feat: 为单次流式聊天接口增加记忆功能

app/controllers/chat_controller.py

@@ -1,5 +1,6 @@
 from ..schemas.chat_schema import *
 from ..dependencies.logger import get_logger
+from ..dependencies.summarizer import get_summarizer
 from .controller_enum import *
 from ..models import UserCharacter, Session, Character, User
 from utils.audio_utils import VAD
@@ -15,6 +16,9 @@ import aiohttp
 # 依赖注入获取logger
 logger = get_logger()
 
+# 依赖注入获取context总结服务
+summarizer = get_summarizer()
+
 # --------------------初始化本地ASR-----------------------
 from utils.stt.modified_funasr import ModifiedRecognizer
 
@@ -55,9 +59,9 @@ def parseChunkDelta(chunk):
         parsed_data = json.loads(decoded_data[6:])
         if 'delta' in parsed_data['choices'][0]:
             delta_content = parsed_data['choices'][0]['delta']
-            return delta_content['content']
+            return -1, delta_content['content']
         else:
-            return "end"
+            return  parsed_data['usage']['total_tokens'] , ""
     except KeyError:
         logger.error(f"error chunk: {chunk}")
         return ""
@@ -142,11 +146,15 @@ async def create_chat_handler(chat: ChatCreateRequest, db, redis):
         "temperature": 1,
         "top_p": 0.9,
     }
+    user_info = {
+        "character":"",
+        "events":[]
+    }
     
     # 将tts和llm信息转化为json字符串
     tts_info_str = json.dumps(tts_info, ensure_ascii=False)
     llm_info_str = json.dumps(llm_info, ensure_ascii=False)
-    user_info_str  = db_user.persona
+    user_info_str  = json.dumps(user_info, ensure_ascii=False)
 
     token = 0
     content = {"user_id": user_id, "messages": messages, "user_info": user_info_str, "tts_info": tts_info_str,
@@ -262,13 +270,16 @@ async def sct_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
         is_first = True
         is_end = False
         session_content = get_session_content(session_id,redis,db)
+        user_info = json.loads(session_content["user_info"])
         messages = json.loads(session_content["messages"])
         current_message = await llm_input_q.get()
         messages.append({'role': 'user', "content": current_message})
+        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
+        messages_send[-1]['content'] = f"用户性格:{user_info['character']}\n事件摘要:{user_info['events']}" + messages_send[-1]['content']
         payload = json.dumps({
             "model": llm_info["model"],
             "stream": True,
-            "messages": messages,
+            "messages": messages_send,
             "max_tokens": 10000,
             "temperature": llm_info["temperature"],
             "top_p": llm_info["top_p"]
@@ -283,8 +294,8 @@ async def sct_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
         async with aiohttp.ClientSession() as client:
             async with client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload) as response:  #发送大模型请求
                 async for chunk in response.content.iter_any():
-                    chunk_data = parseChunkDelta(chunk)
+                    token_count, chunk_data = parseChunkDelta(chunk)
-                    is_end = chunk_data == "end"
+                    is_end = token_count >0
                     if not is_end:
                         llm_response += chunk_data
                     sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)    #断句
@@ -307,6 +318,15 @@ async def sct_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
                         redis.set(session_id,json.dumps(session_content,ensure_ascii=False)) #更新session
                         is_first = True
                         llm_response = ""
+                    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
+                        system_prompt = messages[0]['content']
+                        summary = await summarizer.summarize(messages)
+                        events = user_info['events']
+                        events.append(summary['event'])
+                        session_content['messages'] = [{'role':'system','content':system_prompt}]
+                        session_content['user_info'] = json.dumps({'character': summary['character'], 'events': json.dumps(events,ensure_ascii=False)}, ensure_ascii=False)
+                        redis.set(session_id,json.dumps(session_content,ensure_ascii=False))
+                        logger.debug(f"文本摘要后的session: {session_content}")
     except Exception as e:
         logger.error(f"处理llm返回结果发生错误: {str(e)}")
     chat_finished_event.set()
@@ -443,8 +463,8 @@ async def scl_llm_handler(ws,session_id,response_type,llm_info,tts_info,db,redis
             async with aiohttp.ClientSession() as client:
                 async with client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload) as response:  #发送大模型请求
                     async for chunk in response.content.iter_any():
-                        chunk_data = parseChunkDelta(chunk)
+                        token_count, chunk_data = parseChunkDelta(chunk)
-                        is_end = chunk_data == "end"
+                        is_end = token_count >0
                         if not is_end:
                             llm_response += chunk_data
                         sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)
@@ -613,8 +633,8 @@ async def voice_call_llm_handler(ws,session_id,llm_info,tts_info,db,redis,asr_re
             async with aiohttp.ClientSession() as client:
                 async with client.post(Config.MINIMAX_LLM.URL, headers=headers, data=payload) as response:  #发送大模型请求
                     async for chunk in response.content.iter_any():
-                        chunk_data = parseChunkDelta(chunk)
+                        token_count, chunk_data = parseChunkDelta(chunk)
-                        is_end = chunk_data == "end"
+                        is_end = token_count >0
                         if not is_end:
                             llm_response += chunk_data
                         sentences,current_sentence,is_first = split_string_with_punctuation(current_sentence,chunk_data,is_first,is_end)

app/dependencies/summarizer.py

@@ -0,0 +1,61 @@
+import aiohttp
+import json
+from datetime import datetime
+from config import get_config
+
+
+# 依赖注入获取Config
+Config = get_config()
+
+class Summarizer:
+    def __init__(self):
+        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"}"""
+        self.model = "abab5.5-chat"
+        self.max_token = 10000
+        self.temperature = 0.9
+        self.top_p = 1
+    
+    async def summarize(self,messages):
+        context = ""
+        for message in messages:
+            if message['role'] == 'user':
+                context += "用户："+ message['content'] + '\n'
+            elif message['role'] == 'assistant':
+                context += '玩具：'+ message['content'] + '\n'
+        payload = json.dumps({
+            "model":self.model,
+            "messages":[
+                {
+                    "role":"system",
+                    "content":self.system_prompt
+                },
+                {
+                    "role":"user",
+                    "content":context
+                }
+            ],
+            "max_tokens":self.max_token,
+            "top_p":self.top_p
+        })
+        headers = {
+            'Authorization': f'Bearer {Config.MINIMAX_LLM.API_KEY}',
+            'Content-Type': 'application/json'
+        }
+        async with aiohttp.ClientSession() as session:
+            async with session.post(Config.MINIMAX_LLM.URL, data=payload, headers=headers) as response:
+                content = json.loads(json.loads(await response.text())['choices'][0]['message']['content'])
+                try:
+                    summary = {
+                        'event':datetime.now().strftime("%Y-%m-%d")+"："+content['event'],
+                        'character':content['character']
+                    }
+                except TypeError:
+                    summary = {
+                        'event':datetime.now().strftime("%Y-%m-%d")+"："+content['event'][0],
+                        'character':""
+                    }
+                return summary
+                
+def get_summarizer():
+    summarizer = Summarizer()
+    return summarizer

tests/unit_test/chat_test.py

@@ -101,7 +101,7 @@ class ChatServiceTest:
         payload = json.dumps({
             "user_id": self.user_id,
             "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\"}]",
-            "user_info": "{}", 
+            "user_info": "{\"character\": \"\", 	\"events\": [] }", 
             "tts_info": "{\"language\": 0, \"speaker_id\": 97, \"noise_scale\": 0.1, \"noise_scale_w\": 0.668, \"length_scale\": 1.2}", 
             "llm_info": "{\"model\": \"abab5.5-chat\", \"temperature\": 1, \"top_p\": 0.9}", 
             "token": 0}