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

services/llm_service.py

@@ -0,0 +1,150 @@
+"""
+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()