93 lines
2.5 KiB
Python
93 lines
2.5 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Agent Factory
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Factory functions for creating and configuring agents with standardized toolkits.
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"""
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import os
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from typing import Optional
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from agentscope.agent import ReActAgent
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from agentscope.tool import Toolkit
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from agentscope.formatter import FormatterBase, OpenAIChatFormatter
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from agentscope.model import ChatModelBase, OpenAIChatModel
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from agentscope.memory import InMemoryMemory, MemoryBase
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# Default configurations
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DEFAULT_MODEL_CONFIG = {
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"model_name": "gpt-4o",
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"stream": False,
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}
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def get_default_model() -> OpenAIChatModel:
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"""Create default OpenAI model instance."""
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api_key = os.environ.get("OPENAI_API_KEY")
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if not api_key:
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raise ValueError("OPENAI_API_KEY environment variable is required")
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return OpenAIChatModel(api_key=api_key, **DEFAULT_MODEL_CONFIG)
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def create_agent(
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name: str,
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sys_prompt: str,
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toolkit: Toolkit,
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description: Optional[str] = None,
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model: Optional[ChatModelBase] = None,
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formatter: Optional[FormatterBase] = None,
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memory: Optional[MemoryBase] = None,
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max_iters: int = 10,
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parallel_tool_calls: bool = False,
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**kwargs,
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) -> ReActAgent:
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"""
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Create a ReActAgent with standardized configuration.
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Args:
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name: Agent identifier
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sys_prompt: System prompt template (supports {name} placeholder)
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toolkit: Toolkit instance
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model: Language model (defaults to GPT-4o)
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formatter: Message formatter (defaults to OpenAIChatFormatter)
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memory: Memory instance (defaults to InMemoryMemory)
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max_iters: Maximum reasoning iterations
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parallel_tool_calls: Enable parallel tool execution
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**kwargs: Additional ReActAgent arguments
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Returns:
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Configured ReActAgent instance
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Example:
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>>> agent = create_agent(
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... name="sql_expert",
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... sys_prompt="You are {name}, a SQL database expert",
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... tools=sql_tools
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... )
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"""
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# Set defaults
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if model is None:
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model = get_default_model()
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if formatter is None:
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formatter = OpenAIChatFormatter()
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if memory is None:
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memory = InMemoryMemory()
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# Create agent
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agent = ReActAgent(
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name=name,
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sys_prompt=sys_prompt.format(name=name),
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model=model,
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formatter=formatter,
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toolkit=toolkit,
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memory=memory,
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max_iters=max_iters,
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parallel_tool_calls=parallel_tool_calls,
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**kwargs,
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)
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agent.__doc__ = description
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return agent
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