Merge pull request #5 from bdim404/dev

feat: Enhance stream_chat methods to accept max_tokens parameter

qwen3-tts-backend/core/llm_service.py

@@ -14,7 +14,7 @@ class LLMService:
         self.api_key = api_key
         self.model = model
 
-    async def stream_chat(self, system_prompt: str, user_message: str, on_token=None) -> str:
+    async def stream_chat(self, system_prompt: str, user_message: str, on_token=None, max_tokens: int = 8192) -> str:
         url = f"{self.base_url}/chat/completions"
         headers = {
             "Authorization": f"Bearer {self.api_key}",
@@ -27,6 +27,7 @@ class LLMService:
                 {"role": "user", "content": user_message},
             ],
             "temperature": 0.3,
+            "max_tokens": max_tokens,
             "stream": True,
         }
         full_text = ""
@@ -54,8 +55,8 @@ class LLMService:
                         continue
         return full_text
 
-    async def stream_chat_json(self, system_prompt: str, user_message: str, on_token=None):
+    async def stream_chat_json(self, system_prompt: str, user_message: str, on_token=None, max_tokens: int = 8192):
-        raw = await self.stream_chat(system_prompt, user_message, on_token)
+        raw = await self.stream_chat(system_prompt, user_message, on_token, max_tokens=max_tokens)
         raw = raw.strip()
         if not raw:
             raise ValueError("LLM returned empty response")
@@ -204,7 +205,7 @@ class LLMService:
             '[{"character": "narrator", "text": "叙述文字"}, {"character": "角色名", "text": "对话内容"}, ...]'
         )
         user_message = f"请解析以下章节文本：\n\n{chapter_text}"
-        result = await self.stream_chat_json(system_prompt, user_message, on_token)
+        result = await self.stream_chat_json(system_prompt, user_message, on_token, max_tokens=16384)
         if isinstance(result, list):
             return result
         return []