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Fay年翻更新

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- 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
This commit is contained in:
xszyou 2024-11-20 23:44:47 +08:00
parent f871b6a532
commit 87ed1c4425
31 changed files with 1624 additions and 41 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
config.json
View File

@ -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,
"sound_synthesis_enabled": false,
"playSound": 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"
}
}

19
core/fay_core.py
View File

@ -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()

15
core/member_db.py
View File

@ -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:

4
core/qa_service.py
View File

@ -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):

4
core/recorder.py
View File

@ -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)

19
fay_booter.py
View File

@ -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()

13
gui/flask_server.py
View File

@ -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':

1
gui/static/js/index.js
View File

@ -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,

2
gui/templates/index.html
View File

@ -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">

2
gui/templates/setting.html
View File

@ -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">

133
llm/agent/agent_service.py Normal file
View File

@ -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()

99
llm/agent/fay_agent.py Normal file
View File

@ -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"))

41
llm/agent/tools/DeleteTimer.py Normal file
View File

@ -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)

100
llm/agent/tools/KnowledgeBaseResponder.py Normal file
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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)

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llm/agent/tools/KnowledgeBaseResponder/knowledge_base/1.pdf Normal file
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