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0df7a26e8e
1. 内置RAG知识库（请把docx、pptx、txt文件存放到llm/data目录）； 2. 流式回复逻辑优化； 3. 语音交互逻辑优化； 4. 线程安全增强； 5. 数字人驱动接口增加流式输出开始结束标记； 6. 修复因记忆反思而导致的记忆混乱，无法多轮对话问题； 7. 修复mcp工具获取于调用的线程同步问题； 8. 修复funasr依赖版本问题。
129 lines
4.8 KiB
Python
129 lines
4.8 KiB
Python
import threading
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import time
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from utils import stream_sentence
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from scheduler.thread_manager import MyThread
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import fay_booter
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from core import member_db
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from core.interact import Interact
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# 全局变量，用于存储StreamManager的单例实例
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__streams = None
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# 线程锁，用于保护全局变量的访问
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__streams_lock = threading.Lock()
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def new_instance(max_sentences=1024):
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"""
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创建并返回StreamManager的单例实例
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:param max_sentences: 最大句子缓存数量
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:return: StreamManager实例
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"""
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global __streams
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with __streams_lock:
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if __streams is None:
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__streams = StreamManager(max_sentences)
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return __streams
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class StreamManager:
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"""
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流管理器类，用于管理和处理文本流数据
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"""
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def __init__(self, max_sentences=3):
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"""
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初始化StreamManager
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:param max_sentences: 每个流的最大句子缓存数量
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"""
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if hasattr(self, '_initialized') and self._initialized:
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return
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self.lock = threading.Lock() # 线程锁，用于保护streams字典的访问
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self.streams = {} # 存储用户ID到句子缓存的映射
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self.nlp_streams = {} # 存储用户ID到句子缓存的映射
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self.max_sentences = max_sentences # 最大句子缓存数量
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self.listener_threads = {} # 存储用户ID到监听线程的映射
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self.running = True # 控制监听线程的运行状态
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self._initialized = True # 标记是否已初始化
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self.msgid = "" # 消息ID
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def get_Stream(self, username):
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"""
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获取指定用户ID的文本流，如果不存在则创建新的（线程安全）
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:param username: 用户名
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:return: 对应的句子缓存对象
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"""
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# 注意：这个方法应该在已经获得锁的情况下调用
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# 如果从外部调用，需要先获得锁
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if username not in self.streams or username not in self.nlp_streams:
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# 创建新的流缓存
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self.streams[username] = stream_sentence.SentenceCache(self.max_sentences)
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self.nlp_streams[username] = stream_sentence.SentenceCache(self.max_sentences)
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# 启动监听线程（如果还没有）
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if username not in self.listener_threads:
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stream = self.streams[username]
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nlp_stream = self.nlp_streams[username]
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thread = MyThread(target=self.listen, args=(username, stream, nlp_stream), daemon=True)
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self.listener_threads[username] = thread
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thread.start()
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return self.streams[username], self.nlp_streams[username]
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def write_sentence(self, username, sentence):
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"""
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写入句子到指定用户的文本流（线程安全）
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:param username: 用户名
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:param sentence: 要写入的句子
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:return: 写入是否成功
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"""
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# 检查句子长度，防止过大的句子导致内存问题
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if len(sentence) > 10240: # 10KB限制
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sentence = sentence[:10240]
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if sentence.endswith('_<isfirst>'):
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self.clear_Stream(username)
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# 使用锁保护获取和写入操作
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with self.lock:
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try:
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Stream, nlp_Stream = self.get_Stream(username)
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success = Stream.write(sentence)
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nlp_success = nlp_Stream.write(sentence)
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return success and nlp_success
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except Exception as e:
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print(f"写入句子时出错: {e}")
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return False
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def clear_Stream(self, username):
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"""
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清除指定用户ID的文本流数据
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:param username: 用户名
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"""
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with self.lock:
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if username in self.streams:
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self.streams[username].clear()
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if username in self.nlp_streams:
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self.nlp_streams[username].clear()
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def listen(self, username, stream, nlp_stream):
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while self.running:
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sentence = stream.read()
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if sentence:
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self.execute(username, sentence)
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else:
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time.sleep(0.1)
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def execute(self, username, sentence):
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"""
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执行句子处理逻辑
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:param username: 用户名
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:param sentence: 要处理的句子
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"""
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fay_core = fay_booter.feiFei
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is_first = "_<isfirst>" in sentence
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is_end = "_<isend>" in sentence
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sentence = sentence.replace("_<isfirst>", "").replace("_<isend>", "")
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if sentence or is_first or is_end :
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interact = Interact("stream", 1, {"user": username, "msg": sentence, "isfirst" : is_first, "isend" : is_end})
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fay_core.say(interact, sentence) # 调用核心处理模块进行响应
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time.sleep(0.01) # 短暂休眠以控制处理频率 |