v0.1

能跑但是没写部署教程，主题和记忆识别也没写完

src/plugins/schedule/schedule_llm_module.py

@@ -0,0 +1,55 @@
+import os
+import requests
+from dotenv import load_dotenv
+from typing import Tuple, Union
+
+# 加载环境变量
+load_dotenv()
+
+class LLMModel:
+    # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs):
+    def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-R1", **kwargs):
+        self.model_name = model_name
+        self.params = kwargs
+        self.api_key = os.getenv("SILICONFLOW_KEY")
+        self.base_url = os.getenv("SILICONFLOW_BASE_URL")
+
+    def generate_response(self, prompt: str) -> Tuple[str, str]:
+        """根据输入的提示生成模型的响应"""
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        # 构建请求体
+        data = {
+            "model": self.model_name,
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": 0.9,
+            **self.params
+        }
+        
+        # 发送请求到完整的chat/completions端点
+        api_url = f"{self.base_url.rstrip('/')}/chat/completions"
+        
+        try:
+            response = requests.post(api_url, headers=headers, json=data)
+            response.raise_for_status()  # 检查响应状态
+            
+            result = response.json()
+            if "choices" in result and len(result["choices"]) > 0:
+                content = result["choices"][0]["message"]["content"]
+                reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
+                return content, reasoning_content  # 返回内容和推理内容
+            return "没有返回结果", ""  # 返回两个值
+            
+        except requests.exceptions.RequestException as e:
+            return f"请求失败: {str(e)}", ""  # 返回错误信息和空字符串
+
+# 示例用法
+if __name__ == "__main__":
+    model = LLMModel()  # 默认使用 DeepSeek-V3 模型
+    prompt = "你好，你喜欢我吗？"
+    result, reasoning = model.generate_response(prompt)
+    print("回复内容:", result)
+    print("推理内容:", reasoning)