# 全功能安裝（RAG/代碼解釋器/GUI支持）
pip install -U "qwen-agent[rag,code_interpreter,python_executor,gui]"


# 簡約安裝版本
pip install -U qwen-agent

export DASHSCOPE_API_KEY='your-api-key'

from vllm import LLM, SamplingParams


llm = LLM(model="Qwen2-7B-Chat")

from qwen_agent.tools.base import BaseTool, register_tool
import json5


@register_tool('calculate')
class Calculator(BaseTool):
    description = '基礎運算計算器'
    parameters = [{'name': 'formula', 'type': 'string'}]
    
    def call(self, params: str) -> float:
        return eval(json5.loads(params)['formula'])


# 調用示例
calc = Calculator()
print(calc.call('{"formula": "(3 + 5) * 2"}'))  # 輸出 16.0

from qwen_agent.agents import Assistant


class HistoryAssistant(Assistant):
    def _postprocess_messages(self, messages):
        return messages[-10:]  # 保留最近5輪對話


assistant = HistoryAssistant(llm={'model': 'qwen-max'})

from qwen_agent.agents import Assistant
from qwen_agent.tools import BaseTool, register_tool
import requests
import json5


@register_tool('city_info')
class CityInfoTool(BaseTool):
    description = "城市基礎信息查詢"
    parameters = [{'name': 'name', 'type': 'string'}]
    
    def call(self, params):
        city = json5.loads(params)['name']
        response = requests.get(f"https://api.example.com/cities/{city}")
        return response.json()


# 配置助手
assistant = Assistant(
    llm={'model': 'qwen-max'},
    function_list=['city_info','code_interpreter'],
    system_message="你是一個城市百科助手"
)


# 測試查詢
response = assistant.run([{'role': 'user', 'content': '上海有多少個行政區?'}])
print(response[-1]['content'])

import urllib
from qwen_agent.agents import Assistant


assistant = Assistant(
    function_list=['code_interpreter'],
    system_message="圖像處理專家"
)


def process_image(prompt):
    messages = [{'role':'user', 'content': prompt}]
    for resp in assistant.run(messages):
        if 'function_call' in resp:
            code = resp['function_call']['arguments']
            exec(code)  # 示例簡化執行
    return resp[-1]['content']


process_image('將https://example.com/image.jpg的水平寬度擴大1.5倍')

from qwen_agent import retrieve


# 構建知識庫
retrieve.build_index('documents/')


class DocQA(Assistant):
    def _preprocess(self, query):
        contexts = retrieve.search(query)
        return f"根據文檔：{contexts}\n回答：{query}"


qa = DocQA(llm={'model': 'qwen-max-longcontext'})

from qwen_agent.gui import WebUI


WebUI(assistant).launch(server_name='0.0.0.0', server_port=7860)

class CustomerService(Assistant):
    def __init__(self):
        super().__init__(
            system_message="你是XX公司客服，回答范圍限于產品功能和訂單查詢",
            function_list=[product_lookup, order_status]
        )
    
    def _validate_query(self, query):
        if '價格' in query:
            return "具體產品價格請訪問官網查詢"
        return super()._validate_query(query)

@register_tool('data_analysis')
class DataAnalyzer:
    def call(self, params):
        df = pd.read_csv(params['file'])
        return df.describe().to_markdown()


assistant = Assistant(function_list=['data_analysis', 'code_interpreter'])

# 開啟調試模式
assistant = Assistant(verbose=True)


# 性能監控
from qwen_agent.monitor import perf_counter


@perf_counter
def critical_function():
    pass