Merge branch 'main' into vosk_asr

main/xiaozhi-server/core/connection.py

@@ -18,10 +18,11 @@ from concurrent.futures import ThreadPoolExecutor, TimeoutError
 from core.handle.sendAudioHandle import sendAudioMessage
 from core.handle.receiveAudioHandle import handleAudioMessage
 from core.handle.functionHandler import FunctionHandler
-from plugins_func.register import Action
+from plugins_func.register import Action, ActionResponse
 from config.private_config import PrivateConfig
 from core.auth import AuthMiddleware, AuthenticationError
 from core.utils.auth_code_gen import AuthCodeGenerator
+from core.mcp.manager import MCPManager
 
 TAG = __name__
 
@@ -100,6 +101,8 @@ class ConnectionHandler:
         self.use_function_call_mode = False
         if self.config["selected_module"]["Intent"] == 'function_call':
             self.use_function_call_mode = True
+        
+        self.mcp_manager = MCPManager(self)
 
     async def handle_connection(self, ws):
         try:
@@ -194,7 +197,31 @@ class ConnectionHandler:
         """加载记忆"""
         device_id = self.headers.get("device-id", None)
         self.memory.init_memory(device_id, self.llm)
-        self.intent.set_llm(self.llm)
+        
+        """为意图识别设置LLM，优先使用专用LLM"""
+        # 检查是否配置了专用的意图识别LLM
+        intent_llm_name = self.config["Intent"]["intent_llm"]["llm"]
+        
+        # 记录开始初始化意图识别LLM的时间
+        intent_llm_init_start = time.time()
+        
+        if not self.use_function_call_mode and intent_llm_name and intent_llm_name in self.config["LLM"]:
+            # 如果配置了专用LLM，则创建独立的LLM实例
+            from core.utils import llm as llm_utils
+            intent_llm_config = self.config["LLM"][intent_llm_name]
+            intent_llm_type = intent_llm_config.get("type", intent_llm_name)
+            intent_llm = llm_utils.create_instance(intent_llm_type, intent_llm_config)
+            self.logger.bind(tag=TAG).info(f"为意图识别创建了专用LLM: {intent_llm_name}, 类型: {intent_llm_type}")
+            
+            self.intent.set_llm(intent_llm)
+        else:
+            # 否则使用主LLM
+            self.intent.set_llm(self.llm)
+            self.logger.bind(tag=TAG).info("意图识别使用主LLM")
+            
+        # 记录意图识别LLM初始化耗时
+        intent_llm_init_time = time.time() - intent_llm_init_start
+        self.logger.bind(tag=TAG).info(f"意图识别LLM初始化完成，耗时: {intent_llm_init_time:.4f}秒")
 
         """加载位置信息"""
         self.client_ip_info = get_ip_info(self.client_ip)
@@ -203,6 +230,9 @@ class ConnectionHandler:
             self.prompt = self.prompt + f"\nuser location:{self.client_ip_info}"
             self.dialogue.update_system_message(self.prompt)
 
+        """加载MCP工具"""
+        asyncio.run_coroutine_threadsafe(self.mcp_manager.initialize_servers(), self.loop)
+
     def change_system_prompt(self, prompt):
         self.prompt = prompt
         # 找到原来的role==system，替换原来的系统提示
@@ -324,7 +354,6 @@ class ConnectionHandler:
         functions = None
         if hasattr(self, 'func_handler'):
             functions = self.func_handler.get_functions()
-
         response_message = []
         processed_chars = 0  # 跟踪已处理的字符位置
 
@@ -358,6 +387,9 @@ class ConnectionHandler:
         content_arguments = ""
         for response in llm_responses:
             content, tools_call = response
+            if "content" in response:
+                content = response["content"]
+                tools_call = None
             if content is not None and len(content) > 0:
                 if len(response_message) <= 0 and (content == "```" or "<tool_call>" in content):
                     tool_call_flag = True
@@ -436,8 +468,14 @@ class ConnectionHandler:
                     "id": function_id,
                     "arguments": function_arguments
                 }
-                result = self.func_handler.handle_llm_function_call(self, function_call_data)
-                self._handle_function_result(result, function_call_data, text_index + 1)
+
+                # 处理MCP工具调用
+                if self.mcp_manager.is_mcp_tool(function_name):
+                    result = self._handle_mcp_tool_call(function_call_data)
+                else:
+                    # 处理系统函数
+                    result = self.func_handler.handle_llm_function_call(self, function_call_data)
+                self._handle_function_result(result, function_call_data, text_index+1)
 
         # 处理最后剩余的文本
         full_text = "".join(response_message)
@@ -459,6 +497,42 @@ class ConnectionHandler:
 
         return True
 
+    def _handle_mcp_tool_call(self, function_call_data):
+        function_arguments = function_call_data["arguments"]
+        function_name = function_call_data["name"]
+        try:
+            args_dict = function_arguments
+            if isinstance(function_arguments, str):
+                try:
+                    args_dict = json.loads(function_arguments)
+                except json.JSONDecodeError:
+                    self.logger.bind(tag=TAG).error(f"无法解析 function_arguments: {function_arguments}")
+                    return ActionResponse(action=Action.REQLLM, result="参数解析失败", response="")
+                    
+            tool_result = asyncio.run_coroutine_threadsafe(self.mcp_manager.execute_tool(
+                function_name,
+                args_dict
+            ), self.loop).result()
+            # meta=None content=[TextContent(type='text', text='北京当前天气:\n温度: 21°C\n天气: 晴\n湿度: 6%\n风向: 西北 风\n风力等级: 5级', annotations=None)] isError=False
+            content_text = ""
+            if tool_result is not None and tool_result.content is not None:
+                for content in tool_result.content:
+                    content_type = content.type
+                    if content_type == "text":
+                        content_text = content.text
+                    elif content_type == "image":
+                        pass
+            
+            if len(content_text) > 0:
+                return ActionResponse(action=Action.REQLLM, result=content_text, response="")
+            
+        except Exception as e:
+            self.logger.bind(tag=TAG).error(f"MCP工具调用错误: {e}")
+            return ActionResponse(action=Action.REQLLM, result="工具调用出错", response="")
+
+        return ActionResponse(action=Action.REQLLM, result="工具调用出错", response="")
+            
+
     def _handle_function_result(self, result, function_call_data, text_index):
         if result.action == Action.RESPONSE:  # 直接回复前端
             text = result.response
@@ -582,6 +656,8 @@ class ConnectionHandler:
 
     async def close(self, ws=None):
         """资源清理方法"""
+        # 清理MCP资源
+        await self.mcp_manager.cleanup_all()
 
         # 触发停止事件并清理资源
         if self.stop_event:

main/xiaozhi-server/core/handle/intentHandler.py

@@ -4,6 +4,8 @@ import uuid
 from core.handle.sendAudioHandle import send_stt_message
 from core.handle.helloHandle import checkWakeupWords
 from core.utils.util import remove_punctuation_and_length
+from core.utils.dialogue import Message
+from loguru import logger
 
 TAG = __name__
 logger = setup_logging()
@@ -21,11 +23,11 @@ async def handle_user_intent(conn, text):
         # 使用支持function calling的聊天方法,不再进行意图分析
         return False
     # 使用LLM进行意图分析
-    intent = await analyze_intent_with_llm(conn, text)
-    if not intent:
+    intent_result = await analyze_intent_with_llm(conn, text)
+    if not intent_result:
         return False
     # 处理各种意图
-    return await process_intent_result(conn, intent, text)
+    return await process_intent_result(conn, intent_result, text)
 
 
 async def check_direct_exit(conn, text):
@@ -40,7 +42,6 @@ async def check_direct_exit(conn, text):
     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:
@@ -51,52 +52,65 @@ async def analyze_intent_with_llm(conn, text):
     dialogue = conn.dialogue
     try:
         intent_result = await conn.intent.detect_intent(conn, dialogue.dialogue, text)
-        # 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()
-
+        return intent_result
     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):
+async def process_intent_result(conn, intent_result, 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
+    try:
+        # 尝试将结果解析为JSON
+        intent_data = json.loads(intent_result)
 
-    # 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}")
-        # 调用play_music函数来播放音乐
-        song_name = extract_text_in_brackets(intent)
-        function_id = str(uuid.uuid4().hex)
-        function_name = "play_music"
-        function_arguments = '{ "song_name": "' + song_name + '" }'
+        # 检查是否有function_call
+        if "function_call" in intent_data:
+            # 直接从意图识别获取了function_call
+            logger.bind(tag=TAG).info(f"检测到function_call格式的意图结果: {intent_data['function_call']['name']}")
+            function_name = intent_data["function_call"]["name"]
+            if function_name == "continue_chat":
+                return False
+            function_args = None
+            if "arguments" in intent_data["function_call"]:
+                function_args = intent_data["function_call"]["arguments"]
+            # 确保参数是字符串格式的JSON
+            if isinstance(function_args, dict):
+                function_args = json.dumps(function_args)
 
-        function_call_data = {
-            "name": function_name,
-            "id": function_id,
-            "arguments": function_arguments
-        }
-        conn.func_handler.handle_llm_function_call(conn, function_call_data)
-        return True
+            function_call_data = {
+                "name": function_name,
+                "id": str(uuid.uuid4().hex),
+                "arguments": function_args
+            }
 
-    # 其他意图处理可以在这里扩展
+            await send_stt_message(conn, original_text)
 
-    # 默认返回False，表示继续常规聊天流程
-    return False
+            # 使用executor执行函数调用和结果处理
+            def process_function_call():
+                conn.dialogue.put(Message(role="user", content=original_text))
+                result = conn.func_handler.handle_llm_function_call(conn, function_call_data)
+                if result and function_name != 'play_music':
+                    # 获取当前最新的文本索引
+                    text = result.response
+                    if text is None:
+                        text = result.result
+                    if text is not None:
+                        text_index = conn.tts_last_text_index + 1 if hasattr(conn, 'tts_last_text_index') else 0
+                        conn.recode_first_last_text(text, text_index)
+                        future = conn.executor.submit(conn.speak_and_play, text, text_index)
+                        conn.llm_finish_task = True
+                        conn.tts_queue.put(future)
+                        conn.dialogue.put(Message(role="assistant", content=text))
+
+            # 将函数执行放在线程池中
+            conn.executor.submit(process_function_call)
+            return True
+        return False
+    except json.JSONDecodeError as e:
+        logger.bind(tag=TAG).error(f"处理意图结果时出错: {e}")
+        return False
 
 
 def extract_text_in_brackets(s):
@@ -112,4 +126,4 @@ def extract_text_in_brackets(s):
     if left_bracket_index != -1 and right_bracket_index != -1 and left_bracket_index < right_bracket_index:
         return s[left_bracket_index + 1:right_bracket_index]
     else:
-        return ""
+        return ""

main/xiaozhi-server/core/mcp/MCPClient.py

@@ -0,0 +1,86 @@
+from datetime import timedelta
+from typing import Optional
+from contextlib import AsyncExitStack
+import os, shutil
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+
+from config.logger import setup_logging
+
+TAG = __name__
+
+class MCPClient:
+    def __init__(self, config):
+        # Initialize session and client objects
+        self.session: Optional[ClientSession] = None
+        self.exit_stack = AsyncExitStack()
+        self.logger = setup_logging()
+        self.config = config
+        self.tolls = []
+
+    async def initialize(self):
+        args = self.config.get("args", [])
+
+        command = (
+            shutil.which("npx")
+            if self.config["command"] == "npx"
+            else self.config["command"]
+        )
+        
+        env={**os.environ}
+        if self.config.get("env"):
+            env.update(self.config["env"])
+        
+        server_params = StdioServerParameters(
+            command=command,
+            args=args,
+            env=env
+        )
+        
+        stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
+        self.stdio, self.write = stdio_transport
+        time_out_delta =  timedelta(seconds=15)
+        self.session = await self.exit_stack.enter_async_context(ClientSession(read_stream=self.stdio, write_stream=self.write, read_timeout_seconds=time_out_delta))
+        
+        await self.session.initialize()
+        
+        # List available tools
+        response = await self.session.list_tools()
+        tools = response.tools
+        self.tools = tools
+        self.logger.bind(tag=TAG).info(f"Connected to server with tools:{[tool.name for tool in tools]}")
+    
+    def has_tool(self, tool_name):
+        return any(tool.name == tool_name for tool in self.tools)
+    
+    def get_available_tools(self):
+        available_tools = [{"type": "function", "function":{ 
+            "name": tool.name,
+            "description": tool.description,
+            "parameters": tool.inputSchema
+        } } for tool in self.tools]
+
+        return available_tools
+    
+    async def call_tool(self, tool_name: str, tool_args: dict):
+        self.logger.bind(tag=TAG).info(f"MCPClient Calling tool {tool_name} with args: {tool_args}")
+        try:
+            response = await self.session.call_tool(tool_name, tool_args)
+        except Exception as e:
+            self.logger.bind(tag=TAG).error(f"Error calling tool {tool_name}: {e}")
+            from types import SimpleNamespace
+            error_content = SimpleNamespace(
+                type='text',
+                text=f"Error calling tool {tool_name}: {e}"
+            )
+            error_response = SimpleNamespace(
+                content=[error_content],
+                isError=True
+            )
+            return error_response
+        self.logger.bind(tag=TAG).info(f"MCPClient Response from tool {tool_name}: {response}")
+        return response
+
+    async def cleanup(self):
+        """Clean up resources"""
+        await self.exit_stack.aclose()

main/xiaozhi-server/core/mcp/manager.py

@@ -0,0 +1,110 @@
+"""MCP服务管理器"""
+import os, json
+from typing import Dict, Any, List
+from .MCPClient import MCPClient
+from config.logger import setup_logging
+from core.utils.util import get_project_dir
+from plugins_func.register import register_function, ActionResponse, Action, ToolType
+
+TAG = __name__
+
+class MCPManager:
+    """管理多个MCP服务的集中管理器"""
+
+    def __init__(self,conn) -> None:
+        """
+        初始化MCP管理器
+        """
+        self.conn = conn
+        self.logger = setup_logging()
+        self.config_path = get_project_dir() + 'data/.mcp_server_settings.json'
+        if os.path.exists(self.config_path) == False:
+            self.config_path = ""
+            self.logger.bind(tag=TAG).warning(f"请检查mcp服务配置文件：data/.mcp_server_settings.json")
+        self.client: Dict[str, MCPClient] = {}
+        self.tools = []
+
+    def load_config(self) -> Dict[str, Any]:
+        """加载MCP服务配置
+        Returns:
+            Dict[str, Any]: 服务配置字典
+        """
+        if len(self.config_path) == 0:
+            return {}
+        
+        try:
+            with open(self.config_path, 'r', encoding='utf-8') as f:
+                config = json.load(f)
+            return config.get('mcpServers', {})
+        except Exception as e:
+            self.logger.bind(tag=TAG).error(f"Error loading MCP config from {self.config_path}: {e}")
+            return {}
+
+    async def initialize_servers(self) -> None:
+        """初始化所有MCP服务"""
+        config = self.load_config()
+        for name, srv_config in config.items():
+            if not srv_config.get("command"):
+                self.logger.bind(tag=TAG).warning(f"Skipping server {name}: command not specified")
+                continue
+
+            try:
+                client = MCPClient(srv_config)
+                await client.initialize()
+                self.client[name] = client
+                self.logger.bind(tag=TAG).info(f"Initialized MCP client: {name}")
+                client_tools = client.get_available_tools()
+                self.tools.extend(client_tools)
+                for tool in client_tools:
+                    func_name = "mcp_"+tool["function"]["name"]
+                    register_function(func_name, tool, ToolType.MCP_CLIENT)(self.execute_tool)
+                    self.conn.func_handler.function_registry.register_function(func_name)
+
+            except Exception as e:
+                self.logger.bind(tag=TAG).error(f"Failed to initialize MCP server {name}: {e}")
+        self.conn.func_handler.upload_functions_desc()
+
+    def get_all_tools(self) -> List[Dict[str, Any]]:
+        """获取所有服务的工具function定义
+        Returns:
+            List[Dict[str, Any]]: 所有工具的function定义列表
+        """
+        return self.tools
+
+    def is_mcp_tool(self, tool_name: str) -> bool:
+        """检查是否是MCP工具
+        Args:
+            tool_name: 工具名称
+        Returns:
+            bool: 是否是MCP工具
+        """
+        for tool in self.tools:
+            if tool.get("function") != None and tool["function"].get("name") == tool_name:
+                return True
+        return False
+
+    async def execute_tool(self, tool_name: str, arguments: Dict[str, Any]) -> Any:
+        """执行工具调用
+        Args:
+            tool_name: 工具名称
+            arguments: 工具参数 
+        Returns:
+            Any: 工具执行结果
+        Raises:
+            ValueError: 工具未找到时抛出
+        """
+        self.logger.bind(tag=TAG).info(f"Executing tool {tool_name} with arguments: {arguments}")
+        for client in self.client.values():
+            if client.has_tool(tool_name):
+                return await client.call_tool(tool_name, arguments)
+            
+        raise ValueError(f"Tool {tool_name} not found in any MCP server")
+
+    async def cleanup_all(self) -> None:
+        for name, client in self.client.items():
+            try:
+                await client.cleanup()
+                self.logger.bind(tag=TAG).info(f"Cleaned up MCP client: {name}")
+            except Exception as e:
+                self.logger.bind(tag=TAG).error(f"Error cleaning up MCP client {name}: {e}")
+        self.client.clear()

main/xiaozhi-server/core/providers/intent/base.py

@@ -10,14 +10,21 @@ class IntentProviderBase(ABC):
     def __init__(self, config):
         self.config = config
         self.intent_options = config.get("intent_options", {
+            "handle_exit_intent": "结束聊天, 用户发来如再见之类的表示结束的话, 不想再进行对话的时候",
+            "play_music": "播放音乐, 用户希望你可以播放音乐, 只用于播放音乐的意图",
+            "get_weather": "查询天气, 用户希望查询某个地点的天气情况",
+            "get_news": "查询新闻, 用户希望查询最新新闻或特定类型的新闻",
+            "get_lunar": "用于获取今天的阴历/农历和黄历信息",
+            "get_time": "获取今天日期或者当前时间信息",
             "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")
+        # 获取模型名称和类型信息
+        model_name = getattr(llm, 'model_name', str(llm.__class__.__name__))
+        # 记录更详细的日志
+        logger.bind(tag=TAG).info(f"意图识别设置LLM: {model_name}")
 
     @abstractmethod
     async def detect_intent(self, conn, dialogue_history: List[Dict], text: str) -> str:
@@ -30,5 +37,6 @@ class IntentProviderBase(ABC):
             - "继续聊天"
             - "结束聊天" 
             - "播放音乐 歌名" 或 "随机播放音乐"
+            - "查询天气 地点名" 或 "查询天气 [当前位置]"
         """
         pass

main/xiaozhi-server/core/providers/intent/function_call/function_call.py

@@ -0,0 +1,20 @@
+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, conn, dialogue_history: List[Dict], text: str) -> 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
+            text: 本次对话记录
+        Returns:
+            固定返回"继续聊天"
+        """
+        logger.bind(tag=TAG).debug("Using functionCallProvider, always returning continue chat")
+        return self.intent_options["continue_chat"]

main/xiaozhi-server/core/providers/intent/intent_llm/intent_llm.py

@@ -3,6 +3,9 @@ from ..base import IntentProviderBase
 from plugins_func.functions.play_music import initialize_music_handler
 from config.logger import setup_logging
 import re
+import json
+import hashlib
+import time
 
 TAG = __name__
 logger = setup_logging()
@@ -13,6 +16,10 @@ class IntentProvider(IntentProviderBase):
         super().__init__(config)
         self.llm = None
         self.promot = self.get_intent_system_prompt()
+        # 添加缓存管理
+        self.intent_cache = {}  # 缓存意图识别结果
+        self.cache_expiry = 600  # 缓存有效期10分钟
+        self.cache_max_size = 100  # 最多缓存100个意图
 
     def get_intent_system_prompt(self) -> str:
         """
@@ -22,62 +29,119 @@ class IntentProvider(IntentProviderBase):
         """
         intent_list = []
 
-        """
-        "continue_chat":    "1.继续聊天, 除了播放音乐和结束聊天的时候的选项, 比如日常的聊天和问候, 对话等",
-        "end_chat":         "2.结束聊天, 用户发来如再见之类的表示结束的话, 不想再进行对话的时候",
-        "play_music":       "3.播放音乐, 用户希望你可以播放音乐, 只用于播放音乐的意图"
-        """
-        for key, value in self.intent_options.items():
-            if key == "play_music":
-                intent_list.append("3.播放音乐, 用户希望你可以播放音乐, 只用于播放音乐的意图")
-            elif key == "end_chat":
-                intent_list.append("2.结束聊天, 用户发来如再见之类的表示结束的话, 不想再进行对话的时候")
-            elif key == "continue_chat":
-                intent_list.append("1.继续聊天, 除了播放音乐和结束聊天的时候的选项, 比如日常的聊天和问候, 对话等")
-            else:
-                intent_list.append(value)
-
-        # "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"
         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(使用<start>和<end>标志)：\n"
+            "你是一个意图识别助手。请分析用户的最后一句话，判断用户意图属于以下哪一类：\n"
             "<start>"
             f"{', '.join(intent_list)}"
             "<end>\n"
-            "你需要按照以下的步骤处理用户的对话"
-            "1. 思考出对话的意图是哪一类的"
-            "2. 属于1和2的意图, 直接返回，返回格式如下：\n"
-            "{intent: '用户意图'}\n"
-            "3. 属于3的意图，则继续分析用户希望播放的音乐\n"
-            "4. 如果无法识别出具体歌名，可以返回'随机播放音乐'\n"
-            "{intent: '播放音乐 [获取的音乐名字]'}\n"
-            "下面是几个处理的示例(思考的内容不返回, 只返回json部分, 无额外的内容)\n"
-            "```"
+            "处理步骤:"
+            "1. 思考意图类型，生成function_call格式"
+            "\n\n"
+            "返回格式示例：\n"
+            "1. 播放音乐意图: {\"function_call\": {\"name\": \"play_music\", \"arguments\": {\"song_name\": \"音乐名称\"}}}\n"
+            "2. 查询天气意图: {\"function_call\": {\"name\": \"get_weather\", \"arguments\": {\"location\": \"地点名称\", \"lang\": \"zh_CN\"}}}\n"
+            "3. 查询新闻意图: {\"function_call\": {\"name\": \"get_news\", \"arguments\": {\"category\": \"新闻类别\", \"detail\": false, \"lang\": \"zh_CN\"}}}\n"
+            "4. 结束对话意图: {\"function_call\": {\"name\": \"handle_exit_intent\", \"arguments\": {\"say_goodbye\": \"goodbye\"}}}\n"
+            "5. 获取当天日期时间: {\"function_call\": {\"name\": \"get_time\"}}\n"
+            "6. 获取当前黄历意图: {\"function_call\": {\"name\": \"get_lunar\"}}\n"
+            "7. 继续聊天意图: {\"function_call\": {\"name\": \"continue_chat\"}}\n"
+            "\n"
+            "注意:\n"
+            "- 播放音乐：无歌名时，song_name设为\"random\"\n"
+            "- 查询天气：无地点时，location设为null\n"
+            "- 查询新闻：无类别时，category设为null；查询详情时，detail设为true\n"
+            "- 如果没有明显的意图，应按照继续聊天意图处理\n"
+            "- 只返回纯JSON，不要任何其他内容\n"
+            "\n"
+            "示例分析:\n"
+            "```\n"
+            "用户: 你好小智\n"
+            "返回: {\"function_call\": {\"name\": \"continue_chat\"}}\n"
+            "```\n"
+            "```\n"
             "用户: 你今天怎么样?\n"
-            "思考(不返回): 用户发来的数据是一个问候语，属于继续聊天的意图, 是种类1, 种类1的需求是直接返回\n"
-            "返回结果: {intent: '继续聊天'}\n"
-            "```"
-            "用户: 我今天有点累了, 我们明天再聊吧\n"
-            "思考(不返回): 用户表达了今天不想继续对话，属于结束聊天的意图, 是种类2, 种类2的需求是直接返回\n"
-            "返回结果: {intent: '结束聊天'}\n"
-            "```"
-            "用户: 我今天有点累了, 我们明天再聊吧\n"
-            "思考(不返回): 用户表达了今天不想继续对话，属于结束聊天的意图, 是种类2, 种类2的需求是直接返回\n"
-            "返回结果: {intent: '结束聊天'}\n"
-            "```"
-            "用户: 你可以播放一首中秋月给我听吗\n"
-            "思考(不返回): 用户表达了想听音乐的续签，属于播放音乐的意图, 是种类3, 种类3的需求需要继续判断播放的音乐, 这里用户希望的歌曲名明确给出是中秋月\n"
-            "返回结果: {intent: '播放音乐 [中秋月]'}\n"
-            "```"
-            "你现在可以使用的音乐的名称如下(使用<start>和<end>标志):\n"
+            "返回: {\"function_call\": {\"name\": \"continue_chat\"}}\n"
+            "```\n"
+            "```\n"
+            "用户: 现在是几号了?现在几点了？\n"
+            "返回: {\"function_call\": {\"name\": \"get_time\"}}\n"
+            "```\n"
+            "```\n"
+            "用户: 今天农历是多少？\n"
+            "返回: {\"function_call\": {\"name\": \"get_lunar\"}}\n"
+            "```\n"
+            "```\n"
+            "用户: 我们明天再聊吧\n"
+            "返回: {\"function_call\": {\"name\": \"handle_exit_intent\"}}\n"
+            "```\n"
+            "```\n"
+            "用户: 播放中秋月\n"
+            "返回: {\"function_call\": {\"name\": \"play_music\", \"arguments\": {\"song_name\": \"中秋月\"}}}\n"
+            "```\n"
+            "```\n"
+            "用户: 北京天气怎么样\n"
+            "返回: {\"function_call\": {\"name\": \"get_weather\", \"arguments\": {\"location\": \"北京\", \"lang\": \"zh_CN\"}}}\n"
+            "```\n"
+            "```\n"
+            "用户: 今天天气怎么样\n"
+            "返回: {\"function_call\": {\"name\": \"get_weather\", \"arguments\": {\"location\": null, \"lang\": \"zh_CN\"}}}\n"
+            "```\n"
+            "```\n"
+            "用户: 播报财经新闻\n"
+            "返回: {\"function_call\": {\"name\": \"get_news\", \"arguments\": {\"category\": \"财经\", \"detail\": false, \"lang\": \"zh_CN\"}}}\n"
+            "```\n"
+            "```\n"
+            "用户: 有什么最新新闻\n"
+            "返回: {\"function_call\": {\"name\": \"get_news\", \"arguments\": {\"category\": null, \"detail\": false, \"lang\": \"zh_CN\"}}}\n"
+            "```\n"
+            "```\n"
+            "用户: 详细介绍一下这条新闻\n"
+            "返回: {\"function_call\": {\"name\": \"get_news\", \"arguments\": {\"detail\": true, \"lang\": \"zh_CN\"}}}\n"
+            "```\n"
+            "可用的音乐名称:\n"
         )
         return prompt
+        
+    def clean_cache(self):
+        """清理过期缓存"""
+        now = time.time()
+        # 找出过期键
+        expired_keys = [k for k, v in self.intent_cache.items() if now - v['timestamp'] > self.cache_expiry]
+        for key in expired_keys:
+            del self.intent_cache[key]
+            
+        # 如果缓存太大，移除最旧的条目
+        if len(self.intent_cache) > self.cache_max_size:
+            # 按时间戳排序并保留最新的条目
+            sorted_items = sorted(self.intent_cache.items(), key=lambda x: x[1]['timestamp'])
+            for key, _ in sorted_items[:len(sorted_items) - self.cache_max_size]:
+                del self.intent_cache[key]
 
     async def detect_intent(self, conn, dialogue_history: List[Dict], text: str) -> str:
         if not self.llm:
             raise ValueError("LLM provider not set")
+            
+        # 记录整体开始时间
+        total_start_time = time.time()
+            
+        # 打印使用的模型信息
+        model_info = getattr(self.llm, 'model_name', str(self.llm.__class__.__name__))
+        logger.bind(tag=TAG).info(f"使用意图识别模型: {model_info}")
+            
+        # 计算缓存键
+        cache_key = hashlib.md5(text.encode()).hexdigest()
+        
+        # 检查缓存
+        if cache_key in self.intent_cache:
+            cache_entry = self.intent_cache[cache_key]
+            # 检查缓存是否过期
+            if time.time() - cache_entry['timestamp'] <= self.cache_expiry:
+                cache_time = time.time() - total_start_time
+                logger.bind(tag=TAG).info(f"使用缓存的意图: {cache_key} -> {cache_entry['intent']}, 耗时: {cache_time:.4f}秒")
+                return cache_entry['intent']
+                
+        # 清理缓存
+        self.clean_cache()
 
         # 构建用户最后一句话的提示
         msgStr = ""
@@ -94,18 +158,78 @@ class IntentProvider(IntentProviderBase):
         music_file_names = music_config["music_file_names"]
         prompt_music = f"{self.promot}\n<start>{music_file_names}\n<end>"
         logger.bind(tag=TAG).debug(f"User prompt: {prompt_music}")
+        
+        # 记录预处理完成时间
+        preprocess_time = time.time() - total_start_time
+        logger.bind(tag=TAG).debug(f"意图识别预处理耗时: {preprocess_time:.4f}秒")
+        
         # 使用LLM进行意图识别
+        llm_start_time = time.time()
+        logger.bind(tag=TAG).info(f"开始LLM意图识别调用, 模型: {model_info}")
+        
         intent = self.llm.response_no_stream(
             system_prompt=prompt_music,
             user_prompt=user_prompt
         )
-        # 使用正则表达式提取大括号中的内容  
-        # 使用正则表达式提取 {} 中的内容
-        match = re.search(r'\{.*?\}', intent)
+        
+        # 记录LLM调用完成时间
+        llm_time = time.time() - llm_start_time
+        logger.bind(tag=TAG).info(f"LLM意图识别完成, 模型: {model_info}, 调用耗时: {llm_time:.4f}秒")
+        
+        # 记录后处理开始时间
+        postprocess_start_time = time.time()
+        
+        # 清理和解析响应
+        intent = intent.strip()
+        # 尝试提取JSON部分
+        match = re.search(r'\{.*\}', intent, re.DOTALL)
         if match:
-            result = match.group(0)
-            intent = result
-        else:
-            intent = "{intent: '继续聊天'}"
-        logger.bind(tag=TAG).info(f"Detected intent: {intent}")
-        return intent.strip()
+            intent = match.group(0)
+        
+        # 记录总处理时间
+        total_time = time.time() - total_start_time
+        logger.bind(tag=TAG).info(f"【意图识别性能】模型: {model_info}, 总耗时: {total_time:.4f}秒, LLM调用: {llm_time:.4f}秒, 查询: '{text[:20]}...'")
+        
+        # 尝试解析为JSON
+        try:
+            intent_data = json.loads(intent)
+            # 如果包含function_call，则格式化为适合处理的格式
+            if "function_call" in intent_data:
+                function_data = intent_data["function_call"]
+                function_name = function_data.get("name")
+                function_args = function_data.get("arguments", {})
+                
+                # 记录识别到的function call
+                logger.bind(tag=TAG).info(f"识别到function call: {function_name}, 参数: {function_args}")
+                
+                # 添加到缓存
+                self.intent_cache[cache_key] = {
+                    'intent': intent,
+                    'timestamp': time.time()
+                }
+                
+                # 后处理时间
+                postprocess_time = time.time() - postprocess_start_time
+                logger.bind(tag=TAG).debug(f"意图后处理耗时: {postprocess_time:.4f}秒")
+                
+                # 确保返回完全序列化的JSON字符串
+                return intent
+            else:
+                # 添加到缓存
+                self.intent_cache[cache_key] = {
+                    'intent': intent,
+                    'timestamp': time.time()
+                }
+                
+                # 后处理时间
+                postprocess_time = time.time() - postprocess_start_time
+                logger.bind(tag=TAG).debug(f"意图后处理耗时: {postprocess_time:.4f}秒")
+                
+                # 返回普通意图
+                return intent
+        except json.JSONDecodeError:
+            # 后处理时间
+            postprocess_time = time.time() - postprocess_start_time
+            logger.bind(tag=TAG).error(f"无法解析意图JSON: {intent}, 后处理耗时: {postprocess_time:.4f}秒")
+            # 如果解析失败，默认返回继续聊天意图
+            return "{\"intent\": \"继续聊天\"}"

main/xiaozhi-server/core/providers/llm/xinference/xinference.py

@@ -0,0 +1,85 @@
+from config.logger import setup_logging
+from openai import OpenAI
+import json
+from core.providers.llm.base import LLMProviderBase
+
+TAG = __name__
+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:9997")
+        # Initialize OpenAI client with Xinference 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"
+            
+        logger.bind(tag=TAG).info(f"Initializing Xinference LLM provider with model: {self.model_name}, base_url: {self.base_url}")
+
+        try:
+            self.client = OpenAI(
+                base_url=self.base_url,
+                api_key="xinference"  # Xinference has a similar setup to Ollama where it doesn't need an actual key
+            )
+            logger.bind(tag=TAG).info("Xinference client initialized successfully")
+        except Exception as e:
+            logger.bind(tag=TAG).error(f"Error initializing Xinference client: {e}")
+            raise
+
+    def response(self, session_id, dialogue):
+        try:
+            logger.bind(tag=TAG).debug(f"Sending request to Xinference with model: {self.model_name}, dialogue length: {len(dialogue)}")
+            responses = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=dialogue,
+                stream=True
+            )
+            is_active=True
+            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:
+                        if '<think>' in content:
+                            is_active = False
+                            content = content.split('<think>')[0]
+                        if '</think>' in content:
+                            is_active = True
+                            content = content.split('</think>')[-1]
+                        if is_active:
+                            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 Xinference response generation: {e}")
+            yield "【Xinference服务响应异常】"
+
+    def response_with_functions(self, session_id, dialogue, functions=None):
+        try:
+            logger.bind(tag=TAG).debug(f"Sending function call request to Xinference with model: {self.model_name}, dialogue length: {len(dialogue)}")
+            if functions:
+                logger.bind(tag=TAG).debug(f"Function calls enabled with: {[f.get('function', {}).get('name') for f in functions]}")
+                
+            stream = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=dialogue,
+                stream=True,
+                tools=functions,
+            )
+
+            for chunk in stream:
+                delta = chunk.choices[0].delta
+                content = delta.content
+                tool_calls = delta.tool_calls
+                
+                if content:
+                    yield content, tool_calls
+                elif tool_calls:
+                    yield None, tool_calls
+
+        except Exception as e:
+            logger.bind(tag=TAG).error(f"Error in Xinference function call: {e}")
+            yield {"type": "content", "content": f"【Xinference服务响应异常: {str(e)}】"} 

main/xiaozhi-server/core/utils/util.py

@@ -67,13 +67,11 @@ def is_private_ip(ip_addr):
 
 def get_ip_info(ip_addr):
     try:
-        base_url = "https://freeipapi.com/api/json"
-        url = base_url if is_private_ip(ip_addr) else f"{base_url}/{ip_addr}"
-
+        url = "https://whois.pconline.com.cn/ipJson.jsp?json=true"
         resp = requests.get(url).json()
 
         ip_info = {
-            "city": resp.get("cityName")
+            "city": resp.get("city")
         }
         return ip_info
     except Exception as e: