Fay年翻更新

- 升级Agent（chat_module=agent切换）：升级到langgraph react agent逻辑、集成到主分支fay中、基于自动决策工具调用机制、基于日程跟踪的主动沟通、支持外部观测数据传入；
- 修复因线程同步问题导致的配置文件读写不稳定
- 聊天采纳功能的bug修复

config.json

@@ -8,11 +8,11 @@
         "hobby": "\u53d1\u5446",
         "job": "\u52a9\u7406",
         "name": "\u83f2\u83f2",
-        "voice": "\u6653\u6653(azure)",
+        "voice": "abin",
         "zodiac": "\u86c7"
     },
     "interact": {
-        "QnA": "",
+        "QnA": "qa.csv",
         "maxInteractTime": 15,
         "perception": {
             "chat": 10,
@@ -21,8 +21,7 @@
             "indifferent": 10,
             "join": 10
         },
-        "playSound": true,
+        "playSound": false,
-        "sound_synthesis_enabled": false,
         "visualization": false
     },
     "items": [],
@@ -34,12 +33,11 @@
             "url": ""
         },
         "record": {
-            "channels": 0,
             "device": "",
-            "enabled": true
+            "enabled": false
         },
         "wake_word": "\u4f60\u597d",
-        "wake_word_enabled": true,
+        "wake_word_enabled": false,
         "wake_word_type": "front"
     }
 }

core/fay_core.py

@@ -29,6 +29,7 @@ from llm import nlp_xingchen
 from llm import nlp_langchain
 from llm import nlp_ollama_api
 from llm import nlp_coze
+from llm.agent import fay_agent
 from core import member_db
 import threading
 import functools
@@ -60,7 +61,8 @@ modules = {
     "nlp_xingchen": nlp_xingchen,
     "nlp_langchain": nlp_langchain,
     "nlp_ollama_api": nlp_ollama_api,
-    "nlp_coze": nlp_coze
+    "nlp_coze": nlp_coze,
+    "nlp_agent": fay_agent
 
 }
 
@@ -145,9 +147,9 @@ class FeiFei:
                     uid = member_db.new_instance().find_user(username)
 
                     #记录用户问题
-                    content_db.new_instance().add_content('member','speak',interact.data["msg"], username, uid)
+                    content_id = content_db.new_instance().add_content('member','speak',interact.data["msg"], username, uid)
                     if wsa_server.get_web_instance().is_connected(username):
-                        wsa_server.get_web_instance().add_cmd({"panelReply": {"type":"member","content":interact.data["msg"], "username":username, "uid":uid}, "Username" : username})
+                        wsa_server.get_web_instance().add_cmd({"panelReply": {"type":"member","content":interact.data["msg"], "username":username, "uid":uid, "id":content_id}, "Username" : username})
                     
                     #确定是否命中q&a
                     answer = self.__get_answer(interact.interleaver, interact.data["msg"])
@@ -163,18 +165,17 @@ class FeiFei:
                             wsa_server.get_instance().add_cmd(content)
                         text,textlist = handle_chat_message(interact.data["msg"], username, interact.data.get("observation", ""))
 
-                        # qa_service.QAService().record_qapair(interact.data["msg"], text)#沟通记录缓存到qa文件
                     else: 
                         text = answer
 
                     #记录回复    
                     self.write_to_file("./logs", "answer_result.txt", text)
-                    content_db.new_instance().add_content('fay','speak',text, username, uid)
+                    content_id = content_db.new_instance().add_content('fay','speak',text, username, uid)
 
                     #文字输出：面板、聊天窗、log、数字人
                     if wsa_server.get_web_instance().is_connected(username):
                         wsa_server.get_web_instance().add_cmd({"panelMsg": text, "Username" : username, 'robot': f'http://{cfg.fay_url}:5000/robot/Speaking.jpg'})
-                        wsa_server.get_web_instance().add_cmd({"panelReply": {"type":"fay","content":text, "username":username, "uid":uid}, "Username" : username})
+                        wsa_server.get_web_instance().add_cmd({"panelReply": {"type":"fay","content":text, "username":username, "uid":uid, "id":content_id}, "Username" : username})
                     if len(textlist) > 1:
                         i = 1
                         while i < len(textlist):
@@ -199,8 +200,6 @@ class FeiFei:
                         member_db.new_instance().add_user(username)
                     uid = member_db.new_instance().find_user(username)
 
-                    #TODO 这里可以通过qa来触发指定的脚本操作，如ppt翻页等
-
                     if interact.data.get("text"):
                         #记录回复
                         text = interact.data.get("text")
@@ -219,6 +218,7 @@ class FeiFei:
                     #声音输出
                     MyThread(target=self.say, args=[interact, text]).start()  
                 
+   
             except BaseException as e:
                 print(e)
                 return e
@@ -319,9 +319,6 @@ class FeiFei:
             if audio_url is not None:
                 file_name = 'sample-' + str(int(time.time() * 1000)) + '.wav'
                 result = self.download_wav(audio_url, './samples/', file_name)
-
-            elif not wsa_server.get_instance().get_client_output(interact.data.get('user')):
-                result = None
             elif config_util.config["interact"]["playSound"] or wsa_server.get_instance().is_connected(interact.data.get("user")) or self.__is_send_remote_device_audio(interact):#tts
                 util.printInfo(1,  interact.data.get('user'), '合成音频...')
                 tm = time.time()

core/member_db.py

@@ -83,6 +83,7 @@ class Member_Db:
         else:
             return "notexists"
 
+    #根据username查询uid
     def find_user(self, username):
         conn = sqlite3.connect('user_profiles.db')
         c = conn.cursor()
@@ -94,6 +95,20 @@ class Member_Db:
         else:
            return result[0]
         
+    #根据uid查询username
+    def find_username_by_uid(self, uid):
+        conn = sqlite3.connect('user_profiles.db')
+        c = conn.cursor()
+        c.execute('SELECT username FROM T_Member WHERE id = ?', (uid,))
+        result = c.fetchone()
+        conn.close()
+        if result is None:
+            return 0
+        else:
+           return result[0]
+
+
+
     @synchronized
     def query(self, sql):
         try:

core/qa_service.py

@@ -33,7 +33,7 @@ class QAService:
 
     def question(self, query_type, text):
         if query_type == 'qa':
-            answer_dict = self.__read_qna(cfg.config['interact']['QnA'])
+            answer_dict = self.__read_qna(cfg.config['interact'].get('QnA'))
             answer, action = self.__get_keyword(answer_dict, text, query_type)
             if action:
                 MyThread(target=self.__run, args=[action]).start()
@@ -61,7 +61,7 @@ class QAService:
                     if len(row) >= 2:
                         qna.append([row[0].split(";"), row[1], row[2] if len(row) >= 3 else None])
         except Exception as e:
-            util.log(1, 'qa文件没有指定，不匹配qa')
+            pass
         return qna
 
     def record_qapair(self, question, answer):

core/recorder.py

@@ -46,6 +46,8 @@ class Recorder:
         self.username = 'User' #默认用户，子类实现时会重写
         self.channels = 1
         self.sample_rate = 16000
+        self.is_reading = False
+        self.stream = None
 
     def asrclient(self):
         if self.ASRMode == "ali":
@@ -204,7 +206,7 @@ class Recorder:
                 cfg.load_config()
                 record = cfg.config['source']['record']
                 if not record['enabled'] and not self.is_remote:
-                    time.sleep(0.1)
+                    time.sleep(1)
                     continue
                 self.is_reading = True
                 data = stream.read(1024, exception_on_overflow=False)

fay_booter.py

@@ -14,6 +14,7 @@ from utils import util, config_util, stream_util
 from core.wsa_server import MyServer
 from core import wsa_server
 from core import socket_bridge_service
+from llm.agent import agent_service
 
 feiFei: fay_core.FeiFei = None
 recorderListener: Recorder = None
@@ -96,6 +97,7 @@ class RecorderListener(Recorder):
         try:
             while self.is_reading:
                 time.sleep(0.1)
+            if self.stream is not None:
                 self.stream.stop_stream()
                 self.stream.close()
                 self.paudio.terminate()
@@ -186,7 +188,7 @@ def device_socket_keep_alive():
                 if wsa_server.get_web_instance().is_connected(value.username):
                     wsa_server.get_web_instance().add_cmd({"remote_audio_connect": True, "Username" : value.username}) 
             except Exception as serr:
-                util.printInfo(3, value.username, "远程音频输入输出设备已经断开：{}".format(key))
+                util.printInfo(1, value.username, "远程音频输入输出设备已经断开：{}".format(key))
                 value.stop()
                 delkey = key
                 break
@@ -222,6 +224,8 @@ def accept_audio_device_output_connect():
 
 #数字人端请求获取最新的自动播放消息，若自动播放服务关闭会自动退出自动播放
 def start_auto_play_service(): #TODO 评估一下有无优化的空间
+    if config_util.config['source'].get('automatic_player_url') is None or config_util.config['source'].get('automatic_player_status') is None:
+        return
     url = f"{config_util.config['source']['automatic_player_url']}/get_auto_play_item"
     user = "User" #TODO 临时固死了
     is_auto_server_error = False
@@ -290,6 +294,11 @@ def stop():
             socket_service_instance = None 
     except:
         pass
+
+    if config_util.key_chat_module == "agent":
+        util.log(1, '正在关闭agent服务...')
+        agent_service.agent_stop()
+
     util.log(1, '正在关闭核心服务...')
     feiFei.stop()
     util.log(1, '服务已关闭！')
@@ -325,18 +334,22 @@ def start():
     record = config_util.config['source']['record']
     if record['enabled']:
         util.log(1, '开启录音服务...')
-    recorderListener = RecorderListener(record['device'], feiFei)  # 监听麦克风
+    recorderListener = RecorderListener('device', feiFei)  # 监听麦克风
     recorderListener.start()
 
     #启动声音沟通接口服务
     util.log(1,'启动声音沟通接口服务...')
     deviceSocketThread = MyThread(target=accept_audio_device_output_connect)
     deviceSocketThread.start()
-
     socket_service_instance = socket_bridge_service.new_instance()
     socket_bridge_service_Thread = MyThread(target=socket_service_instance.start_service)
     socket_bridge_service_Thread.start()
 
+    #启动agent服务
+    if config_util.key_chat_module == "agent":
+        util.log(1,'启动agent服务...')
+        agent_service.agent_start()
+
     #启动自动播放服务
     util.log(1,'启动自动播放服务...')
     MyThread(target=start_auto_play_service).start()

gui/flask_server.py

@@ -46,6 +46,7 @@ def verify_password(username, password):
     if username in users and users[username] == password:
         return username
 
+
 def __get_template():
     try:
         return render_template('index.html')
@@ -68,6 +69,7 @@ def __get_device_list():
         print(f"Error getting device list: {e}")
         return []
 
+
 @__app.route('/api/submit', methods=['post'])
 def api_submit():
     data = request.values.get('data')
@@ -252,7 +254,7 @@ def api_send():
         if not username or not msg:
             return jsonify({'result': 'error', 'message': '用户名和消息内容不能为空'})
         interact = Interact("text", 1, {'user': username, 'msg': msg})
-        util.printInfo(3, "文字发送按钮", '{}'.format(interact.data["msg"]), time.time())
+        util.printInfo(1, username, '[文字发送按钮]{}'.format(interact.data["msg"]), time.time())
         fay_booter.feiFei.on_interact(interact)
         return '{"result":"successful"}'
     except json.JSONDecodeError:
@@ -263,10 +265,11 @@ def api_send():
 # 获取指定用户的消息记录
 @__app.route('/api/get-msg', methods=['post'])
 def api_get_Msg():
-    data = request.form.get('data')
-    if not data:
-        return jsonify({'list': [], 'message': '未提供数据'})
     try:
+        data = request.form.get('data')
+        if data is None:
+            data = request.get_json()
+        else:
             data = json.loads(data)
         uid = member_db.new_instance().find_user(data["username"])
         contentdb = content_db.new_instance()
@@ -310,7 +313,7 @@ def api_send_v1_chat_completions():
         model = data.get('model', 'fay')
         observation = data.get('observation', '')
         interact = Interact("text", 1, {'user': username, 'msg': last_content, 'observation': observation})
-        util.printInfo(3, "文字沟通接口", '{}'.format(interact.data["msg"]), time.time())
+        util.printInfo(1, username, '[文字沟通接口]{}'.format(interact.data["msg"]), time.time())
         text = fay_booter.feiFei.on_interact(interact)
 
         if model == 'fay-streaming':

gui/static/js/index.js

@@ -190,6 +190,7 @@ class FayInterface {
         }
         if (vueInstance.selectedUser && data.panelReply.username === vueInstance.selectedUser[1]) {
           vueInstance.messages.push({
+            id: data.panelReply.id,
             username: data.panelReply.username,
             content: data.panelReply.content,
             type: data.panelReply.type,

gui/templates/index.html

@@ -14,7 +14,7 @@
   <body >
     <div  id="app" class="main_bg">  
       <div class="main_left">  
-        <div class="main_left_logo" ><img   src="{{ url_for('static',filename='images/logo.png') }}" alt=""> 
+        <div class="main_left_logo" ><img   src="{{ url_for('static',filename='images/Logo.png') }}" alt=""> 
         </div>
 
       <div class="main_left_menu">

gui/templates/setting.html

@@ -14,7 +14,7 @@
   <body>
     <div class="main_bg" id="app">	
       <div class="main_left">	
-        <div class="main_left_logo" ><img src="{{ url_for('static',filename='images/logo.png') }}" alt=""> 
+        <div class="main_left_logo" ><img src="{{ url_for('static',filename='images/Logo.png') }}" alt=""> 
         </div>
 
       <div class="main_left_menu">

llm/agent/agent_service.py

@@ -0,0 +1,133 @@
+import sqlite3
+import threading
+import datetime
+import time
+import os
+from scheduler.thread_manager import MyThread
+from core import member_db
+from core.interact import Interact
+from utils import util
+import fay_booter
+
+scheduled_tasks = {}
+agent_running = False
+
+
+# 数据库初始化
+def init_db():
+    conn = sqlite3.connect('timer.db')
+    cursor = conn.cursor()
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS timer (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            time TEXT NOT NULL,
+            repeat_rule TEXT NOT NULL,
+            content TEXT NOT NULL,
+            uid INTEGER
+        )
+    ''')
+    conn.commit()
+    conn.close()
+
+    
+
+# 插入测试数据
+def insert_test_data():
+    conn = sqlite3.connect('timer.db')
+    cursor = conn.cursor()
+    cursor.execute("INSERT INTO timer (time, repeat_rule, content) VALUES (?, ?, ?)", ('16:20', '1010001', 'Meeting Reminder'))
+    conn.commit()
+    conn.close()
+
+# 解析重复规则返回待执行时间，None代表不在今天的待执行计划
+def parse_repeat_rule(rule, task_time):
+    today = datetime.datetime.now()
+    if rule == '0000000':  # 不重复
+        task_datetime = datetime.datetime.combine(today.date(), task_time)
+        if task_datetime > today:
+            return task_datetime
+        else:
+            return None
+    for i, day in enumerate(rule):
+        if day == '1' and today.weekday() == i:
+            task_datetime = datetime.datetime.combine(today.date(), task_time)
+            if task_datetime > today:
+                return task_datetime
+    return None
+
+# 执行任务
+def execute_task(task_time, id, content, uid):
+    username = member_db.new_instance().find_username_by_uid(uid=uid)
+    if not username:
+        username = "User"
+    interact = Interact("text", 1, {'user': username, 'msg': "执行任务->\n" + content, 'observation': ""})
+    util.printInfo(3, "系统", '执行任务：{}'.format(interact.data["msg"]), time.time())
+    text = fay_booter.feiFei.on_interact(interact)
+    if text is not None and id in scheduled_tasks:
+        del scheduled_tasks[id]
+        # 如果不重复，执行后删除记录
+        conn = sqlite3.connect('timer.db')
+        cursor = conn.cursor()
+        cursor.execute("DELETE FROM timer WHERE repeat_rule = '0000000' AND id = ?", (id,))
+        conn.commit()
+        conn.close()
+
+
+# 30秒扫描一次数据库，当扫描到今天的不存在于定时任务列表的记录，则添加到定时任务列表。执行完的记录从定时任务列表中清除。
+def check_and_execute():
+    while agent_running:
+        conn = sqlite3.connect('timer.db')
+        cursor = conn.cursor()
+        cursor.execute("SELECT * FROM timer")
+        rows = cursor.fetchall()
+        for row in rows:
+            id, task_time_str, repeat_rule, content, uid = row
+            task_time = datetime.datetime.strptime(task_time_str, '%H:%M').time()
+            next_execution = parse_repeat_rule(repeat_rule, task_time)
+
+            if next_execution and id not in scheduled_tasks:
+                timer_thread = threading.Timer((next_execution - datetime.datetime.now()).total_seconds(), execute_task, [next_execution, id, content, uid])
+                timer_thread.start()
+                scheduled_tasks[id] = timer_thread
+
+        conn.close()
+        time.sleep(30)  # 30秒扫描一次
+
+# agent启动
+def agent_start():
+    global agent_running
+    
+    agent_running = True
+    #初始计划
+    if not os.path.exists("./timer.db"):
+        init_db()
+        content ="""执行任务->
+            你是一个数字人，你的责任是陪伴主人生活、工作：
+            1、在每天早上8点提醒主人起床;
+            2、每天12:00及18:30提醒主人吃饭;  
+            3、每天21:00陪主人聊聊天; 
+            4、每天23:00提醒主人睡觉。 
+            """
+        interact = Interact("text", 1, {'user': 'User', 'msg': content, 'observation': ""})
+        util.printInfo(3, "系统", '执行任务：{}'.format(interact.data["msg"]), time.time())
+        text = fay_booter.feiFei.on_interact(interact)
+        if text is None:
+            util.printInfo(3, "系统", '任务执行失败', time.time())
+        
+    check_and_execute_thread = MyThread(target=check_and_execute)
+    check_and_execute_thread.start()
+
+    
+
+def agent_stop():
+    global agent_running 
+    global scheduled_tasks
+    # 取消所有定时任务
+    for task in scheduled_tasks.values():
+        task.cancel()
+    agent_running = False
+    scheduled_tasks = {}
+    
+
+if __name__ == "__main__":
+    agent_start()

llm/agent/fay_agent.py

@@ -0,0 +1,99 @@
+import os
+import time
+from llm.agent.tools.MyTimer import MyTimer
+from llm.agent.tools.Weather import Weather
+from llm.agent.tools.QueryTimerDB import QueryTimerDB
+from llm.agent.tools.DeleteTimer import DeleteTimer
+from llm.agent.tools.QueryTime import QueryTime
+from llm.agent.tools.PythonExecutor import PythonExecutor
+from llm.agent.tools.WebPageRetriever import WebPageRetriever
+from llm.agent.tools.WebPageScraper import WebPageScraper
+from llm.agent.tools.ToRemind import ToRemind
+from langgraph.prebuilt import create_react_agent
+from langchain_openai import ChatOpenAI
+from langgraph.checkpoint.memory import MemorySaver
+import utils.config_util as cfg
+from utils import util
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from core import content_db
+from core import member_db
+
+class FayAgentCore():
+    def __init__(self, uid=0, observation=""):
+        self.observation=observation
+        cfg.load_config()
+        os.environ["OPENAI_API_KEY"] = cfg.key_gpt_api_key
+        os.environ["OPENAI_API_BASE"] = cfg.gpt_base_url
+        os.environ["LANGCHAIN_TRACING_V2"] = "true"
+        os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+        os.environ["LANGCHAIN_API_KEY"] = "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"
+        os.environ["LANGCHAIN_PROJECT"] = "pr-best-artist-21"
+
+        #创建llm
+        self.llm = ChatOpenAI(model=cfg.gpt_model_engine)
+        
+        #创建agent graph
+        my_timer = MyTimer(uid=uid)#传入uid
+        weather_tool = Weather()
+        query_timer_db_tool = QueryTimerDB()
+        delete_timer_tool = DeleteTimer()
+        python_executor = PythonExecutor()
+        web_page_retriever = WebPageRetriever()
+        web_page_scraper = WebPageScraper()
+        to_remind = ToRemind()
+        self.tools = [my_timer, weather_tool, query_timer_db_tool, delete_timer_tool, python_executor, web_page_retriever, web_page_scraper, to_remind]
+        self.attr_info = ", ".join(f"{key}: {value}" for key, value in cfg.config["attribute"].items())
+        self.prompt_template = """
+            现在时间是：{now_time}。你是一个数字人，负责协助主人处理问题和陪伴主人生活、工作。你的个人资料是：{attr_info}。通过外部设备观测到：{observation}。\n请依据以信息为主人服务。
+            """.format(now_time=QueryTime().run(""), attr_info=self.attr_info, observation=self.observation)
+        self.memory = MemorySaver()
+        self.agent = create_react_agent(self.llm, self.tools, checkpointer=self.memory)
+
+        self.total_tokens = 0
+        self.total_cost = 0
+
+     #载入记忆
+    def get_history_messages(self, uid):
+        chat_history = []
+        history = content_db.new_instance().get_list('all','desc', 100, uid)
+        if history and len(history) > 0:
+            i = 0
+            while i < len(history):
+                if history[i][0] == "member":
+                    chat_history.append(HumanMessage(content=history[i][2], user=member_db.new_instance().find_username_by_uid(uid=uid)))
+                else:
+                    chat_history.append(AIMessage(content=history[i][2]))
+                i += 1
+        return chat_history
+
+        
+    def run(self, input_text, uid=0):      
+        result = ""
+        messages = self.get_history_messages(uid)
+        messages.insert(0, SystemMessage(self.prompt_template))
+        messages.append(HumanMessage(content=input_text))
+
+        try:
+            for chunk in self.agent.stream(
+                {"messages": messages}, {"configurable": {"thread_id": "tid{}".format(uid)}}
+            ):
+                if chunk.get("agent"):
+                    if chunk['agent']['messages'][0].content:
+                        result = chunk['agent']['messages'][0].content
+                    cb = chunk['agent']['messages'][0].response_metadata['token_usage']['total_tokens']
+                    self.total_tokens = self.total_tokens + cb
+                    
+            util.log(1, "本次消耗token:{}，共消耗token:{}".format(cb, self.total_tokens))          
+        except Exception as e:
+            print(e)
+        return result
+
+def question(cont, uid=0, observation=""):
+    starttime = time.time()
+    agent = FayAgentCore(uid=uid, observation=observation)
+    response_text = agent.run(cont, uid)
+    util.log(1, "接口调用耗时 :" + str(time.time() - starttime))
+    return response_text
+if __name__ == "__main__":
+    agent = FayAgentCore()
+    print(agent.run("你好"))

llm/agent/tools/DeleteTimer.py

@@ -0,0 +1,41 @@
+import sqlite3
+from typing import Any
+
+from langchain.tools import BaseTool
+
+from llm.agent import agent_service
+
+
+class DeleteTimer(BaseTool):
+    name: str = "DeleteTimer"
+    description: str = "用于删除某一个日程，接受任务id作为参数，如：2"
+
+    def __init__(self):
+        super().__init__()
+
+    def _run(self, para) -> str:
+        try:
+            id = int(para)
+        except ValueError:
+            return "输入的 ID 无效，必须是数字。"
+        
+        try:
+            with sqlite3.connect('timer.db') as conn:
+                cursor = conn.cursor()
+                cursor.execute("DELETE FROM timer WHERE id = ?", (id,))
+                conn.commit()
+        except sqlite3.Error as e:
+            return f"数据库错误: {e}"
+
+        if id in agent_service.scheduled_tasks:
+            agent_service.scheduled_tasks[id].cancel()
+            del agent_service.scheduled_tasks[id]
+
+        return f"任务 {id} 取消成功。"
+        
+
+
+if __name__ == "__main__":
+    tool = DeleteTimer()
+    result = tool.run("1")
+    print(result)

llm/agent/tools/KnowledgeBaseResponder.py

@@ -0,0 +1,100 @@
+import os
+from typing import Any
+
+from langchain.tools import BaseTool
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.embeddings.openai import OpenAIEmbeddings
+from langchain.indexes.vectorstore import VectorstoreIndexCreator, VectorStoreIndexWrapper
+from langchain_community.vectorstores.chroma import Chroma
+from langchain_openai import ChatOpenAI
+import hashlib
+#若要使用请自行配置
+os.environ["OPENAI_API_KEY"] = ""
+os.environ["OPENAI_API_BASE"] = "https://api.openai.com/v1"
+index_name = "knowledge_data"
+folder_path = "agent/tools/KnowledgeBaseResponder/knowledge_base"  
+local_persist_path = "agent/tools/KnowledgeBaseResponder"
+md5_file_path = os.path.join(local_persist_path, "pdf_md5.txt")
+#
+class KnowledgeBaseResponder(BaseTool):
+    name = "KnowledgeBaseResponder"
+    description = """此工具用于连接本地知识库获取问题答案，使用时请传入相关问题作为参数，例如：“草梅最适合的生长温度”"""
+
+    def __init__(self):
+        super().__init__()
+
+    async def _arun(self, *args: Any, **kwargs: Any) -> Any:
+        # 用例中没有用到 arun 不予具体实现
+        pass
+
+
+    def _run(self, para: str) -> str:
+        self.save_all()
+        result = self.question(para)
+        return result
+
+    def generate_file_md5(self, file_path):
+        hasher = hashlib.md5()
+        with open(file_path, 'rb') as afile:
+            buf = afile.read()
+            hasher.update(buf)
+        return hasher.hexdigest()
+
+    def load_md5_list(self):
+        if os.path.exists(md5_file_path):
+            with open(md5_file_path, 'r') as file:
+                return {line.split(",")[0]: line.split(",")[1].strip() for line in file}
+        return {}
+
+    def update_md5_list(self, file_name, md5_value):
+        md5_list = self.load_md5_list()
+        md5_list[file_name] = md5_value
+        with open(md5_file_path, 'w') as file:
+            for name, md5 in md5_list.items():
+                file.write(f"{name},{md5}\n")
+
+    def load_all_pdfs(self, folder_path):
+        md5_list = self.load_md5_list()
+        for file_name in os.listdir(folder_path):
+            if file_name.endswith(".pdf"):
+                file_path = os.path.join(folder_path, file_name)
+                file_md5 = self.generate_file_md5(file_path)
+                if file_name not in md5_list or md5_list[file_name] != file_md5:
+                    print(f"正在加载 {file_name} 到索引...")
+                    self.load_pdf_and_save_to_index(file_path, index_name)
+                    self.update_md5_list(file_name, file_md5)
+
+    def get_index_path(self, index_name):
+        return os.path.join(local_persist_path, index_name)
+
+    def load_pdf_and_save_to_index(self, file_path, index_name):
+        loader = PyPDFLoader(file_path)
+        embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+        index = VectorstoreIndexCreator(embedding=embedding, vectorstore_kwargs={"persist_directory": self.get_index_path(index_name)}).from_loaders([loader])
+        index.vectorstore.persist()
+
+    def load_index(self, index_name):
+        index_path = self.get_index_path(index_name)
+        embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+        vectordb = Chroma(persist_directory=index_path, embedding_function=embedding)
+        return VectorStoreIndexWrapper(vectorstore=vectordb)
+
+    def save_all(self):
+        self.load_all_pdfs(folder_path)
+
+    def question(self, cont):
+        try:
+            info = cont
+            index = self.load_index(index_name)    
+            llm = ChatOpenAI(model="gpt-4-0125-preview")
+            ans = index.query(info, llm, chain_type="map_reduce")
+            return ans
+        except Exception as e:
+            print(f"请求失败: {e}")
+            return "抱歉，我现在太忙了，休息一会，请稍后再试。"
+
+
+if __name__ == "__main__":
+    tool = KnowledgeBaseResponder()
+    info = tool.run("草莓最适合的生长温度")
+    print(info)