书生大模型关卡任务-Lagent 自定义你的 Agent 智能体

1.环境配置

开发机选择 30% A100,镜像选择为 Cuda12.2-conda。

首先来为 Lagent 配置一个可用的环境。

# 创建环境
conda create -n agent_camp3 python=3.10 -y
# 激活环境
conda activate agent_camp3
# 安装 torch
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
# 安装其他依赖包
pip install termcolor==2.4.0
pip install lmdeploy==0.5.2

接下来,我们通过源码安装的方式安装 lagent。

# 创建目录以存放代码
mkdir -p /root/agent_camp3
cd /root/agent_camp3
git clone https://github.com/InternLM/lagent.git
cd lagent && git checkout 81e7ace && pip install -e . && cd ..
pip install griffe==0.48.0

2.Lagent Web Demo 使用

接下来,我们将使用 Lagent 的 Web Demo 来体验 InternLM2.5-7B-Chat 的智能体能力。

首先,我们先使用 LMDeploy 部署 InternLM2.5-7B-Chat,并启动一个 API Server。

conda activate agent_camp3
lmdeploy serve api_server /share/new_models/Shanghai_AI_Laboratory/internlm2_5-7b-chat --model-name internlm2_5-7b-chat

然后,我们在另一个窗口中启动 Lagent 的 Web Demo。

cd /root/agent_camp3/lagent
conda activate agent_camp3
streamlit run examples/internlm2_agent_web_demo.py

端口映射:

修改模型名称一栏为 internlm2_5-7b-chat,修改模型 ip一栏为127.0.0.1:23333

输入后需要按下回车以确认!

然后,我们在插件选择一栏选择 ArxivSearch,并输入指令“帮我搜索一下 MindSearch 论文”。

3.基于 Lagent 自定义智能体

Lagent 中关于工具部分的介绍文档位于 https://lagent.readthedocs.io/zh-cn/latest/tutorials/action.html 。

使用 Lagent 自定义工具主要分为以下几步:

  1. 继承 BaseAction 类
  2. 实现简单工具的 run 方法;或者实现工具包内每个子工具的功能
  3. 简单工具的 run 方法可选被 tool_api 装饰;工具包内每个子工具的功能都需要被 tool_api 装饰

下面我们将实现一个调用 MagicMaker API 以完成文生图的功能。

首先,我们先来创建工具文件:

cd /root/agent_camp3/lagent
touch lagent/actions/magicmaker.py

然后,我们将下面的代码复制进入 /root/agent_camp3/lagent/lagent/actions/magicmaker.py

import json
import requests

from lagent.actions.base_action import BaseAction, tool_api
from lagent.actions.parser import BaseParser, JsonParser
from lagent.schema import ActionReturn, ActionStatusCode


class MagicMaker(BaseAction):
    styles_option = [
        'dongman',  # 动漫
        'guofeng',  # 国风
        'xieshi',   # 写实
        'youhua',   # 油画
        'manghe',   # 盲盒
    ]
    aspect_ratio_options = [
        '16:9', '4:3', '3:2', '1:1',
        '2:3', '3:4', '9:16'
    ]

    def __init__(self,
                 style='guofeng',
                 aspect_ratio='4:3'):
        super().__init__()
        if style in self.styles_option:
            self.style = style
        else:
            raise ValueError(f'The style must be one of {self.styles_option}')
        
        if aspect_ratio in self.aspect_ratio_options:
            self.aspect_ratio = aspect_ratio
        else:
            raise ValueError(f'The aspect ratio must be one of {aspect_ratio}')
    
    @tool_api
    def generate_image(self, keywords: str) -> dict:
        """Run magicmaker and get the generated image according to the keywords.

        Args:
            keywords (:class:`str`): the keywords to generate image

        Returns:
            :class:`dict`: the generated image
                * image (str): path to the generated image
        """
        try:
            response = requests.post(
                url='https://magicmaker.openxlab.org.cn/gw/edit-anything/api/v1/bff/sd/generate',
                data=json.dumps({
                    "official": True,
                    "prompt": keywords,
                    "style": self.style,
                    "poseT": False,
                    "aspectRatio": self.aspect_ratio
                }),
                headers={'content-type': 'application/json'}
            )
        except Exception as exc:
            return ActionReturn(
                errmsg=f'MagicMaker exception: {exc}',
                state=ActionStatusCode.HTTP_ERROR)
        image_url = response.json()['data']['imgUrl']
        return {'image': image_url}

最后,我们修改 /root/agent_camp3/lagent/examples/internlm2_agent_web_demo.py 来适配我们的自定义工具。

  1. 在 from lagent.actions import ActionExecutor, ArxivSearch, IPythonInterpreter 的下一行添加 from lagent.actions.magicmaker import MagicMaker
  2. 在第27行添加 MagicMaker()
from lagent.actions import ActionExecutor, ArxivSearch, IPythonInterpreter
+ from lagent.actions.magicmaker import MagicMaker
from lagent.agents.internlm2_agent import INTERPRETER_CN, META_CN, PLUGIN_CN, Internlm2Agent, Internlm2Protocol

...
        action_list = [
            ArxivSearch(),
+             MagicMaker(),
        ]

接下来,启动 Web Demo,要求生成山水画

发现调用工具成功,但是没有生成图片链接,原因是Magic Maker没用了

4.自定义Agent

参考:

图片生成 API 文档 | 讯飞开放平台文档中心 (xfyun.cn)

我们编写工具(密钥需要在科大讯飞申请)/root/agent_camp3/lagent/lagent/actions/imagecreator.py

import json
import requests
from lagent.actions.base_action import BaseAction, tool_api
from lagent.schema import ActionReturn, ActionStatusCode

import time

from datetime import datetime
from wsgiref.handlers import format_date_time
from time import mktime
import hashlib
import base64
import hmac
from urllib.parse import urlencode
import json
from PIL import Image
from io import BytesIO

class AssembleHeaderException(Exception):
    def __init__(self, msg):
        self.message = msg


class Url:
    def __init__(this, host, path, schema):
        this.host = host
        this.path = path
        this.schema = schema
        pass


# calculate sha256 and encode to base64
def sha256base64(data):
    sha256 = hashlib.sha256()
    sha256.update(data)
    digest = base64.b64encode(sha256.digest()).decode(encoding='utf-8')
    return digest


def parse_url(requset_url):
    stidx = requset_url.index("://")
    host = requset_url[stidx + 3:]
    schema = requset_url[:stidx + 3]
    edidx = host.index("/")
    if edidx <= 0:
        raise AssembleHeaderException("invalid request url:" + requset_url)
    path = host[edidx:]
    host = host[:edidx]
    u = Url(host, path, schema)
    return u


# 生成鉴权url
def assemble_ws_auth_url(requset_url, method="GET", api_key="", api_secret=""):
    u = parse_url(requset_url)
    host = u.host
    path = u.path
    now = datetime.now()
    date = format_date_time(mktime(now.timetuple()))
    # print(date)
    # date = "Thu, 12 Dec 2019 01:57:27 GMT"
    signature_origin = "host: {}\ndate: {}\n{} {} HTTP/1.1".format(host, date, method, path)
    # print(signature_origin)
    signature_sha = hmac.new(api_secret.encode('utf-8'), signature_origin.encode('utf-8'),
                             digestmod=hashlib.sha256).digest()
    signature_sha = base64.b64encode(signature_sha).decode(encoding='utf-8')
    authorization_origin = "api_key=\"%s\", algorithm=\"%s\", headers=\"%s\", signature=\"%s\"" % (
        api_key, "hmac-sha256", "host date request-line", signature_sha)
    authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8')
    # print(authorization_origin)
    values = {
        "host": host,
        "date": date,
        "authorization": authorization
    }

    return requset_url + "?" + urlencode(values)

# 生成请求body体
def getBody(appid,text):
    body= {
        "header": {
            "app_id": appid,
            "uid":"123456789"
        },
        "parameter": {
            "chat": {
                "domain": "general",
                "temperature":0.5,
                "max_tokens":4096
            }
        },
        "payload": {
            "message":{
                "text":[
                    {
                        "role":"user",
                        "content":text
                    }
                ]
            }
        }
    }
    return body

# 发起请求并返回结果
def main(text,appid,apikey,apisecret):
    host = 'http://spark-api.cn-huabei-1.xf-yun.com/v2.1/tti'
    url = assemble_ws_auth_url(host,method='POST',api_key=apikey,api_secret=apisecret)
    content = getBody(appid,text)
    print(time.time())
    response = requests.post(url,json=content,headers={'content-type': "application/json"}).text
    print(time.time())
    return response

#将base64 的图片数据存在本地
def base64_to_image(base64_data, save_path):
    # 解码base64数据
    img_data = base64.b64decode(base64_data)

    # 将解码后的数据转换为图片
    img = Image.open(BytesIO(img_data))

    # 保存图片到本地
    img.save(save_path)


# 解析并保存到指定位置
def parser_Message(message):
    data = json.loads(message)
    # print("data" + str(message))
    code = data['header']['code']
    if code != 0:
        print(f'请求错误: {code}, {data}')
    else:
        text = data["payload"]["choices"]["text"]
        imageContent = text[0]
        # if('image' == imageContent["content_type"]):
        imageBase = imageContent["content"]
        imageName = data['header']['sid']
        savePath = f"/root/agent_camp3/lagent/tmp_dir/{imageName}.jpg"
        base64_to_image(imageBase,savePath)
        print("图片保存路径:" + savePath)

def get_path(desc):
    #运行前请配置以下鉴权三要素,获取途径:https://console.xfyun.cn/services/tti
    APPID =''
    APISecret = ''
    APIKEY = ''#!!!!!!!!!!!!这里要填入科大讯飞对应服务的密钥
    res = main(desc,appid=APPID,apikey=APIKEY,apisecret=APISecret)
    # print(res)
    #保存到指定位置
    parser_Message(res)
    # print(res)
    data = json.loads(res)
    save_path = f"http://127.0.0.1:7860/imgs/{data['header']['sid']}.jpg"
    return save_path

class ImageCreator(BaseAction):
    def __init__(self):
            super().__init__()

    @tool_api
    def create_image(self, keywords: str) -> dict:
        """Create an image based on the input text using the specified API.

        Args:
            text (:class:`str`): the text to generate image

        Returns:
            :class:`dict`: the generated image information
                * image_path (str): path to the generated image
        """
        try:
            save_path=get_path(keywords)
            return {'image': save_path}
        except Exception as exc:
            print(exc)
            return ActionReturn(
                errmsg=f'ImageCreator exception: {exc}',
                state=ActionStatusCode.HTTP_ERROR)
            
if __name__ == "__main__":
    imc=ImageCreator()
    print(imc.create_image("山水画"))

 这里和Magic Maker不同的是文生图直接返回的是图片的二进制码,所以我们把生成的图片存到目录/root/agent_camp3/lagent/tmp_dir/中,然后我们要把该目录的图片以get请求的方式映射出来,编写、运行如下脚本/root/agent_camp3/app.py:

from flask import Flask, send_from_directory, abort

app = Flask(__name__)

# 设置图片存储目录
img_dir = '/root/agent_camp3/lagent/tmp_dir/'

@app.route('/imgs/<path:filename>')
def img_proxy(filename):
    """
    Serve images from the specified directory.
    """
    return send_from_directory(img_dir, filename)

if __name__ == '__main__':
    # 运行 Flask 应用
    app.run(host='127.0.0.1', port=7860)

这样,我们就能访问http://127.0.0.1:7860/imgs/ +图片文件名(如cht000b897f@dx1924cd6c77bb894550.jpg)访问到图片。

最后修改web_demo:

运行:

端口映射:

再次让模型调用工具:

点击超链:

发现成功按要求生成图片。

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