wehub-resource-sync
36 lines
2.1 KiB
Python
36 lines
2.1 KiB
Python
from autoagent.types import Agent
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from autoagent.tools import tool_dummy
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from typing import Union
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from autoagent.registry import register_plugin_agent # import the register_agent function from the registry
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@register_plugin_agent(name = "Dummy Agent", func_name="get_dummy_agent") # You must register the agent in the registry, otherwise the agent will not be loaded. The name of register_agent is get_xxx_agent.
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def get_dummy_agent(model: str):
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"""
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This is a dummy agent, it's used for demonstrating the usage of the autoagent.
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Args:
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model: The model to be used for the agent.
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Returns:
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An agent instance.
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"""
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def dummy_instructions(context_variables: dict):
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"""
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The function should take the context_variables as an argument, and return a string. The context_variables is a dictionary, and it's track the important variables of the agent in the whole conversation.
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The instructions should be concise and clear, and it's very important for the agent to follow the instructions.
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"""
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tmp_variables = context_variables.get("tmp_variables", {})
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return f"""..."""
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return Agent(
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name="Dummy Agent", # The name of the agent, you can change it in different scenes.
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model=model, # The default model is gpt-4o-2024-08-06, you can change it to other models if user specified.
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instructions="..." or dummy_instructions, # the instructions of the agent, the instructions can be a string or a function that returns a string. If it is a function, the function should take the context_variables as an argument, and return a string. The instructions should be concise and clear, and it's very important for the agent to follow the instructions.
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functions=[tool_dummy], # The tools of the agent, you can add different tools in different scenes.
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)
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"""
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Form to create an agent:
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agent_name = "Dummy Agent"
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agent_description = "This is a dummy agent, it's used for demonstrating the usage of the autoagent."
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agent_instructions = "..." | "...{global_variables}..."
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agent_tools = [tool_dummy]
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""" |