update: 增加了智谱大模型，增加了修改max_tokens接口

app/concrete.py

@@ -1,5 +1,6 @@
 from utils.xf_asr_utils import xf_asr_websocket_factory, make_first_frame, make_continue_frame, make_last_frame, parse_xfasr_recv
 from volcenginesdkarkruntime import Ark
+from zhipuai import ZhipuAI
 from .model import Assistant
 from .abstract import *
 from .public import *
@@ -37,7 +38,7 @@ class XF_ASR(ASR):
         self.segment_duration_threshold = 25 #超时时间为25秒
         self.segment_start_time = None
     
-    async def stream_recognize(self, chunk):
+    async def stream_recognize(self, assistant, chunk):
         if self.status == FIRST_FRAME and chunk['meta_info']['is_end']: #如果是第一帧，且为end，则判断为杂音
             raise SideNoiseError()
         if self.websocket is None: #如果websocket未建立，则建立一个新的连接
@@ -188,6 +189,50 @@ class VOLCENGINE_LLM(LLM):
             raise AbnormalLLMFrame(f"error volcengine llm_chunk:{llm_chunk}")
         return llm_chunk.choices[0].delta.content
 
+class ZHIPU_LLM(LLM):
+    def __init__(self):
+        self.token = 0
+        self.client = ZhipuAI(api_key=Config.ZHIPU_LLM.API_KEY)
+    
+    async def chat(self, assistant, prompt):
+        llm_info = json.loads(assistant.llm_info)
+        messages = json.loads(assistant.messages)
+        messages.append({'role':'user','content':prompt})
+        stream = self.client.chat.completions.create(
+            model = llm_info['model'],
+            messages=messages,
+            stream=True,
+            temperature=llm_info['temperature'],
+            top_p=llm_info['top_p'],
+            max_tokens=llm_info['max_tokens']
+        )
+        for chunk in stream:
+            try:
+                chunk_msg = self.__parseChunk(chunk)
+                msg_frame = {"is_end":False,"code":200,"msg":chunk_msg}
+                yield msg_frame
+            except LLMResponseEnd:
+                msg_frame = {"is_end":True,"code":200,"msg":""}
+                assistant.token = self.token
+                if self.token > llm_info['max_tokens'] * 0.8:  #如果token超过80%，则重置session
+                    msg_frame['code'] = '201'
+                    assistant.messages = json.dumps([{'role':'system','content':assistant.system_prompt},{'role':'user','content':prompt}])
+                yield msg_frame
+    
+    def __parseChunk(self, llm_chunk):
+        if llm_chunk.usage:
+            self.token = llm_chunk.usage.total_tokens
+            raise LLMResponseEnd()
+        if not llm_chunk.choices:
+            raise AbnormalLLMFrame(f"error zhipu llm_chunk:{llm_chunk}")
+        return llm_chunk.choices[0].delta.content
+
+class MIXED_LLM(LLM):
+    def __init__(self):
+        self.minimax = MINIMAX_LLM()
+        self.volcengine = VOLCENGINE_LLM()
+        self.zhipu = ZHIPU_LLM()
+
 class VITS_TTS(TTS):
     def __init__(self):
         pass
@@ -211,6 +256,8 @@ class LLMFactory:
             return MINIMAX_LLM()
         if llm_type == 'VOLCENGINE':
             return VOLCENGINE_LLM()
+        if llm_type == 'ZHIPU':
+            return ZHIPU_LLM()
 
 class TTSFactory:
     def create_tts(self,tts_type:str) -> TTS:
@@ -324,8 +371,8 @@ class Agent():
         return self.user_audio_service_chain.user_audio_process(audio, recorder=self.recorder)
     
     # 进行流式语音识别
-    async def stream_recognize(self, chunk):
-        return await self.asr.stream_recognize(chunk)
+    async def stream_recognize(self, assistant, chunk):
+        return await self.asr.stream_recognize(assistant,chunk)
     
     # 进行Prompt加工
     def prompt_process(self, asr_results):