forked from killua/TakwayPlatform
由于大模型返回后处理改为同步，所以改回requests库
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parent
f11339ff92
commit
75016e3009
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@ -10,7 +10,7 @@ from config import get_config
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import uuid
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import json
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import asyncio
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import httpx
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import requests
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# 依赖注入获取logger
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logger = get_logger()
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@ -49,10 +49,10 @@ def get_session_content(session_id,redis,db):
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#解析大模型流式返回内容
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def parseChunkDelta(chunk):
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try:
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if chunk == "":
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if chunk == b"":
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return ""
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chunk_json_str = chunk[6:]
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parsed_data = json.loads(chunk_json_str)
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decoded_data = chunk.decode('utf-8')
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parsed_data = json.loads(decoded_data[6:])
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if 'delta' in parsed_data['choices'][0]:
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delta_content = parsed_data['choices'][0]['delta']
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return delta_content['content']
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@ -242,72 +242,36 @@ async def sct_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
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'Authorization': f"Bearer {Config.MINIMAX_LLM.API_KEY}",
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'Content-Type': 'application/json'
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}
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async with httpx.AsyncClient() as client:
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response = await client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload)
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async for chunk in response.aiter_lines():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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if not is_end:
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llm_response += chunk_data
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sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)
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for sentence in sentences:
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if response_type == RESPONSE_TEXT:
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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elif response_type == RESPONSE_AUDIO:
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sr,audio = tts.synthesize(sentence, tts_info["speaker_id"], tts_info["language"], tts_info["noise_scale"], tts_info["noise_scale_w"], tts_info["length_scale"],return_bytes=True)
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_bytes(audio)
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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logger.debug(f"websocket返回: {sentence}")
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if is_end:
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logger.debug(f"llm返回结果: {llm_response}")
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await ws.send_text(json.dumps({"type": "end", "code": 200, "msg": ""}, ensure_ascii=False))
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is_end = False
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session_content = get_session_content(session_id,redis,db)
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messages = json.loads(session_content["messages"])
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messages.append({'role': 'assistant', "content": llm_response})
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session_content["messages"] = json.dumps(messages,ensure_ascii=False) #更新对话
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redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
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is_first = True
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llm_response = ""
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chat_finished_event.set()
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#大模型返回断句
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async def sct_llm_response_handler(session_id,redis,db,llm_response_q,split_result_q,llm_response_finish_event):
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logger.debug("llm返回处理函数启动")
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llm_response = ""
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current_sentence = ""
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is_first = True
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while not (llm_response_finish_event.is_set() and llm_response_q.empty()):
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llm_chunk = await llm_response_q.get()
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llm_response += llm_chunk
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sentences, current_sentence, is_first = split_string_with_punctuation(current_sentence, llm_chunk, is_first)
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response = requests.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload,stream=True)
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for chunk in response.iter_lines():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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if not is_end:
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llm_response += chunk_data
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sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)
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for sentence in sentences:
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await split_result_q.put(sentence)
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session_content = get_session_content(session_id,redis,db)
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messages = json.loads(session_content["messages"])
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messages.append({'role': 'assistant', "content": llm_response})
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session_content["messages"] = json.dumps(messages,ensure_ascii=False) #更新对话
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redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
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logger.debug(f"llm返回结果: {llm_response}")
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if response_type == RESPONSE_TEXT:
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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elif response_type == RESPONSE_AUDIO:
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sr,audio = tts.synthesize(sentence, tts_info["speaker_id"], tts_info["language"], tts_info["noise_scale"], tts_info["noise_scale_w"], tts_info["length_scale"],return_bytes=True)
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_bytes(audio)
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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logger.debug(f"websocket返回: {sentence}")
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if is_end:
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logger.debug(f"llm返回结果: {llm_response}")
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await ws.send_text(json.dumps({"type": "end", "code": 200, "msg": ""}, ensure_ascii=False))
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is_end = False
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#文本返回及语音合成
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async def sct_response_handler(ws,tts_info,response_type,split_result_q,llm_response_finish_event,chat_finish_event):
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logger.debug("返回处理函数启动")
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while not (llm_response_finish_event.is_set() and split_result_q.empty()):
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sentence = await split_result_q.get()
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if response_type == RESPONSE_TEXT:
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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elif response_type == RESPONSE_AUDIO:
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sr,audio = tts.synthesize(sentence, tts_info["speaker_id"], tts_info["language"], tts_info["noise_scale"], tts_info["noise_scale_w"], tts_info["length_scale"],return_bytes=True)
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_bytes(audio)
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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logger.debug(f"websocket返回: {sentence}")
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chat_finish_event.set()
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session_content = get_session_content(session_id,redis,db)
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messages = json.loads(session_content["messages"])
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messages.append({'role': 'assistant', "content": llm_response})
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session_content["messages"] = json.dumps(messages,ensure_ascii=False) #更新对话
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redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
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is_first = True
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llm_response = ""
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chat_finished_event.set()
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async def streaming_chat_temporary_handler(ws: WebSocket, db, redis):
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logger.debug("streaming chat temporary websocket 连接建立")
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@ -417,36 +381,36 @@ async def scl_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
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'Authorization': f"Bearer {Config.MINIMAX_LLM.API_KEY}",
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'Content-Type': 'application/json'
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}
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async with httpx.AsyncClient() as client:
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response = await client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload)
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async for chunk in response.aiter_lines():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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if not is_end:
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llm_response += chunk_data
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sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)
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for sentence in sentences:
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if response_type == RESPONSE_TEXT:
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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elif response_type == RESPONSE_AUDIO:
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sr,audio = tts.synthesize(sentence, tts_info["speaker_id"], tts_info["language"], tts_info["noise_scale"], tts_info["noise_scale_w"], tts_info["length_scale"],return_bytes=True)
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_bytes(audio)
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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logger.debug(f"websocket返回: {sentence}")
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if is_end:
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logger.debug(f"llm返回结果: {llm_response}")
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await ws.send_text(json.dumps({"type": "end", "code": 200, "msg": ""}, ensure_ascii=False))
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is_end = False
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response = requests.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload,stream=True)
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for chunk in response.iter_lines():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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if not is_end:
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llm_response += chunk_data
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sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)
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for sentence in sentences:
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if response_type == RESPONSE_TEXT:
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logger.debug(f"websocket返回: {sentence}")
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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elif response_type == RESPONSE_AUDIO:
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sr,audio = tts.synthesize(sentence, tts_info["speaker_id"], tts_info["language"], tts_info["noise_scale"], tts_info["noise_scale_w"], tts_info["length_scale"],return_bytes=True)
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response_message = {"type": "text", "code":200, "msg": sentence}
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await ws.send_bytes(audio)
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await ws.send_text(json.dumps(response_message, ensure_ascii=False))
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logger.debug(f"websocket返回: {sentence}")
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if is_end:
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logger.debug(f"llm返回结果: {llm_response}")
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await ws.send_text(json.dumps({"type": "end", "code": 200, "msg": ""}, ensure_ascii=False))
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is_end = False
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session_content = get_session_content(session_id,redis,db)
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messages = json.loads(session_content["messages"])
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messages.append({'role': 'assistant', "content": llm_response})
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session_content["messages"] = json.dumps(messages,ensure_ascii=False) #更新对话
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redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
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is_first = True
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llm_response = ""
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session_content = get_session_content(session_id,redis,db)
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messages = json.loads(session_content["messages"])
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messages.append({'role': 'assistant', "content": llm_response})
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session_content["messages"] = json.dumps(messages,ensure_ascii=False) #更新对话
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redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
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is_first = True
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llm_response = ""
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except asyncio.TimeoutError:
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continue
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chat_finished_event.set()
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@ -562,32 +526,31 @@ async def voice_call_llm_handler(ws,session_id,llm_info,tts_info,db,redis,asr_re
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'Authorization': f"Bearer {Config.MINIMAX_LLM.API_KEY}",
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'Content-Type': 'application/json'
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}
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async with httpx.AsyncClient() as client:
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response = await client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload)
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async for chunk in response.aiter_lines():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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if not is_end:
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llm_response += chunk_data
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sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)
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for sentence in sentences:
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sr,audio = tts.synthesize(sentence, tts_info["language"], tts_info["speaker_id"], tts_info["noise_scale"], tts_info["noise_scale_w"], tts_info["length_scale"], return_bytes=True)
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text_response = {"type": "llm_text", "code": 200, "msg": sentence}
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await ws.send_bytes(audio) #返回音频二进制流数据
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await ws.send_text(json.dumps(text_response, ensure_ascii=False)) #返回文本数据
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logger.debug(f"llm返回结果: {sentence}")
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if is_end:
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logger.debug(f"llm返回结果: {llm_response}")
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await ws.send_text(json.dumps({"type": "end", "code": 200, "msg": ""}, ensure_ascii=False))
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is_end = False
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response = requests.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload,stream=True)
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for chunk in response.iter_lines():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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if not is_end:
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llm_response += chunk_data
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sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)
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for sentence in sentences:
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sr,audio = tts.synthesize(sentence, tts_info["language"], tts_info["speaker_id"], tts_info["noise_scale"], tts_info["noise_scale_w"], tts_info["length_scale"], return_bytes=True)
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text_response = {"type": "llm_text", "code": 200, "msg": sentence}
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await ws.send_bytes(audio) #返回音频二进制流数据
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await ws.send_text(json.dumps(text_response, ensure_ascii=False)) #返回文本数据
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logger.debug(f"llm返回结果: {sentence}")
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if is_end:
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logger.debug(f"llm返回结果: {llm_response}")
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await ws.send_text(json.dumps({"type": "end", "code": 200, "msg": ""}, ensure_ascii=False))
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is_end = False
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session_content = get_session_content(session_id,redis,db)
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messages = json.loads(session_content["messages"])
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messages.append({'role': 'assistant', "content": llm_response})
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session_content["messages"] = json.dumps(messages,ensure_ascii=False) #更新对话
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redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
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is_first = True
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llm_response = ""
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session_content = get_session_content(session_id,redis,db)
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messages = json.loads(session_content["messages"])
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messages.append({'role': 'assistant', "content": llm_response})
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session_content["messages"] = json.dumps(messages,ensure_ascii=False) #更新对话
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redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
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is_first = True
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llm_response = ""
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except asyncio.TimeoutError:
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continue
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voice_call_end_event.set()
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