24年年底，因为deepseekV3的爆火，我开始注意到这样一个来说性价比极高的大模型，于是第一时间将其接入了此前编写的QQ机器人插件中。最近几周，随着R1版本的发布，我又有了尝试本地部署蒸馏版本的想法（不过估计最多到14B Xp）。此外我也有一些让其对话风格更加近似于人类的想法，将在近期进行尝试。

from openai import OpenAI
from nonebot import on_command,on_message
from nonebot.rule import to_me
from nonebot.adapters import Message
from nonebot.params import CommandArg
import json,os
from dotenv import load_dotenv

# 加载.env文件中的环境变量
load_dotenv()

api_key = os.getenv("OPENAI_API_KEY", "default_key_if_not_set")

json_list = []
#echo = on_command('ds', priority=5)
echo = on_message(rule=to_me(), priority=5)
# for backward compatibility, you can still use `https://api.deepseek.com/v1` as `base_url`.
client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com")

@echo.handle()
async def handle_first_receive(args: Message = CommandArg()):

    with open('demo_bot/plugins/DeepSeek/deepseek_result.json', 'r', encoding= 'utf-8') as file:
        json_list = json.load(file)

    if len(json_list) == 11:
        del json_list[1]
        del json_list[1]

    if len(json_list) == 0:
        json_list = [{"role": "user","content":f"{args}"}]
    else:
        json_list.append({"role": "user","content":f"{args}"})


    response = client.chat.completions.create(
        model="deepseek-chat",
        messages=json_list,
        max_tokens=1024,
        temperature=0.7,
        stream=False
    )

    json_list.append({"role": "assistant","content": f"{response.choices[0].message.content}"})
    with open('demo_bot/plugins/DeepSeek/deepseek_result.json', 'w', encoding= 'utf-8') as f:
        f.write(f"{json.dumps(json_list,ensure_ascii=False)}")
    await echo.finish(response.choices[0].message.content)


clean = on_command('cl', priority=5)

@clean.handle()
async def handle_first_receive():
    with open('demo_bot/plugins/DeepSeek/deepseek_result.json', 'r', encoding= 'utf-8') as file:
        json_list = json.load(file)
        json_list = json_list[0]  
    with open('demo_bot/plugins/DeepSeek/deepseek_result.json', 'w', encoding= 'utf-8') as f:
        f.write(f"[{json.dumps(json_list,ensure_ascii=False)}]")
    await clean.finish("记忆已清除。")

role = on_command('role', priority=5)

@role.handle()
async def handle_first_receive(args: Message = CommandArg()):
    with open('demo_bot/plugins/DeepSeek/deepseek_result.json', 'r', encoding= 'utf-8') as file:
        json_list = json.load(file)
        json_list[0] = {"role": "system", "content":f"{args}"}
    with open('demo_bot/plugins/DeepSeek/deepseek_result.json', 'w', encoding= 'utf-8') as f:
        f.write(f"{json.dumps(json_list,ensure_ascii=False)}")
    await role.finish("已重设角色。")

此外，出于对diy一个自动对焦镜头的执念（当初一时冲动花了50r买了个球机镜头回来，现在还在吃灰），我又研究了自动对焦算法（然而由于极度缺乏专业知识，大部分都是看不懂的……），学习了如何调用人脸检测模型（在deep seek的帮助下），本着Ctrl+C/V的编程精神（），勉强拼凑出了一个理论验证版本，然而效果并不尽如人意。（详见下一条学习日志）