Merge branch 'main' into MVP

This commit is contained in:
Erlei Chen
2025-03-28 23:13:24 +08:00
21 changed files with 731 additions and 141 deletions
+3 -3
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@@ -5,15 +5,15 @@ WORKDIR /app
COPY main/xiaozhi-server/requirements.txt .
# 优化apt安装
# 安装Python依赖
RUN pip install --no-cache-dir -r requirements.txt
# 第阶段:生产镜像
# 第阶段:生产镜像
FROM python:3.10-slim
WORKDIR /opt/xiaozhi-esp32-server
# 优化apt安装
# 安装系统依赖
RUN apt-get update && \
apt-get install -y --no-install-recommends libopus0 ffmpeg && \
apt-get clean && \
+32
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@@ -11,6 +11,7 @@ manager-api 该项目基于SpringBoot框架开发。
JDK 21
Maven 3.8+
MySQL 8.0+
Redis 5.0+
Vue 3.x
# 创建数据库
@@ -43,6 +44,37 @@ spring:
password: 123456
```
# 连接Redis
如果还没有Redis,你可以通过docker安装redis
```
docker run --name xiaozhi-esp32-server-redis -d -p 6379:6379 redis
```
# 确认项目Redis连接信息
`src/main/resources/application-dev.yml`中配置Redis连接信息
```
spring:
data:
redis:
host: localhost
port: 6379
password:
database: 0
```
`src/main/resources/application.yml`中配置Redis启用状态
```
renren:
redis:
open: true
```
# 测试启动
本项目为SpringBoot项目,启动方式为:
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@@ -39,8 +39,8 @@ export default {
},
methods: {
confirm() {
if (!this.deviceCode.trim()) {
this.$message.error('请输入设备验证码');
if (!/^\d{6}$/.test(this.deviceCode)) {
this.$message.error('请输入6位数字设备验证码');
return;
}
this.$emit('confirm',this.deviceCode)
+2 -2
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@@ -3,7 +3,7 @@ import sys
from loguru import logger
from config.settings import load_config
SERVER_VERSION = "0.1.15"
SERVER_VERSION = "0.1.16"
def setup_logging():
"""从配置文件中读取日志配置,并设置日志输出格式和级别"""
@@ -13,7 +13,7 @@ def setup_logging():
log_format_file = log_config.get("log_format_file", "{time:YYYY-MM-DD HH:mm:ss} - {version_{selected_module}} - {name} - {level} - {extra[tag]} - {message}")
selected_module = config.get("selected_module")
selected_module_str = ''.join([key[0] + value[0] for key, value in selected_module.items()])
selected_module_str = ''.join([value[0] + value[1] for key, value in selected_module.items()])
log_format = log_format.replace("{version}", SERVER_VERSION)
log_format = log_format.replace("{selected_module}", selected_module_str)
+81 -5
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@@ -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}")
"""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:
@@ -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 ""
+86
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@@ -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()
+110
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@@ -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()
@@ -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""随机播放音乐"
- "查询天气 地点名""查询天气 [当前位置]"
"""
pass
@@ -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"]
@@ -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。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: 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}")
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}")
# 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}")
# 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}")
# 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}")
# 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}")
# 如果解析失败,默认返回继续聊天意图
return "{\"intent\": \"继续聊天\"}"
@@ -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)}"}
@@ -0,0 +1,31 @@
{
"des": [
"在data目录下创建.mcp_server_settings.json文件,可以选择下面的MCP服务,也可以自行添加新的MCP服务。",
"后面不断测试补充好用的mcp服务,欢迎大家一起补充。",
"记得删除注释行,des属性仅为说明,不会被解析。",
"des和link属性,仅为说明安装方式,方便大家查看原始链接,不是必须项。"
],
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/Users/username/Desktop",
"/path/to/other/allowed/dir"
],
"link":"https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem"
},
"playwright": {
"command": "npx",
"args": ["-y", "@executeautomation/playwright-mcp-server"],
"des" : "run 'npx playwright install' first",
"link": "https://github.com/executeautomation/mcp-playwright"
},
"windows-cli": {
"command": "npx",
"args": ["-y", "@simonb97/server-win-cli"],
"link": "https://github.com/SimonB97/win-cli-mcp-server"
}
}
}
@@ -45,10 +45,10 @@ def fetch_news_from_rss(rss_url):
try:
response = requests.get(rss_url)
response.raise_for_status()
# 解析XML
root = ET.fromstring(response.content)
# 查找所有item元素(新闻条目)
news_items = []
for item in root.findall('.//item'):
@@ -56,14 +56,14 @@ def fetch_news_from_rss(rss_url):
link = item.find('link').text if item.find('link') is not None else "#"
description = item.find('description').text if item.find('description') is not None else "无描述"
pubDate = item.find('pubDate').text if item.find('pubDate') is not None else "未知时间"
news_items.append({
'title': title,
'link': link,
'description': description,
'pubDate': pubDate
})
return news_items
except Exception as e:
logger.bind(tag=TAG).error(f"获取RSS新闻失败: {e}")
@@ -75,9 +75,9 @@ def fetch_news_detail(url):
try:
response = requests.get(url)
response.raise_for_status()
soup = BeautifulSoup(response.content, 'html.parser')
# 尝试提取正文内容 (这里的选择器需要根据实际网站结构调整)
content_div = soup.select_one('.content_desc, .content, article, .article-content')
if content_div:
@@ -98,7 +98,7 @@ def map_category(category_text):
"""将用户输入的中文类别映射到配置文件中的类别键"""
if not category_text:
return None
# 类别映射字典,目前支持社会、国际、财经新闻,如需更多类型,参见配置文件
category_map = {
# 社会新闻
@@ -113,10 +113,10 @@ def map_category(category_text):
"金融": "finance",
"经济": "finance"
}
# 转换为小写并去除空格
normalized_category = category_text.lower().strip()
# 返回映射结果,如果没有匹配项则返回原始输入
return category_map.get(normalized_category, category_text)
@@ -129,21 +129,22 @@ def get_news(conn, category: str = None, detail: bool = False, lang: str = "zh_C
if detail:
if not hasattr(conn, 'last_news_link') or not conn.last_news_link or 'link' not in conn.last_news_link:
return ActionResponse(Action.REQLLM, "抱歉,没有找到最近查询的新闻,请先获取一条新闻。", None)
link = conn.last_news_link.get('link')
title = conn.last_news_link.get('title', '未知标题')
if link == '#':
return ActionResponse(Action.REQLLM, "抱歉,该新闻没有可用的链接获取详细内容。", None)
logger.bind(tag=TAG).debug(f"获取新闻详情: {title}, URL={link}")
# 获取新闻详情
detail_content = fetch_news_detail(link)
if not detail_content or detail_content == "无法获取详细内容":
return ActionResponse(Action.REQLLM, f"抱歉,无法获取《{title}》的详细内容,可能是链接已失效或网站结构发生变化。", None)
return ActionResponse(Action.REQLLM,
f"抱歉,无法获取《{title}》的详细内容,可能是链接已失效或网站结构发生变化。", None)
# 构建详情报告
detail_report = (
f"根据下列数据,用{lang}回应用户的新闻详情查询请求:\n\n"
@@ -152,33 +153,33 @@ def get_news(conn, category: str = None, detail: bool = False, lang: str = "zh_C
f"(请对上述新闻内容进行总结,提取关键信息,以自然、流畅的方式向用户播报,"
f"不要提及这是总结,就像是在讲述一个完整的新闻故事)"
)
return ActionResponse(Action.REQLLM, detail_report, None)
# 否则,获取新闻列表并随机选择一条
# 从配置中获取RSS URL
rss_config = conn.config["plugins"]["get_news"]
default_rss_url = rss_config.get("default_rss_url", "https://www.chinanews.com.cn/rss/society.xml")
# 将用户输入的类别映射到配置中的类别键
mapped_category = map_category(category)
# 如果提供了类别,尝试从配置中获取对应的URL
rss_url = default_rss_url
if mapped_category and mapped_category in rss_config.get("category_urls", {}):
rss_url = rss_config["category_urls"][mapped_category]
logger.bind(tag=TAG).info(f"获取新闻: 原始类别={category}, 映射类别={mapped_category}, URL={rss_url}")
# 获取新闻列表
news_items = fetch_news_from_rss(rss_url)
if not news_items:
return ActionResponse(Action.REQLLM, "抱歉,未能获取到新闻信息,请稍后再试。", None)
# 随机选择一条新闻
selected_news = random.choice(news_items)
# 保存当前新闻链接到连接对象,以便后续查询详情
if not hasattr(conn, 'last_news_link'):
conn.last_news_link = {}
@@ -186,7 +187,7 @@ def get_news(conn, category: str = None, detail: bool = False, lang: str = "zh_C
'link': selected_news.get('link', '#'),
'title': selected_news.get('title', '未知标题')
}
# 构建新闻报告
news_report = (
f"根据下列数据,用{lang}回应用户的新闻查询请求:\n\n"
@@ -197,9 +198,9 @@ def get_news(conn, category: str = None, detail: bool = False, lang: str = "zh_C
f"直接读出新闻即可,不需要额外多余的内容。"
f"如果用户询问更多详情,告知用户可以说'请详细介绍这条新闻'获取更多内容)"
)
return ActionResponse(Action.REQLLM, news_report, None)
except Exception as e:
logger.bind(tag=TAG).error(f"获取新闻出错: {e}")
return ActionResponse(Action.REQLLM, "抱歉,获取新闻时发生错误,请稍后再试。", None)
@@ -52,7 +52,7 @@ WEATHER_CODE_MAP = {
"405": "雨雪天气", "406": "阵雨夹雪", "407": "阵雪", "408": "小到中雪", "409": "中到大雪", "410": "大到暴雪",
"456": "阵雨夹雪", "457": "阵雪", "499": "",
"500": "薄雾", "501": "", "502": "", "503": "扬沙", "504": "浮尘",
"507": "沙尘暴", "508": "强沙尘暴",
"507": "沙尘暴", "508": "强沙尘暴",
"509": "浓雾", "510": "强浓雾", "511": "中度霾", "512": "重度霾", "513": "严重霾", "514": "大雾", "515": "特强浓雾",
"900": "", "901": "", "999": "未知"
}
@@ -120,4 +120,4 @@ def get_weather(conn, location: str = None, lang: str = "zh_CN"):
"参数为0的值不需要报告给用户,每次都报告体感温度,根据语境选择合适的参数内容告知用户,并对参数给出相应评价)"
)
return ActionResponse(Action.REQLLM, weather_report, None)
return ActionResponse(Action.REQLLM, weather_report, None)
@@ -69,7 +69,7 @@ handle_device_function_desc = {
"description": "动作名称,可选值:get(获取),set(设置),raise(提高),lower(降低)"
},
"value": {
"type": "int",
"type": "number",
"description": "值大小,可选值:0-100之间的整数"
}
},
@@ -12,6 +12,7 @@ class ToolType(Enum):
CHANGE_SYS_PROMPT = (3, "修改系统提示词,切换角色性格或职责")
SYSTEM_CTL = (4, "系统控制,影响正常的对话流程,如退出、播放音乐等,需要传递conn参数")
IOT_CTL = (5, "IOT设备控制,需要传递conn参数")
MCP_CLIENT = (6, "MCP客户端")
def __init__(self, code, message):
self.code = code
+3 -1
View File
@@ -22,4 +22,6 @@ mem0ai==0.1.62
bs4==0.0.2
modelscope==1.23.2
sherpa_onnx==1.11.0
cnlunar==0.2.0
mcp==1.4.1
cnlunar==0.2.0