释放 Qwen-Agent 的力量：通过 RAG 应用彻底改变 AI 援助

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

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/Qwen1.5-72B-Chat',
    # '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)