/{issue_id}")
+async def get_similar(issue_id: int, n_results: int = Query(default=5, le=20)):
+    try:
+        results = await search_similar_by_id(issue_id, n_results)
+        return {"available": True, "results": results, "query_issue_id": issue_id}
+    except Exception as e:
+        return {"available": False, "results": [], "error": str(e)}
+
+
+@router.post("/similar/search")
+async def search_similar(req: SearchRequest):
+    filters = {}
+    if req.project_id is not None:
+        filters["project_id"] = str(req.project_id)
+    if req.category:
+        filters["category"] = req.category
+    try:
+        results = await search_similar_by_text(
+            req.query, req.n_results, filters or None
+        )
+        return {"available": True, "results": results}
+    except Exception as e:
+        return {"available": False, "results": [], "error": str(e)}
+
+
+@router.get("/embeddings/stats")
+async def embedding_stats():
+    return vector_store.stats()

ai-service/routers/health.py

@@ -0,0 +1,21 @@
+from fastapi import APIRouter
+from services.ollama_client import ollama_client
+from db.vector_store import vector_store
+
+router = APIRouter(tags=["health"])
+
+
+@router.get("/health")
+async def health_check():
+    ollama_status = await ollama_client.check_health()
+    return {
+        "status": "ok",
+        "service": "tk-ai-service",
+        "ollama": ollama_status,
+        "embeddings": vector_store.stats(),
+    }
+
+
+@router.get("/models")
+async def list_models():
+    return await ollama_client.check_health()

ai-service/routers/rag.py

@@ -0,0 +1,57 @@
+from fastapi import APIRouter
+from pydantic import BaseModel
+from services.rag_service import (
+    rag_suggest_solution,
+    rag_ask,
+    rag_analyze_pattern,
+    rag_classify_with_context,
+)
+
+router = APIRouter(tags=["rag"])
+
+
+class AskRequest(BaseModel):
+    question: str
+    project_id: int | None = None
+
+
+class PatternRequest(BaseModel):
+    description: str
+    n_results: int = 10
+
+
+class ClassifyRequest(BaseModel):
+    description: str
+    detail_notes: str = ""
+
+
+@router.post("/rag/suggest-solution/{issue_id}")
+async def suggest_solution(issue_id: int):
+    try:
+        return await rag_suggest_solution(issue_id)
+    except Exception as e:
+        return {"available": False, "error": str(e)}
+
+
+@router.post("/rag/ask")
+async def ask_question(req: AskRequest):
+    try:
+        return await rag_ask(req.question, req.project_id)
+    except Exception as e:
+        return {"available": False, "error": str(e)}
+
+
+@router.post("/rag/pattern")
+async def analyze_pattern(req: PatternRequest):
+    try:
+        return await rag_analyze_pattern(req.description, req.n_results)
+    except Exception as e:
+        return {"available": False, "error": str(e)}
+
+
+@router.post("/rag/classify")
+async def classify_with_rag(req: ClassifyRequest):
+    try:
+        return await rag_classify_with_context(req.description, req.detail_notes)
+    except Exception as e:
+        return {"available": False, "error": str(e)}

ai-service/services/classification_service.py

@@ -0,0 +1,60 @@
+import json
+from services.ollama_client import ollama_client
+from config import settings
+
+
+CLASSIFY_PROMPT_PATH = "prompts/classify_issue.txt"
+SUMMARIZE_PROMPT_PATH = "prompts/summarize_issue.txt"
+
+
+def _load_prompt(path: str) -> str:
+    with open(path, "r", encoding="utf-8") as f:
+        return f.read()
+
+
+async def classify_issue(description: str, detail_notes: str = "") -> dict:
+    template = _load_prompt(CLASSIFY_PROMPT_PATH)
+    prompt = template.format(
+        description=description or "",
+        detail_notes=detail_notes or "",
+    )
+    raw = await ollama_client.generate_text(prompt)
+    try:
+        start = raw.find("{")
+        end = raw.rfind("}") + 1
+        if start >= 0 and end > start:
+            return json.loads(raw[start:end])
+    except json.JSONDecodeError:
+        pass
+    return {"raw_response": raw, "parse_error": True}
+
+
+async def summarize_issue(
+    description: str, detail_notes: str = "", solution: str = ""
+) -> dict:
+    template = _load_prompt(SUMMARIZE_PROMPT_PATH)
+    prompt = template.format(
+        description=description or "",
+        detail_notes=detail_notes or "",
+        solution=solution or "",
+    )
+    raw = await ollama_client.generate_text(prompt)
+    try:
+        start = raw.find("{")
+        end = raw.rfind("}") + 1
+        if start >= 0 and end > start:
+            return json.loads(raw[start:end])
+    except json.JSONDecodeError:
+        pass
+    return {"summary": raw.strip()}
+
+
+async def classify_and_summarize(
+    description: str, detail_notes: str = ""
+) -> dict:
+    classification = await classify_issue(description, detail_notes)
+    summary_result = await summarize_issue(description, detail_notes)
+    return {
+        "classification": classification,
+        "summary": summary_result.get("summary", ""),
+    }

ai-service/services/db_client.py

@@ -0,0 +1,97 @@
+from urllib.parse import quote_plus
+
+from sqlalchemy import create_engine, text
+from config import settings
+
+
+def get_engine():
+    password = quote_plus(settings.DB_PASSWORD)
+    url = (
+        f"mysql+pymysql://{settings.DB_USER}:{password}"
+        f"@{settings.DB_HOST}:{settings.DB_PORT}/{settings.DB_NAME}"
+    )
+    return create_engine(url, pool_pre_ping=True, pool_size=5)
+
+
+engine = get_engine()
+
+
+def get_all_issues() -> list[dict]:
+    with engine.connect() as conn:
+        result = conn.execute(
+            text(
+                "SELECT id, category, description, detail_notes, "
+                "final_description, final_category, solution, "
+                "management_comment, cause_detail, project_id, "
+                "review_status, report_date, responsible_department, "
+                "location_info "
+                "FROM qc_issues ORDER BY id"
+            )
+        )
+        return [dict(row._mapping) for row in result]
+
+
+def get_issue_by_id(issue_id: int) -> dict | None:
+    with engine.connect() as conn:
+        result = conn.execute(
+            text(
+                "SELECT id, category, description, detail_notes, "
+                "final_description, final_category, solution, "
+                "management_comment, cause_detail, project_id, "
+                "review_status, report_date, responsible_department, "
+                "location_info "
+                "FROM qc_issues WHERE id = :id"
+            ),
+            {"id": issue_id},
+        )
+        row = result.fetchone()
+        return dict(row._mapping) if row else None
+
+
+def get_issues_since(last_id: int) -> list[dict]:
+    with engine.connect() as conn:
+        result = conn.execute(
+            text(
+                "SELECT id, category, description, detail_notes, "
+                "final_description, final_category, solution, "
+                "management_comment, cause_detail, project_id, "
+                "review_status, report_date, responsible_department, "
+                "location_info "
+                "FROM qc_issues WHERE id > :last_id ORDER BY id"
+            ),
+            {"last_id": last_id},
+        )
+        return [dict(row._mapping) for row in result]
+
+
+def get_daily_qc_stats(date_str: str) -> dict:
+    with engine.connect() as conn:
+        result = conn.execute(
+            text(
+                "SELECT "
+                "  COUNT(*) as total, "
+                "  SUM(CASE WHEN DATE(report_date) = :d THEN 1 ELSE 0 END) as new_today, "
+                "  SUM(CASE WHEN review_status = 'in_progress' THEN 1 ELSE 0 END) as in_progress, "
+                "  SUM(CASE WHEN review_status = 'completed' THEN 1 ELSE 0 END) as completed, "
+                "  SUM(CASE WHEN review_status = 'pending_review' THEN 1 ELSE 0 END) as pending "
+                "FROM qc_issues"
+            ),
+            {"d": date_str},
+        )
+        row = result.fetchone()
+        return dict(row._mapping) if row else {}
+
+
+def get_issues_for_date(date_str: str) -> list[dict]:
+    with engine.connect() as conn:
+        result = conn.execute(
+            text(
+                "SELECT id, category, description, detail_notes, "
+                "review_status, responsible_department, solution "
+                "FROM qc_issues "
+                "WHERE DATE(report_date) = :d "
+                "ORDER BY id"
+            ),
+            {"d": date_str},
+        )
+        return [dict(row._mapping) for row in result]

ai-service/services/embedding_service.py

@@ -0,0 +1,144 @@
+from services.ollama_client import ollama_client
+from db.vector_store import vector_store
+from db.metadata_store import metadata_store
+from services.db_client import get_all_issues, get_issue_by_id, get_issues_since
+
+
+def build_document_text(issue: dict) -> str:
+    parts = []
+    if issue.get("description"):
+        parts.append(issue["description"])
+    if issue.get("final_description"):
+        parts.append(issue["final_description"])
+    if issue.get("detail_notes"):
+        parts.append(issue["detail_notes"])
+    if issue.get("solution"):
+        parts.append(f"해결: {issue['solution']}")
+    if issue.get("management_comment"):
+        parts.append(f"의견: {issue['management_comment']}")
+    if issue.get("cause_detail"):
+        parts.append(f"원인: {issue['cause_detail']}")
+    return " ".join(parts)
+
+
+def build_metadata(issue: dict) -> dict:
+    meta = {"issue_id": issue["id"]}
+    for key in [
+        "category", "project_id", "review_status",
+        "responsible_department", "location_info",
+    ]:
+        val = issue.get(key)
+        if val is not None:
+            meta[key] = str(val)
+    rd = issue.get("report_date")
+    if rd:
+        meta["report_date"] = str(rd)[:10]
+    meta["has_solution"] = "true" if issue.get("solution") else "false"
+    return meta
+
+
+async def sync_all_issues() -> dict:
+    issues = get_all_issues()
+    synced = 0
+    skipped = 0
+    for issue in issues:
+        doc_text = build_document_text(issue)
+        if not doc_text.strip():
+            skipped += 1
+            continue
+        try:
+            embedding = await ollama_client.generate_embedding(doc_text)
+            vector_store.upsert(
+                doc_id=f"issue_{issue['id']}",
+                document=doc_text,
+                embedding=embedding,
+                metadata=build_metadata(issue),
+            )
+            synced += 1
+        except Exception as e:
+            skipped += 1
+    if issues:
+        max_id = max(i["id"] for i in issues)
+        metadata_store.set_last_synced_id(max_id)
+    return {"synced": synced, "skipped": skipped, "total": len(issues)}
+
+
+async def sync_single_issue(issue_id: int) -> dict:
+    issue = get_issue_by_id(issue_id)
+    if not issue:
+        return {"status": "not_found"}
+    doc_text = build_document_text(issue)
+    if not doc_text.strip():
+        return {"status": "empty_text"}
+    embedding = await ollama_client.generate_embedding(doc_text)
+    vector_store.upsert(
+        doc_id=f"issue_{issue['id']}",
+        document=doc_text,
+        embedding=embedding,
+        metadata=build_metadata(issue),
+    )
+    return {"status": "synced", "issue_id": issue_id}
+
+
+async def sync_incremental() -> dict:
+    last_id = metadata_store.get_last_synced_id()
+    issues = get_issues_since(last_id)
+    synced = 0
+    for issue in issues:
+        doc_text = build_document_text(issue)
+        if not doc_text.strip():
+            continue
+        try:
+            embedding = await ollama_client.generate_embedding(doc_text)
+            vector_store.upsert(
+                doc_id=f"issue_{issue['id']}",
+                document=doc_text,
+                embedding=embedding,
+                metadata=build_metadata(issue),
+            )
+            synced += 1
+        except Exception:
+            pass
+    if issues:
+        max_id = max(i["id"] for i in issues)
+        metadata_store.set_last_synced_id(max_id)
+    return {"synced": synced, "new_issues": len(issues)}
+
+
+async def search_similar_by_id(issue_id: int, n_results: int = 5) -> list[dict]:
+    issue = get_issue_by_id(issue_id)
+    if not issue:
+        return []
+    doc_text = build_document_text(issue)
+    if not doc_text.strip():
+        return []
+    embedding = await ollama_client.generate_embedding(doc_text)
+    results = vector_store.query(
+        embedding=embedding,
+        n_results=n_results + 1,
+    )
+    # exclude self
+    filtered = []
+    for r in results:
+        if r["id"] != f"issue_{issue_id}":
+            filtered.append(r)
+    return filtered[:n_results]
+
+
+async def search_similar_by_text(query: str, n_results: int = 5, filters: dict = None) -> list[dict]:
+    embedding = await ollama_client.generate_embedding(query)
+    where = None
+    if filters:
+        conditions = []
+        for k, v in filters.items():
+            if v is not None:
+                conditions.append({k: str(v)})
+        if len(conditions) == 1:
+            where = conditions[0]
+        elif len(conditions) > 1:
+            where = {"$and": conditions}
+    return vector_store.query(
+        embedding=embedding,
+        n_results=n_results,
+        where=where,
+    )

ai-service/services/ollama_client.py

@@ -0,0 +1,57 @@
+import httpx
+from config import settings
+
+
+class OllamaClient:
+    def __init__(self):
+        self.base_url = settings.OLLAMA_BASE_URL
+        self.timeout = httpx.Timeout(float(settings.OLLAMA_TIMEOUT), connect=10.0)
+
+    async def generate_embedding(self, text: str) -> list[float]:
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.post(
+                f"{self.base_url}/api/embeddings",
+                json={"model": settings.OLLAMA_EMBED_MODEL, "prompt": text},
+            )
+            response.raise_for_status()
+            return response.json()["embedding"]
+
+    async def batch_embeddings(self, texts: list[str]) -> list[list[float]]:
+        results = []
+        for text in texts:
+            emb = await self.generate_embedding(text)
+            results.append(emb)
+        return results
+
+    async def generate_text(self, prompt: str, system: str = None) -> str:
+        messages = []
+        if system:
+            messages.append({"role": "system", "content": system})
+        messages.append({"role": "user", "content": prompt})
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.post(
+                f"{self.base_url}/api/chat",
+                json={
+                    "model": settings.OLLAMA_TEXT_MODEL,
+                    "messages": messages,
+                    "stream": False,
+                    "options": {"temperature": 0.3, "num_predict": 2048},
+                },
+            )
+            response.raise_for_status()
+            return response.json()["message"]["content"]
+
+    async def check_health(self) -> dict:
+        try:
+            async with httpx.AsyncClient(timeout=httpx.Timeout(5.0)) as client:
+                response = await client.get(f"{self.base_url}/api/tags")
+                models = response.json().get("models", [])
+                return {
+                    "status": "connected",
+                    "models": [m["name"] for m in models],
+                }
+        except Exception:
+            return {"status": "disconnected"}
+
+
+ollama_client = OllamaClient()

ai-service/services/rag_service.py

@@ -0,0 +1,164 @@
+from services.ollama_client import ollama_client
+from services.embedding_service import search_similar_by_text, build_document_text
+from services.db_client import get_issue_by_id
+
+
+def _load_prompt(path: str) -> str:
+    with open(path, "r", encoding="utf-8") as f:
+        return f.read()
+
+
+def _format_retrieved_issues(results: list[dict]) -> str:
+    if not results:
+        return "관련 과거 사례가 없습니다."
+    lines = []
+    for i, r in enumerate(results, 1):
+        meta = r.get("metadata", {})
+        similarity = round(r.get("similarity", 0) * 100)
+        doc = (r.get("document", ""))[:500]
+        cat = meta.get("category", "")
+        dept = meta.get("responsible_department", "")
+        status = meta.get("review_status", "")
+        has_sol = meta.get("has_solution", "false")
+        date = meta.get("report_date", "")
+        issue_id = meta.get("issue_id", r["id"])
+        lines.append(
+            f"[사례 {i}] No.{issue_id} (유사도 {similarity}%)\n"
+            f"  분류: {cat} | 부서: {dept} | 상태: {status} | 날짜: {date} | 해결여부: {'O' if has_sol == 'true' else 'X'}\n"
+            f"  내용: {doc}"
+        )
+    return "\n\n".join(lines)
+
+
+async def rag_suggest_solution(issue_id: int) -> dict:
+    """과거 유사 이슈의 해결 사례를 참고하여 해결방안을 제안"""
+    issue = get_issue_by_id(issue_id)
+    if not issue:
+        return {"available": False, "error": "이슈를 찾을 수 없습니다"}
+
+    doc_text = build_document_text(issue)
+    if not doc_text.strip():
+        return {"available": False, "error": "이슈 내용이 비어있습니다"}
+
+    # 해결 완료된 유사 이슈 검색
+    similar = await search_similar_by_text(
+        doc_text, n_results=5, filters={"has_solution": "true"}
+    )
+    # 해결 안 된 것도 포함 (참고용)
+    if len(similar) < 3:
+        all_similar = await search_similar_by_text(doc_text, n_results=5)
+        seen = {r["id"] for r in similar}
+        for r in all_similar:
+            if r["id"] not in seen:
+                similar.append(r)
+            if len(similar) >= 5:
+                break
+
+    context = _format_retrieved_issues(similar)
+    template = _load_prompt("prompts/rag_suggest_solution.txt")
+    prompt = template.format(
+        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}

ai-service/services/report_service.py

@@ -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),
+        },
+    }

docker-compose.yml

@@ -307,6 +307,40 @@ services:
     networks:
       - tk-network
 
+  # =================================================================
+  # AI Service
+  # =================================================================
+
+  ai-service:
+    build:
+      context: ./ai-service
+      dockerfile: Dockerfile
+    container_name: tk-ai-service
+    restart: unless-stopped
+    ports:
+      - "30400:8000"
+    environment:
+      - OLLAMA_BASE_URL=${OLLAMA_BASE_URL:-http://100.111.160.84:11434}
+      - OLLAMA_TEXT_MODEL=${OLLAMA_TEXT_MODEL:-qwen2.5:14b-instruct-q4_K_M}
+      - OLLAMA_EMBED_MODEL=${OLLAMA_EMBED_MODEL:-bge-m3}
+      - OLLAMA_TIMEOUT=${OLLAMA_TIMEOUT:-120}
+      - DB_HOST=mariadb
+      - DB_PORT=3306
+      - DB_USER=${MYSQL_USER:-hyungi_user}
+      - DB_PASSWORD=${MYSQL_PASSWORD}
+      - DB_NAME=${MYSQL_DATABASE:-hyungi}
+      - SECRET_KEY=${SSO_JWT_SECRET}
+      - SYSTEM1_API_URL=http://system1-api:3005
+      - CHROMA_PERSIST_DIR=/app/data/chroma
+      - TZ=Asia/Seoul
+    volumes:
+      - ai_data:/app/data
+    depends_on:
+      mariadb:
+        condition: service_healthy
+    networks:
+      - tk-network
+
   # =================================================================
   # Gateway
   # =================================================================
@@ -382,6 +416,7 @@ volumes:
   system3_uploads:
     external: true
     name: tkqc-package_uploads
+  ai_data:
 networks:
   tk-network:
     driver: bridge

gateway/nginx.conf

@@ -53,6 +53,18 @@ server {
         proxy_set_header X-Forwarded-Proto $scheme;
     }
 
+    # ===== AI Service API =====
+    location /ai-api/ {
+        proxy_pass http://ai-service:8000/api/ai/;
+        proxy_http_version 1.1;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_set_header X-Forwarded-Proto $scheme;
+        proxy_read_timeout 120s;
+        proxy_send_timeout 120s;
+    }
+
     # ===== System 1 Web (나머지 모든 경로) =====
     location / {
         proxy_pass http://system1-web:80;

system3-nonconformance/api/routers/page_permissions.py

@@ -52,6 +52,7 @@ DEFAULT_PAGES = {
     'issues_dashboard': {'title': '현황판', 'default_access': True},
     'reports': {'title': '보고서', 'default_access': False},
     'reports_daily': {'title': '일일보고서', 'default_access': False},
+    'ai_assistant': {'title': 'AI 어시스턴트', 'default_access': False},
 }
 
 

system3-nonconformance/web/ai-assistant.html

@@ -0,0 +1,284 @@
+<!DOCTYPE html>
+<html lang="ko">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AI 어시스턴트</title>
+    <link rel="preload" href="https://cdn.tailwindcss.com" as="script">
+    <script src="https://cdn.tailwindcss.com"></script>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+    <link rel="stylesheet" href="/static/css/tkqc-common.css?v=20260306">
+    <link rel="stylesheet" href="/static/css/ai-assistant.css?v=20260306">
+</head>
+<body>
+    <!-- 로딩 스크린 -->
+    <div id="loadingScreen" class="fixed inset-0 bg-white z-50 flex items-center justify-center">
+        <div class="text-center">
+            <div class="animate-spin rounded-full h-12 w-12 border-b-2 border-purple-600 mx-auto mb-4"></div>
+            <p class="text-gray-600">AI 어시스턴트를 불러오는 중...</p>
+        </div>
+    </div>
+
+    <!-- 메인 콘텐츠 -->
+    <div id="mainContent" class="min-h-screen">
+        <!-- 공통 헤더 -->
+        <div id="commonHeader"></div>
+
+        <main class="container mx-auto px-4 py-8 content-fade-in" style="padding-top: 72px;">
+            <!-- 페이지 헤더 -->
+            <div class="bg-white rounded-xl shadow-sm p-6 mb-6">
+                <div class="flex items-center justify-between">
+                    <div>
+                        <h1 class="text-2xl font-bold text-gray-900 flex items-center">
+                            <i class="fas fa-robot text-purple-500 mr-3"></i>
+                            AI 어시스턴트
+                        </h1>
+                        <p class="text-gray-600 mt-1">AI 기반 부적합 분석, 검색, 질의응답을 한곳에서 사용하세요</p>
+                    </div>
+                </div>
+            </div>
+
+            <!-- 1. 상태 카드 (3열 그리드) -->
+            <div class="grid grid-cols-1 md:grid-cols-3 gap-4 mb-6">
+                <div class="status-card bg-white rounded-xl shadow-sm p-5 border-l-4 border-purple-500">
+                    <div class="flex items-center justify-between">
+                        <div>
+                            <p class="text-sm text-gray-500">AI 서비스</p>
+                            <p class="text-lg font-bold mt-1" id="aiStatusText">확인 중...</p>
+                        </div>
+                        <div id="aiStatusIcon" class="w-10 h-10 rounded-full bg-gray-100 flex items-center justify-center">
+                            <i class="fas fa-spinner fa-spin text-gray-400"></i>
+                        </div>
+                    </div>
+                </div>
+                <div class="status-card bg-white rounded-xl shadow-sm p-5 border-l-4 border-indigo-500">
+                    <div class="flex items-center justify-between">
+                        <div>
+                            <p class="text-sm text-gray-500">임베딩 데이터</p>
+                            <p class="text-lg font-bold mt-1" id="aiEmbeddingCount">-</p>
+                        </div>
+                        <div class="w-10 h-10 rounded-full bg-indigo-50 flex items-center justify-center">
+                            <i class="fas fa-database text-indigo-500"></i>
+                        </div>
+                    </div>
+                </div>
+                <div class="status-card bg-white rounded-xl shadow-sm p-5 border-l-4 border-blue-500">
+                    <div class="flex items-center justify-between">
+                        <div>
+                            <p class="text-sm text-gray-500">AI 모델</p>
+                            <p class="text-lg font-bold mt-1" id="aiModelName">-</p>
+                        </div>
+                        <div class="w-10 h-10 rounded-full bg-blue-50 flex items-center justify-center">
+                            <i class="fas fa-brain text-blue-500"></i>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- 2. AI Q&A (메인 — 채팅형) -->
+            <div class="section-card bg-white rounded-xl shadow-sm p-6 mb-6">
+                <div class="flex items-center justify-between mb-4">
+                    <div class="flex items-center">
+                        <i class="fas fa-comments text-purple-500 mr-2"></i>
+                        <h2 class="text-lg font-semibold text-gray-800">AI Q&A</h2>
+                        <span class="ml-2 text-xs bg-purple-100 text-purple-600 px-2 py-0.5 rounded-full">과거 사례 기반</span>
+                    </div>
+                    <button onclick="clearChat()" class="text-sm text-gray-400 hover:text-gray-600 transition-colors">
+                        <i class="fas fa-trash-alt mr-1"></i>대화 초기화
+                    </button>
+                </div>
+
+                <!-- 채팅 히스토리 -->
+                <div id="chatContainer" class="chat-container bg-gray-50 rounded-lg p-4 mb-4 min-h-[200px]">
+                    <div class="text-center text-gray-400 text-sm py-8" id="chatPlaceholder">
+                        <i class="fas fa-robot text-4xl mb-3 text-gray-300"></i>
+                        <p>부적합 관련 질문을 입력하세요.</p>
+                        <p class="text-xs mt-1">과거 사례를 분석하여 답변합니다.</p>
+                    </div>
+                </div>
+
+                <!-- 빠른 질문 템플릿 -->
+                <div class="flex flex-wrap gap-2 mb-3">
+                    <button onclick="setQuickQuestion('최근 가장 많이 발생하는 부적합 유형은?')"
+                            class="quick-question-btn text-xs px-3 py-1.5 rounded-full bg-white text-gray-600">
+                        <i class="fas fa-chart-pie mr-1 text-purple-400"></i>많이 발생하는 유형
+                    </button>
+                    <button onclick="setQuickQuestion('용접 불량의 주요 원인과 해결방법은?')"
+                            class="quick-question-btn text-xs px-3 py-1.5 rounded-full bg-white text-gray-600">
+                        <i class="fas fa-fire mr-1 text-orange-400"></i>용접 불량 원인
+                    </button>
+                    <button onclick="setQuickQuestion('자재 관련 부적합을 줄이려면 어떻게 해야 하나요?')"
+                            class="quick-question-btn text-xs px-3 py-1.5 rounded-full bg-white text-gray-600">
+                        <i class="fas fa-box mr-1 text-blue-400"></i>자재 부적합 개선
+                    </button>
+                    <button onclick="setQuickQuestion('반복적으로 발생하는 부적합 패턴이 있나요?')"
+                            class="quick-question-btn text-xs px-3 py-1.5 rounded-full bg-white text-gray-600">
+                        <i class="fas fa-redo mr-1 text-red-400"></i>반복 패턴 분석
+                    </button>
+                </div>
+
+                <!-- 입력 영역 -->
+                <div class="flex flex-col md:flex-row gap-2">
+                    <div class="flex-1">
+                        <textarea id="qaQuestion" rows="3"
+                                  class="w-full px-4 py-3 border border-gray-300 rounded-lg focus:ring-2 focus:ring-purple-500 focus:border-purple-500 resize-none text-sm"
+                                  placeholder="질문을 입력하세요... (Ctrl+Enter로 전송)"
+                                  onkeydown="if(event.ctrlKey && event.key==='Enter') submitQuestion()"></textarea>
+                    </div>
+                    <div class="flex flex-col gap-2 md:w-48">
+                        <select id="qaProjectFilter" class="px-3 py-2 border border-gray-300 rounded-lg text-sm focus:ring-2 focus:ring-purple-500">
+                            <option value="">전체 프로젝트</option>
+                        </select>
+                        <button onclick="submitQuestion()"
+                                class="px-4 py-2 bg-purple-600 text-white rounded-lg hover:bg-purple-700 transition-colors text-sm font-medium">
+                            <i class="fas fa-paper-plane mr-1"></i>질문하기
+                        </button>
+                    </div>
+                </div>
+            </div>
+
+            <!-- 3. 시맨틱 검색 -->
+            <div class="section-card bg-white rounded-xl shadow-sm p-6 mb-6">
+                <div class="flex items-center mb-4">
+                    <i class="fas fa-search text-indigo-500 mr-2"></i>
+                    <h2 class="text-lg font-semibold text-gray-800">시맨틱 검색</h2>
+                    <span class="ml-2 text-xs bg-indigo-100 text-indigo-600 px-2 py-0.5 rounded-full">유사 부적합 찾기</span>
+                </div>
+
+                <div class="flex flex-col md:flex-row gap-2 mb-4">
+                    <input type="text" id="searchQuery"
+                           class="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-indigo-500 focus:border-indigo-500 text-sm"
+                           placeholder="부적합 내용을 자연어로 검색하세요... (예: 볼트 누락, 용접 불량)"
+                           onkeydown="if(event.key==='Enter') executeSemanticSearch()">
+                    <select id="searchProjectFilter" class="px-3 py-2 border border-gray-300 rounded-lg text-sm focus:ring-2 focus:ring-indigo-500 md:w-40">
+                        <option value="">전체 프로젝트</option>
+                    </select>
+                    <select id="searchCategoryFilter" class="px-3 py-2 border border-gray-300 rounded-lg text-sm focus:ring-2 focus:ring-indigo-500 md:w-40">
+                        <option value="">전체 카테고리</option>
+                        <option value="civil">토목</option>
+                        <option value="architecture">건축</option>
+                        <option value="mechanical">기계</option>
+                        <option value="electrical">전기</option>
+                        <option value="piping">배관</option>
+                        <option value="instrument">계장</option>
+                        <option value="painting">도장</option>
+                        <option value="insulation">보온</option>
+                        <option value="fireproof">내화</option>
+                        <option value="other">기타</option>
+                    </select>
+                    <select id="searchResultCount" class="px-3 py-2 border border-gray-300 rounded-lg text-sm focus:ring-2 focus:ring-indigo-500 md:w-28">
+                        <option value="5">5건</option>
+                        <option value="10" selected>10건</option>
+                        <option value="20">20건</option>
+                    </select>
+                    <button onclick="executeSemanticSearch()"
+                            class="px-4 py-2 bg-indigo-600 text-white rounded-lg hover:bg-indigo-700 transition-colors text-sm whitespace-nowrap">
+                        <i class="fas fa-search mr-1"></i>검색
+                    </button>
+                </div>
+
+                <div id="searchLoading" class="hidden text-center py-4">
+                    <i class="fas fa-spinner fa-spin text-indigo-500 mr-1"></i>
+                    <span class="text-sm text-gray-500">AI 검색 중...</span>
+                </div>
+                <div id="searchResults" class="space-y-2">
+                    <!-- 검색 결과 -->
+                </div>
+            </div>
+
+            <!-- 4. 패턴 분석 -->
+            <div class="section-card bg-white rounded-xl shadow-sm p-6 mb-6">
+                <div class="flex items-center mb-4">
+                    <i class="fas fa-chart-bar text-green-500 mr-2"></i>
+                    <h2 class="text-lg font-semibold text-gray-800">패턴 분석</h2>
+                    <span class="ml-2 text-xs bg-green-100 text-green-600 px-2 py-0.5 rounded-full">부적합 패턴 파악</span>
+                </div>
+
+                <div class="flex flex-col md:flex-row gap-2 mb-4">
+                    <textarea id="patternInput" rows="2"
+                              class="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-green-500 focus:border-green-500 resize-none text-sm"
+                              placeholder="분석할 부적합 내용을 입력하세요... (예: 배관 용접부 결함)"></textarea>
+                    <button onclick="executePatternAnalysis()"
+                            class="px-4 py-2 bg-green-600 text-white rounded-lg hover:bg-green-700 transition-colors text-sm whitespace-nowrap self-end">
+                        <i class="fas fa-chart-bar mr-1"></i>패턴 분석
+                    </button>
+                </div>
+
+                <div id="patternLoading" class="hidden text-center py-4">
+                    <i class="fas fa-spinner fa-spin text-green-500 mr-1"></i>
+                    <span class="text-sm text-gray-500">패턴 분석 중...</span>
+                </div>
+                <div id="patternResults" class="hidden">
+                    <!-- 패턴 분석 결과 -->
+                </div>
+            </div>
+
+            <!-- 5. AI 분류 테스트 -->
+            <div class="section-card bg-white rounded-xl shadow-sm p-6 mb-6">
+                <div class="flex items-center mb-4">
+                    <i class="fas fa-tags text-amber-500 mr-2"></i>
+                    <h2 class="text-lg font-semibold text-gray-800">AI 분류 테스트</h2>
+                    <span class="ml-2 text-xs bg-amber-100 text-amber-600 px-2 py-0.5 rounded-full">기본 vs RAG 비교</span>
+                </div>
+
+                <div class="grid grid-cols-1 md:grid-cols-2 gap-4 mb-4">
+                    <div>
+                        <label class="block text-sm font-medium text-gray-700 mb-1">부적합 설명</label>
+                        <textarea id="classifyDescription" rows="3"
+                                  class="w-full px-4 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-amber-500 focus:border-amber-500 resize-none text-sm"
+                                  placeholder="부적합 설명을 입력하세요..."></textarea>
+                    </div>
+                    <div>
+                        <label class="block text-sm font-medium text-gray-700 mb-1">상세 내용 (선택)</label>
+                        <textarea id="classifyDetail" rows="3"
+                                  class="w-full px-4 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-amber-500 focus:border-amber-500 resize-none text-sm"
+                                  placeholder="상세 내용을 입력하세요..."></textarea>
+                    </div>
+                </div>
+                <div class="flex gap-2 mb-4">
+                    <button onclick="executeClassification(false)"
+                            class="px-4 py-2 bg-amber-500 text-white rounded-lg hover:bg-amber-600 transition-colors text-sm">
+                        <i class="fas fa-tag mr-1"></i>기본 분류
+                    </button>
+                    <button onclick="executeClassification(true)"
+                            class="px-4 py-2 bg-purple-600 text-white rounded-lg hover:bg-purple-700 transition-colors text-sm">
+                        <i class="fas fa-tags mr-1"></i>RAG 분류
+                    </button>
+                </div>
+
+                <div id="classifyLoading" class="hidden text-center py-4">
+                    <i class="fas fa-spinner fa-spin text-amber-500 mr-1"></i>
+                    <span class="text-sm text-gray-500">AI 분류 중...</span>
+                </div>
+                <div id="classifyResults" class="hidden">
+                    <!-- 분류 결과 -->
+                </div>
+            </div>
+        </main>
+    </div>
+
+    <!-- AI 이슈 상세 모달 -->
+    <div id="aiIssueModal" class="fixed inset-0 bg-black bg-opacity-50 hidden z-50 flex items-center justify-center" onclick="if(event.target===this)this.classList.add('hidden')">
+        <div class="bg-white rounded-xl shadow-2xl w-full max-w-lg mx-4 max-h-[80vh] overflow-y-auto">
+            <div class="sticky top-0 bg-white border-b px-5 py-3 flex justify-between items-center rounded-t-xl">
+                <h3 id="aiIssueModalTitle" class="font-bold text-gray-800"></h3>
+                <button onclick="document.getElementById('aiIssueModal').classList.add('hidden')" class="text-gray-400 hover:text-gray-600">
+                    <i class="fas fa-times"></i>
+                </button>
+            </div>
+            <div id="aiIssueModalBody" class="p-5 text-sm text-gray-700 space-y-3"></div>
+        </div>
+    </div>
+
+    <!-- 스크립트 -->
+    <script src="/static/js/core/permissions.js?v=20260306"></script>
+    <script src="/static/js/components/common-header.js?v=20260306"></script>
+    <script src="/static/js/core/page-manager.js?v=20260306"></script>
+    <script src="/static/js/core/auth-manager.js?v=20260306"></script>
+    <script src="/static/js/utils/issue-helpers.js?v=20260306"></script>
+    <script src="/static/js/utils/toast.js?v=20260306"></script>
+    <script src="/static/js/components/mobile-bottom-nav.js?v=20260306"></script>
+    <script src="/static/js/api.js?v=20260306"></script>
+    <script src="/static/js/pages/ai-assistant.js?v=20260306"></script>
+</body>
+</html>

system3-nonconformance/web/issues-dashboard.html

@@ -115,6 +115,57 @@
                 </div>
             </div>
 
+            <!-- AI 시맨틱 검색 -->
+            <div class="bg-white rounded-xl shadow-sm p-6 mb-6">
+                <div class="flex items-center mb-3">
+                    <i class="fas fa-robot text-purple-500 mr-2"></i>
+                    <h3 class="text-sm font-semibold text-gray-700">AI 유사 부적합 검색</h3>
+                </div>
+                <div class="flex space-x-2">
+                    <input type="text" id="aiSearchQuery"
+                           class="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-purple-500 focus:border-purple-500"
+                           placeholder="부적합 내용을 자연어로 검색하세요... (예: 볼트 누락, 용접 불량)"
+                           onkeydown="if(event.key==='Enter') aiSemanticSearch()">
+                    <button onclick="aiSemanticSearch()"
+                            class="px-4 py-2 bg-purple-500 text-white rounded-lg hover:bg-purple-600 transition-colors whitespace-nowrap">
+                        <i class="fas fa-search mr-1"></i>AI 검색
+                    </button>
+                </div>
+                <div id="aiSearchLoading" class="hidden mt-3 text-center">
+                    <i class="fas fa-spinner fa-spin text-purple-500 mr-1"></i>
+                    <span class="text-sm text-gray-500">AI 검색 중...</span>
+                </div>
+                <div id="aiSearchResults" class="hidden mt-3 space-y-2">
+                    <!-- 검색 결과 -->
+                </div>
+
+                <!-- RAG Q&A -->
+                <div class="mt-4 pt-4 border-t border-gray-200">
+                    <div class="flex items-center mb-2">
+                        <i class="fas fa-comments text-indigo-500 mr-2"></i>
+                        <h4 class="text-sm font-semibold text-gray-700">AI Q&A (과거 사례 기반)</h4>
+                    </div>
+                    <div class="flex space-x-2">
+                        <input type="text" id="aiQaQuestion"
+                               class="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-indigo-500 focus:border-indigo-500"
+                               placeholder="질문하세요... (예: 최근 자재 누락이 왜 많아?, 용접 불량 해결방법은?)"
+                               onkeydown="if(event.key==='Enter') aiAskQuestion()">
+                        <button onclick="aiAskQuestion()"
+                                class="px-4 py-2 bg-indigo-500 text-white rounded-lg hover:bg-indigo-600 transition-colors whitespace-nowrap">
+                            <i class="fas fa-paper-plane mr-1"></i>질문
+                        </button>
+                    </div>
+                    <div id="aiQaLoading" class="hidden mt-3 text-center">
+                        <i class="fas fa-spinner fa-spin text-indigo-500 mr-1"></i>
+                        <span class="text-sm text-gray-500">과거 사례 분석 중...</span>
+                    </div>
+                    <div id="aiQaResult" class="hidden mt-3 bg-indigo-50 border border-indigo-200 rounded-lg p-4">
+                        <div id="aiQaAnswer" class="text-sm text-gray-700 whitespace-pre-line"></div>
+                        <div id="aiQaSources" class="mt-2 text-xs text-indigo-500"></div>
+                    </div>
+                </div>
+            </div>
+
             <!-- 프로젝트 선택 및 필터 -->
             <div class="bg-white rounded-xl shadow-sm p-6 mb-6">
                 <div class="flex flex-col md:flex-row md:items-center md:justify-between space-y-4 md:space-y-0">
@@ -549,15 +600,29 @@
         </div>
     </div>
 
+    <!-- AI 이슈 상세 모달 -->
+    <div id="aiIssueModal" class="fixed inset-0 bg-black bg-opacity-50 hidden z-50 flex items-center justify-center" onclick="if(event.target===this)this.classList.add('hidden')">
+        <div class="bg-white rounded-xl shadow-2xl w-full max-w-lg mx-4 max-h-[80vh] overflow-y-auto">
+            <div class="sticky top-0 bg-white border-b px-5 py-3 flex justify-between items-center rounded-t-xl">
+                <h3 id="aiIssueModalTitle" class="font-bold text-gray-800"></h3>
+                <button onclick="document.getElementById('aiIssueModal').classList.add('hidden')" class="text-gray-400 hover:text-gray-600">
+                    <i class="fas fa-times"></i>
+                </button>
+            </div>
+            <div id="aiIssueModalBody" class="p-5 text-sm text-gray-700 space-y-3"></div>
+        </div>
+    </div>
+
     <!-- 스크립트 -->
-    <script src="/static/js/core/permissions.js?v=20260213"></script>
-    <script src="/static/js/components/common-header.js?v=20260213"></script>
-    <script src="/static/js/core/page-manager.js?v=20260213"></script>
-    <script src="/static/js/core/auth-manager.js?v=20260213"></script>
-    <script src="/static/js/utils/issue-helpers.js?v=20260213"></script>
-    <script src="/static/js/utils/photo-modal.js?v=20260213"></script>
-    <script src="/static/js/utils/toast.js?v=20260213"></script>
-    <script src="/static/js/components/mobile-bottom-nav.js?v=20260213"></script>
-    <script src="/static/js/pages/issues-dashboard.js?v=20260213"></script>
+    <script src="/static/js/core/permissions.js?v=20260306"></script>
+    <script src="/static/js/components/common-header.js?v=20260306"></script>
+    <script src="/static/js/core/page-manager.js?v=20260306"></script>
+    <script src="/static/js/core/auth-manager.js?v=20260306"></script>
+    <script src="/static/js/utils/issue-helpers.js?v=20260306"></script>
+    <script src="/static/js/utils/photo-modal.js?v=20260306"></script>
+    <script src="/static/js/utils/toast.js?v=20260306"></script>
+    <script src="/static/js/components/mobile-bottom-nav.js?v=20260306"></script>
+    <script src="/static/js/api.js?v=20260306"></script>
+    <script src="/static/js/pages/issues-dashboard.js?v=20260306"></script>
 </body>
 </html>

system3-nonconformance/web/issues-inbox.html

@@ -204,6 +204,26 @@
                         </div>
                     </div>
 
+                    <!-- AI 분류 추천 -->
+                    <div class="bg-purple-50 border border-purple-200 rounded-lg p-3">
+                        <div class="flex items-center justify-between">
+                            <span class="text-sm font-medium text-purple-700">
+                                <i class="fas fa-robot mr-1"></i>AI 분류 추천
+                            </span>
+                            <button id="aiClassifyBtn" onclick="aiClassifyCurrentIssue()"
+                                    class="px-3 py-1 bg-purple-500 text-white text-xs rounded-lg hover:bg-purple-600 transition-colors">
+                                <i class="fas fa-magic mr-1"></i>AI 분석
+                            </button>
+                        </div>
+                        <div id="aiClassifyLoading" class="hidden mt-2 text-center">
+                            <i class="fas fa-spinner fa-spin text-purple-500 mr-1"></i>
+                            <span class="text-xs text-purple-600">AI 분석 중...</span>
+                        </div>
+                        <div id="aiClassifyResult" class="hidden mt-2 text-sm text-purple-800 space-y-1">
+                            <!-- AI 결과가 여기에 표시됩니다 -->
+                        </div>
+                    </div>
+
                     <!-- 수정 폼 -->
                     <div class="grid grid-cols-1 md:grid-cols-2 gap-4">
                         <div>
@@ -350,13 +370,14 @@
     <!-- Scripts -->
     <script src="/static/js/date-utils.js?v=20260213"></script>
     <script src="/static/js/core/permissions.js?v=20260213"></script>
-    <script src="/static/js/components/common-header.js?v=20260213"></script>
-    <script src="/static/js/core/page-manager.js?v=20260213"></script>
-    <script src="/static/js/components/mobile-calendar.js?v=20260213"></script>
-    <script src="/static/js/utils/issue-helpers.js?v=20260213"></script>
-    <script src="/static/js/utils/photo-modal.js?v=20260213"></script>
-    <script src="/static/js/utils/toast.js?v=20260213"></script>
-    <script src="/static/js/components/mobile-bottom-nav.js?v=20260213"></script>
-    <script src="/static/js/pages/issues-inbox.js?v=20260213"></script>
+    <script src="/static/js/components/common-header.js?v=20260306"></script>
+    <script src="/static/js/core/page-manager.js?v=20260306"></script>
+    <script src="/static/js/components/mobile-calendar.js?v=20260306"></script>
+    <script src="/static/js/utils/issue-helpers.js?v=20260306"></script>
+    <script src="/static/js/utils/photo-modal.js?v=20260306"></script>
+    <script src="/static/js/utils/toast.js?v=20260306"></script>
+    <script src="/static/js/components/mobile-bottom-nav.js?v=20260306"></script>
+    <script src="/static/js/api.js?v=20260306"></script>
+    <script src="/static/js/pages/issues-inbox.js?v=20260306"></script>
 </body>
 </html>

system3-nonconformance/web/issues-management.html

@@ -161,6 +161,44 @@
                         <!-- 동적으로 생성될 내용 -->
                     </div>
 
+                    <!-- AI 유사 부적합 패널 -->
+                    <div id="aiSimilarPanel" class="mt-6 border-t pt-6 hidden">
+                        <div class="flex items-center justify-between mb-3">
+                            <h3 class="text-sm font-semibold text-gray-700">
+                                <i class="fas fa-robot text-purple-500 mr-2"></i>AI 유사 부적합
+                            </h3>
+                            <button id="aiSimilarRefresh" onclick="loadSimilarIssues()" class="text-xs text-purple-500 hover:text-purple-700">
+                                <i class="fas fa-sync-alt mr-1"></i>검색
+                            </button>
+                        </div>
+                        <div id="aiSimilarLoading" class="hidden text-center py-4">
+                            <i class="fas fa-spinner fa-spin text-purple-500 mr-2"></i>
+                            <span class="text-sm text-gray-500">유사 이슈 검색 중...</span>
+                        </div>
+                        <div id="aiSimilarResults" class="space-y-2">
+                            <!-- 유사 이슈 목록 -->
+                        </div>
+                        <div id="aiSimilarEmpty" class="hidden text-center py-3">
+                            <p class="text-sm text-gray-400">유사한 부적합이 없습니다</p>
+                        </div>
+
+                        <!-- RAG 해결방안 제안 -->
+                        <div class="mt-4 pt-3 border-t border-purple-100">
+                            <button id="aiSuggestSolutionBtn" onclick="aiSuggestSolution()"
+                                    class="w-full px-3 py-2 bg-gradient-to-r from-purple-500 to-indigo-500 text-white text-sm rounded-lg hover:from-purple-600 hover:to-indigo-600 transition-all">
+                                <i class="fas fa-lightbulb mr-2"></i>AI 해결방안 제안 (과거 사례 기반)
+                            </button>
+                            <div id="aiSuggestLoading" class="hidden mt-2 text-center py-3">
+                                <i class="fas fa-spinner fa-spin text-purple-500 mr-1"></i>
+                                <span class="text-xs text-gray-500">과거 사례 분석 중...</span>
+                            </div>
+                            <div id="aiSuggestResult" class="hidden mt-2 bg-indigo-50 border border-indigo-200 rounded-lg p-3">
+                                <div id="aiSuggestContent" class="text-sm text-gray-700 whitespace-pre-line"></div>
+                                <div id="aiSuggestSources" class="mt-2 text-xs text-indigo-500"></div>
+                            </div>
+                        </div>
+                    </div>
+
                     <!-- 모달 푸터 -->
                     <div class="flex justify-end space-x-3 mt-6 pt-6 border-t">
                         <button onclick="closeIssueDetailModal()" class="px-4 py-2 text-gray-600 hover:text-gray-800">
@@ -299,13 +337,14 @@
 
     <!-- Scripts -->
     <script src="/static/js/date-utils.js?v=20260213"></script>
-    <script src="/static/js/core/permissions.js?v=20260213"></script>
-    <script src="/static/js/components/common-header.js?v=20260213"></script>
-    <script src="/static/js/core/page-manager.js?v=20260213"></script>
-    <script src="/static/js/utils/issue-helpers.js?v=20260213"></script>
-    <script src="/static/js/utils/photo-modal.js?v=20260213"></script>
-    <script src="/static/js/utils/toast.js?v=20260213"></script>
-    <script src="/static/js/components/mobile-bottom-nav.js?v=20260213"></script>
-    <script src="/static/js/pages/issues-management.js?v=20260213"></script>
+    <script src="/static/js/core/permissions.js?v=20260306"></script>
+    <script src="/static/js/components/common-header.js?v=20260306"></script>
+    <script src="/static/js/core/page-manager.js?v=20260306"></script>
+    <script src="/static/js/utils/issue-helpers.js?v=20260306"></script>
+    <script src="/static/js/utils/photo-modal.js?v=20260306"></script>
+    <script src="/static/js/utils/toast.js?v=20260306"></script>
+    <script src="/static/js/components/mobile-bottom-nav.js?v=20260306"></script>
+    <script src="/static/js/api.js?v=20260306"></script>
+    <script src="/static/js/pages/issues-management.js?v=20260306"></script>
 </body>
 </html>

system3-nonconformance/web/nginx.conf

@@ -48,6 +48,18 @@ server {
         proxy_buffering off;
     }
 
+    # AI API 프록시
+    location /ai-api/ {
+        proxy_pass http://ai-service:8000/api/ai/;
+        proxy_http_version 1.1;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_set_header X-Forwarded-Proto $scheme;
+        proxy_read_timeout 120s;
+        proxy_send_timeout 120s;
+    }
+
     # 모바일 전용 페이지
     location /m/ {
         alias /usr/share/nginx/html/m/;

system3-nonconformance/web/static/css/ai-assistant.css

@@ -0,0 +1,162 @@
+/* ai-assistant.css — AI 어시스턴트 페이지 전용 스타일 */
+
+/* 페이드인 애니메이션 */
+.fade-in { opacity: 0; animation: fadeIn 0.5s ease-in forwards; }
+@keyframes fadeIn { to { opacity: 1; } }
+
+.header-fade-in { opacity: 0; animation: headerFadeIn 0.6s ease-out forwards; }
+@keyframes headerFadeIn { to { opacity: 1; transform: translateY(0); } from { transform: translateY(-10px); } }
+
+.content-fade-in { opacity: 0; animation: contentFadeIn 0.7s ease-out 0.2s forwards; }
+@keyframes contentFadeIn { to { opacity: 1; transform: translateY(0); } from { transform: translateY(20px); } }
+
+/* 채팅 컨테이너 */
+.chat-container {
+    max-height: 500px;
+    overflow-y: auto;
+    scroll-behavior: smooth;
+}
+
+.chat-container::-webkit-scrollbar {
+    width: 6px;
+}
+
+.chat-container::-webkit-scrollbar-track {
+    background: #f1f5f9;
+    border-radius: 3px;
+}
+
+.chat-container::-webkit-scrollbar-thumb {
+    background: #cbd5e1;
+    border-radius: 3px;
+}
+
+.chat-container::-webkit-scrollbar-thumb:hover {
+    background: #94a3b8;
+}
+
+/* 채팅 말풍선 */
+.chat-bubble {
+    max-width: 85%;
+    animation: bubbleIn 0.3s ease-out;
+}
+
+@keyframes bubbleIn {
+    from {
+        opacity: 0;
+        transform: translateY(10px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.chat-bubble-user {
+    background: #7c3aed;
+    color: white;
+    border-radius: 18px 18px 4px 18px;
+    padding: 10px 16px;
+    margin-left: auto;
+}
+
+.chat-bubble-ai {
+    background: #f1f5f9;
+    color: #1e293b;
+    border-radius: 18px 18px 18px 4px;
+    padding: 10px 16px;
+}
+
+.chat-bubble-ai .source-link {
+    color: #7c3aed;
+    cursor: pointer;
+    text-decoration: underline;
+    text-decoration-style: dotted;
+}
+
+.chat-bubble-ai .source-link:hover {
+    color: #6d28d9;
+    text-decoration-style: solid;
+}
+
+/* 로딩 도트 애니메이션 */
+.typing-indicator {
+    display: flex;
+    gap: 4px;
+    padding: 12px 16px;
+}
+
+.typing-dot {
+    width: 8px;
+    height: 8px;
+    background: #94a3b8;
+    border-radius: 50%;
+    animation: typingBounce 1.4s infinite ease-in-out;
+}
+
+.typing-dot:nth-child(2) { animation-delay: 0.2s; }
+.typing-dot:nth-child(3) { animation-delay: 0.4s; }
+
+@keyframes typingBounce {
+    0%, 80%, 100% { transform: scale(0.6); opacity: 0.4; }
+    40% { transform: scale(1); opacity: 1; }
+}
+
+/* 빠른 질문 버튼 */
+.quick-question-btn {
+    transition: all 0.2s ease;
+    border: 1px solid #e2e8f0;
+}
+
+.quick-question-btn:hover {
+    border-color: #7c3aed;
+    background: #faf5ff;
+    transform: translateY(-1px);
+    box-shadow: 0 2px 8px rgba(124, 58, 237, 0.12);
+}
+
+/* 상태 카드 */
+.status-card {
+    transition: all 0.2s ease;
+}
+
+.status-card:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08);
+}
+
+/* 섹션 카드 */
+.section-card {
+    transition: all 0.2s ease;
+}
+
+/* 결과 아이템 */
+.result-item {
+    transition: all 0.15s ease;
+    cursor: pointer;
+}
+
+.result-item:hover {
+    background: #f8fafc;
+    border-color: #7c3aed;
+}
+
+/* 모바일 반응형 */
+@media (max-width: 768px) {
+    .chat-container {
+        max-height: 400px;
+    }
+
+    .chat-bubble {
+        max-width: 92%;
+    }
+
+    button, a, [onclick], select {
+        min-height: 44px;
+        min-width: 44px;
+    }
+
+    body {
+        padding-bottom: calc(64px + env(safe-area-inset-bottom)) !important;
+    }
+}

system3-nonconformance/web/static/js/api.js

@@ -308,6 +308,161 @@ function checkPageAccess(pageName) {
     return user;
 }
 
+// AI API
+const AiAPI = {
+    getSimilarIssues: async (issueId, limit = 5) => {
+        try {
+            const res = await fetch(`/ai-api/similar/${issueId}?n_results=${limit}`, {
+                headers: { 'Authorization': `Bearer ${TokenManager.getToken()}` }
+            });
+            if (!res.ok) return { available: false, results: [] };
+            return await res.json();
+        } catch (e) {
+            console.warn('AI 유사 검색 실패:', e);
+            return { available: false, results: [] };
+        }
+    },
+    searchSimilar: async (query, limit = 5, filters = {}) => {
+        try {
+            const res = await fetch('/ai-api/similar/search', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'Authorization': `Bearer ${TokenManager.getToken()}`
+                },
+                body: JSON.stringify({ query, n_results: limit, ...filters })
+            });
+            if (!res.ok) return { available: false, results: [] };
+            return await res.json();
+        } catch (e) {
+            console.warn('AI 검색 실패:', e);
+            return { available: false, results: [] };
+        }
+    },
+    classifyIssue: async (description, detailNotes = '') => {
+        try {
+            const res = await fetch('/ai-api/classify', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'Authorization': `Bearer ${TokenManager.getToken()}`
+                },
+                body: JSON.stringify({ description, detail_notes: detailNotes })
+            });
+            if (!res.ok) return { available: false };
+            return await res.json();
+        } catch (e) {
+            console.warn('AI 분류 실패:', e);
+            return { available: false };
+        }
+    },
+    generateDailyReport: async (date, projectId) => {
+        try {
+            const res = await fetch('/ai-api/report/daily', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'Authorization': `Bearer ${TokenManager.getToken()}`
+                },
+                body: JSON.stringify({ date, project_id: projectId })
+            });
+            if (!res.ok) return { available: false };
+            return await res.json();
+        } catch (e) {
+            console.warn('AI 보고서 생성 실패:', e);
+            return { available: false };
+        }
+    },
+    syncEmbeddings: async () => {
+        try {
+            const res = await fetch('/ai-api/embeddings/sync', {
+                method: 'POST',
+                headers: { 'Authorization': `Bearer ${TokenManager.getToken()}` }
+            });
+            if (!res.ok) return { status: 'error' };
+            return await res.json();
+        } catch (e) {
+            return { status: 'error' };
+        }
+    },
+    checkHealth: async () => {
+        try {
+            const res = await fetch('/ai-api/health');
+            return await res.json();
+        } catch (e) {
+            return { status: 'disconnected' };
+        }
+    },
+    // RAG: 해결방안 제안
+    suggestSolution: async (issueId) => {
+        try {
+            const res = await fetch(`/ai-api/rag/suggest-solution/${issueId}`, {
+                method: 'POST',
+                headers: { 'Authorization': `Bearer ${TokenManager.getToken()}` }
+            });
+            if (!res.ok) return { available: false };
+            return await res.json();
+        } catch (e) {
+            console.warn('AI 해결방안 제안 실패:', e);
+            return { available: false };
+        }
+    },
+    // RAG: 자연어 질의
+    askQuestion: async (question, projectId = null) => {
+        try {
+            const res = await fetch('/ai-api/rag/ask', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'Authorization': `Bearer ${TokenManager.getToken()}`
+                },
+                body: JSON.stringify({ question, project_id: projectId })
+            });
+            if (!res.ok) return { available: false };
+            return await res.json();
+        } catch (e) {
+            console.warn('AI 질의 실패:', e);
+            return { available: false };
+        }
+    },
+    // RAG: 패턴 분석
+    analyzePattern: async (description) => {
+        try {
+            const res = await fetch('/ai-api/rag/pattern', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'Authorization': `Bearer ${TokenManager.getToken()}`
+                },
+                body: JSON.stringify({ description })
+            });
+            if (!res.ok) return { available: false };
+            return await res.json();
+        } catch (e) {
+            console.warn('AI 패턴 분석 실패:', e);
+            return { available: false };
+        }
+    },
+    // RAG: 강화 분류 (과거 사례 참고)
+    classifyWithRAG: async (description, detailNotes = '') => {
+        try {
+            const res = await fetch('/ai-api/rag/classify', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'Authorization': `Bearer ${TokenManager.getToken()}`
+                },
+                body: JSON.stringify({ description, detail_notes: detailNotes })
+            });
+            if (!res.ok) return { available: false };
+            return await res.json();
+        } catch (e) {
+            console.warn('AI RAG 분류 실패:', e);
+            return { available: false };
+        }
+    }
+};
+
 // 프로젝트 API
 const ProjectsAPI = {
     getAll: (activeOnly = false) => {

system3-nonconformance/web/static/js/components/common-header.js

@@ -86,6 +86,15 @@ class CommonHeader {
                     }
                 ]
             },
+            {
+                id: 'ai_assistant',
+                title: 'AI 어시스턴트',
+                icon: 'fas fa-robot',
+                url: '/ai-assistant.html',
+                pageName: 'ai_assistant',
+                color: 'text-purple-600',
+                bgColor: 'text-purple-600 hover:bg-purple-50'
+            },
         ];
     }
 

system3-nonconformance/web/static/js/core/permissions.js

@@ -20,7 +20,8 @@ class PagePermissionManager {
             'issues_inbox': { title: '수신함', defaultAccess: true },
             'issues_management': { title: '관리함', defaultAccess: false },
             'issues_archive': { title: '폐기함', defaultAccess: false },
-            'reports': { title: '보고서', defaultAccess: false }
+            'reports': { title: '보고서', defaultAccess: false },
+            'ai_assistant': { title: 'AI 어시스턴트', defaultAccess: false }
         };
     }
 

system3-nonconformance/web/static/js/pages/ai-assistant.js

@@ -0,0 +1,584 @@
+/**
+ * ai-assistant.js — AI 어시스턴트 페이지 스크립트
+ */
+
+let currentUser = null;
+let projects = [];
+let chatHistory = [];
+
+// 애니메이션 함수들
+function animateHeaderAppearance() {
+    const header = document.getElementById('commonHeader');
+    if (header) {
+        header.classList.add('header-fade-in');
+    }
+}
+
+// 페이지 초기화
+async function initializeAiAssistant() {
+    try {
+        currentUser = await window.authManager.checkAuth();
+        if (!currentUser) {
+            document.getElementById('loadingScreen').style.display = 'none';
+            window.location.href = '/';
+            return;
+        }
+
+        window.pagePermissionManager.setUser(currentUser);
+        await window.pagePermissionManager.loadPagePermissions();
+
+        if (!window.pagePermissionManager.canAccessPage('ai_assistant')) {
+            alert('AI 어시스턴트 접근 권한이 없습니다.');
+            window.location.href = '/';
+            return;
+        }
+
+        if (window.commonHeader) {
+            await window.commonHeader.init(currentUser, 'ai_assistant');
+            setTimeout(() => animateHeaderAppearance(), 100);
+        }
+
+        await loadProjects();
+        checkAiHealth();
+        document.getElementById('loadingScreen').style.display = 'none';
+
+    } catch (error) {
+        console.error('AI 어시스턴트 초기화 실패:', error);
+        alert('페이지를 불러오는데 실패했습니다.');
+        document.getElementById('loadingScreen').style.display = 'none';
+    }
+}
+
+// AuthManager 대기 후 초기화
+document.addEventListener('DOMContentLoaded', () => {
+    const checkAuthManager = () => {
+        if (window.authManager) {
+            initializeAiAssistant();
+        } else {
+            setTimeout(checkAuthManager, 100);
+        }
+    };
+    checkAuthManager();
+});
+
+// 프로젝트 로드
+async function loadProjects() {
+    try {
+        const apiUrl = window.API_BASE_URL || '/api';
+        const response = await fetch(`${apiUrl}/projects/`, {
+            headers: {
+                'Authorization': `Bearer ${TokenManager.getToken()}`,
+                'Content-Type': 'application/json'
+            }
+        });
+
+        if (response.ok) {
+            projects = await response.json();
+            updateProjectFilters();
+        }
+    } catch (error) {
+        console.error('프로젝트 로드 실패:', error);
+    }
+}
+
+function updateProjectFilters() {
+    const selects = ['qaProjectFilter', 'searchProjectFilter'];
+    selects.forEach(id => {
+        const select = document.getElementById(id);
+        if (!select) return;
+        // 기존 옵션 유지 (첫 번째 "전체 프로젝트")
+        while (select.options.length > 1) select.remove(1);
+        projects.forEach(p => {
+            const opt = document.createElement('option');
+            opt.value = p.id;
+            opt.textContent = p.project_name;
+            select.appendChild(opt);
+        });
+    });
+}
+
+// ─── AI 상태 체크 ───────────────────────────────────────
+async function checkAiHealth() {
+    try {
+        const health = await AiAPI.checkHealth();
+        const statusText = document.getElementById('aiStatusText');
+        const statusIcon = document.getElementById('aiStatusIcon');
+        const embeddingCount = document.getElementById('aiEmbeddingCount');
+        const modelName = document.getElementById('aiModelName');
+
+        if (health.status === 'healthy' || health.status === 'ok') {
+            statusText.textContent = '연결됨';
+            statusText.classList.add('text-green-600');
+            statusIcon.innerHTML = '<i class="fas fa-check-circle text-green-500 text-xl"></i>';
+            statusIcon.className = 'w-10 h-10 rounded-full bg-green-50 flex items-center justify-center';
+        } else {
+            statusText.textContent = '연결 안됨';
+            statusText.classList.add('text-red-500');
+            statusIcon.innerHTML = '<i class="fas fa-times-circle text-red-500 text-xl"></i>';
+            statusIcon.className = 'w-10 h-10 rounded-full bg-red-50 flex items-center justify-center';
+        }
+
+        const embCount = health.embedding_count
+            ?? health.total_embeddings
+            ?? health.embeddings?.total_documents;
+        if (embCount !== undefined) {
+            embeddingCount.textContent = embCount.toLocaleString() + '건';
+        }
+
+        const model = health.model
+            || health.llm_model
+            || (health.ollama?.models?.[0]);
+        if (model) {
+            modelName.textContent = model;
+        }
+    } catch (error) {
+        console.error('AI 상태 체크 실패:', error);
+        document.getElementById('aiStatusText').textContent = '오류';
+    }
+}
+
+// ─── Q&A 채팅 ───────────────────────────────────────────
+function setQuickQuestion(text) {
+    document.getElementById('qaQuestion').value = text;
+    document.getElementById('qaQuestion').focus();
+}
+
+async function submitQuestion() {
+    const input = document.getElementById('qaQuestion');
+    const question = input.value.trim();
+    if (!question) return;
+
+    const projectId = document.getElementById('qaProjectFilter').value || null;
+
+    // 플레이스홀더 제거
+    const placeholder = document.getElementById('chatPlaceholder');
+    if (placeholder) placeholder.remove();
+
+    // 사용자 메시지 추가
+    appendChatMessage('user', question);
+    input.value = '';
+    chatHistory.push({ role: 'user', content: question });
+
+    // 로딩 표시
+    appendChatLoading();
+
+    try {
+        const result = await AiAPI.askQuestion(question, projectId);
+        removeChatLoading();
+
+        if (result.available === false) {
+            appendChatMessage('ai', 'AI 서비스에 연결할 수 없습니다. 잠시 후 다시 시도해주세요.');
+            return;
+        }
+
+        const answer = result.answer || result.response || '답변을 생성할 수 없습니다.';
+        const sources = result.sources || result.related_issues || [];
+        appendChatMessage('ai', answer, sources);
+        chatHistory.push({ role: 'ai', content: answer });
+
+    } catch (error) {
+        removeChatLoading();
+        appendChatMessage('ai', '오류가 발생했습니다: ' + error.message);
+    }
+}
+
+function appendChatMessage(role, content, sources) {
+    const container = document.getElementById('chatContainer');
+
+    const wrapper = document.createElement('div');
+    wrapper.className = `flex ${role === 'user' ? 'justify-end' : 'justify-start'} mb-3`;
+
+    const bubble = document.createElement('div');
+    bubble.className = `chat-bubble ${role === 'user' ? 'chat-bubble-user' : 'chat-bubble-ai'}`;
+
+    // 내용 렌더링
+    const contentDiv = document.createElement('div');
+    contentDiv.className = 'text-sm whitespace-pre-line';
+    contentDiv.textContent = content;
+    bubble.appendChild(contentDiv);
+
+    // AI 답변 참고 사례
+    if (role === 'ai' && sources && sources.length > 0) {
+        const sourcesDiv = document.createElement('div');
+        sourcesDiv.className = 'mt-2 pt-2 border-t border-gray-200';
+
+        const sourcesTitle = document.createElement('p');
+        sourcesTitle.className = 'text-xs text-gray-500 mb-1';
+        sourcesTitle.textContent = '참고 사례:';
+        sourcesDiv.appendChild(sourcesTitle);
+
+        sources.forEach(source => {
+            const issueId = source.issue_id || source.id;
+            const desc = source.description || source.title || `이슈 #${issueId}`;
+            const similarity = source.similarity ? ` (${(source.similarity * 100).toFixed(0)}%)` : '';
+
+            const link = document.createElement('span');
+            link.className = 'source-link text-xs block';
+            link.textContent = `#${issueId} ${desc}${similarity}`;
+            link.onclick = () => showAiIssueDetail(issueId);
+            sourcesDiv.appendChild(link);
+        });
+
+        bubble.appendChild(sourcesDiv);
+    }
+
+    wrapper.appendChild(bubble);
+    container.appendChild(wrapper);
+    container.scrollTop = container.scrollHeight;
+}
+
+function appendChatLoading() {
+    const container = document.getElementById('chatContainer');
+    const wrapper = document.createElement('div');
+    wrapper.className = 'flex justify-start mb-3';
+    wrapper.id = 'chatLoadingBubble';
+
+    wrapper.innerHTML = `
+        <div class="chat-bubble chat-bubble-ai">
+            <div class="typing-indicator">
+                <div class="typing-dot"></div>
+                <div class="typing-dot"></div>
+                <div class="typing-dot"></div>
+            </div>
+        </div>
+    `;
+
+    container.appendChild(wrapper);
+    container.scrollTop = container.scrollHeight;
+}
+
+function removeChatLoading() {
+    const el = document.getElementById('chatLoadingBubble');
+    if (el) el.remove();
+}
+
+function clearChat() {
+    const container = document.getElementById('chatContainer');
+    container.innerHTML = `
+        <div class="text-center text-gray-400 text-sm py-8" id="chatPlaceholder">
+            <i class="fas fa-robot text-4xl mb-3 text-gray-300"></i>
+            <p>부적합 관련 질문을 입력하세요.</p>
+            <p class="text-xs mt-1">과거 사례를 분석하여 답변합니다.</p>
+        </div>
+    `;
+    chatHistory = [];
+}
+
+// ─── 시맨틱 검색 ────────────────────────────────────────
+async function executeSemanticSearch() {
+    const query = document.getElementById('searchQuery').value.trim();
+    if (!query) return;
+
+    const projectId = document.getElementById('searchProjectFilter').value || undefined;
+    const category = document.getElementById('searchCategoryFilter').value || undefined;
+    const limit = parseInt(document.getElementById('searchResultCount').value);
+
+    const loading = document.getElementById('searchLoading');
+    const resultsDiv = document.getElementById('searchResults');
+    loading.classList.remove('hidden');
+    resultsDiv.innerHTML = '';
+
+    try {
+        const filters = {};
+        if (projectId) filters.project_id = parseInt(projectId);
+        if (category) filters.category = category;
+
+        const result = await AiAPI.searchSimilar(query, limit, filters);
+        loading.classList.add('hidden');
+
+        if (!result.results || result.results.length === 0) {
+            resultsDiv.innerHTML = '<p class="text-sm text-gray-500 text-center py-4">검색 결과가 없습니다.</p>';
+            return;
+        }
+
+        result.results.forEach((item, idx) => {
+            const issueId = item.issue_id || item.id;
+            const desc = item.description || '';
+            const similarity = item.similarity ? (item.similarity * 100).toFixed(1) : '-';
+            const category = item.category || '';
+            const status = item.status || item.review_status || '';
+
+            const card = document.createElement('div');
+            card.className = 'result-item border border-gray-200 rounded-lg p-3 flex items-start gap-3';
+            card.onclick = () => showAiIssueDetail(issueId);
+
+            card.innerHTML = `
+                <div class="flex-shrink-0 w-8 h-8 rounded-full bg-indigo-50 flex items-center justify-center text-xs font-bold text-indigo-600">
+                    ${idx + 1}
+                </div>
+                <div class="flex-1 min-w-0">
+                    <div class="flex items-center gap-2 mb-1">
+                        <span class="text-xs font-mono text-gray-400">#${issueId}</span>
+                        ${category ? `<span class="text-xs bg-gray-100 text-gray-600 px-1.5 py-0.5 rounded">${category}</span>` : ''}
+                        ${status ? `<span class="text-xs bg-blue-50 text-blue-600 px-1.5 py-0.5 rounded">${status}</span>` : ''}
+                    </div>
+                    <p class="text-sm text-gray-700 truncate">${desc}</p>
+                </div>
+                <div class="flex-shrink-0 text-right">
+                    <span class="text-sm font-bold text-indigo-600">${similarity}%</span>
+                    <p class="text-xs text-gray-400">유사도</p>
+                </div>
+            `;
+            resultsDiv.appendChild(card);
+        });
+    } catch (error) {
+        loading.classList.add('hidden');
+        resultsDiv.innerHTML = `<p class="text-sm text-red-500 text-center py-4">검색 중 오류가 발생했습니다.</p>`;
+    }
+}
+
+// ─── 패턴 분석 ──────────────────────────────────────────
+async function executePatternAnalysis() {
+    const input = document.getElementById('patternInput').value.trim();
+    if (!input) return;
+
+    const loading = document.getElementById('patternLoading');
+    const resultsDiv = document.getElementById('patternResults');
+    loading.classList.remove('hidden');
+    resultsDiv.classList.add('hidden');
+
+    try {
+        const result = await AiAPI.analyzePattern(input);
+        loading.classList.add('hidden');
+        resultsDiv.classList.remove('hidden');
+
+        if (result.available === false) {
+            resultsDiv.innerHTML = '<p class="text-sm text-gray-500 text-center py-4">AI 서비스에 연결할 수 없습니다.</p>';
+            return;
+        }
+
+        let html = '';
+
+        // 분석 결과
+        const analysis = result.analysis || result.pattern || result.answer || '';
+        if (analysis) {
+            html += `
+                <div class="bg-green-50 border border-green-200 rounded-lg p-4 mb-3">
+                    <h4 class="text-sm font-semibold text-green-800 mb-2">
+                        <i class="fas fa-chart-bar mr-1"></i>분석 결과
+                    </h4>
+                    <div class="text-sm text-gray-700 whitespace-pre-line">${analysis}</div>
+                </div>
+            `;
+        }
+
+        // 관련 이슈
+        const relatedIssues = result.related_issues || result.sources || [];
+        if (relatedIssues.length > 0) {
+            html += `
+                <div class="border border-gray-200 rounded-lg p-4">
+                    <h4 class="text-sm font-semibold text-gray-700 mb-2">
+                        <i class="fas fa-link mr-1"></i>관련 이슈 (${relatedIssues.length}건)
+                    </h4>
+                    <div class="space-y-1">
+                        ${relatedIssues.map(issue => {
+                            const id = issue.issue_id || issue.id;
+                            const desc = issue.description || '';
+                            return `<div class="result-item text-sm p-2 rounded border border-gray-100" onclick="showAiIssueDetail(${id})">
+                                <span class="font-mono text-gray-400 text-xs">#${id}</span>
+                                <span class="text-gray-700">${desc}</span>
+                            </div>`;
+                        }).join('')}
+                    </div>
+                </div>
+            `;
+        }
+
+        resultsDiv.innerHTML = html || '<p class="text-sm text-gray-500 text-center py-4">분석 결과가 없습니다.</p>';
+    } catch (error) {
+        loading.classList.add('hidden');
+        resultsDiv.classList.remove('hidden');
+        resultsDiv.innerHTML = `<p class="text-sm text-red-500 text-center py-4">분석 중 오류가 발생했습니다.</p>`;
+    }
+}
+
+// ─── AI 분류 ────────────────────────────────────────────
+async function executeClassification(useRAG) {
+    const description = document.getElementById('classifyDescription').value.trim();
+    if (!description) {
+        alert('부적합 설명을 입력해주세요.');
+        return;
+    }
+
+    const detailNotes = document.getElementById('classifyDetail').value.trim();
+    const loading = document.getElementById('classifyLoading');
+    const resultsDiv = document.getElementById('classifyResults');
+    loading.classList.remove('hidden');
+    resultsDiv.classList.add('hidden');
+
+    try {
+        const result = useRAG
+            ? await AiAPI.classifyWithRAG(description, detailNotes)
+            : await AiAPI.classifyIssue(description, detailNotes);
+
+        loading.classList.add('hidden');
+        resultsDiv.classList.remove('hidden');
+
+        if (result.available === false) {
+            resultsDiv.innerHTML = '<p class="text-sm text-gray-500 text-center py-4">AI 서비스에 연결할 수 없습니다.</p>';
+            return;
+        }
+
+        const methodLabel = useRAG ? 'RAG 분류 (과거 사례 참고)' : '기본 분류';
+        const methodColor = useRAG ? 'purple' : 'amber';
+
+        let html = `
+            <div class="bg-${methodColor}-50 border border-${methodColor}-200 rounded-lg p-4">
+                <h4 class="text-sm font-semibold text-${methodColor}-800 mb-3">
+                    <i class="fas fa-${useRAG ? 'tags' : 'tag'} mr-1"></i>${methodLabel}
+                </h4>
+                <div class="grid grid-cols-2 gap-3">
+        `;
+
+        // 분류 결과 필드 표시
+        const fields = [
+            { key: 'category', label: '카테고리' },
+            { key: 'discipline', label: '공종' },
+            { key: 'severity', label: '심각도' },
+            { key: 'root_cause', label: '근본 원인' },
+            { key: 'priority', label: '우선순위' },
+            { key: 'suggested_action', label: '권장 조치' }
+        ];
+
+        fields.forEach(field => {
+            const val = result[field.key] || result.classification?.[field.key];
+            if (val) {
+                html += `
+                    <div>
+                        <span class="text-xs text-gray-500">${field.label}</span>
+                        <p class="text-sm font-medium text-gray-800">${val}</p>
+                    </div>
+                `;
+            }
+        });
+
+        html += '</div>';
+
+        // 신뢰도
+        const confidence = result.confidence || result.classification?.confidence;
+        if (confidence) {
+            const pct = (typeof confidence === 'number' && confidence <= 1)
+                ? (confidence * 100).toFixed(0)
+                : confidence;
+            html += `
+                <div class="mt-3 pt-3 border-t border-${methodColor}-200">
+                    <span class="text-xs text-gray-500">신뢰도</span>
+                    <div class="flex items-center gap-2 mt-1">
+                        <div class="flex-1 bg-gray-200 rounded-full h-2">
+                            <div class="bg-${methodColor}-500 h-2 rounded-full" style="width: ${pct}%"></div>
+                        </div>
+                        <span class="text-sm font-bold text-${methodColor}-600">${pct}%</span>
+                    </div>
+                </div>
+            `;
+        }
+
+        // RAG 참고 사례
+        const sources = result.sources || result.related_issues || result.similar_cases || [];
+        if (sources.length > 0) {
+            html += `
+                <div class="mt-3 pt-3 border-t border-${methodColor}-200">
+                    <span class="text-xs text-gray-500">참고 사례</span>
+                    <div class="mt-1 space-y-1">
+                        ${sources.map(s => {
+                            const id = s.issue_id || s.id;
+                            const desc = s.description || '';
+                            return `<div class="result-item text-xs p-1.5 rounded border border-gray-100" onclick="showAiIssueDetail(${id})">
+                                <span class="font-mono text-gray-400">#${id}</span> ${desc}
+                            </div>`;
+                        }).join('')}
+                    </div>
+                </div>
+            `;
+        }
+
+        html += '</div>';
+        resultsDiv.innerHTML = html;
+    } catch (error) {
+        loading.classList.add('hidden');
+        resultsDiv.classList.remove('hidden');
+        resultsDiv.innerHTML = `<p class="text-sm text-red-500 text-center py-4">분류 중 오류가 발생했습니다.</p>`;
+    }
+}
+
+// ─── 이슈 상세 모달 ─────────────────────────────────────
+async function showAiIssueDetail(issueId) {
+    const modal = document.getElementById('aiIssueModal');
+    const title = document.getElementById('aiIssueModalTitle');
+    const body = document.getElementById('aiIssueModalBody');
+
+    title.textContent = `이슈 #${issueId}`;
+    body.innerHTML = '<div class="text-center py-8"><i class="fas fa-spinner fa-spin text-purple-500 text-xl"></i><p class="text-sm text-gray-500 mt-2">불러오는 중...</p></div>';
+    modal.classList.remove('hidden');
+
+    try {
+        const apiUrl = window.API_BASE_URL || '/api';
+        const response = await fetch(`${apiUrl}/issues/${issueId}`, {
+            headers: {
+                'Authorization': `Bearer ${TokenManager.getToken()}`,
+                'Content-Type': 'application/json'
+            }
+        });
+
+        if (!response.ok) throw new Error('이슈를 불러올 수 없습니다.');
+        const issue = await response.json();
+
+        const statusMap = {
+            'pending': '대기',
+            'in_progress': '진행 중',
+            'completed': '완료',
+            'rejected': '반려'
+        };
+
+        body.innerHTML = `
+            <div class="space-y-3">
+                <div>
+                    <span class="text-xs text-gray-500">프로젝트</span>
+                    <p class="font-medium">${issue.project_name || '-'}</p>
+                </div>
+                <div>
+                    <span class="text-xs text-gray-500">설명</span>
+                    <p class="font-medium">${issue.description || '-'}</p>
+                </div>
+                ${issue.detail_notes ? `<div>
+                    <span class="text-xs text-gray-500">상세 내용</span>
+                    <p class="whitespace-pre-line">${issue.detail_notes}</p>
+                </div>` : ''}
+                <div class="grid grid-cols-2 gap-3">
+                    <div>
+                        <span class="text-xs text-gray-500">카테고리</span>
+                        <p>${issue.category || '-'}</p>
+                    </div>
+                    <div>
+                        <span class="text-xs text-gray-500">상태</span>
+                        <p>${statusMap[issue.review_status] || issue.review_status || '-'}</p>
+                    </div>
+                    <div>
+                        <span class="text-xs text-gray-500">위치</span>
+                        <p>${issue.location || '-'}</p>
+                    </div>
+                    <div>
+                        <span class="text-xs text-gray-500">담당자</span>
+                        <p>${issue.assigned_to_name || issue.assignee_name || '-'}</p>
+                    </div>
+                </div>
+                ${issue.resolution ? `<div>
+                    <span class="text-xs text-gray-500">해결 방안</span>
+                    <p class="whitespace-pre-line">${issue.resolution}</p>
+                </div>` : ''}
+                <div>
+                    <span class="text-xs text-gray-500">등록일</span>
+                    <p>${issue.created_at ? new Date(issue.created_at).toLocaleDateString('ko-KR') : '-'}</p>
+                </div>
+            </div>
+        `;
+    } catch (error) {
+        body.innerHTML = `<p class="text-sm text-red-500 text-center py-4">이슈를 불러오는데 실패했습니다.</p>`;
+    }
+}
+
+// 엔트리 포인트
+function initializeAiAssistantApp() {
+    console.log('AI 어시스턴트 스크립트 로드 완료');
+}
+initializeAiAssistantApp();

system3-nonconformance/web/static/js/pages/issues-dashboard.js

@@ -1786,8 +1786,130 @@ document.addEventListener('DOMContentLoaded', function() {
     }
 });
 
-// API 스크립트 동적 로드
-const script = document.createElement('script');
-script.src = '/static/js/api.js?v=20260213';
-script.onload = initializeDashboardApp;
-document.body.appendChild(script);
+// AI 시맨틱 검색
+async function aiSemanticSearch() {
+    const query = document.getElementById('aiSearchQuery')?.value?.trim();
+    if (!query || typeof AiAPI === 'undefined') return;
+
+    const loading = document.getElementById('aiSearchLoading');
+    const results = document.getElementById('aiSearchResults');
+    if (loading) loading.classList.remove('hidden');
+    if (results) { results.classList.add('hidden'); results.innerHTML = ''; }
+
+    const data = await AiAPI.searchSimilar(query, 8);
+    if (loading) loading.classList.add('hidden');
+
+    if (!data.available || !data.results || data.results.length === 0) {
+        results.innerHTML = '<p class="text-sm text-gray-400 text-center py-2">검색 결과가 없습니다</p>';
+        results.classList.remove('hidden');
+        return;
+    }
+
+    results.innerHTML = data.results.map(r => {
+        const meta = r.metadata || {};
+        const similarity = Math.round((r.similarity || 0) * 100);
+        const issueId = meta.issue_id || r.id.replace('issue_', '');
+        const doc = (r.document || '').substring(0, 100);
+        const cat = meta.category || '';
+        const status = meta.review_status || '';
+        return `
+            <div class="flex items-start space-x-3 bg-gray-50 rounded-lg p-3 hover:bg-purple-50 transition-colors cursor-pointer"
+                 onclick="showAiIssueModal(${issueId})"
+                <div class="flex-shrink-0 w-10 h-10 rounded-full bg-purple-100 flex items-center justify-center">
+                    <span class="text-xs font-bold text-purple-700">${similarity}%</span>
+                </div>
+                <div class="flex-1 min-w-0">
+                    <div class="flex items-center space-x-2 mb-1">
+                        <span class="text-sm font-medium text-gray-800">No.${issueId}</span>
+                        ${cat ? `<span class="text-xs px-1.5 py-0.5 rounded bg-purple-100 text-purple-700">${cat}</span>` : ''}
+                        ${status ? `<span class="text-xs text-gray-400">${status}</span>` : ''}
+                    </div>
+                    <p class="text-xs text-gray-500 truncate">${doc}</p>
+                </div>
+            </div>
+        `;
+    }).join('');
+    results.classList.remove('hidden');
+}
+
+// RAG Q&A
+async function aiAskQuestion() {
+    const question = document.getElementById('aiQaQuestion')?.value?.trim();
+    if (!question || typeof AiAPI === 'undefined') return;
+
+    const loading = document.getElementById('aiQaLoading');
+    const result = document.getElementById('aiQaResult');
+    const answer = document.getElementById('aiQaAnswer');
+    const sources = document.getElementById('aiQaSources');
+
+    if (loading) loading.classList.remove('hidden');
+    if (result) result.classList.add('hidden');
+
+    const projectId = document.getElementById('projectFilter')?.value || null;
+    const data = await AiAPI.askQuestion(question, projectId ? parseInt(projectId) : null);
+
+    if (loading) loading.classList.add('hidden');
+
+    if (!data.available) {
+        if (answer) answer.textContent = 'AI 서비스를 사용할 수 없습니다';
+        if (result) result.classList.remove('hidden');
+        return;
+    }
+
+    if (answer) answer.textContent = data.answer || '';
+    if (sources && data.sources) {
+        const refs = data.sources.slice(0, 5).map(s =>
+            `No.${s.id}(${s.similarity}%)`
+        ).join(', ');
+        sources.textContent = refs ? `참고: ${refs}` : '';
+    }
+    if (result) result.classList.remove('hidden');
+}
+
+// AI 이슈 상세 모달
+async function showAiIssueModal(issueId) {
+    const modal = document.getElementById('aiIssueModal');
+    const title = document.getElementById('aiIssueModalTitle');
+    const body = document.getElementById('aiIssueModalBody');
+    if (!modal || !body) return;
+
+    title.textContent = `부적합 No.${issueId}`;
+    body.innerHTML = '<div class="text-center py-4"><i class="fas fa-spinner fa-spin text-purple-500"></i> 로딩 중...</div>';
+    modal.classList.remove('hidden');
+
+    try {
+        const token = typeof TokenManager !== 'undefined' ? TokenManager.getToken() : null;
+        const headers = token ? { 'Authorization': `Bearer ${token}` } : {};
+        const res = await fetch(`/api/issues/${issueId}`, { headers });
+        if (!res.ok) throw new Error('fetch failed');
+        const issue = await res.json();
+
+        const categoryText = typeof getCategoryText === 'function' ? getCategoryText(issue.category || issue.final_category) : (issue.category || issue.final_category || '-');
+        const statusText = typeof getStatusText === 'function' ? getStatusText(issue.review_status) : (issue.review_status || '-');
+        const deptText = typeof getDepartmentText === 'function' ? getDepartmentText(issue.responsible_department) : (issue.responsible_department || '-');
+
+        body.innerHTML = `
+            <div class="flex flex-wrap gap-2 mb-3">
+                <span class="px-2 py-1 text-xs rounded-full bg-blue-100 text-blue-700">${categoryText}</span>
+                <span class="px-2 py-1 text-xs rounded-full bg-green-100 text-green-700">${statusText}</span>
+                <span class="px-2 py-1 text-xs rounded-full bg-orange-100 text-orange-700">${deptText}</span>
+                ${issue.report_date ? `<span class="px-2 py-1 text-xs rounded-full bg-gray-100 text-gray-600">${issue.report_date}</span>` : ''}
+            </div>
+            ${issue.description ? `<div><strong class="text-gray-600">설명:</strong><p class="mt-1 whitespace-pre-line">${issue.description}</p></div>` : ''}
+            ${issue.detail_notes ? `<div><strong class="text-gray-600">상세:</strong><p class="mt-1 whitespace-pre-line">${issue.detail_notes}</p></div>` : ''}
+            ${issue.final_description ? `<div><strong class="text-gray-600">최종 판정:</strong><p class="mt-1 whitespace-pre-line">${issue.final_description}</p></div>` : ''}
+            ${issue.solution ? `<div><strong class="text-gray-600">해결방안:</strong><p class="mt-1 whitespace-pre-line">${issue.solution}</p></div>` : ''}
+            ${issue.cause_detail ? `<div><strong class="text-gray-600">원인:</strong><p class="mt-1 whitespace-pre-line">${issue.cause_detail}</p></div>` : ''}
+            ${issue.management_comment ? `<div><strong class="text-gray-600">관리 의견:</strong><p class="mt-1 whitespace-pre-line">${issue.management_comment}</p></div>` : ''}
+            <div class="pt-3 border-t text-right">
+                <a href="/issues-management.html#issue-${issueId}" class="text-xs text-purple-500 hover:underline">관리함에서 보기 →</a>
+            </div>
+        `;
+    } catch (e) {
+        body.innerHTML = `<p class="text-red-500">이슈를 불러올 수 없습니다</p>
+            <a href="/issues-management.html#issue-${issueId}" class="text-xs text-purple-500 hover:underline">관리함에서 보기 →</a>`;
+    }
+}
+
+// 초기화
+initializeDashboardApp();

system3-nonconformance/web/static/js/pages/issues-inbox.js

@@ -879,14 +879,81 @@ function showError(message) {
     alert(message);
 }
 
-// API 스크립트 동적 로딩
-const script = document.createElement('script');
-script.src = '/static/js/api.js?v=20260213';
-script.onload = function() {
-    console.log('API 스크립트 로드 완료 (issues-inbox.html)');
+// AI 분류 추천
+async function aiClassifyCurrentIssue() {
+    if (!currentIssueId || typeof AiAPI === 'undefined') return;
+    const issue = issues.find(i => i.id === currentIssueId);
+    if (!issue) return;
+
+    const btn = document.getElementById('aiClassifyBtn');
+    const loading = document.getElementById('aiClassifyLoading');
+    const result = document.getElementById('aiClassifyResult');
+    if (btn) btn.disabled = true;
+    if (loading) loading.classList.remove('hidden');
+    if (result) result.classList.add('hidden');
+
+    // RAG 강화 분류 사용 (과거 사례 참고)
+    const classifyFn = AiAPI.classifyWithRAG || AiAPI.classifyIssue;
+    const data = await classifyFn(
+        issue.description || issue.final_description || '',
+        issue.detail_notes || ''
+    );
+
+    if (loading) loading.classList.add('hidden');
+    if (btn) btn.disabled = false;
+
+    if (!data.available) {
+        if (result) {
+            result.innerHTML = '<p class="text-xs text-red-500">AI 서비스를 사용할 수 없습니다</p>';
+            result.classList.remove('hidden');
+        }
+        return;
+    }
+
+    const categoryMap = {
+        'material_missing': '자재 누락',
+        'design_error': '설계 오류',
+        'incoming_defect': '반입 불량',
+        'inspection_miss': '검사 누락',
+    };
+    const deptMap = {
+        'production': '생산',
+        'quality': '품질',
+        'purchasing': '구매',
+        'design': '설계',
+        'sales': '영업',
+    };
+
+    const cat = data.category || '';
+    const dept = data.responsible_department || '';
+    const severity = data.severity || '';
+    const summary = data.summary || '';
+    const confidence = data.category_confidence ? Math.round(data.category_confidence * 100) : '';
+
+    result.innerHTML = `
+        <div class="space-y-1">
+            <p><strong>분류:</strong> ${categoryMap[cat] || cat} ${confidence ? `(${confidence}%)` : ''}</p>
+            <p><strong>부서:</strong> ${deptMap[dept] || dept}</p>
+            <p><strong>심각도:</strong> ${severity}</p>
+            ${summary ? `<p><strong>요약:</strong> ${summary}</p>` : ''}
+            <button onclick="applyAiClassification('${cat}')"
+                    class="mt-2 px-3 py-1 bg-purple-600 text-white text-xs rounded hover:bg-purple-700">
+                <i class="fas fa-check mr-1"></i>적용
+            </button>
+        </div>
+    `;
+    result.classList.remove('hidden');
+}
+
+function applyAiClassification(category) {
+    const reviewCategory = document.getElementById('reviewCategory');
+    if (reviewCategory && category) {
+        reviewCategory.value = category;
+    }
+    if (window.showToast) {
+        window.showToast('AI 추천이 적용되었습니다', 'success');
+    }
+}
+
+// 초기화 (api.js는 HTML에서 로드됨)
 initializeInbox();
-};
-script.onerror = function() {
-    console.error('API 스크립트 로드 실패');
-};
-document.head.appendChild(script);

system3-nonconformance/web/static/js/pages/issues-management.js

@@ -930,13 +930,100 @@ async function openIssueDetailModal(issueId) {
 
     // 모달 표시
     document.getElementById('issueDetailModal').classList.remove('hidden');
+
+    // AI 유사 부적합 자동 로드
+    const aiPanel = document.getElementById('aiSimilarPanel');
+    if (aiPanel) {
+        aiPanel.classList.remove('hidden');
+        loadSimilarIssues();
+    }
 }
 
 function closeIssueDetailModal() {
     document.getElementById('issueDetailModal').classList.add('hidden');
+    const aiPanel = document.getElementById('aiSimilarPanel');
+    if (aiPanel) aiPanel.classList.add('hidden');
+    // RAG 결과 초기화
+    const suggestResult = document.getElementById('aiSuggestResult');
+    if (suggestResult) suggestResult.classList.add('hidden');
     currentModalIssueId = null;
 }
 
+// RAG: AI 해결방안 제안
+async function aiSuggestSolution() {
+    if (!currentModalIssueId || typeof AiAPI === 'undefined') return;
+    const btn = document.getElementById('aiSuggestSolutionBtn');
+    const loading = document.getElementById('aiSuggestLoading');
+    const result = document.getElementById('aiSuggestResult');
+    const content = document.getElementById('aiSuggestContent');
+    const sources = document.getElementById('aiSuggestSources');
+
+    if (btn) btn.disabled = true;
+    if (loading) loading.classList.remove('hidden');
+    if (result) result.classList.add('hidden');
+
+    const data = await AiAPI.suggestSolution(currentModalIssueId);
+
+    if (loading) loading.classList.add('hidden');
+    if (btn) btn.disabled = false;
+
+    if (!data.available) {
+        if (content) content.textContent = 'AI 서비스를 사용할 수 없습니다';
+        if (result) result.classList.remove('hidden');
+        return;
+    }
+
+    if (content) content.textContent = data.suggestion || '';
+    if (sources && data.referenced_issues) {
+        const refs = data.referenced_issues
+            .filter(r => r.has_solution)
+            .map(r => `No.${r.id}(${r.similarity}%)`)
+            .join(', ');
+        sources.textContent = refs ? `참고 사례: ${refs}` : '';
+    }
+    if (result) result.classList.remove('hidden');
+}
+
+// AI 유사 부적합 검색
+async function loadSimilarIssues() {
+    if (!currentModalIssueId || typeof AiAPI === 'undefined') return;
+    const loading = document.getElementById('aiSimilarLoading');
+    const results = document.getElementById('aiSimilarResults');
+    const empty = document.getElementById('aiSimilarEmpty');
+    if (loading) loading.classList.remove('hidden');
+    if (results) results.innerHTML = '';
+    if (empty) empty.classList.add('hidden');
+
+    const data = await AiAPI.getSimilarIssues(currentModalIssueId, 5);
+    if (loading) loading.classList.add('hidden');
+
+    if (!data.available || !data.results || data.results.length === 0) {
+        if (empty) empty.classList.remove('hidden');
+        return;
+    }
+
+    results.innerHTML = data.results.map(r => {
+        const meta = r.metadata || {};
+        const similarity = Math.round((r.similarity || 0) * 100);
+        const issueId = meta.issue_id || r.id.replace('issue_', '');
+        const doc = (r.document || '').substring(0, 80);
+        const cat = meta.category || '';
+        return `
+            <div class="bg-purple-50 border border-purple-100 rounded-lg p-3 cursor-pointer hover:bg-purple-100 transition-colors"
+                 onclick="openIssueDetailModal(${issueId})"
+                <div class="flex items-center justify-between mb-1">
+                    <span class="text-xs font-medium text-purple-700">No.${issueId}</span>
+                    <span class="text-xs px-2 py-0.5 rounded-full ${similarity >= 70 ? 'bg-purple-200 text-purple-800' : 'bg-gray-200 text-gray-600'}">
+                        ${similarity}% 유사
+                    </span>
+                </div>
+                <p class="text-xs text-gray-600 line-clamp-2">${doc}...</p>
+                ${cat ? `<span class="text-xs text-purple-500 mt-1 inline-block">${cat}</span>` : ''}
+            </div>
+        `;
+    }).join('');
+}
+
 function createModalContent(issue, project) {
     return `
         <div class="grid grid-cols-1 md:grid-cols-2 gap-6">
@@ -1186,17 +1273,8 @@ function getPriorityBadge(priority) {
     return `<span class="badge ${p.class}">${p.text}</span>`;
 }
 
-// API 스크립트 동적 로딩
-const script = document.createElement('script');
-script.src = '/static/js/api.js?v=20260213';
-script.onload = function() {
-    console.log('✅ API 스크립트 로드 완료 (issues-management.js)');
+// 초기화 (api.js는 HTML에서 로드됨)
 initializeManagement();
-};
-script.onerror = function() {
-    console.error('❌ API 스크립트 로드 실패');
-};
-document.head.appendChild(script);
 
 // 추가 정보 모달 관련 함수들
 let selectedIssueId = null;