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/chat/llm_generator.py

@@ -0,0 +1,108 @@
+from typing import Dict, Any, List, Optional, Union, Tuple
+from openai import OpenAI
+from functools import partial
+from .config import BotConfig
+from ...common.database import Database
+import random
+import os
+import aiohttp
+from dotenv import load_dotenv
+from .relationship_manager import relationship_manager
+
+# 获取当前文件的绝对路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
+load_dotenv(os.path.join(root_dir, '.env'))
+
+
+
+class LLMResponseGenerator:
+    def __init__(self, config: BotConfig):
+        self.config = config
+        self.API_KEY = os.getenv('SILICONFLOW_KEY')
+        self.BASE_URL =os.getenv('SILICONFLOW_BASE_URL')
+        self.client = OpenAI(
+            api_key=self.API_KEY,
+            base_url=self.BASE_URL
+        )
+
+        self.db = Database.get_instance()
+        # 当前使用的模型类型
+        self.current_model_type = 'r1'  # 默认使用 R1
+
+    async def generate_response(self, text: str) -> Optional[str]:
+        """根据当前模型类型选择对应的生成函数"""
+        if random.random() < self.config.MODEL_R1_PROBABILITY:
+            self.current_model_type = "r1"
+        else:
+            self.current_model_type = "v3"
+        
+        print(f"+++++++++++++++++麦麦{self.current_model_type}思考中+++++++++++++++++")
+        if self.current_model_type == 'r1':
+            model_response = await self._generate_v3_response(text)
+        else:
+            model_response = await self._generate_v3_response(text)
+        # 打印情感标签
+        print(f'麦麦的回复------------------------------是：{model_response}')
+        
+        return model_response
+
+    async def _generate_r1_response(self, text: str) -> Optional[str]:
+        """使用 DeepSeek-R1 模型生成回复"""
+        messages = [{"role": "user", "content": text}]
+        async with aiohttp.ClientSession() as session:
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self.API_KEY}"
+            }
+            payload = {
+                "model": "Pro/deepseek-ai/DeepSeek-R1",
+                "messages": messages,
+                "stream": False,
+                "max_tokens": 1024,
+                "temperature": 0.8
+            }
+            async with session.post(f"{self.BASE_URL}/chat/completions", 
+                                    headers=headers, 
+                                    json=payload) as response:
+                result = await 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", "")
+                    print(f"Content: {content}")
+                    print(f"Reasoning: {reasoning_content}")
+                    return content
+                
+        return None
+
+    async def _generate_v3_response(self, text: str) -> Optional[str]:
+        """使用 DeepSeek-V3 模型生成回复"""
+
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.API_KEY}"
+        }
+        
+        payload = {
+            "model": "Pro/deepseek-ai/DeepSeek-V3",
+            "messages": [{"role": "user", "content": text}],
+            "max_tokens": 1024,
+            "temperature": 0.8
+        }
+        
+        async with aiohttp.ClientSession() as session:
+            async with session.post(f"{self.BASE_URL}/chat/completions", 
+                                    headers=headers, 
+                                    json=payload) as response:
+                result = await response.json()
+                
+                if "choices" in result and len(result["choices"]) > 0:
+                    content = result["choices"][0]["message"]["content"]
+                    return content
+                else:
+                    print(f"[ERROR] V3 回复发送生成失败: {result}")
+                
+        return None
+
+    
+llm_response = LLMResponseGenerator(config=BotConfig())