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Qwen Agent

Qwen-Agent 是一个基于 Qwen 的指令跟随、工具使用、计划和记忆能力来开发 LLM 应用程序的框架。它还附带了一些示例应用程序，例如浏览器助手、代码解释器和自定义助手。

1. 安装

git clone https://github.com/QwenLM/Qwen-Agent.git
cd Qwen-Agent
pip install -e ./

2. 开发自定义智能体

Qwen-Agent 提供包括语言模型和提示词等原子级组件，及智能体等高级组件在内的多种组件。以下示例选取助理组件进行展示，阐述了如何整合自定义工具以及如何迅速开发出一个能够应用这些工具的代理程序。

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import json
import os

import json5
import urllib.parse
from qwen_agent.agents import Assistant
from qwen_agent.tools.base import BaseTool, register_tool

llm_cfg = {
    # Use the model service provided by DashScope:
    'model': 'qwen-max',
    'model_server': 'dashscope',
    # 'api_key': 'YOUR_DASHSCOPE_API_KEY',
    # It will use the `DASHSCOPE_API_KEY' environment variable if 'api_key' is not set here.

    # Use your own model service compatible with OpenAI API:
    # 'model': 'Qwen/Qwen2-72B-Instruct',
    # 'model_server': 'http://localhost:8000/v1',  # api_base
    # 'api_key': 'EMPTY',

    # (Optional) LLM hyperparameters for generation:
    'generate_cfg': {
        'top_p': 0.8
    }
}
system = 'According to the user\'s request, you first draw a picture and then automatically run code to download the picture ' + \
          'and select an image operation from the given document to process the image'

# Add a custom tool named my_image_gen：
@register_tool('my_image_gen')
class MyImageGen(BaseTool):
    description = 'AI painting (image generation) service, input text description, and return the image URL drawn based on text information.'
    parameters = [{
        'name': 'prompt',
        'type': 'string',
        'description': 'Detailed description of the desired image content, in English',
        'required': True
    }]

    def call(self, params: str, **kwargs) -> str:
        prompt = json5.loads(params)['prompt']
        prompt = urllib.parse.quote(prompt)
        return json.dumps(
            {'image_url': f'https://image.pollinations.ai/prompt/{prompt}'},
            ensure_ascii=False)


tools = ['my_image_gen', 'code_interpreter']  # code_interpreter is a built-in tool in Qwen-Agent
bot = Assistant(llm=llm_cfg,
                system_message=system,
                function_list=tools,
                files=[os.path.abspath('doc.pdf')])

messages = []
while True:
    query = input('user question: ')
    messages.append({'role': 'user', 'content': query})
    response = []
    for response in bot.run(messages=messages):
        print('bot response:', response)
    messages.extend(response)

我们在官方仓库中了解更多使用案例。