#update: 添加mixed模型方案

app/concrete.py

@@ -87,6 +87,12 @@ class MINIMAX_LLM(LLM):
     
     async def chat(self, assistant, prompt):
         llm_info = json.loads(assistant.llm_info)
+        
+        # 临时方案，后续需要修改
+        user_info = json.loads(assistant.user_info)
+        if user_info['llm_type'] == "MIXED":
+            llm_info['model'] = 'abab6.5s-chat'
+            
         messages = json.loads(assistant.messages)
         messages.append({'role':'user','content':prompt})
         payload = json.dumps({  #整理payload
@@ -146,6 +152,12 @@ class VOLCENGINE_LLM(LLM):
     
     async def chat(self, assistant, prompt):
         llm_info = json.loads(assistant.llm_info)
+        
+        # 临时方案，后续需要修改
+        user_info = json.loads(assistant.user_info)
+        if user_info['llm_type'] == "MIXED":
+            llm_info['model'] = 'doubao-32k-lite'
+            
         model = self.__get_model(llm_info)
         messages = json.loads(assistant.messages)
         messages.append({'role':'user','content':prompt})
@@ -196,6 +208,12 @@ class ZHIPU_LLM(LLM):
     
     async def chat(self, assistant, prompt):
         llm_info = json.loads(assistant.llm_info)
+        
+        # 临时方案，后续需要修改
+        user_info = json.loads(assistant.user_info)
+        if user_info['llm_type'] == "MIXED":
+            llm_info['model'] = 'glm-4-air'
+            
         messages = json.loads(assistant.messages)
         messages.append({'role':'user','content':prompt})
         stream = self.client.chat.completions.create(
@@ -233,6 +251,42 @@ class MIXED_LLM(LLM):
         self.volcengine = VOLCENGINE_LLM()
         self.zhipu = ZHIPU_LLM()
     
+    async def chat(self, assistant, prompt):
+        minimax_result = self.minimax.chat(assistant, prompt)
+        volcengine_result = self.volcengine.chat(assistant, prompt)
+        zhipu_result = self.zhipu.chat(assistant, prompt)
+        
+        minimax_task = asyncio.create_task(minimax_result.__anext__())
+        volcengine_task = asyncio.create_task(volcengine_result.__anext__())
+        zhipu_task = asyncio.create_task(zhipu_result.__anext__())
+        
+        done, pending = await asyncio.wait([minimax_task, volcengine_task, zhipu_task], return_when=asyncio.FIRST_COMPLETED)
+        first_task = done.pop()
+        if first_task == minimax_task:
+            logger.debug("使用MINIMAX模型")
+            yield await minimax_task
+            while True:
+                try:
+                    yield await minimax_result.__anext__()
+                except StopAsyncIteration:
+                    break
+        elif first_task == volcengine_task:
+            logger.debug("使用豆包模型")
+            yield await volcengine_task
+            while True:
+                try:
+                    yield await volcengine_result.__anext__()
+                except StopAsyncIteration:
+                    break
+        elif first_task == zhipu_task:
+            logger.debug("使用智谱模型")
+            yield await zhipu_task
+            while True:
+                try:
+                    yield await zhipu_result.__anext__()
+                except StopAsyncIteration:
+                    break
+
 class VITS_TTS(TTS):
     def __init__(self):
         pass
@@ -258,6 +312,8 @@ class LLMFactory:
             return VOLCENGINE_LLM()
         if llm_type == 'ZHIPU':
             return ZHIPU_LLM()
+        if llm_type == 'MIXED':
+            return MIXED_LLM()
 
 class TTSFactory:
     def create_tts(self,tts_type:str) -> TTS:

main.py

@@ -226,14 +226,19 @@ async def streaming_chat(ws: WebSocket):
         agent.recorder.input_text = prompt
         logger.debug("开始调用大模型")
         llm_frames = await agent.chat(assistant, prompt)
+        
+        start_time = time.time()
+        is_first_response = True
+        
         async for llm_frame in llm_frames:
+            if is_first_response:
+                end_time = time.time()
+                logger.debug(f"第一帧返回耗时：{round(end_time-start_time,3)}s")
+                is_first_response = False
             resp_msgs = agent.llm_msg_process(llm_frame)
             for resp_msg in resp_msgs: 
                 llm_text += resp_msg
-                tts_start_time = time.time()
                 tts_audio = agent.synthetize(assistant, resp_msg)
-                tts_end_time = time.time()
-                logger.debug(f"TTS生成音频耗时：{tts_end_time-tts_start_time}s")
                 agent.tts_audio_process(tts_audio)
                 await ws.send_bytes(agent.encode(resp_msg, tts_audio))
                 logger.debug(f'websocket返回：{resp_msg}')