本地记忆+意图识别 (#250)

* 增加本地记忆功能，使用llm总结记忆 * update：增加统一非流式输出输出 * 增加意图识别内容，使用llm进行识别 * 初始化记忆模块 * 完善意图识别处理后的流程 * 通过使用function call实现意图识别 * update:优化意图识别的配置 * update:function call最优设置成doubao-pro-32k-functioncall-241028 --------- Co-authored-by: 玄凤科技 <eric230308@gmail.com> Co-authored-by: hrz <1710360675@qq.com>
2026-07-22 07:03:53 +08:00 · 2025-03-09 21:33:45 +08:00
parent 9c2b2a2dcc
commit 63f34e5a82
19 changed files with 858 additions and 108 deletions
@@ -65,21 +65,45 @@ CMD_exit:

 # 具体处理时选择的模块(The module selected for specific processing)
 selected_module:
-  ASR: FunASR
+  # 语音活动检测模块，默认使用SileroVAD模型
  VAD: SileroVAD
+  # 语音识别模块，默认使用FunASR本地模型
+  ASR: FunASR
  # 将根据配置名称对应的type调用实际的LLM适配器
  LLM: ChatGLMLLM
  # TTS将根据配置名称对应的type调用实际的TTS适配器
  TTS: EdgeTTS
-  Memory: mem0ai
+  # 记忆模块，默认使用本地压缩记忆，如果想使用超长记忆，推荐使用mem0ai
+  Memory: nomem
+  # 意图识别模块，默认不开启。开启后，可以播放音乐、控制音量、识别退出指令
+  # 如果意图识别设置成 function_call，建议使用：DoubaoLLM，推荐把的model_name设置为：doubao-pro-32k-functioncall-241028
+  Intent: nointent

+# 意图识别，是用于理解用户意图的模块，例如：播放音乐
+Intent:
+  # 不使用意图识别
+  nointent:
+    type: nointent
+  # 意图识别使用大模型，优点：通用性强，缺点：增加串行前置意图识别模块，会增加处理时间
+  intent_llm:
+    type: intent_llm
+  # 意图识别使用function_call，缺点：需要所选择的LLM支持function_call，优点：按需调用工具、速度快
+  function_call:
+    type: function_call
+    
 Memory:
  mem0ai:
    type: mem0ai
    # https://app.mem0.ai/dashboard/api-keys
    # 每月有1000次免费调用
    api_key: 你的mem0ai api key
-    
+  nomem:
+    # 不想使用记忆功能，可以使用nomem 
+    type: nomem
+  mem_local_short:
+    # 本地记忆功能，通过selected_module的llm总结，数据保存在本地，不会上传到服务器 
+    type: mem_local_short
+
 ASR:
  FunASR:
    type: fun_local
@@ -106,6 +130,13 @@ LLM:
    base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
    model_name: qwen-turbo
    api_key: 你的deepseek web key
+  DoubaoLLM:
+    # 定义LLM API类型
+    type: openai
+    # 可在这里开通 https://console.volcengine.com/ark/region:ark+cn-beijing/model/detail?Id=doubao-pro-32k&projectName=undefined
+    base_url: https://ark.cn-beijing.volces.com/api/v3
+    model_name: doubao-pro-32k-functioncall-241028
+    api_key: 你的doubao web key
  DeepSeekLLM:
    # 定义LLM API类型
    type: openai
@@ -383,18 +414,6 @@ module_test:

 # 本地音乐播放配置
 music:
-  music_commands:
-    - "来一首歌"
-    - "唱一首歌"
-    - "播放音乐"
-    - "来点音乐"
-    - "背景音乐"
-    - "放首歌"
-    - "播放歌曲"
-    - "来点背景音乐"
-    - "我想听歌"
-    - "我要听歌"
-    - "放点音乐"
  music_dir: "./music"  # 音乐文件存放路径，将从该目录及子目录下搜索音乐文件
  music_ext: # 音乐文件类型，p3格式效率最高
    - ".mp3"
@@ -0,0 +1,36 @@
+FunctionCallConfig = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "handle_exit_intent",
+                    "description": "当用户想结束对话或需要退出系统时调用",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "say_goodbye": {
+                                "type": "string",
+                                "description": "和用户友好结束对话的告别语"
+                            }
+                        },
+                        "required": []
+                    }
+                }
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "play_music",
+                    "description": "唱歌、听歌、播放音乐方法。比如用户说播放音乐，参数为：random，比如用户说播放两只老虎，参数为：两只老虎",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "song_name": {
+                                "type": "string",
+                                "description": "歌曲名称，如果没有指定具体歌名则为'random'"
+                            }
+                        },
+                        "required": ["song_name"]
+                    }
+                }
+            }
+        ]
@@ -15,6 +15,7 @@ from core.utils.util import get_string_no_punctuation_or_emoji
 from concurrent.futures import ThreadPoolExecutor, TimeoutError
 from core.handle.sendAudioHandle import sendAudioMessage
 from core.handle.receiveAudioHandle import handleAudioMessage
+from core.handle.intentHandler import Action, get_functions, handle_llm_function_call
 from config.private_config import PrivateConfig
 from core.auth import AuthMiddleware, AuthenticationError
 from core.utils.auth_code_gen import AuthCodeGenerator
@@ -27,7 +28,7 @@ class TTSException(RuntimeError):


 class ConnectionHandler:
-    def __init__(self, config: Dict[str, Any], _vad, _asr, _llm, _tts, _music, _memory):
+    def __init__(self, config: Dict[str, Any], _vad, _asr, _llm, _tts, _music, _memory, _intent):
        self.config = config
        self.logger = setup_logging()
        self.auth = AuthMiddleware(config)
@@ -55,6 +56,7 @@ class ConnectionHandler:
        self.llm = _llm
        self.tts = _tts
        self.memory = _memory
+        self.intent = _intent

        # vad相关变量
        self.client_audio_buffer = bytes()
@@ -88,6 +90,12 @@ class ConnectionHandler:
        self.auth_code_gen = AuthCodeGenerator.get_instance()
        self.is_device_verified = False  # 添加设备验证状态标志
        self.music_handler = _music
+        self.close_after_chat = False  # 是否在聊天结束后关闭连接
+        self.use_function_call_mode = False
+        if self.config["selected_module"]["Intent"] == 'function_call':
+            self.use_function_call_mode = True
+
+        self.logger.bind(tag=TAG).info(f"use_function_call_mode:{self.use_function_call_mode}")

    async def handle_connection(self, ws):
        try:
@@ -101,7 +109,8 @@ class ConnectionHandler:
            await self.auth.authenticate(self.headers)

            device_id = self.headers.get("device-id", None)
-            self.memory.set_role_id(device_id)
+            self.memory.init_memory(device_id, self.llm)
+            self.intent.set_llm(self.llm)

            # Load private configuration if device_id is provided
            bUsePrivateConfig = self.config.get("use_private_config", False)
@@ -204,7 +213,6 @@ class ConnectionHandler:
            return False
        return not self.is_device_verified

-
    def chat(self, query):
        if self.isNeedAuth():
            self.llm_finish_task = True
@@ -213,6 +221,7 @@ class ConnectionHandler:
            return True

        self.dialogue.put(Message(role="user", content=query))
+
        response_message = []
        processed_chars = 0  # 跟踪已处理的字符位置
        try:
@@ -220,10 +229,10 @@ class ConnectionHandler:
            # 使用带记忆的对话
            future = asyncio.run_coroutine_threadsafe(self.memory.query_memory(query), self.loop)
            memory_str = future.result()
-            
-            self.logger.bind(tag=TAG).info(f"记忆内容: {memory_str}")
+
+            self.logger.bind(tag=TAG).debug(f"记忆内容: {memory_str}")
            llm_responses = self.llm.response(
-                self.session_id, 
+                self.session_id,
                self.dialogue.get_llm_dialogue_with_memory(memory_str)
            )
        except Exception as e:
@@ -245,7 +254,7 @@ class ConnectionHandler:
            current_text = full_text[processed_chars:]  # 从未处理的位置开始

            # 查找最后一个有效标点
-            punctuations = ("。", "？", "！", "?", "!", ";", "；", ":", "：")
+            punctuations = ("。", "？", "！", "；", "：")
            last_punct_pos = -1
            for punct in punctuations:
                pos = current_text.rfind(punct)
@@ -282,6 +291,119 @@ class ConnectionHandler:
        self.logger.bind(tag=TAG).debug(json.dumps(self.dialogue.get_llm_dialogue(), indent=4, ensure_ascii=False))
        return True

+    def chat_with_function_calling(self, query):
+        self.logger.bind(tag=TAG).debug(f"Chat with function calling start: {query}")
+        """Chat with function calling for intent detection using streaming"""
+        if self.isNeedAuth():
+            self.llm_finish_task = True
+            future = asyncio.run_coroutine_threadsafe(self._check_and_broadcast_auth_code(), self.loop)
+            future.result()
+            return True
+
+        self.dialogue.put(Message(role="user", content=query))
+
+        # Define intent functions
+        functions = get_functions()
+
+        response_message = []
+        processed_chars = 0  # 跟踪已处理的字符位置
+        function_call_data = None  # 存储function call数据
+
+        try:
+            start_time = time.time()
+
+            # 使用带记忆的对话
+            future = asyncio.run_coroutine_threadsafe(self.memory.query_memory(query), self.loop)
+            memory_str = future.result()
+
+            # self.logger.bind(tag=TAG).info(f"记忆内容: {memory_str}")
+
+            # 使用支持functions的streaming接口
+            llm_responses = self.llm.response_with_functions(
+                self.session_id,
+                self.dialogue.get_llm_dialogue_with_memory(memory_str),
+                functions=functions
+            )
+        except Exception as e:
+            self.logger.bind(tag=TAG).error(f"LLM 处理出错 {query}: {e}")
+            return None
+
+        self.llm_finish_task = False
+        text_index = 0
+
+        # 处理流式响应
+        for response in llm_responses:
+            if response["type"] == "content":
+                content = response["content"]
+                response_message.append(content)
+
+                if self.client_abort:
+                    break
+
+                end_time = time.time()
+                self.logger.bind(tag=TAG).debug(f"大模型返回时间: {end_time - start_time} 秒, 生成token={content}")
+
+                # 处理文本分段和TTS逻辑
+                # 合并当前全部文本并处理未分割部分
+                full_text = "".join(response_message)
+                current_text = full_text[processed_chars:]  # 从未处理的位置开始
+
+                # 查找最后一个有效标点
+                punctuations = ("。", "？", "！", "；", "：")
+                last_punct_pos = -1
+                for punct in punctuations:
+                    pos = current_text.rfind(punct)
+                    if pos > last_punct_pos:
+                        last_punct_pos = pos
+
+                # 找到分割点则处理
+                if last_punct_pos != -1:
+                    segment_text_raw = current_text[:last_punct_pos + 1]
+                    segment_text = get_string_no_punctuation_or_emoji(segment_text_raw)
+                    if segment_text:
+                        text_index += 1
+                        self.recode_first_last_text(segment_text, text_index)
+                        future = self.executor.submit(self.speak_and_play, segment_text, text_index)
+                        self.tts_queue.put(future)
+                        processed_chars += len(segment_text_raw)  # 更新已处理字符位置
+
+            elif response["type"] == "function_call":
+                # Extract function call data
+                function_call_data = {
+                    "name": response["function_call"]["function"]["name"],
+                    "arguments": response["function_call"]["function"]["arguments"]
+                }
+                self.logger.bind(tag=TAG).info(f"Function call detected: {function_call_data}")
+
+        # 处理最后剩余的文本
+        full_text = "".join(response_message)
+        remaining_text = full_text[processed_chars:]
+        if remaining_text:
+            segment_text = get_string_no_punctuation_or_emoji(remaining_text)
+            if segment_text:
+                text_index += 1
+                self.recode_first_last_text(segment_text, text_index)
+                future = self.executor.submit(self.speak_and_play, segment_text, text_index)
+                self.tts_queue.put(future)
+
+        # 存储对话内容
+        self.dialogue.put(Message(role="assistant", content="".join(response_message)))
+
+        # 处理function call
+        if function_call_data:
+            result = handle_llm_function_call(self, function_call_data)
+            if result.action == Action.RESPONSE:
+                text = result.response
+                text_index += 1
+                self.recode_first_last_text(text, text_index)
+                future = self.executor.submit(self.speak_and_play, text, text_index)
+                self.tts_queue.put(future)
+
+        self.llm_finish_task = True
+        self.logger.bind(tag=TAG).debug(json.dumps(self.dialogue.get_llm_dialogue(), indent=4, ensure_ascii=False))
+
+        return True
+
    def _tts_priority_thread(self):
        while not self.stop_event.is_set():
            text = None
@@ -372,3 +494,14 @@ class ConnectionHandler:
        self.client_have_voice_last_time = 0
        self.client_voice_stop = False
        self.logger.bind(tag=TAG).debug("VAD states reset.")
+
+    def chat_and_close(self, text):
+        """Chat with the user and then close the connection"""
+        try:
+            # Use the existing chat method
+            self.chat(text)
+
+            # After chat is complete, close the connection
+            self.close_after_chat = True
+        except Exception as e:
+            self.logger.bind(tag=TAG).error(f"Chat and close error: {str(e)}")
@@ -0,0 +1,170 @@
+from config.logger import setup_logging
+import json
+from core.handle.sendAudioHandle import send_stt_message
+from core.utils.dialogue import Message
+from config.functionCallConfig import FunctionCallConfig
+import asyncio
+from enum import Enum
+
+TAG = __name__
+logger = setup_logging()
+
+
+class Action(Enum):
+    NOTFOUND = (0, "没有找到函数")
+    NONE = (1, "啥也不干")
+    RESPONSE = (2, "直接回复")
+    REQLLM = (3, "调用函数后再请求llm生成回复")
+
+    def __init__(self, code, message):
+        self.code = code
+        self.message = message
+
+
+class ActionResponse:
+    def __init__(self, action: Action, result, response):
+        self.action = action  # 动作类型
+        self.result = result  # 动作产生的结果
+        self.response = response  # 直接回复的内容
+
+
+def get_functions():
+    """获取功能调用配置"""
+    return FunctionCallConfig
+
+
+def handle_llm_function_call(conn, function_call_data):
+    try:
+        function_name = function_call_data["name"]
+
+        if function_name == "handle_exit_intent":
+            # 处理退出意图
+            try:
+                say_goodbye = json.loads(function_call_data["arguments"]).get("say_goodbye", "再见")
+                conn.close_after_chat = True
+                logger.bind(tag=TAG).info(f"退出意图已处理:{say_goodbye}")
+                return ActionResponse(action=Action.RESPONSE, result="退出意图已处理", response=say_goodbye)
+            except Exception as e:
+                logger.bind(tag=TAG).error(f"处理退出意图错误: {e}")
+
+        elif function_name == "play_music":
+            # 处理音乐播放意图
+            try:
+                song_name = "random"
+                arguments = function_call_data["arguments"]
+                if arguments is not None and len(arguments) > 0:
+                    args = json.loads(arguments)
+                    song_name = args.get("song_name", "random")
+                music_intent = f"播放音乐 {song_name}" if song_name != "random" else "随机播放音乐"
+
+                # 执行音乐播放命令
+                future = asyncio.run_coroutine_threadsafe(
+                    conn.music_handler.handle_music_command(conn, music_intent),
+                    conn.loop
+                )
+                future.result()
+                return ActionResponse(action=Action.RESPONSE, result="退出意图已处理", response="还想听什么歌？")
+            except Exception as e:
+                logger.bind(tag=TAG).error(f"处理音乐意图错误: {e}")
+        else:
+            return ActionResponse(action=Action.NOTFOUND, result="没有找到对应的函数", response="")
+    except Exception as e:
+        logger.bind(tag=TAG).error(f"处理function call错误: {e}")
+
+    return None
+
+
+async def handle_user_intent(conn, text):
+    """
+    Handle user intent before starting chat
+    
+    Args:
+        conn: Connection object
+        text: User's text input
+    
+    Returns:
+        bool: True if intent was handled, False if should proceed to chat
+    """
+    # 检查是否有明确的退出命令
+    if await check_direct_exit(conn, text):
+        return True
+
+    if conn.use_function_call_mode:
+        # 使用支持function calling的聊天方法,不再进行意图分析
+        return False
+
+    logger.bind(tag=TAG).info(f"分析用户意图: {text}")
+
+    # 使用LLM进行意图分析
+    intent = await analyze_intent_with_llm(conn, text)
+
+    if not intent:
+        return False
+
+    # 处理各种意图
+    return await process_intent_result(conn, intent, text)
+
+
+async def check_direct_exit(conn, text):
+    """检查是否有明确的退出命令"""
+    cmd_exit = conn.cmd_exit
+    for cmd in cmd_exit:
+        if text == cmd:
+            logger.bind(tag=TAG).info(f"识别到明确的退出命令: {text}")
+            await conn.close()
+            return True
+    return False
+
+
+async def analyze_intent_with_llm(conn, text):
+    """使用LLM分析用户意图"""
+    if not hasattr(conn, 'intent') or not conn.intent:
+        logger.bind(tag=TAG).warning("意图识别服务未初始化")
+        return None
+
+    # 创建对话历史记录
+    dialogue = conn.dialogue
+    dialogue.put(Message(role="user", content=text))
+
+    try:
+        intent_result = await conn.intent.detect_intent(dialogue.dialogue)
+        logger.bind(tag=TAG).info(f"意图识别结果: {intent_result}")
+
+        # 尝试解析JSON结果
+        try:
+            intent_data = json.loads(intent_result)
+            if "intent" in intent_data:
+                return intent_data["intent"]
+        except json.JSONDecodeError:
+            # 如果不是JSON格式，尝试直接获取意图文本
+            return intent_result.strip()
+
+    except Exception as e:
+        logger.bind(tag=TAG).error(f"意图识别失败: {str(e)}")
+
+    return None
+
+
+async def process_intent_result(conn, intent, original_text):
+    """处理意图识别结果"""
+    # 处理退出意图
+    if "结束聊天" in intent:
+        logger.bind(tag=TAG).info(f"识别到退出意图: {intent}")
+
+        # 如果正在播放音乐，可以关了 TODO
+
+        # 如果是明确的离别意图，发送告别语并关闭连接
+        await send_stt_message(conn, original_text)
+        conn.executor.submit(conn.chat_and_close, original_text)
+        return True
+
+    # 处理播放音乐意图
+    if "播放音乐" in intent:
+        logger.bind(tag=TAG).info(f"识别到音乐播放意图: {intent}")
+        await conn.music_handler.handle_music_command(conn, intent)
+        return True
+
+    # 其他意图处理可以在这里扩展
+
+    # 默认返回False，表示继续常规聊天流程
+    return False
@@ -15,7 +15,7 @@ logger = setup_logging()

 def _extract_song_name(text):
    """从用户输入中提取歌名"""
-    for keyword in ["听", "播放", "放", "唱"]:
+    for keyword in ["播放音乐"]:
        if keyword in text:
            parts = text.split(keyword)
            if len(parts) > 1:
@@ -36,6 +36,7 @@ def _find_best_match(potential_song, music_files):
            best_match = music_file
    return best_match

+
 class MusicManager:
    def __init__(self, music_dir, music_ext):
        self.music_dir = Path(music_dir)
@@ -55,23 +56,20 @@ class MusicManager:
                    music_files.append(str(file.relative_to(self.music_dir)))
        return music_files

+
 class MusicHandler:
    def __init__(self, config):
        self.config = config
-        self.music_related_keywords = []

        if "music" in self.config:
            self.music_config = self.config["music"]
            self.music_dir = os.path.abspath(
                self.music_config.get("music_dir", "./music")  # 默认路径修改
            )
-            self.music_related_keywords = self.music_config.get("music_commands", [])
            self.music_ext = self.music_config.get("music_ext", (".mp3", ".wav", ".p3"))
            self.refresh_time = self.music_config.get("refresh_time", 60)
        else:
            self.music_dir = os.path.abspath("./music")
-            self.music_related_keywords = ["来一首歌", "唱一首歌", "播放音乐", "来点音乐", "背景音乐", "放首歌",
-                                           "播放歌曲", "来点背景音乐", "我想听歌", "我要听歌", "放点音乐"]
            self.music_ext = (".mp3", ".wav", ".p3")
            self.refresh_time = 60

@@ -100,13 +98,9 @@ class MusicHandler:
                    logger.bind(tag=TAG).info(f"找到最匹配的歌曲: {best_match}")
                    await self.play_local_music(conn, specific_file=best_match)
                    return True
-
        # 检查是否是通用播放音乐命令
-        if any(cmd in clean_text for cmd in self.music_related_keywords):
-            await self.play_local_music(conn)
-            return True
-
-        return False
+        await self.play_local_music(conn)
+        return True

    async def play_local_music(self, conn, specific_file=None):
        """播放本地音乐文件"""
@@ -117,26 +111,18 @@ class MusicHandler:

            # 确保路径正确性
            if specific_file:
-                music_path = os.path.join(self.music_dir, specific_file)
-                if not os.path.exists(music_path):
-                    logger.bind(tag=TAG).error(f"指定的音乐文件不存在: {music_path}")
-                    return
                selected_music = specific_file
+                music_path = os.path.join(self.music_dir, specific_file)
            else:
-                if time.time() - self.scan_time > self.refresh_time:
-                    # 刷新音乐文件列表
-                    self.music_files = MusicManager(self.music_dir, self.music_ext).get_music_files()
-                    self.scan_time = time.time()
-                    logger.bind(tag=TAG).debug(f"刷新的音乐文件列表: {self.music_files}")
-
                if not self.music_files:
                    logger.bind(tag=TAG).error("未找到MP3音乐文件")
                    return
                selected_music = random.choice(self.music_files)
                music_path = os.path.join(self.music_dir, selected_music)
-                if not os.path.exists(music_path):
-                    logger.bind(tag=TAG).error(f"选定的音乐文件不存在: {music_path}")
-                    return
+
+            if not os.path.exists(music_path):
+                logger.bind(tag=TAG).error(f"选定的音乐文件不存在: {music_path}")
+                return
            text = f"正在播放{selected_music}"
            await send_stt_message(conn, text)
            conn.tts_first_text_index = 0
@@ -2,6 +2,7 @@ from config.logger import setup_logging
 import time
 from core.utils.util import remove_punctuation_and_length
 from core.handle.sendAudioHandle import send_stt_message
+from core.handle.intentHandler import handle_user_intent

 TAG = __name__
 logger = setup_logging()
@@ -33,13 +34,7 @@ async def handleAudioMessage(conn, audio):
        else:
            text, file_path = await conn.asr.speech_to_text(conn.asr_audio, conn.session_id)
            logger.bind(tag=TAG).info(f"识别文本: {text}")
-            text_len, text_without_punctuation = remove_punctuation_and_length(text)
-            if await conn.music_handler.handle_music_command(conn, text_without_punctuation):
-                conn.asr_server_receive = True
-                conn.asr_audio.clear()
-                return
-            if text_len <= conn.max_cmd_length and await handleCMDMessage(conn, text_without_punctuation):
-                return
+            text_len, _ = remove_punctuation_and_length(text)
            if text_len > 0:
                await startToChat(conn, text)
            else:
@@ -48,20 +43,22 @@ async def handleAudioMessage(conn, audio):
        conn.reset_vad_states()


-async def handleCMDMessage(conn, text):
-    cmd_exit = conn.cmd_exit
-    for cmd in cmd_exit:
-        if text == cmd:
-            logger.bind(tag=TAG).info("识别到明确的退出命令".format(text))
-            await conn.close()
-            return True
-    return False
-
-
 async def startToChat(conn, text):
-    # 异步发送 stt 信息
+    # 首先进行意图分析
+    intent_handled = await handle_user_intent(conn, text)
+    
+    if intent_handled:
+        # 如果意图已被处理，不再进行聊天
+        conn.asr_server_receive = True
+        return
+    
+    # 意图未被处理，继续常规聊天流程
    await send_stt_message(conn, text)
-    conn.executor.submit(conn.chat, text)
+    if conn.use_function_call_mode:
+        # 使用支持function calling的聊天方法
+        conn.executor.submit(conn.chat_with_function_calling, text)
+    else:
+        conn.executor.submit(conn.chat, text)


 async def no_voice_close_connect(conn):
@@ -7,14 +7,6 @@ from core.utils.util import remove_punctuation_and_length, get_string_no_punctua
 TAG = __name__
 logger = setup_logging()

-
-async def isLLMWantToFinish(last_text):
-    _, last_text_without_punctuation = remove_punctuation_and_length(last_text)
-    if "再见" in last_text_without_punctuation or "拜拜" in last_text_without_punctuation:
-        return True
-    return False
-
-
 async def sendAudioMessage(conn, audios, text, text_index=0):
    # 发送句子开始消息
    if text_index == conn.tts_first_text_index:
@@ -46,10 +38,9 @@ async def sendAudioMessage(conn, audios, text, text_index=0):
    # 发送结束消息（如果是最后一个文本）
    if conn.llm_finish_task and text_index == conn.tts_last_text_index:
        await send_tts_message(conn, 'stop', None)
-        if await isLLMWantToFinish(text):
+        if conn.close_after_chat:
            await conn.close()

-
 async def send_tts_message(conn, state, text=None):
    """发送 TTS 状态消息"""
    message = {
@@ -0,0 +1,33 @@
+from abc import ABC, abstractmethod
+from typing import List, Dict
+from config.logger import setup_logging
+
+TAG = __name__
+logger = setup_logging()
+
+class IntentProviderBase(ABC):
+    def __init__(self, config):
+        self.config = config
+        self.intent_options = config.get("intent_options", {
+            "continue_chat": "继续聊天",
+            "end_chat": "结束聊天",
+            "play_music": "播放音乐"
+        })
+
+    def set_llm(self, llm):
+        self.llm = llm
+        logger.bind(tag=TAG).debug("Set LLM for intent provider")
+        
+    @abstractmethod
+    async def detect_intent(self, dialogue_history: List[Dict]) -> str:
+        """
+        检测用户最后一句话的意图
+        Args:
+            dialogue_history: 对话历史记录列表，每条记录包含role和content
+        Returns:
+            返回识别出的意图，格式为:
+            - "继续聊天"
+            - "结束聊天" 
+            - "播放音乐 歌名" 或 "随机播放音乐"
+        """
+        pass
@@ -0,0 +1,61 @@
+from typing import List, Dict
+from ..base import IntentProviderBase
+from config.logger import setup_logging
+
+TAG = __name__
+logger = setup_logging()
+
+
+
+class IntentProvider(IntentProviderBase):
+    def __init__(self, config):
+        super().__init__(config)
+        self.llm = None
+        self.promot = self.get_intent_system_prompt()
+
+    def get_intent_system_prompt(self) -> str:
+        """
+        根据配置的意图选项动态生成系统提示词
+        Returns:
+            格式化后的系统提示词
+        """
+        intent_list = []
+        for key, value in self.intent_options.items():
+            if key == "play_music":
+                intent_list.append(f"{value} [歌名]")
+            else:
+                intent_list.append(value)
+                
+        prompt = (
+            "你是一个意图识别助手。你需要根据和用户的对话记录，重点分析用户的最后一句话，判断用户意图属于以下哪一类：\n"
+            f"{', '.join(intent_list)}\n"
+            "如果是唱歌、听歌、播放音乐，请指定歌名，格式为'播放音乐 [识别出的歌名]'。\n"
+            "如果听不出具体歌名，可以返回'随机播放音乐'。\n"
+            "只需要返回意图结果的json，不要解释。"
+            "返回格式如下：\n"
+            "{intent: '用户意图'}"
+        )
+        return prompt
+    
+    async def detect_intent(self, dialogue_history: List[Dict]) -> str:
+        if not self.llm:
+            raise ValueError("LLM provider not set")
+
+        # 构建用户最后一句话的提示
+        msgStr = ""
+        for msg in dialogue_history:
+            if msg.role == "user":
+                msgStr += f"User: {msg.content}\n"
+            elif msg.role== "assistant":
+                msgStr += f"Assistant: {msg.content}\n"
+
+        user_prompt = f"请分析用户的意图：\n{msgStr}"
+        
+        # 使用LLM进行意图识别
+        intent = self.llm.response_no_stream(
+            system_prompt=self.promot,
+            user_prompt=user_prompt
+        )
+  
+        logger.bind(tag=TAG).info(f"Detected intent: {intent}")
+        return intent.strip()
@@ -0,0 +1,18 @@
+from ..base import IntentProviderBase
+from typing import List, Dict
+from config.logger import setup_logging
+
+TAG = __name__
+logger = setup_logging()
+
+class IntentProvider(IntentProviderBase):
+    async def detect_intent(self, dialogue_history: List[Dict]) -> str:
+        """
+        默认的意图识别实现，始终返回继续聊天
+        Args:
+            dialogue_history: 对话历史记录列表
+        Returns:
+            固定返回"继续聊天"
+        """
+        logger.bind(tag=TAG).debug("Using NoIntentProvider, always returning continue chat")
+        return self.intent_options["continue_chat"]
@@ -1,8 +1,38 @@
 from abc import ABC, abstractmethod
+from config.logger import setup_logging

+TAG = __name__
+logger = setup_logging()

 class LLMProviderBase(ABC):
    @abstractmethod
    def response(self, session_id, dialogue):
        """LLM response generator"""
        pass
+
+    def response_no_stream(self, system_prompt, user_prompt):
+        try:
+            # 构造对话格式
+            dialogue = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ]
+            result = ""
+            for part in self.response("", dialogue):
+                result += part
+            return result
+
+        except Exception as e:
+            logger.bind(tag=TAG).error(f"Error in Ollama response generation: {e}")
+            return "【LLM服务响应异常】"
+    
+    def response_with_functions(self, session_id, dialogue, functions=None):
+        """
+        Default implementation for function calling (streaming)
+        This should be overridden by providers that support function calls
+        
+        Returns: generator that yields either text tokens or a special function call token
+        """
+        # For providers that don't support functions, just return regular response
+        for token in self.response(session_id, dialogue):
+            yield {"type": "content", "content": token}
@@ -1,5 +1,6 @@
 from config.logger import setup_logging
-import requests, json
+from openai import OpenAI
+import json
 from core.providers.llm.base import LLMProviderBase

 TAG = __name__
@@ -8,39 +9,73 @@ logger = setup_logging()

 class LLMProvider(LLMProviderBase):
    def __init__(self, config):
-
        self.model_name = config.get("model_name")
        self.base_url = config.get("base_url", "http://localhost:11434")
+        # Initialize OpenAI client with Ollama base URL
+        #如果没有v1，增加v1
+        if not self.base_url.endswith("/v1"):
+            self.base_url = f"{self.base_url}/v1"
+            
+        self.client = OpenAI(
+            base_url=self.base_url,
+            api_key="ollama"  # Ollama doesn't need an API key but OpenAI client requires one
+        )

-    def response(self, session_id, dialogue):        
+    def response(self, session_id, dialogue):
        try:
-            # Convert dialogue format to Ollama format
-            prompt = ""
-            for msg in dialogue:
-                if msg["role"] == "system":
-                    prompt += f"System: {msg['content']}\n"
-                elif msg["role"] == "user":
-                    prompt += f"User: {msg['content']}\n"
-                elif msg["role"] == "assistant":
-                    prompt += f"Assistant: {msg['content']}\n"
-
-            # Make request to Ollama API
-            response = requests.post(
-                f"{self.base_url}/api/generate",
-                json={
-                    "model": self.model_name,
-                    "prompt": prompt,
-                    "stream": True
-                },
+            responses = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=dialogue,
                stream=True
            )
-
-            for line in response.iter_lines():
-                if line:
-                    json_response = json.loads(line)
-                    if "response" in json_response:
-                        yield json_response["response"]
+            
+            for chunk in responses:
+                try:
+                    delta = chunk.choices[0].delta if getattr(chunk, 'choices', None) else None
+                    content = delta.content if hasattr(delta, 'content') else ''
+                    if content:
+                        yield content
+                except Exception as e:
+                    logger.bind(tag=TAG).error(f"Error processing chunk: {e}")

        except Exception as e:
            logger.bind(tag=TAG).error(f"Error in Ollama response generation: {e}")
            yield "【Ollama服务响应异常】"
+
+    def response_with_functions(self, session_id, dialogue, functions=None):
+        try:
+            stream = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=dialogue,
+                stream=True,
+                tools=functions,
+            )
+            
+            current_function_call = None
+            current_content = ""
+            
+            for chunk in stream:
+                delta = chunk.choices[0].delta
+                
+                if delta.content:
+                    current_content += delta.content
+                    yield {"type": "content", "content": delta.content}
+                
+                if delta.tool_calls:
+                    tool_call = delta.tool_calls[0]
+                    # Handle the function call data using proper attribute access
+                    if not current_function_call:
+                        current_function_call = {
+                            "function": {
+                                "name": tool_call.function.name,
+                                "arguments": tool_call.function.arguments
+                            }
+                        }
+            
+            if current_function_call:
+                logger.bind(tag=TAG).debug(f"ollama Function call detected: {current_function_call}")
+                yield {"type": "function_call", "function_call": current_function_call}
+
+        except Exception as e:
+            logger.bind(tag=TAG).error(f"Error in Ollama function call: {e}")
+            yield {"type": "content", "content": f"【Ollama服务响应异常: {str(e)}】"}
@@ -43,3 +43,41 @@ class LLMProvider(LLMProviderBase):

        except Exception as e:
            logger.bind(tag=TAG).error(f"Error in response generation: {e}")
+
+    def response_with_functions(self, session_id, dialogue, functions=None):
+        try:
+            stream = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=dialogue,
+                stream=True,
+                tools=functions,
+            )
+            
+            current_function_call = None
+            current_content = ""
+            
+            for chunk in stream:
+                delta = chunk.choices[0].delta
+                
+                if delta.content:
+                    current_content += delta.content
+                    yield {"type": "content", "content": delta.content}
+                
+                if delta.tool_calls:
+                    tool_call = delta.tool_calls[0]
+                    # Handle the function call data using proper attribute access
+                    if not current_function_call:
+                        current_function_call = {
+                            "function": {
+                                "name": tool_call.function.name,
+                                "arguments": tool_call.function.arguments
+                            }
+                        }
+            
+            if current_function_call:
+                logger.bind(tag=TAG).debug(f"openai Function call detected: {current_function_call}")
+                yield {"type": "function_call", "function_call": current_function_call}
+                
+        except Exception as e:
+            self.logger.bind(tag=TAG).error(f"Error in function call streaming: {e}")
+            yield {"type": "content", "content": f"【OpenAI服务响应异常: {e}】"}
@@ -8,6 +8,7 @@ class MemoryProviderBase(ABC):
    def __init__(self, config):
        self.config = config
        self.role_id = None
+        self.llm = None

    @abstractmethod
    async def save_memory(self, msgs):
@@ -19,5 +20,6 @@ class MemoryProviderBase(ABC):
        """Query memories for specific role based on similarity"""
        return "please implement query method"

-    def set_role_id(self, role_id: str):
-        self.role_id = role_id
+    def init_memory(self, role_id, llm):
+        self.role_id = role_id    
+        self.llm = llm
@@ -0,0 +1,156 @@
+from ..base import MemoryProviderBase, logger
+import time
+import json
+import os
+import yaml
+from core.utils.util import get_project_dir
+
+short_term_memory_prompt = """
+# 时空记忆编织者
+
+## 核心使命
+构建可生长的动态记忆网络，在有限空间内保留关键信息的同时，智能维护信息演变轨迹
+根据对话记录，总结user的重要信息，以便在未来的对话中提供更个性化的服务
+
+## 记忆法则
+### 1. 三维度记忆评估（每次更新必执行）
+| 维度       | 评估标准                  | 权重分 |
+|------------|---------------------------|--------|
+| 时效性     | 信息新鲜度（按对话轮次） | 40%    |
+| 情感强度   | 含💖标记/重复提及次数     | 35%    |
+| 关联密度   | 与其他信息的连接数量      | 25%    |
+
+### 2. 动态更新机制
+**名字变更处理示例：**
+原始记忆："曾用名": ["张三"], "现用名": "张三丰"
+触发条件：当检测到「我叫X」「称呼我Y」等命名信号时
+操作流程：
+1. 将旧名移入"曾用名"列表
+2. 记录命名时间轴："2024-02-15 14:32:启用张三丰"
+3. 在记忆立方追加：「从张三到张三丰的身份蜕变」
+
+### 3. 空间优化策略
+- **信息压缩术**：用符号体系提升密度
+  - ✅"张三丰[北/软工/🐱]"
+  - ❌"北京软件工程师，养猫"
+- **淘汰预警**：当总字数≥900时触发
+  1. 删除权重分<60且3轮未提及的信息
+  2. 合并相似条目（保留时间戳最近的）
+
+## 记忆结构
+输出格式必须为可解析的json字符串，不需要解释、注释和说明，保存记忆时仅从对话提取信息，不要混入示例内容
+```json
+{
+  "时空档案": {
+    "身份图谱": {
+      "现用名": "",
+      "特征标记": [] 
+    },
+    "记忆立方": [
+      {
+        "事件": "入职新公司",
+        "时间戳": "2024-03-20",
+        "情感值": 0.9,
+        "关联项": ["下午茶"],
+        "保鲜期": 30 
+      }
+    ]
+  },
+  "关系网络": {
+    "高频话题": {"职场": 12},
+    "暗线联系": [""]
+  },
+  "待响应": {
+    "紧急事项": ["需立即处理的任务"], 
+    "潜在关怀": ["可主动提供的帮助"]
+  },
+  "高光语录": [
+    "最打动人心的瞬间，强烈的情感表达，user的原话"
+  ]
+}
+```
+"""
+
+def extract_json_data(json_code):
+    start = json_code.find("```json")
+    # 从start开始找到下一个```结束
+    end = json_code.find("```", start+1)
+    #print("start:", start, "end:", end)
+    if start == -1 or end == -1:
+        try:
+            jsonData = json.loads(json_code)
+            return json_code
+        except Exception as e:
+            print("Error:", e)
+        return ""
+    jsonData = json_code[start+7:end]
+    return jsonData
+
+TAG = __name__
+
+class MemoryProvider(MemoryProviderBase):
+    def __init__(self, config):
+        super().__init__(config)
+        self.short_momery = ""
+        self.memory_path = get_project_dir() + 'data/.memory.yaml'
+        self.load_memory()
+
+    def init_memory(self, role_id, llm):
+        super().init_memory(role_id, llm)
+        self.load_memory()
+    
+    def load_memory(self):
+        all_memory = {}
+        if os.path.exists(self.memory_path):
+            with open(self.memory_path, 'r', encoding='utf-8') as f:
+                all_memory = yaml.safe_load(f) or {}
+        if self.role_id in all_memory:
+            self.short_momery = all_memory[self.role_id]
+    
+    def save_memory_to_file(self):
+        all_memory = {}
+        if os.path.exists(self.memory_path):
+              with open(self.memory_path, 'r', encoding='utf-8') as f:
+                  all_memory = yaml.safe_load(f) or {}
+        all_memory[self.role_id] = self.short_momery
+        with open(self.memory_path, 'w', encoding='utf-8') as f:
+            yaml.dump(all_memory, f, allow_unicode=True)
+        
+    async def save_memory(self, msgs):
+        if self.llm is None:
+            logger.bind(tag=TAG).error("LLM is not set for memory provider")
+            return None
+        
+        if len(msgs) < 2:
+            return None
+        
+        msgStr = ""
+        for msg in msgs:
+            if msg.role == "user":
+                msgStr += f"User: {msg.content}\n"
+            elif msg.role== "assistant":
+                msgStr += f"Assistant: {msg.content}\n"
+        if len(self.short_momery) > 0:
+            msgStr+="历史记忆：\n"
+            msgStr+=self.short_momery
+        
+        #当前时间
+        time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        msgStr += f"当前时间：{time_str}"
+
+        result = self.llm.response_no_stream(short_term_memory_prompt, msgStr)
+ 
+        json_str = extract_json_data(result)
+        try:
+            json_data = json.loads(json_str) # 检查json格式是否正确
+            self.short_momery = json_str
+        except Exception as e:
+            print("Error:", e)
+        
+        self.save_memory_to_file()
+        logger.bind(tag=TAG).info(f"Save memory successful - Role: {self.role_id}")
+
+        return self.short_momery
+    
+    async def query_memory(self, query: str)-> str:
+        return self.short_momery
@@ -0,0 +1,18 @@
+'''
+不使用记忆，可以选择此模块
+'''
+from ..base import MemoryProviderBase, logger
+
+TAG = __name__
+
+class MemoryProvider(MemoryProviderBase):
+    def __init__(self, config):
+        super().__init__(config)
+      
+    async def save_memory(self, msgs):
+        logger.bind(tag=TAG).debug("nomem mode: No memory saving is performed.")
+        return None
+
+    async def query_memory(self, query: str)-> str:
+        logger.bind(tag=TAG).debug("nomem mode: No memory query is performed.")
+        return ""
@@ -26,6 +26,9 @@ class Dialogue:
        return dialogue

    def get_llm_dialogue_with_memory(self, memory_str: str = None) -> List[Dict[str, str]]:
+        if memory_str is None or len(memory_str) == 0:
+            return self.get_llm_dialogue()
+        
        # 构建带记忆的对话
        dialogue = []
        
@@ -0,0 +1,17 @@
+import os
+import sys
+from config.logger import setup_logging
+import importlib
+
+logger = setup_logging()
+
+
+def create_instance(class_name, *args, **kwargs):
+    # 创建intent实例
+    if os.path.exists(os.path.join('core', 'providers', 'intent', class_name, f'{class_name}.py')):
+        lib_name = f'core.providers.intent.{class_name}.{class_name}'
+        if lib_name not in sys.modules:
+            sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
+        return sys.modules[lib_name].IntentProvider(*args, **kwargs)
+
+    raise ValueError(f"不支持的intent类型: {class_name}，请检查该配置的type是否设置正确")
@@ -4,7 +4,7 @@ from config.logger import setup_logging
 from core.connection import ConnectionHandler
 from core.handle.musicHandler import MusicHandler
 from core.utils.util import get_local_ip
-from core.utils import asr, vad, llm, tts, memory
+from core.utils import asr, vad, llm, tts, memory, intent

 TAG = __name__

@@ -13,11 +13,11 @@ class WebSocketServer:
    def __init__(self, config: dict):
        self.config = config
        self.logger = setup_logging()
-        self._vad, self._asr, self._llm, self._tts, self._music, self._memory = self._create_processing_instances()
+        self._vad, self._asr, self._llm, self._tts, self._music, self._memory, self.intent = self._create_processing_instances()
        self.active_connections = set()  # 添加全局连接记录

    def _create_processing_instances(self):
-        memory_cls_name = self.config["selected_module"].get("Memory", "mem0ai") # 默认使用mem0ai
+        memory_cls_name = self.config["selected_module"].get("Memory", "nomem") # 默认使用nomem
        has_memory_cfg = self.config.get("Memory") and memory_cls_name in self.config["Memory"]
        memory_cfg = self.config["Memory"][memory_cls_name] if has_memory_cfg else {}

@@ -52,6 +52,13 @@ class WebSocketServer:
            ),
            MusicHandler(self.config),
            memory.create_instance(memory_cls_name, memory_cfg),
+            intent.create_instance(
+                self.config["selected_module"]["Intent"]
+                if not 'type' in self.config["Intent"][self.config["selected_module"]["Intent"]]
+                else
+                self.config["Intent"][self.config["selected_module"]["Intent"]]["type"],
+                self.config["Intent"][self.config["selected_module"]["Intent"]]
+            ),
        )

    async def start(self):
@@ -71,7 +78,7 @@ class WebSocketServer:
    async def _handle_connection(self, websocket):
        """处理新连接，每次创建独立的ConnectionHandler"""
        # 创建ConnectionHandler时传入当前server实例
-        handler = ConnectionHandler(self.config, self._vad, self._asr, self._llm, self._tts, self._music, self._memory)
+        handler = ConnectionHandler(self.config, self._vad, self._asr, self._llm, self._tts, self._music, self._memory, self.intent)
        self.active_connections.add(handler)
        try:
            await handler.handle_connection(websocket)