1. lagent
参考文档
https://github.com/InternLM/Tutorial/blob/camp2/agent/lagent.md
- 使用 LMDeploy 部署
python
conda activate agent
lmdeploy serve api_server /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-7b \
--server-name 127.0.0.1 \
--model-name internlm2-chat-7b \
--cache-max-entry-count 0.1
2.启动并使用 Lagent Web Demo
python
conda activate agent
cd /root/agent/lagent/examples
streamlit run internlm2_agent_web_demo.py --server.address 127.0.0.1 --server.port 7860
-
使用自带的arxive search插件进行论文搜索:
-
自定义工具(天气查询)进行调用
准备和风天气的API
首先通过 touch /root/agent/lagent/lagent/actions/weather.py(大小写敏感)新建工具,文件使用的代码为:
python
import json
import os
import requests
from typing import Optional, Type
from lagent.actions.base_action import BaseAction, tool_api
from lagent.actions.parser import BaseParser, JsonParser
from lagent.schema import ActionReturn, ActionStatusCode
class WeatherQuery(BaseAction):
"""天气查询插件,用于查询天气信息。"""
def __init__(self,
key: Optional[str] = None, # 可选参数:API 密钥
description: Optional[dict] = None,
parser: Type[BaseParser] = JsonParser,
enable: bool = True) -> None:
super().__init__(description, parser, enable)
# 从环境变量或传入的参数中获取天气 API 密钥
key = os.environ.get('WEATHER_API_KEY', key)
if key is None:
raise ValueError(
'请设置天气 API 密钥,可以通过环境变量 WEATHER_API_KEY 设置,或者在实例化时传入 key 参数'
)
self.key = key
# 设置用于查询城市信息和天气信息的 URL
self.location_query_url = 'https://geoapi.qweather.com/v2/city/lookup'
self.weather_query_url = 'https://devapi.qweather.com/v7/weather/now'
@tool_api
def run(self, query: str) -> ActionReturn:
"""执行天气查询的方法。
Args:
query (str): 要查询的城市名称。
"""
tool_return = ActionReturn(type=self.name)
# 调用 _search 方法查询天气信息
status_code, response = self._search(query)
if status_code == -1:
tool_return.errmsg = response
tool_return.state = ActionStatusCode.HTTP_ERROR
elif status_code == 200:
# 解析查询结果并返回
parsed_res = self._parse_results(response)
tool_return.result = [dict(type='text', content=str(parsed_res))]
tool_return.state = ActionStatusCode.SUCCESS
else:
tool_return.errmsg = str(status_code)
tool_return.state = ActionStatusCode.API_ERROR
return tool_return
def _parse_results(self, results: dict) -> str:
"""解析从 QWeather API 返回的天气信息。
Args:
results (dict): QWeather API 返回的天气信息,JSON 格式。
Returns:
str: 解析后的天气信息。
"""
now = results['now']
data = [
f'数据观测时间: {now["obsTime"]}',
f'温度: {now["temp"]}°C',
f'体感温度: {now["feelsLike"]}°C',
f'天气: {now["text"]}',
f'风向: {now["windDir"]},角度为 {now["wind360"]}°',
f'风力等级: {now["windScale"]},风速为 {now["windSpeed"]} km/h',
f'相对湿度: {now["humidity"]}',
f'当前小时累计降水量: {now["precip"]} mm',
f'大气压强: {now["pressure"]} 百帕',
f'能见度: {now["vis"]} km',
]
return '\n'.join(data)
def _search(self, query: str):
"""查询城市代码和天气信息。
Args:
query (str): 要查询的城市名称。
Returns:
tuple: 包含状态码和响应结果的元组。
"""
# 查询城市代码
try:
city_code_response = requests.get(
self.location_query_url,
params={'key': self.key, 'location': query}
)
except Exception as e:
return -1, str(e)
if city_code_response.status_code != 200:
return city_code_response.status_code, city_code_response.json()
city_code_response = city_code_response.json()
if len(city_code_response['location']) == 0:
return -1, '未查询到城市'
city_code = city_code_response['location'][0]['id']
# 查询天气信息
try:
weather_response = requests.get(
self.weather_query_url,
params={'key': self.key, 'location': city_code}
)
except Exception as e:
return -1, str(e)
return weather_response.status_code, weather_response.json()
运行结果
但是换成明天的就不行了
2. AgentLego:组装智能体"乐高"
参考文档
https://github.com/InternLM/Tutorial/blob/camp2/agent/agentlego.md#1-直接使用-agentlego
直接使用 AgentLego
安装相关依赖后,上传一张街景图片,使用目标检测工具。
通过 touch /root/agent/direct_use.py(大小写敏感)的方式在 /root/agent 目录下新建 direct_use.py 以直接使用目标检测工具,direct_use.py 的代码如下:
python
import re
import cv2
from agentlego.apis import load_tool
# load tool
tool = load_tool('ObjectDetection', device='cuda')
# apply tool
visualization = tool('/root/agent/road.jpg')
print(visualization)
# visualize
image = cv2.imread('/root/agent/road.jpg')
preds = visualization.split('\n')
pattern = r'(\w+) \((\d+), (\d+), (\d+), (\d+)\), score (\d+)'
for pred in preds:
name, x1, y1, x2, y2, score = re.match(pattern, pred).groups()
x1, y1, x2, y2, score = int(x1), int(y1), int(x2), int(y2), int(score)
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 1)
cv2.putText(image, f'{name} {score}', (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 1)
cv2.imwrite('/root/agent/road_detection_direct.jpg', image)
运行结果如下:
可以发现其识别出了car/person/truck/bicycle/bus等物体
更换一张图片进行测试:
会发现他没有识别出物体
"No object found"
再次更换一张街景图片进行识别
这是香港街头的一张图片,虽然比较复杂,但可以看出还是可以识别出大部分物体的。但图片左侧有一些人没有识别出来。
作为智能体工具使用
这样的使用方式可以在Web页面中,新增agent和工具吗,然后指定agent调用工具
3. 使用自定义工具
首先通过 touch /root/agent/agentlego/agentlego/tools/magicmaker_image_generation.py(大小写敏感)的方法新建工具文件。
python
import json
import requests
import numpy as np
from agentlego.types import Annotated, ImageIO, Info
from agentlego.utils import require
from .base import BaseTool
class MagicMakerImageGeneration(BaseTool):
default_desc = ('This tool can call the api of magicmaker to '
'generate an image according to the given keywords.')
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'
]
@require('opencv-python')
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}')
def apply(self,
keywords: Annotated[str,
Info('A series of Chinese keywords separated by comma.')]
) -> ImageIO:
import cv2
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'}
)
image_url = response.json()['data']['imgUrl']
image_response = requests.get(image_url)
image = cv2.cvtColor(cv2.imdecode(np.frombuffer(image_response.content, np.uint8), cv2.IMREAD_COLOR),cv2.COLOR_BGR2RGB)
return ImageIO(image)