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/dify/dify.py

@@ -0,0 +1,36 @@
+import logging
+import requests
+from core.providers.llm.base import LLMProviderBase
+
+logger = logging.getLogger(__name__)
+
+class LLMProvider(LLMProviderBase):
+    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】"