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57 lines
1.9 KiB
57 lines
1.9 KiB
2 weeks ago
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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("GROQ_API_KEY")
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# Model
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model = OpenAIChat(
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openai_api_base="https://api.groq.com/openai/v1",
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openai_api_key=api_key,
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model_name="llama-3.1-70b-versatile",
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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="Crypto-Tax-Optimization-Agent",
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system_prompt="You are a friendly tax expert specializing in cryptocurrency investments. Provide approachable insights on optimizing tax savings for crypto transactions.",
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llm=model,
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max_loops=1,
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dynamic_temperature_enabled=True,
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user_name="User",
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output_type="string",
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streaming_on=True,
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)
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agent2 = Agent(
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agent_name="Crypto-Investment-Strategies-Agent",
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system_prompt="You are a conversational financial analyst focused on cryptocurrency investments. Offer debatable advice on investment strategies that minimize tax liabilities.",
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llm=model,
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max_loops=1,
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dynamic_temperature_enabled=True,
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user_name="User",
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output_type="string",
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streaming_on=True,
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)
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agents = [agent1, agent2]
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chat = GroupChat(
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name="Crypto Tax Optimization Debate",
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description="Debate on optimizing tax savings for cryptocurrency transactions and investments",
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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 can one optimize tax savings for cryptocurrency transactions and investments? I bought some Bitcoin and Ethereum last year and want to minimize my tax liabilities this year."
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)
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print(history.model_dump_json(indent=2))
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