update:加了一点注释

app/abstract.py

@@ -31,7 +31,7 @@ class PromptService(ABC):
 
 class LLMMsgService(ABC):
     @abstractmethod
-    def llm_msg_process(self, llm_chunk:list, **kwargs) -> list:
+    def llm_msg_process(self, llm_chunks:list, **kwargs) -> list:
         pass
     
 class TTSAudioService(ABC):

app/concrete.py

@@ -78,7 +78,7 @@ class MINIMAX_LLM(LLM):
         llm_info = json.loads(assistant.llm_info)
         messages = json.loads(assistant.messages)
         messages.append({'role':'user','content':prompt})
-        payload = json.dumps({
+        payload = json.dumps({  #整理payload
             "model": llm_info['model'],
             "stream": True,
             "messages": messages,
@@ -91,15 +91,15 @@ class MINIMAX_LLM(LLM):
             'Content-Type': 'application/json'
         }
         async with aiohttp.ClientSession() as client:
-            async with client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload) as response:
+            async with client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload) as response:  #调用大模型
                 async for chunk in response.content.iter_any():
                     try:
-                        chunk_msg = self.__parseChunk(chunk)
+                        chunk_msg = self.__parseChunk(chunk)  #解析llm返回
                         msg_frame = {"is_end":False,"code":200,"msg":chunk_msg}
                         yield msg_frame
                     except LLMResponseEnd:
                         msg_frame = {"is_end":True,"msg":""}
-                        if self.token > llm_info['max_tokens'] * 0.8:
+                        if self.token > llm_info['max_tokens'] * 0.8:  #如果token超过80%，则重置session
                             msg_frame['msg'] = 'max_token reached'
                             as_query = db.query(Assistant).filter(Assistant.id == assistant.id).first()
                             as_query.messages = json.dumps([{'role':'system','content':assistant.system_prompt},{'role':'user','content':prompt}])
@@ -169,12 +169,14 @@ class BasicPromptService(PromptService):
         else:
             raise NoAsrResultsError()
 
+# 记录用户音频
 class UserAudioRecordService(UserAudioService):
     def user_audio_process(self, audio:str, **kwargs):
         audio_decoded = base64.b64decode(audio)
         kwargs['recorder'].user_audio += audio_decoded
         return audio,kwargs
-    
+
+# 记录TTS音频
 class TTSAudioRecordService(TTSAudioService):
     def tts_audio_process(self, audio:bytes, **kwargs):
         kwargs['recorder'].tts_audio += audio
@@ -214,7 +216,7 @@ class LLMMsgServiceChain:
         self.services.append(service)
     def llm_msg_process(self, llm_msg):
         kwargs = {}
-        llm_chunks = self.spliter.split(llm_msg)
+        llm_chunks = self.spliter.split(llm_msg)    #首先对llm返回进行断句
         for service in self.services:
             llm_chunks , kwargs = service.llm_msg_process(llm_chunks, **kwargs)
         return llm_chunks
@@ -247,7 +249,7 @@ class Agent():
         
         self.llm = llmFactory.create_llm(llm_type)
         
-        self.llm_chunk_service_chain = LLMMsgServiceChain()
+        self.llm_msg_service_chain = LLMMsgServiceChain()
         
         self.tts = ttsFactory.create_tts(tts_type)
         
@@ -275,7 +277,7 @@ class Agent():
     
     # 对大模型的返回进行处理
     def llm_msg_process(self, llm_chunk, db):
-        return self.llm_chunk_service_chain.llm_msg_process(llm_chunk)
+        return self.llm_msg_service_chain.llm_msg_process(llm_chunk)
     
     # 进行TTS合成
     def synthetize(self, assistant, text, db):

main.py

@@ -172,7 +172,7 @@ async def streaming_chat(ws: WebSocket,db=Depends(get_db)):
     await ws.accept()
     logger.debug("WebSocket连接成功")
     try:
-        agent = Agent(asr_type=Config.ASR, llm_type=Config.LLM, tts_type=Config.TTS)
+        agent = None
         assistant = None
         asr_results = []
         llm_text = ""
@@ -182,6 +182,8 @@ async def streaming_chat(ws: WebSocket,db=Depends(get_db)):
             if assistant is None:
                 assistant = db.query(Assistant).filter(Assistant.id == chunk['meta_info']['session_id']).first()
                 agent.init_recorder(assistant.user_id)
+            if not agent:
+                agent = Agent(asr_type=Config.ASR, llm_type=Config.LLM, tts_type=Config.TTS)
             chunk["audio"] = agent.user_audio_process(chunk["audio"], db)
             asr_results = await agent.stream_recognize(chunk, db)
         kid_text = asr_results[0]['text'] #asr结果的[0]默认为孩子(主要用户)的asr结果