LLM和TTS采用适配器模式进行解耦，方便扩展更多服务平台调用

core/utils/llm.py

@@ -1,7 +1,10 @@
+import os
+import sys
 import json
 import logging
 import openai
 import requests
+import importlib
 from datetime import datetime
 from core.utils.util import is_segment
 from core.utils.util import get_string_no_punctuation_or_emoji
@@ -11,132 +14,15 @@ from abc import ABC, abstractmethod
 logger = logging.getLogger(__name__)
 
 
-class LLM(ABC):
-    @abstractmethod
-    def response(self, session_id, dialogue):
-        """LLM response generator"""
-        pass
-
-
-class DeepSeekLLM(LLM):
-    def __init__(self, config):
-        self.model_name = config.get("model_name")
-        self.api_key = config.get("api_key")
-        self.base_url = config.get("url")
-        self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
-
-    def response(self, session_id, dialogue):
-        logger.info(f"Generating response using {dialogue}")
-        try:
-            responses = self.client.chat.completions.create(
-                model=self.model_name,
-                messages=dialogue,
-                stream=True
-            )
-            for chunk in responses:
-                # 检查是否存在有效的choice且content不为空
-                if chunk.choices and len(chunk.choices) > 0:
-                    delta = chunk.choices[0].delta
-                    content = getattr(delta, 'content', '')
-                    if content:  # 仅在content非空时生成
-                        yield content
-        except Exception as e:
-            logger.error(f"Error in response generation: {e}")
-
-
-class ChatGLMLLM(LLM):
-    def __init__(self, config):
-        self.model_name = config.get("model_name")
-        self.api_key = config.get("api_key")
-        self.base_url = config.get("url")
-        self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
-
-    def response(self, session_id, dialogue):
-        try:
-            responses = self.client.chat.completions.create(
-                model=self.model_name,
-                messages=dialogue,
-                stream=True
-            )
-            for chunk in responses:
-                # 检查是否存在有效的choice且content不为空
-                if chunk.choices and len(chunk.choices) > 0:
-                    delta = chunk.choices[0].delta
-                    content = getattr(delta, 'content', '')
-                    if content:  # 仅在content非空时生成
-                        yield content
-        except Exception as e:
-            logger.error(f"Error in response generation: {e}")
-
-class AliLLM(LLM):
-    def __init__(self, config):
-        self.model_name = config.get("model_name")
-        self.api_key = config.get("api_key")
-        self.base_url = config.get("base_url")
-        self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
-
-    def response(self, session_id, dialogue):
-        try:
-            responses = self.client.chat.completions.create(
-                model=self.model_name,
-                messages=dialogue,
-                stream=True
-            )
-            for chunk in responses:
-                # 检查是否存在有效的choice且content不为空
-                if chunk.choices and len(chunk.choices) > 0:
-                    delta = chunk.choices[0].delta
-                    content = getattr(delta, 'content', '')
-                    if content:  # 仅在content非空时生成
-                        yield content
-        except Exception as e:
-            logger.error(f"Error in response generation: {e}")
-
-class DifyLLM(LLM):
-    def __init__(self, config):
-        self.api_key = config["api_key"]
-        self.base_url = config.get("base_url", "https://api.dify.ai/v1").rstrip('/')
-
-    def response(self, session_id, dialogue):
-        try:
-            # 取最后一条用户消息
-            last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
-
-            # 发起流式请求
-            with requests.post(
-                    f"{self.base_url}/chat-messages",
-                    headers={"Authorization": f"Bearer {self.api_key}"},
-                    json={
-                        "query": last_msg["content"],
-                        "response_mode": "streaming",
-                        "user": session_id,
-                        "inputs": {}
-                    },
-                    stream=True
-            ) as r:
-                for line in r.iter_lines():
-                    if line.startswith(b'data: '):
-                        event = json.loads(line[6:])
-                        if event.get('answer'):
-                            yield event['answer']
-
-        except Exception:
-            yield "【服务响应异常】"
-
-
 def create_instance(class_name, *args, **kwargs):
-    # 获取类对象
-    cls_map = {
-        "DeepSeekLLM": DeepSeekLLM,
-        "ChatGLMLLM": ChatGLMLLM,
-        "DifyLLM": DifyLLM,
-        "AliLLM": AliLLM,
-        # 可扩展其他LLM实现
-    }
+    # 创建LLM实例
+    if os.path.exists(os.path.join('core', 'providers', 'llm', class_name, f'{class_name}.py')):
+        lib_name = f'core.providers.llm.{class_name}.{class_name}'
+        if lib_name not in sys.modules:
+            sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
+            return sys.modules[lib_name].LLMProvider(*args, **kwargs)
 
-    if cls := cls_map.get(class_name):
-        return cls(*args, **kwargs)
-    raise ValueError(f"不支持的LLM类型: {class_name}")
+    raise ValueError(f"不支持的LLM类型: {class_name}，请检查该配置的type是否设置正确")
 
 
 if __name__ == "__main__":
@@ -145,7 +31,10 @@ if __name__ == "__main__":
     """
     config = read_config(get_project_dir() + "config.yaml")
     llm = create_instance(
-        config["selected_module"]["LLM"],
+        config["selected_module"]["LLM"]
+        if not "type" in config["LLM"][config["selected_module"]["LLM"]]
+        else
+        config["LLM"][config["selected_module"]["LLM"]]["type"],
         config["LLM"][config["selected_module"]["LLM"]]
     )