155 lines
6.4 KiB
Python
155 lines
6.4 KiB
Python
from nonebot.typing import T_State
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from nonebot import on_command
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from nonebot.adapters import Message
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from nonebot.params import ArgPlainText, CommandArg
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from nonebot.permission import SUPERUSER
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from typing import Optional
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#from .acgnapis import *
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from nonebot_plugin_alconna import on_alconna
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from nonebot_plugin_alconna.uniseg import UniMessage, Target, MsgTarget, UniMsg, Image
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from arclet.alconna import Alconna, Args, AllParam, Arparma
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from .util import *
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import traceback
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from azure.ai.inference.aio import ChatCompletionsClient
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from azure.ai.inference.models import SystemMessage, UserMessage, AssistantMessage, TextContentItem, ImageContentItem, ImageUrl, CompletionsFinishReason
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from azure.core.credentials import AzureKeyCredential
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from azure.core.exceptions import HttpResponseError
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from .__init__ import __plugin_meta__
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from PIL import Image
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from .config import config
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from .models import MarshoContext
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changemdl = on_command("changemodel",permission=SUPERUSER)
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resetmem = on_command("reset",permission=SUPERUSER)
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setprompt_cmd = on_command("prompt",permission=SUPERUSER)
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praises_cmd = on_command("praises",permission=SUPERUSER)
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add_usermsg_cmd = on_command("usermsg",permission=SUPERUSER)
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add_assistantmsg_cmd = on_command("assistantmsg",permission=SUPERUSER)
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nekocmd = on_alconna(
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Alconna(
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"marsho",
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Args["text?",AllParam],
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),
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aliases={"neko"}
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)
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model_name = "gpt-4o-mini"
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context = MarshoContext()
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context_limit = 15
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context_count = 0
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@add_usermsg_cmd.handle()
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async def add_usermsg(arg: Message = CommandArg()):
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if msg := arg.extract_plain_text():
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context.append(UserMessage(content=msg))
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await UniMessage("已添加用户消息").send()
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@add_assistantmsg_cmd.handle()
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async def add_assistantmsg(arg: Message = CommandArg()):
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if msg := arg.extract_plain_text():
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context.append(AssistantMessage(content=msg))
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await UniMessage("已添加助手消息").send()
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@praises_cmd.handle()
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async def getpraises():
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await UniMessage(build_praises()).send()
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@setprompt_cmd.handle() #用不了了
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async def setprompt(arg: Message = CommandArg()):
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global spell, context
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if prompt := arg.extract_plain_text():
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spell = SystemMessage(content=prompt)
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await setprompt_cmd.finish("已设置提示词")
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else:
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spell = SystemMessage(content="")
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context = []
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await setprompt_cmd.finish("已清除提示词")
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@resetmem.handle()
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async def reset():
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global context_count
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context.reset()
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context_count = 0
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await resetmem.finish("上下文已重置")
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@changemdl.got("model",prompt="请输入模型名")
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async def changemodel(model : str = ArgPlainText()):
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global model_name
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model_name = model
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await changemdl.finish("已切换")
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@nekocmd.handle()
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async def neko(
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message: UniMsg,
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text = None
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):
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global context_limit, context_count
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token = config.marshoai_token
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endpoint = "https://models.inference.ai.azure.com"
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#msg = await UniMessage.generate(message=message)
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client = ChatCompletionsClient(
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endpoint=endpoint,
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credential=AzureKeyCredential(token),
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)
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if not text:
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await UniMessage(
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"""MarshoAI Alpha? by Asankilp
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用法：
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marsho <聊天内容>
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与 Marsho 进行对话。当模型为gpt时，可以带上图片进行对话。
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changemodel
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切换 AI 模型。仅超级用户可用。
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reset
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重置上下文。仅超级用户可用。
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注意事项：
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当 Marsho 回复消息为None或以content_filter开头的错误信息时，表示该消息被内容过滤器过滤，请调整你的聊天内容确保其合规。
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当回复以RateLimitReached开头的错误信息时，该 AI 模型的次数配额已用尽，请联系Bot管理员。
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※本AI的回答"按原样"提供，不提供担保，不代表开发者任何立场。AI也会犯错，请仔细甄别回答的准确性。
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当前使用的模型："""+model_name).send()
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return
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if context_count >= context_limit:
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await UniMessage("上下文数量达到阈值。已自动重置上下文。").send()
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context.reset()
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context_count = 0
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# await UniMessage(str(text)).send()
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try:
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usermsg = [TextContentItem(text=str(text).replace("[image]",""))]
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if model_name == "gpt-4o" or model_name == "gpt-4o-mini":
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for i in message:
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if i.type == "image":
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imgurl = i.data["url"]
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print(imgurl)
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await download_file(str(imgurl))
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picmsg = ImageContentItem(image_url=ImageUrl.load(
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image_file="./azureaipic.png",
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image_format=Image.open("azureaipic.png").format
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)
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)
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usermsg.append(picmsg)
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#await UniMessage(str(context+[UserMessage(content=usermsg)])).send()
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else:
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usermsg = str(text)
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#await UniMessage('非gpt').send()
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response = await client.complete(
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messages=context.build()+[UserMessage(content=usermsg)],
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model=model_name
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)
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#await UniMessage(str(response)).send()
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choice = response.choices[0]
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if choice["finish_reason"] == CompletionsFinishReason.STOPPED:
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context.append(UserMessage(content=usermsg))
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context.append(choice.message)
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context_count += 1
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elif choice["finish_reason"] == CompletionsFinishReason.CONTENT_FILTERED:
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await UniMessage("*已被内容过滤器过滤。*").send()
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#await UniMessage(str(choice)).send()
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await UniMessage(str(choice.message.content)).send(reply_to=True)
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#requests_limit = response.headers.get('x-ratelimit-limit-requests')
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#request_id = response.headers.get('x-request-id')
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#remaining_requests = response.headers.get('x-ratelimit-remaining-requests')
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#remaining_tokens = response.headers.get('x-ratelimit-remaining-tokens')
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#await UniMessage(f""" 剩余token：{remaining_tokens}"""
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# ).send()
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except Exception as e:
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await UniMessage(str(e)).send()
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# await UniMessage(str(e.reason)).send()
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traceback.print_exc()
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return
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