feat: 为单次流式聊天接口增加记忆功能
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parent
0593bf0482
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@ -1,5 +1,6 @@
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from ..schemas.chat_schema import *
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from ..dependencies.logger import get_logger
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from ..dependencies.summarizer import get_summarizer
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from .controller_enum import *
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from ..models import UserCharacter, Session, Character, User
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from utils.audio_utils import VAD
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@ -15,6 +16,9 @@ import aiohttp
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# 依赖注入获取logger
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logger = get_logger()
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# 依赖注入获取context总结服务
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summarizer = get_summarizer()
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# --------------------初始化本地ASR-----------------------
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from utils.stt.modified_funasr import ModifiedRecognizer
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@ -55,9 +59,9 @@ def parseChunkDelta(chunk):
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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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return -1, delta_content['content']
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else:
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return "end"
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return parsed_data['usage']['total_tokens'] , ""
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except KeyError:
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logger.error(f"error chunk: {chunk}")
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return ""
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@ -142,11 +146,15 @@ async def create_chat_handler(chat: ChatCreateRequest, db, redis):
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"temperature": 1,
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"top_p": 0.9,
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}
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user_info = {
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"character":"",
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"events":[]
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}
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# 将tts和llm信息转化为json字符串
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tts_info_str = json.dumps(tts_info, ensure_ascii=False)
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llm_info_str = json.dumps(llm_info, ensure_ascii=False)
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user_info_str = db_user.persona
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user_info_str = json.dumps(user_info, ensure_ascii=False)
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token = 0
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content = {"user_id": user_id, "messages": messages, "user_info": user_info_str, "tts_info": tts_info_str,
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@ -262,13 +270,16 @@ async def sct_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
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is_first = True
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is_end = False
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session_content = get_session_content(session_id,redis,db)
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user_info = json.loads(session_content["user_info"])
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messages = json.loads(session_content["messages"])
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current_message = await llm_input_q.get()
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messages.append({'role': 'user', "content": current_message})
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messages_send = messages #创造一个message副本，在其中最后一条数据前面添加用户信息
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messages_send[-1]['content'] = f"用户性格:{user_info['character']}\n事件摘要:{user_info['events']}" + messages_send[-1]['content']
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payload = json.dumps({
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"model": llm_info["model"],
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"stream": True,
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"messages": messages,
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"messages": messages_send,
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"max_tokens": 10000,
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"temperature": llm_info["temperature"],
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"top_p": llm_info["top_p"]
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@ -283,8 +294,8 @@ async def sct_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
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async with aiohttp.ClientSession() as client:
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async with client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload) as response: #发送大模型请求
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async for chunk in response.content.iter_any():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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token_count, chunk_data = parseChunkDelta(chunk)
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is_end = token_count >0
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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) #断句
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@ -307,6 +318,15 @@ async def sct_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
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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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if is_end and token_count > summarizer.max_token * 0.6: #如果llm返回的token数大于60%的最大token数，则进行文本摘要
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system_prompt = messages[0]['content']
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summary = await summarizer.summarize(messages)
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events = user_info['events']
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events.append(summary['event'])
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session_content['messages'] = [{'role':'system','content':system_prompt}]
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session_content['user_info'] = json.dumps({'character': summary['character'], 'events': json.dumps(events,ensure_ascii=False)}, ensure_ascii=False)
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redis.set(session_id,json.dumps(session_content,ensure_ascii=False))
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logger.debug(f"文本摘要后的session: {session_content}")
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except Exception as e:
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logger.error(f"处理llm返回结果发生错误: {str(e)}")
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chat_finished_event.set()
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@ -443,8 +463,8 @@ async def scl_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
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async with aiohttp.ClientSession() as client:
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async with client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload) as response: #发送大模型请求
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async for chunk in response.content.iter_any():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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token_count, chunk_data = parseChunkDelta(chunk)
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is_end = token_count >0
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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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@ -613,8 +633,8 @@ async def voice_call_llm_handler(ws,session_id,llm_info,tts_info,db,redis,asr_re
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async with aiohttp.ClientSession() as client:
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async with client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload) as response: #发送大模型请求
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async for chunk in response.content.iter_any():
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chunk_data = parseChunkDelta(chunk)
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is_end = chunk_data == "end"
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token_count, chunk_data = parseChunkDelta(chunk)
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is_end = token_count >0
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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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@ -0,0 +1,61 @@
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import aiohttp
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import json
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from datetime import datetime
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from config import get_config
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# 依赖注入获取Config
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Config = get_config()
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class Summarizer:
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def __init__(self):
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self.system_prompt = """你是一台对话总结机器，你的职责是整理用户与玩具之间的对话，最终提炼出对话中发生的事件，以及用户性格\n\n你的输出必须为一个json，里面有两个字段，一个是event，一个是character，将你总结出的事件写入event，将你总结出的用户性格写入character\nevent和character均为字符串\n返回示例：{"event":"在幼儿园看葫芦娃，老鹰抓小鸡","character":"活泼可爱"}"""
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self.model = "abab5.5-chat"
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self.max_token = 10000
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self.temperature = 0.9
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self.top_p = 1
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async def summarize(self,messages):
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context = ""
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for message in messages:
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if message['role'] == 'user':
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context += "用户："+ message['content'] + '\n'
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elif message['role'] == 'assistant':
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context += '玩具：'+ message['content'] + '\n'
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payload = json.dumps({
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"model":self.model,
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"messages":[
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{
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"role":"system",
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"content":self.system_prompt
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},
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{
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"role":"user",
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"content":context
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}
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],
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"max_tokens":self.max_token,
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"top_p":self.top_p
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})
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headers = {
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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 aiohttp.ClientSession() as session:
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async with session.post(Config.MINIMAX_LLM.URL, data=payload, headers=headers) as response:
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content = json.loads(json.loads(await response.text())['choices'][0]['message']['content'])
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try:
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summary = {
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'event':datetime.now().strftime("%Y-%m-%d")+"："+content['event'],
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'character':content['character']
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}
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except TypeError:
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summary = {
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'event':datetime.now().strftime("%Y-%m-%d")+"："+content['event'][0],
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'character':""
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}
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return summary
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def get_summarizer():
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summarizer = Summarizer()
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return summarizer
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@ -101,7 +101,7 @@ class ChatServiceTest:
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payload = json.dumps({
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"user_id": self.user_id,
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"messages": "[{\"role\": \"system\", \"content\": \"我们正在角色扮演对话游戏中，你需要始终保持角色扮演并待在角色设定的情景中，你扮演的角色信息如下：\\n角色名称: 海绵宝宝。\\n角色背景: 厨师，做汉堡\\n角色所处环境: 海绵宝宝住在深海的大菠萝里面\\n角色的常用问候语: 你好啊，海绵宝宝。\\n\\n你需要用简单、通俗易懂的口语化方式进行对话，在没有经过允许的情况下，你需要保持上述角色，不得擅自跳出角色设定。\\n\"}]",
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"user_info": "{}",
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"user_info": "{\"character\": \"\", \"events\": [] }",
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"tts_info": "{\"language\": 0, \"speaker_id\": 97, \"noise_scale\": 0.1, \"noise_scale_w\": 0.668, \"length_scale\": 1.2}",
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"llm_info": "{\"model\": \"abab5.5-chat\", \"temperature\": 1, \"top_p\": 0.9}",
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"token": 0}
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