v0.2.1 添加了对官方api的支持

图像识别还得用硅基

env.example

@@ -17,6 +17,8 @@ CHAT_ANY_WHERE_KEY=
 SILICONFLOW_KEY=
 CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
 SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
+DEEP_SEEK_KEY=
+DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
 
 
 

src/plugins/chat/bot_config_toml

@@ -17,6 +17,7 @@ check_interval = 120
 register_interval = 10
 
 [response]
+api_using = "siliconflow"
 model_r1_probability = 0.8
 model_v3_probability = 0.1
 model_r1_distill_probability = 0.1

src/plugins/chat/config.py

@@ -40,6 +40,7 @@ class BotConfig:
     EMOJI_CHECK_INTERVAL: int = 120  # 表情包检查间隔（分钟）
     EMOJI_REGISTER_INTERVAL: int = 10  # 表情包注册间隔（分钟）
     
+    API_USING: str = "siliconflow"  # 使用的API
     MODEL_R1_PROBABILITY: float = 0.8  # R1模型概率
     MODEL_V3_PROBABILITY: float = 0.1  # V3模型概率
     MODEL_R1_DISTILL_PROBABILITY: float = 0.1  # R1蒸馏模型概率
@@ -76,7 +77,8 @@ class BotConfig:
                 config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
                 config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
                 config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY)
-            
+                config.API_USING = response_config.get("api_using", config.API_USING)
+                
             # 消息配置
             if "message" in toml_dict:
                 msg_config = toml_dict["message"]
@@ -108,7 +110,11 @@ class LLMConfig:
     # 基础配置
     SILICONFLOW_API_KEY: str = None
     SILICONFLOW_BASE_URL: str = None
+    DEEP_SEEK_API_KEY: str = None
+    DEEP_SEEK_BASE_URL: str = None
 
 llm_config = LLMConfig()
 llm_config.SILICONFLOW_API_KEY = os.getenv('SILICONFLOW_KEY')
 llm_config.SILICONFLOW_BASE_URL = os.getenv('SILICONFLOW_BASE_URL')
+llm_config.DEEP_SEEK_API_KEY = os.getenv('DEEP_SEEK_KEY')
+llm_config.DEEP_SEEK_BASE_URL = os.getenv('DEEP_SEEK_BASE_URL')

src/plugins/chat/llm_generator.py

@@ -26,11 +26,17 @@ load_dotenv(os.path.join(root_dir, '.env'))
 class LLMResponseGenerator:
     def __init__(self, config: BotConfig):
         self.config = config
-        self.client = OpenAI(
-            api_key=llm_config.SILICONFLOW_API_KEY,
-            base_url=llm_config.SILICONFLOW_BASE_URL
-        )
-
+        if self.config.API_USING == "siliconflow":
+            self.client = OpenAI(
+                api_key=llm_config.SILICONFLOW_API_KEY,
+                base_url=llm_config.SILICONFLOW_BASE_URL
+            )
+        elif self.config.API_USING == "deepseek":
+            self.client = OpenAI(
+                api_key=llm_config.DEEP_SEEK_API_KEY,
+                base_url=llm_config.DEEP_SEEK_BASE_URL
+            )
+            
         self.db = Database.get_instance()
         
         # 当前使用的模型类型
@@ -140,19 +146,33 @@ class LLMResponseGenerator:
 
     async def _generate_r1_response(self, message: Message) -> Optional[str]:
         """使用 DeepSeek-R1 模型生成回复"""
-        return await self._generate_base_response(
-            message, 
-            "Pro/deepseek-ai/DeepSeek-R1",
-            {"temperature": 0.7, "max_tokens": 1024}
-        )
+        if self.config.API_USING == "deepseek":
+            return await self._generate_base_response(
+                message, 
+                "deepseek-reasoner",
+                {"temperature": 0.7, "max_tokens": 1024}
+            )
+        else:
+            return await self._generate_base_response(
+                message, 
+                "Pro/deepseek-ai/DeepSeek-R1",
+                {"temperature": 0.7, "max_tokens": 1024}
+            )
 
     async def _generate_v3_response(self, message: Message) -> Optional[str]:
         """使用 DeepSeek-V3 模型生成回复"""
-        return await self._generate_base_response(
-            message, 
-            "Pro/deepseek-ai/DeepSeek-V3",
-            {"temperature": 0.8, "max_tokens": 1024}
-        )
+        if self.config.API_USING == "deepseek":
+            return await self._generate_base_response(
+                message, 
+                "deepseek-chat",
+                {"temperature": 0.8, "max_tokens": 1024}
+            )
+        else:
+            return await self._generate_base_response(
+                message, 
+                "Pro/deepseek-ai/DeepSeek-V3",
+                {"temperature": 0.8, "max_tokens": 1024}
+            )
 
     async def _generate_r1_distill_response(self, message: Message) -> Optional[str]:
         """使用 DeepSeek-R1-Distill-Qwen-32B 模型生成回复"""
@@ -192,9 +212,13 @@ class LLMResponseGenerator:
             messages = [{"role": "user", "content": prompt}]
             
             loop = asyncio.get_event_loop()
+            if self.config.API_USING == "deepseek":
+                model = "deepseek-chat"
+            else:
+                model = "Pro/deepseek-ai/DeepSeek-V3"
             create_completion = partial(
                 self.client.chat.completions.create,
-                model="Pro/deepseek-ai/DeepSeek-V3",
+                model=model,
                 messages=messages,
                 stream=False,
                 max_tokens=30,