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80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
# -*- coding: utf-8 -*-
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"""The main entry point of the agent skill example."""
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import asyncio
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import os
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from agentscope.agent import ReActAgent
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from agentscope.formatter import DashScopeChatFormatter
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from agentscope.memory import InMemoryMemory
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from agentscope.message import Msg
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from agentscope.model import DashScopeChatModel
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from agentscope.tool import (
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Toolkit,
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execute_shell_command,
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execute_python_code,
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view_text_file,
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)
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async def main() -> None:
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"""The main entry point for the ReAct agent example."""
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toolkit = Toolkit()
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# To use agent skills, your agent must be equipped with text file viewing
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# tools.
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toolkit.register_tool_function(execute_shell_command)
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toolkit.register_tool_function(execute_python_code)
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toolkit.register_tool_function(view_text_file)
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# Register the agent skill
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toolkit.register_agent_skill(
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"./skill/analyzing-agentscope-library",
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)
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agent = ReActAgent(
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name="Friday",
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sys_prompt="""You are a helpful assistant named Friday.
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# IMPORTANT
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- Don't make any assumptions. All your knowledge about AgentScope library must come from your equipped skills.
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""", # noqa: E501
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model=DashScopeChatModel(
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api_key=os.environ.get("DASHSCOPE_API_KEY"),
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model_name="qwen3-max",
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enable_thinking=False,
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stream=True,
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),
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formatter=DashScopeChatFormatter(),
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toolkit=toolkit,
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memory=InMemoryMemory(),
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)
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# First, let's take a look at the agent's system prompt
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print("\033[1;32mAgent System Prompt:\033[0m")
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print(agent.sys_prompt)
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print("\n")
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print(
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"\033[1;32mResponse to Question 'What skills do you have?':\033[0m",
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)
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# We prepare two questions
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await agent(
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Msg("user", "What skills do you have?", "user"),
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)
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print(
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"\n\033[1;32mResponse to Question 'How to create my own tool function "
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"for the agent in agentscope?':\033[0m",
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)
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# The second question
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await agent(
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Msg(
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"user",
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"How to custom tool function for the agent in agentscope?",
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"user",
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),
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)
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asyncio.run(main())
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