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/services/llm_service.py

@@ -0,0 +1,173 @@
+"""
+Ollama LLM API 服務
+"""
+import json
+from typing import Optional, List, Generator
+
+import requests
+
+from app.core.config import settings
+
+
+class LLMService:
+    """Ollama LLM API 服務"""
+
+    def __init__(self):
+        self.api_url = settings.OLLAMA_API_URL
+        self.default_model = settings.OLLAMA_DEFAULT_MODEL
+
+    def list_models(self) -> List[str]:
+        """
+        列出可用模型
+
+        Returns:
+            模型 ID 列表
+        """
+        try:
+            response = requests.get(f"{self.api_url}/v1/models", timeout=10)
+            response.raise_for_status()
+            models = response.json()
+            return [m["id"] for m in models.get("data", [])]
+        except Exception as e:
+            return [f"Error: {str(e)}"]
+
+    def chat(
+        self,
+        messages: List[dict],
+        model: Optional[str] = None,
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None,
+    ) -> str:
+        """
+        聊天完成請求
+
+        Args:
+            messages: 對話訊息列表 [{"role": "user", "content": "..."}]
+            model: 模型名稱，預設使用設定檔中的模型
+            temperature: 溫度參數 (0-1)
+            max_tokens: 最大 token 數
+
+        Returns:
+            AI 回應內容
+        """
+        chat_request = {
+            "model": model or self.default_model,
+            "messages": messages,
+            "temperature": temperature,
+        }
+
+        if max_tokens:
+            chat_request["max_tokens"] = max_tokens
+
+        try:
+            response = requests.post(
+                f"{self.api_url}/v1/chat/completions",
+                json=chat_request,
+                timeout=60,
+            )
+            response.raise_for_status()
+            result = response.json()
+            return result["choices"][0]["message"]["content"]
+        except Exception as e:
+            return f"Error: {str(e)}"
+
+    def chat_stream(
+        self,
+        messages: List[dict],
+        model: Optional[str] = None,
+        temperature: float = 0.7,
+    ) -> Generator[str, None, None]:
+        """
+        串流聊天請求
+
+        Args:
+            messages: 對話訊息列表
+            model: 模型名稱
+            temperature: 溫度參數
+
+        Yields:
+            AI 回應內容片段
+        """
+        chat_request = {
+            "model": model or self.default_model,
+            "messages": messages,
+            "temperature": temperature,
+            "stream": True,
+        }
+
+        try:
+            response = requests.post(
+                f"{self.api_url}/v1/chat/completions",
+                json=chat_request,
+                stream=True,
+                timeout=120,
+            )
+            response.raise_for_status()
+
+            for line in response.iter_lines():
+                if line:
+                    if line.startswith(b"data: "):
+                        data_str = line[6:].decode("utf-8")
+                        if data_str.strip() != "[DONE]":
+                            try:
+                                data = json.loads(data_str)
+                                if "choices" in data:
+                                    delta = data["choices"][0].get("delta", {})
+                                    if "content" in delta:
+                                        yield delta["content"]
+                            except json.JSONDecodeError:
+                                continue
+        except Exception as e:
+            yield f"Error: {str(e)}"
+
+    def simple_ask(
+        self,
+        question: str,
+        system_prompt: str = "You are a helpful assistant.",
+        model: Optional[str] = None,
+    ) -> str:
+        """
+        簡單問答
+
+        Args:
+            question: 使用者問題
+            system_prompt: 系統提示詞
+            model: 模型名稱
+
+        Returns:
+            AI 回應
+        """
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": question},
+        ]
+        return self.chat(messages, model=model)
+
+    def analyze_kpi(self, kpi_data: dict) -> str:
+        """
+        分析 KPI 數據
+
+        Args:
+            kpi_data: KPI 相關數據
+
+        Returns:
+            AI 分析結果
+        """
+        system_prompt = """你是一位專業的 KPI 分析師。
+請根據提供的 KPI 數據，給出專業的分析和建議。
+回應請使用繁體中文，並保持專業且易懂。"""
+
+        question = f"""請分析以下 KPI 數據：
+
+{json.dumps(kpi_data, ensure_ascii=False, indent=2)}
+
+請提供：
+1. 數據摘要
+2. 表現評估
+3. 改善建議"""
+
+        return self.simple_ask(question, system_prompt)
+
+
+# 單例
+llm_service = LLMService()