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68 lines
1.7 KiB
68 lines
1.7 KiB
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import os
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from dotenv import load_dotenv
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from swarm_models import OpenAIChat
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from swarms import Agent, GroupChat, expertise_based
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if __name__ == "__main__":
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load_dotenv()
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# Get the OpenAI API key from the environment variable
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api_key = os.getenv("OPENAI_API_KEY")
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# Create an instance of the OpenAIChat class
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model = OpenAIChat(
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openai_api_key=api_key,
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model_name="gpt-4o-mini",
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temperature=0.1,
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)
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# Example agents
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agent1 = Agent(
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agent_name="Financial-Analysis-Agent",
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system_prompt="You are a financial analyst specializing in investment strategies.",
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llm=model,
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max_loops=1,
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autosave=False,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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output_type="string",
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streaming_on=False,
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)
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agent2 = Agent(
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agent_name="Tax-Adviser-Agent",
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system_prompt="You are a tax adviser who provides clear and concise guidance on tax-related queries.",
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llm=model,
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max_loops=1,
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autosave=False,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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output_type="string",
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streaming_on=False,
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)
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agents = [agent1, agent2]
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chat = GroupChat(
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name="Investment Advisory",
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description="Financial and tax analysis group",
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agents=agents,
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speaker_fn=expertise_based,
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)
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history = chat.run(
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"How to optimize tax strategy for investments?"
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)
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print(history.model_dump_json(indent=2))
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