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/providers/llm/openai/openai.py

@@ -0,0 +1,32 @@
+import logging
+import openai
+from core.providers.llm.base import LLMProviderBase
+
+logger = logging.getLogger(__name__)
+
+class LLMProvider(LLMProviderBase):
+    def __init__(self, config):
+        self.model_name = config.get("model_name")
+        self.api_key = config.get("api_key")
+        if 'base_url' in config:
+            self.base_url = config.get("base_url")
+        else:
+            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}")