Initial commit: KPI Management System Backend

Features:
- FastAPI backend with JWT authentication
- MySQL database with SQLAlchemy ORM
- KPI workflow: draft → pending → approved → evaluation → completed
- Ollama LLM API integration for AI features
- Gitea API integration for version control
- Complete API endpoints for KPI, dashboard, notifications

Tables: KPI_D_* prefix naming convention

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

app/api/llm.py

@@ -0,0 +1,140 @@
+"""
+LLM API
+"""
+from typing import Optional, List
+
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+
+from app.api.deps import get_current_user
+from app.models.employee import Employee
+from app.services.llm_service import llm_service
+
+router = APIRouter(prefix="/api/llm", tags=["LLM"])
+
+
+class ChatMessage(BaseModel):
+    """聊天訊息"""
+
+    role: str  # system, user, assistant
+    content: str
+
+
+class ChatRequest(BaseModel):
+    """聊天請求"""
+
+    messages: List[ChatMessage]
+    model: Optional[str] = None
+    temperature: float = 0.7
+    stream: bool = False
+
+
+class ChatResponse(BaseModel):
+    """聊天回應"""
+
+    content: str
+    model: str
+
+
+class SimpleAskRequest(BaseModel):
+    """簡單問答請求"""
+
+    question: str
+    system_prompt: Optional[str] = "You are a helpful assistant."
+    model: Optional[str] = None
+
+
+class KPIAnalyzeRequest(BaseModel):
+    """KPI 分析請求"""
+
+    kpi_data: dict
+
+
+@router.get("/models")
+def list_models(current_user: Employee = Depends(get_current_user)):
+    """
+    列出可用的 LLM 模型
+    """
+    models = llm_service.list_models()
+    return {"models": models}
+
+
+@router.post("/chat", response_model=ChatResponse)
+def chat(
+    data: ChatRequest,
+    current_user: Employee = Depends(get_current_user),
+):
+    """
+    聊天完成請求
+    """
+    if data.stream:
+        raise HTTPException(
+            status_code=400,
+            detail="請使用 /chat/stream 端點進行串流請求",
+        )
+
+    messages = [{"role": m.role, "content": m.content} for m in data.messages]
+    content = llm_service.chat(
+        messages=messages,
+        model=data.model,
+        temperature=data.temperature,
+    )
+
+    return ChatResponse(
+        content=content,
+        model=data.model or llm_service.default_model,
+    )
+
+
+@router.post("/chat/stream")
+async def chat_stream(
+    data: ChatRequest,
+    current_user: Employee = Depends(get_current_user),
+):
+    """
+    串流聊天請求
+    """
+    messages = [{"role": m.role, "content": m.content} for m in data.messages]
+
+    def generate():
+        for chunk in llm_service.chat_stream(
+            messages=messages,
+            model=data.model,
+            temperature=data.temperature,
+        ):
+            yield f"data: {chunk}\n\n"
+        yield "data: [DONE]\n\n"
+
+    return StreamingResponse(
+        generate(),
+        media_type="text/event-stream",
+    )
+
+
+@router.post("/ask")
+def simple_ask(
+    data: SimpleAskRequest,
+    current_user: Employee = Depends(get_current_user),
+):
+    """
+    簡單問答
+    """
+    response = llm_service.simple_ask(
+        question=data.question,
+        system_prompt=data.system_prompt,
+        model=data.model,
+    )
+    return {"answer": response}
+
+
+@router.post("/analyze-kpi")
+def analyze_kpi(
+    data: KPIAnalyzeRequest,
+    current_user: Employee = Depends(get_current_user),
+):
+    """
+    AI 分析 KPI 數據
+    """
+    analysis = llm_service.analyze_kpi(data.kpi_data)
+    return {"analysis": analysis}