Phase 0: 專案初始化 - 建立專案結構、環境設定與 LLM 服務模組

.env.example

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+# Flask
+FLASK_SECRET_KEY=your-secret-key-change-in-production
+FLASK_DEBUG=True
+
+# Database (MySQL)
+DB_HOST=your-db-host
+DB_PORT=3306
+DB_NAME=your-db-name
+DB_USER=your-db-user
+DB_PASSWORD=your-db-password
+
+# Ollama API
+OLLAMA_API_URL=https://your-ollama-api-url
+OLLAMA_DEFAULT_MODEL=qwen2.5:3b
+
+# Gitea
+GITEA_URL=https://your-gitea-url
+GITEA_USER=your-username
+GITEA_TOKEN=your-token

.gitignore

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+# Environment
+.env
+venv/
+env/
+.venv/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+logs/
+
+# Database
+*.db
+*.sqlite3
+
+# Uploads
+uploads/

README.md

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+# DIT_C
+
+## 快速開始
+
+```bash
+# 安裝依賴
+pip install -r requirements.txt
+
+# 設定環境變數
+cp .env.example .env
+# 編輯 .env 填入正確的資料庫與 API 資訊
+
+# 啟動服務
+python app.py
+```
+
+## 測試端點
+
+- 健康檢查: `GET /health`
+- LLM 連線測試: `GET /test-llm`
+
+## 專案結構
+
+```
+DIT_C/
+├── app.py              # 主程式入口
+├── config.py           # 設定檔
+├── models/             # 資料庫模型
+├── routes/             # 路由模組
+├── services/           # 商業邏輯 (含 LLM 服務)
+├── utils/              # 工具函式
+├── templates/          # HTML 模板
+└── static/             # 靜態資源
+```

app.py

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+"""
+DIT_C 主程式入口
+"""
+
+from flask import Flask, jsonify
+from config import Config
+from models import db
+
+
+def create_app(config_class=Config):
+    """應用程式工廠"""
+    app = Flask(__name__)
+    app.config.from_object(config_class)
+
+    # 初始化擴展
+    db.init_app(app)
+
+    # 註冊 Blueprint (後續擴展)
+    # from routes.auth import auth_bp
+    # from routes.admin import admin_bp
+    # from routes.api import api_bp
+    # app.register_blueprint(auth_bp)
+    # app.register_blueprint(admin_bp)
+    # app.register_blueprint(api_bp)
+
+    # 健康檢查端點
+    @app.route('/health')
+    def health_check():
+        return jsonify({"status": "ok", "message": "DIT_C is running"})
+
+    # 測試 LLM 連線
+    @app.route('/test-llm')
+    def test_llm():
+        from services.llm_service import llm_service, LLMServiceError
+        try:
+            models = llm_service.get_available_models()
+            return jsonify({
+                "status": "ok",
+                "available_models": models,
+                "default_model": Config.OLLAMA_DEFAULT_MODEL
+            })
+        except LLMServiceError as e:
+            return jsonify({"status": "error", "message": str(e)}), 500
+
+    return app
+
+
+if __name__ == '__main__':
+    app = create_app()
+    app.run(host='0.0.0.0', port=5000, debug=Config.DEBUG)

config.py

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+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+class Config:
+    """應用程式設定"""
+
+    # Flask
+    SECRET_KEY = os.getenv('FLASK_SECRET_KEY', 'dev-secret-key')
+    DEBUG = os.getenv('FLASK_DEBUG', 'False').lower() == 'true'
+
+    # Database (MySQL)
+    DB_HOST = os.getenv('DB_HOST', 'localhost')
+    DB_PORT = int(os.getenv('DB_PORT', 3306))
+    DB_NAME = os.getenv('DB_NAME', 'database')
+    DB_USER = os.getenv('DB_USER', 'root')
+    DB_PASSWORD = os.getenv('DB_PASSWORD', '')
+
+    # SQLAlchemy 連線字串
+    SQLALCHEMY_DATABASE_URI = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}"
+    )
+    SQLALCHEMY_TRACK_MODIFICATIONS = False
+
+    # Table 前綴
+    TABLE_PREFIX = 'DIT_C_'
+
+    # Ollama API
+    OLLAMA_API_URL = os.getenv('OLLAMA_API_URL', 'https://ollama_pjapi.theaken.com')
+    OLLAMA_DEFAULT_MODEL = os.getenv('OLLAMA_DEFAULT_MODEL', 'qwen2.5:3b')
+
+    # Gitea
+    GITEA_URL = os.getenv('GITEA_URL', '')
+    GITEA_USER = os.getenv('GITEA_USER', '')
+    GITEA_TOKEN = os.getenv('GITEA_TOKEN', '')

models/__init__.py

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+from flask_sqlalchemy import SQLAlchemy
+
+db = SQLAlchemy()

requirements.txt

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+flask>=3.0.0
+flask-sqlalchemy>=3.1.0
+flask-login>=0.6.3
+python-dotenv>=1.0.0
+pymysql>=1.1.0
+cryptography>=41.0.0
+requests>=2.31.0

routes/__init__.py

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+# Routes module

services/__init__.py

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+# Services module

services/llm_service.py

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+"""
+Ollama LLM API 服務模組
+支援一般請求與串流模式
+"""
+
+import requests
+import json
+from typing import Generator, Optional
+from config import Config
+
+
+class LLMService:
+    """Ollama API 服務封裝"""
+
+    def __init__(self, api_url: str = None, default_model: str = None):
+        self.api_url = api_url or Config.OLLAMA_API_URL
+        self.default_model = default_model or Config.OLLAMA_DEFAULT_MODEL
+
+    def get_available_models(self) -> list:
+        """取得可用模型列表"""
+        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 requests.RequestException as e:
+            raise LLMServiceError(f"無法取得模型列表: {e}")
+
+    def chat(
+        self,
+        messages: list,
+        model: str = None,
+        temperature: float = 0.7,
+        system_prompt: str = None
+    ) -> str:
+        """
+        發送聊天請求 (非串流)
+
+        Args:
+            messages: 訊息列表 [{"role": "user", "content": "..."}]
+            model: 模型名稱
+            temperature: 溫度參數 (0-1)
+            system_prompt: 系統提示詞
+
+        Returns:
+            AI 回應內容
+        """
+        model = model or self.default_model
+
+        # 加入系統提示詞
+        if system_prompt:
+            messages = [{"role": "system", "content": system_prompt}] + messages
+
+        payload = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+            "stream": False
+        }
+
+        try:
+            response = requests.post(
+                f"{self.api_url}/v1/chat/completions",
+                json=payload,
+                timeout=60
+            )
+            response.raise_for_status()
+            result = response.json()
+            return result['choices'][0]['message']['content']
+        except requests.RequestException as e:
+            raise LLMServiceError(f"聊天請求失敗: {e}")
+
+    def chat_stream(
+        self,
+        messages: list,
+        model: str = None,
+        temperature: float = 0.7,
+        system_prompt: str = None
+    ) -> Generator[str, None, None]:
+        """
+        發送聊天請求 (串流模式)
+
+        Args:
+            messages: 訊息列表
+            model: 模型名稱
+            temperature: 溫度參數
+            system_prompt: 系統提示詞
+
+        Yields:
+            串流回應的每個片段
+        """
+        model = model or self.default_model
+
+        if system_prompt:
+            messages = [{"role": "system", "content": system_prompt}] + messages
+
+        payload = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+            "stream": True
+        }
+
+        try:
+            response = requests.post(
+                f"{self.api_url}/v1/chat/completions",
+                json=payload,
+                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 requests.RequestException as e:
+            raise LLMServiceError(f"串流請求失敗: {e}")
+
+    def simple_query(self, prompt: str, system_prompt: str = None) -> str:
+        """
+        簡單查詢 (單一問題)
+
+        Args:
+            prompt: 使用者問題
+            system_prompt: 系統提示詞
+
+        Returns:
+            AI 回應
+        """
+        messages = [{"role": "user", "content": prompt}]
+        return self.chat(messages, system_prompt=system_prompt)
+
+
+class LLMServiceError(Exception):
+    """LLM 服務錯誤"""
+    pass
+
+
+# 全域實例
+llm_service = LLMService()

utils/__init__.py

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+# Utils module