关于OneBot的QQ机器人探索2
一个QQ聊天机器人，按照OneBot v11协议进行开发
目前实现的主要功能有：
- echo 消息回显
- 搜索歌曲并发送音乐卡片
- AI聊天，概率性主动回复，支持在QQ中使用指令切换AI模型和提示词
- bing每日一图获取并发送
- 群聊管理之敏感词检测并禁言
关于OneBot的QQ机器人探索（第二期）
这是我去年编写的一个QQ聊天机器人，按照OneBot v11协议进行开发
当时刚入门，编写的代码耦合性较高，因此该代码仅能作为探索记录
代码太乱了，无法过多解释，有需要者自己看看吧（悲）
目前实现的主要功能有：
- echo 消息回显
- 搜索歌曲并发送音乐卡片
- AI聊天，概率性主动回复，支持在QQ中使用指令切换AI模型和提示词
- bing每日一图获取并发送
- 群聊管理之敏感词检测并禁言
入口函数：
import asyncio
from bot import Bot, Command
from msgProcFunctions import *
async def main():
cmd = Command("")
# bot = Bot("ws://localhost:3001", cmd)
# bot = Bot("ws://www.lyxyz5223.com:3001", cmd)
bot = Bot("ws://localhost:5223", cmd)
# bot = Bot("ws://localhost:3001", cmd)
cmd.cmds.append(msgProc)
await bot.connect()
await bot.run()
print("aa")
if __name__=='__main__':
asyncio.run(main())
聊天机器人聊天消息分发
- 由于代码开源方面问题，源OneBotv11接口的QQ消息数据处理相关类 暂不公开
wyyyyAPI = "https://docs-neteasecloudmusicapi.vercel.app/search?keywords="
wyyyyAPI = "https://music.163.com/api/search/get/web?csrf_token=hlpretag=&hlposttag=&type=1&offset=0&total=true&limit=10&s="
wyyyySearchList = []
wyyyySearch_Music_ID_List = []
import base64
import os
import time
from bot import Bot,Message_Type
from event import Event
from message import MessageList
import requests as req
import asyncio
import threading
import AIAPI#,AIollama
import random
import threading
# 全局读取敏感词列表
def is_base64(s):
try:
# 空字符串不是合法base64
if not s or len(s) % 4 != 0:
return False
base64.b64decode(s, validate=True)
return True
except Exception:
return False
# 1. 读取敏感词时记录来源文件
def load_badwords():
badwords = []
folder_path = os.path.join(os.path.dirname(__file__), "BadWords")
if not os.path.exists(folder_path):
print("敏感词文件夹不存在：", folder_path)
return badwords
for filename in os.listdir(folder_path):
if filename.endswith(".txt"):
file_path = os.path.join(folder_path, filename)
try:
with open(file_path, "r", encoding="utf-8") as f:
for line in f:
word = line.strip()
if word and is_base64(word):
try:
decoded = base64.b64decode(word).decode("utf-8")
badwords.append((decoded, filename))
except Exception as e:
print(f"敏感词解码失败（文件{filename}）：", word, e)
badwords.append((word, filename))
elif word:
badwords.append((word, filename))
except Exception as e:
print(f"敏感词文件读取失败：{file_path}", e)
return badwords
BADWORDS = load_badwords()
print("敏感词列表加载完成，共", len(BADWORDS), "个敏感词")
print("敏感词列表：", BADWORDS[:10]) # 打印前10个敏感词
# 2. 检查敏感词时返回命中词和文件名，并打印
def check_badwords(message: str):
"""
检查消息是否包含敏感词
:param message: 消息内容
:return: 命中的敏感词及其文件名列表
"""
hit_words = []
for word, filename in BADWORDS:
if word and word in message:
print(f"命中敏感词：{word}，文件：{filename}")
hit_words.append((word, filename))
return hit_words
async def echoProc(bot: Bot, m:Event, id:str, msg_type: Message_Type):
msg = m.check().get_msg()
f = 0
send_msg = []
for i in msg:
if f == 0 and i["type"] == "at" and str(i["data"]["qq"]) == str(m.check().raw["self_id"]):
f = 1
continue
else:
send_msg.append(i)
if send_msg[0]["type"] == "text":
send_msg[0]["data"]["text"] = str(send_msg[0]["data"]["text"]).strip()[6:]
await bot.send_msg(id,msg_type,send_msg)
async def bingProc(bot: Bot, zhiling:str, id:str,msg_type: Message_Type):
zhiling = req.request(
"get",
"https://cn.bing.com/HPImageArchive.aspx",
params={"format": "js", "idx": 0, "n": 1}
)
print(zhiling.text)
zhiling = zhiling.json()
zhiling = "https://cn.bing.com" + zhiling["images"][0]["url"]
#"https://cn.bing.com/HPImageArchive.aspx?format=js&idx=0&n=1"
await bot.send_msg(id,msg_type,[MessageList.image(str(zhiling))])
async def musicProc(bot: Bot, zhiling:str, id:str, msg_type: Message_Type):
if(zhiling == "/music"):
await bot.send_msg(id,msg_type,[MessageList.text("点歌格式(4种)：\n首选：/music search 关键字，然后再/music select 列表数字序号 \n/music type id\n/music 1 title url\n/music 2 title openUrl audioUrl")])
# await bot.send_msg(id,[MessageList.text("点歌格式：\n/music type id\n/music 1 title url\n/music 2 title openUrl audioUrl")])
# await bot.send_msg(id,[MessageList.text("type：\n1：普通点歌\n2：普通点歌\n163：网易云点歌\nxm：虾米点歌\nqq：QQ音乐点歌")])
# await bot.send_msg(id,[MessageList.text("id：\n1：普通点歌：歌曲名\n2：普通点歌：歌曲名\n163：网易云id\nxm：虾米id\nqq：QQ音乐id")])
# await bot.send_msg(id,[MessageList.text("url：\n普通点歌：歌曲url")])
# await bot.send_msg(id,[MessageList.text("openUrl：\n普通点歌：点击打开的url")])
# await bot.send_msg(id,[MessageList.text("audioUrl：\n普通点歌：歌曲url")])
# await bot.send_msg(id,[MessageList.text("示例：\n/music 1 歌曲名 歌曲url\n/music 2 歌曲名 点击打开的url 歌曲url\n/music 163 网易云id\n/music xm 虾米id\n/music qq QQ音乐id")])
# await bot.send_msg(id,[MessageList.text("注意：\n/music 1 2 163 xm qq 后面必须跟空格")])
await bot.send_msg(id,msg_type,[{
"type": "music",
"data": {
"type": "custom",
"url": "https://lw-sycdn.kuwo.cn/87898d2bfa406816e7a24dee4434e67c/66b3a70e/resource/30106/trackmedia/M5000004J5Dg2Aifkc.mp3?bitrate$128&from=vip",
"audio": "https://lw-sycdn.kuwo.cn/87898d2bfa406816e7a24dee4434e67c/66b3a70e/resource/30106/trackmedia/M5000004J5Dg2Aifkc.mp3?bitrate$128&from=vip",
"title": "Aurora - Hoaprox/Dang Minh"
}
}])
pass
musicNameAndUrl = zhiling.replace("/music", "").split()
print("musicNameAndUrl",musicNameAndUrl)
if musicNameAndUrl[0] == "163" or musicNameAndUrl[0] == "xm" or musicNameAndUrl[0] == "qq":
await bot.send_msg(id,msg_type,[MessageList.Music(musicNameAndUrl[0], musicNameAndUrl[1])])# id点歌 /music type id
musicTitle = " ".join(musicNameAndUrl[1:len(musicNameAndUrl)-1])
musicNameAndUrl[0] = musicNameAndUrl[0].lower()#指令小写字母化
match musicNameAndUrl[0]:
case "1":
await bot.send_msg(id,msg_type,[MessageList.CustomMusic(url=musicNameAndUrl.pop(), audio=musicNameAndUrl.pop(),title=musicTitle)])# 普通点歌 /music 1 title Url
case "2":
await bot.send_msg(id,msg_type,[MessageList.CustomMusic(url=musicNameAndUrl[len(musicNameAndUrl)-2], audio=musicNameAndUrl.pop(),title=musicTitle)])# 普通点歌 /music 2 title OpenUrl AudioUrl
case "search":
cont = req.request("get",wyyyyAPI + " ".join(musicNameAndUrl[1:])).json()
#cont = json.dumps(cont)
#print(cont)
global wyyyySearchList,wyyyySearch_Music_ID_List
wyyyySearchList = []
wyyyySearch_Music_ID_List = []
countNum = 1
for i in cont["result"]["songs"]:
artists = ""
for ii in i["artists"]:
artists += ii["name"]
artists += "/"
artists = artists[:-1]
wyyyySearchList.append(str(countNum) +"." + i["name"]+" - "+ artists)
wyyyySearch_Music_ID_List.append(i["id"])
countNum += 1
print(i["name"])
await bot.send_msg(id,msg_type,[MessageList.text("\n".join(wyyyySearchList))])
pass
case "select":
Ordinal = int(musicNameAndUrl[1])-1
await bot.send_msg(id,msg_type,[MessageList.text("已为您点歌："+wyyyySearchList[Ordinal])])
await bot.send_msg(id,msg_type,[MessageList.Music("163",wyyyySearch_Music_ID_List[Ordinal])])
pass
async def AIProc(bot: Bot, zhiling:str, id:str, msg_type: Message_Type, m: Event, api:str,model:str):
chatText = zhiling.replace("/ai", "")
sender_id = m.check().raw["sender"]["user_id"]# 发送者QQ号
msg_id = m.check().raw["message_id"]# 消息ID
# msg_list : list = []
# if msg_type == Message_Type.group:
# msg_list.append(MessageList.At(sender_id))
# msg_list.append(MessageList.text(" "))
# msg_list.append(MessageList.text("喵~"))
# await bot.send_msg(id,msg_type,msg_list)
def ai(text:str):
text_AI = asyncio.run(AIAPI.chat(content=text,id=id,msg_type=msg_type,memory=True,api=api,model=model))
msg_list = []
msg_list.append(MessageList.reply(msg_id))
if msg_type == Message_Type.group:
msg_list.append(MessageList.At(sender_id))
msg_list.append(MessageList.text("\n"))
msg_list.append(MessageList.text(text_AI))
asyncio.run(bot.send_msg(id,msg_type,msg_list))
th = threading.Thread(target=ai,daemon=True,args=(chatText,))
th.start()
print("Thread Exit")
# await bot.send_msg(id,msg_type,[MessageList.text(await AIollama.chat(chatText))])
# class HashPairKey:#用于区分不同群聊的结构体
# def __init__(self):
# self.id = ""
# self.msg_type = Message_Type.group
# def __init__(self,id:str,msg_type:Message_Type):
# self.id = id
# self.msg_type = msg_type
async def sendMsgProc(bot: Bot, zhiling:str, id:str,msg_type: Message_Type):
text = zhiling.replace("/send_msg", "")
text = text.strip()
# /send_msg type id text
z = text.split(" ")
contents = " ".join(z[2:])
if len(z) < 3:
return
if z[0] == "group":
await bot.send_msg(z[1],Message_Type.group,[MessageList.text(contents)])
elif z[0] == "private":
await bot.send_msg(z[1],Message_Type.private,[MessageList.text(contents)])
async def sendPicProc(bot: Bot, zhiling:str, id:str,msg_type: Message_Type):
text = zhiling.replace("/send_pic", "")
text = text.strip()
# /send_pic type id picUrl [picUrl] ...
z = text.split(" ")
if len(z) < 3:
return
# contents = " ".join(z[2:])
imgUrls = z[2:]
contents = []
for i in imgUrls:
contents.append(MessageList.image(i))
if z[0] == "group":
await bot.send_msg(z[1],Message_Type.group,contents)
elif z[0] == "private":
await bot.send_msg(z[1],Message_Type.private,contents)
calcMsg : dict = {
str : int
}
maxMsgThenReply : dict = {
str : int
}
aiModel : dict = {
str : [str,str]
#id : [ai api,ai model]
}
badword_count = {} # 结构: {(group_id, user_id): [count, first_time]}
BADWORD_LIMIT = 3 # 1分钟内最多允许3次
BADWORD_PERIOD = 60 # 秒， 表示1分钟一次刷新计数
defaultModel = ["deepseek",""]
async def msgProc(bot: Bot, m: Event):
print(m.check().raw)
zhiling = m.check().get_msg()
print("消息内容：",zhiling)
zhiling = m.check().get_raw_msg()
at = m.check().get_the_first_at_object_qq_number_str()
id : str = ""
msg_type : Message_Type
self_id :str = str(m.check().raw["self_id"])
sender_id = m.check().raw["sender"]["user_id"]# 发送者QQ号
if m.check().raw["message_type"] == "private":
id = m.check().raw["user_id"]
msg_type = Message_Type.private
elif m.check().raw["message_type"] == "group":
id = m.check().raw["group_id"]
msg_type = Message_Type.group
id = str(id)
memHashKey = id + ',' + msg_type.value
global calcMsg,maxMsgThenReply,aiModel
if memHashKey not in calcMsg.keys():
calcMsg[memHashKey] = 0
if memHashKey not in maxMsgThenReply.keys():
maxMsgThenReply[memHashKey] = 0
calcMsg[memHashKey] += 1 # 消息计数+1，当达到一定值将重置为0，并回复消息
if maxMsgThenReply[memHashKey] == 0:
maxMsgThenReply[memHashKey] = random.randint(30, 50)# [30,50]区间，是闭区间
# maxMsgThenReply[memHashKey] = random.randint(10, 20)
if memHashKey not in aiModel.keys():
aiModel[memHashKey] = defaultModel
print("calcMsg[memHashKey]:", calcMsg[memHashKey])
print("maxMsgThenReply[memHashKey]:", maxMsgThenReply[memHashKey])
print("aiModel[memHashKey]:", aiModel[memHashKey])
match zhiling:
case _ if msg_type == Message_Type.group: # 所有群聊消息检查
msgText = zhiling.strip()
# 智能禁言检测
ban_duration = 60 # 禁言时长，单位秒
key = (id, sender_id)
now = time.time()
if check_badwords(msgText):
# 初始化或重置窗口
if key not in badword_count or now - badword_count[key][1] > BADWORD_PERIOD:
badword_count[key] = [1, now, [msgText]]
else:
badword_count[key][0] += 1
badword_count[key][2].append(msgText)
# 判断是否超限
if badword_count[key][0] >= BADWORD_LIMIT:
print(f"已禁言成员：{sender_id}，1分钟内所有违规消息：{badword_count[key][2]}")
await bot.send_msg(id, msg_type,
[MessageList.At(sender_id), MessageList.text(
f"1分钟内多次违规，已禁言！禁言成员：{sender_id}" # \n违规内容：\n" + "\n".join(badword_count[key][2])
)]
)
await bot.set_group_ban(group_id=id, user_id=sender_id, duration=ban_duration)
badword_count[key] = [0, now, []] # 重置计数和消息列表
# else:
# await bot.send_msg(
# id, msg_type,
# [MessageList.text(f"检测到违规词，请注意言辞！（{badword_count[key][0]}/{BADWORD_LIMIT}）")]
# )
match zhiling:
case _ if msg_type == Message_Type.group and at != self_id:
zhiling = m.check().get_msg()
msgText = ""
for msgJson in zhiling:
if msgJson["type"] != "at":
find_it = True
if msgJson["type"] == "text":
msgText += msgJson["data"]["text"]
#await bot.send_group_msg(id,[MessageList.text(msgText)])
elif msgJson["type"] == "at":
msgText += "@" + msgJson["data"]["qq"]
elif msgJson["type"] == "image":
pass
msgText = msgText.strip()
msgList = msgText.split()
if msgList[0].lower() == "/changeai":
if len(msgList) == 3:
model = [msgList[1],msgList[2]]
elif len(msgList) == 2:
model = [msgList[1],""]
else:
model = defaultModel
aiModel[memHashKey] = model
await bot.send_msg(id,msg_type,[MessageList.text("已切换AI模型")])
if msgText != "" and calcMsg[memHashKey] > maxMsgThenReply[memHashKey]:
print("calcMsg[memHashKey] > maxMsgThenReply[memHashKey]", calcMsg[memHashKey],">",maxMsgThenReply[memHashKey])
calcMsg[memHashKey] = 0
maxMsgThenReply[memHashKey] = 0
def sendAIResponse():
AI_Response = []
AI_Response.append(MessageList.text(asyncio.run(AIAPI.chat(content=msgText,id=id,msg_type=msg_type,memory=False,api=aiModel[memHashKey][0],model=aiModel[memHashKey][1]))))
asyncio.run(bot.send_msg(id, msg_type, AI_Response))
print("AI Intelligence Response Thread Start")
threading.Thread(target=sendAIResponse,daemon=True).start()
case _ if msg_type == Message_Type.private or (msg_type == Message_Type.group and at == self_id):
# case _:
zhiling = m.check().get_msg()
find_it = False
msgText = ""
for msgJson in zhiling:
if msgJson["type"] != "at":
find_it = True
if msgJson["type"] == "text":
msgText += msgJson["data"]["text"]
#await bot.send_group_msg(id,[MessageList.text(msgText)])
elif msgJson["type"] == "at":
if msgJson["data"]["qq"] != self_id:
msgText += "@" + msgJson["data"]["qq"]
elif msgJson["type"] == "image":
pass
# msgText += msgJson["data"]["file"]
if find_it == False:
await bot.send_msg(id,msg_type,[MessageList.text("你干嘛！？")])
else:
zhiling = msgText.strip()
z = zhiling.lower()
match zhiling:
case _ if "/echo" in zhiling and zhiling[:5] == "/echo":
await echoProc(bot, m, id, msg_type)
case "/bing":
await bingProc(bot, zhiling, id, msg_type)
case "/dev":
await bot.send_msg(id,msg_type,[MessageList.text("调试信息：\n" + str(m.check().raw))])
case "/random":
await bot.send_msg(id,msg_type,[MessageList.image("https://moe.jitsu.top/img/")])
case "/help":
await bot.send_msg(id,msg_type,[MessageList.text("/bing——必应每日一图\n/dev——发送调试信息\n/random——随机二次元\n/help——帮助\n/echo——重复话语\n/music——点歌（签证以停止服务，将无法发送音乐卡片）\n/ai——与AI对话\n/changeAI——切换AI模型\n/ShowCurrentAI——显示当前AI模型\n/ChangePrompt——修改提示词\n")])
case _ if "/music" in zhiling and zhiling[:6] == "/music":
await musicProc(bot, zhiling, id, msg_type)
case _ if "/changeai" in zhiling.lower() and zhiling[:9].lower() == "/changeai":
msgList = zhiling.split()
if msgList[0].lower() == "/changeai":
if len(msgList) == 3:
model = [msgList[1],msgList[2]]
elif len(msgList) == 2:
model = [msgList[1],""]
else:
model = defaultModel
aiModel[memHashKey] = model
await bot.send_msg(id,msg_type,[MessageList.text("已切换AI模型")])
case _ if "/showcurrentai" in zhiling.lower() and zhiling[:14].lower() == "/showcurrentai":
await bot.send_msg(id,msg_type,[MessageList.text("当前AI模型：\n" + aiModel[memHashKey][0] + "->" + (aiModel[memHashKey][1] if aiModel[memHashKey][1] != "" else "默认"))])
case _ if "/changeprompt" in zhiling.lower() and zhiling[:13].lower() == "/changeprompt":
await AIAPI.changePrompt(prompt=zhiling[14:],id=id,msg_type=msg_type)
await bot.send_msg(id,msg_type,[MessageList.text("提示词已修改，已清空当前聊天记录")])
case _ if "/send_msg" in zhiling.lower() and zhiling[:9].lower() == "/send_msg":
await sendMsgProc(bot, zhiling, id, msg_type)
case _ if "/send_pic" in zhiling.lower() and zhiling[:9].lower() == "/send_pic":
await sendPicProc(bot, zhiling, id, msg_type)
# case _:
case _ if "/ai" in zhiling and zhiling[:3] == "/ai":
await AIProc(bot, zhiling, id, msg_type, m,aiModel[memHashKey][0],aiModel[memHashKey][1])
下面是接入本地ollama服务的代码
import ollama
import json
from ollama import AsyncClient
#client = ollama.Client(host="localhost:11434")
client = AsyncClient(host="http://localhost:11434")
ChatCount=0
messages = []
async def chat(content:str) -> str:
global ChatCount,messages
message = {
'role': 'user',
'content': content
}
messages.append(message)
msg_to_send = json.dumps(messages)
response = await client.chat(model='qwen2:7b',messages=messages,stream=False)
message = {
"role": "assistant",
"content": response["message"]["content"]
}
messages.append(message)
ChatCount += 1
if(ChatCount > 10):
messages = []
return response["message"]["content"]
然后是发送AI回复的处理函数
# Please install OpenAI SDK first: `pip3 install openai`
import os# 获取模块文件路径
from openai import OpenAI,AsyncOpenAI
import asyncio
import json
from enum import Enum
from bot import Message_Type
messages : dict = {str : list}
# 定义一个异步函数chat，用于与AI进行对话
# DeepSeek的model有"deepseek-chat"和"deepseek-reasoner"
# kimi的model有moonshot-v1-8k moonshot-v1-32k moonshot-v1-128k moonshot-v1-8k-vision-preview moonshot-v1-32k-vision-preview moonshot-v1-128k-vision-preview，后者昂贵
async def chat(content:str, id:str="all", msg_type:Message_Type=Message_Type.group, memory:bool=True, api:str="deepseek", model:str="deepseek-chat", prompt:str="") -> str:
model = model.strip()
hashKey = id + ',' + msg_type.value
apis = {}
with open(os.path.join(os.path.dirname(__file__),"ai_apis.json"), "r", encoding="utf-8") as api_file:
apis = json.load(api_file)
if api not in apis.keys() and model == "":
raise Exception(f"API {api} not found")
# if model not in apis[api]["models"]:
# raise Exception(f"Model {model} not found in API {api}")
if model == "":
model = apis[api]["models"][0]
# 创建一个AsyncOpenAI客户端
client = AsyncOpenAI(api_key=apis[api]["key"], base_url=apis[api]["url"])
# 定义一个全局变量messages，用于存储对话历史
global messages
# 定义一个字典message，用于存储用户发送的消息
message = {
'role': 'user',
'content': content
}
# 定义一个空字符串response，用于存储AI的回复
response = ''
try:
# 打印正在获取AI回复
print("getting AI response")
# 如果group_id不在messages的键中，则将group_id添加到messages的键中，并将对应的值设为一个空列表
if hashKey not in messages.keys():
if prompt == "":
with open(os.path.join(os.path.dirname(__file__),"AIPrompt.txt"), "r", encoding="utf-8") as prompt_file:
prompt = prompt_file.read()
messages[hashKey] = [# 此处可用于存放提示词
{
"role": "system",
# "content": "请你扮演一个刚从美国留学回国的人，说话时候会故意中文夹杂部分英文单词，显得非常fancy，对话中总是带有很强的优越感。"
"content": prompt
},
]
else:
messages[hashKey] = []
# 将message添加到messages[hashKey]中
send_messages = messages[hashKey] + [message]
# 调用client.chat.completions.create方法，获取AI的回复
response = await client.chat.completions.with_raw_response.create(
model=model,
messages=send_messages,
stream=False,
n=1# 下文choices中元素的个数
)
# 打印获取到的AI回复
print("Succeeded gotten AI response")
if response.status_code != 200 or response.text == '':
raise Exception(f"DeepSeek API response status code: {response.status_code}, response: {response.text if response.text != '' else '(null)'}")
responseJson = json.loads(response.text)
print("AI Response:"+responseJson["choices"][0]["message"]["content"])
# 如果memory为True，则将message和AI的回复添加到messages[hashKey]中
if memory:
messages[hashKey].append(message)
messages[hashKey].append(responseJson["choices"][0]["message"])
# 关闭client
await client.close()
# 返回AI的回复
return responseJson["choices"][0]["message"]["content"]
except Exception as e:
# 如果发生异常，则打印异常信息
import traceback,sys
# tb = traceback.extract_tb(sys.exc_info()[2])
print(sys.exc_info())
# 关闭client
await client.close()
# 返回异常信息和API的返回信息
# return str(sys.exc_info()) + '\nException contents: ' + str(e) + "\nHttp Response: " + str(response)
return ''
async def changePrompt(prompt:str, id:str="all", msg_type:Message_Type=Message_Type.group):
global messages
hashKey = id + ',' + msg_type.value
messages[hashKey] = [
{
"role": "system",
"content": prompt
},
]
if __name__ == "__main__":
# path = os.path.dirname(__file__) + "\\"
# with open(path + "AIPrompt2.txt", "r", encoding="utf-8") as prompt_file:
# content = prompt_file.read()
# print(content)
async def main():
print(await chat("写一个正则表达式，要求匹配一个字符串中包含另一个字符串(b)，且b前面没有b的第一个字符，若有，则b前面的该字符前必须有该字符"))
asyncio.run(main())
其中，AIAPI.py使用到的ai_apis.json格式如下
{
"localhost":{
"name": "localhost",
"url": "http://localhost:11434",
"key": "",
"models": [
"wangshenzhi/gemma2-9b-chinese-chat:latest",
"qwen3:8b",
"qwen2:7b"
]
},
"kimi":{
"name": "kimi",
"url": "https://api.moonshot.cn/v1",
"key": "",
"models": [
"moonshot-v1-8k",
"moonshot-v1-32k",
"moonshot-v1-128k",
"moonshot-v1-8k-vision-preview",
"moonshot-v1-32k-vision-preview",
"moonshot-v1-128k-vision-preview"
]
},
"deepseek":{
"name": "deepseek",
"url": "https://api.deepseek.com",
"key": "",
"models": [
"deepseek-chat",
"deepseek-reasoner"
]
}
}
浙公网安备 33010602011771号