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118 lines
2.8 KiB
118 lines
2.8 KiB
import os
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from dotenv import load_dotenv
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from swarms import Agent, SequentialWorkflow
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from swarm_models import OpenAIChat
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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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# Initialize specialized agents
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data_extractor_agent = Agent(
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agent_name="Data-Extractor",
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system_prompt=None,
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llm=model,
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max_loops=1,
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autosave=True,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="data_extractor_agent.json",
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user_name="pe_firm",
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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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)
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summarizer_agent = Agent(
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agent_name="Document-Summarizer",
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system_prompt=None,
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llm=model,
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max_loops=1,
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autosave=True,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="summarizer_agent.json",
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user_name="pe_firm",
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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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)
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financial_analyst_agent = Agent(
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agent_name="Financial-Analyst",
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system_prompt=None,
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llm=model,
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max_loops=1,
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autosave=True,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="financial_analyst_agent.json",
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user_name="pe_firm",
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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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)
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market_analyst_agent = Agent(
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agent_name="Market-Analyst",
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system_prompt=None,
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llm=model,
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max_loops=1,
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autosave=True,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="market_analyst_agent.json",
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user_name="pe_firm",
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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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)
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operational_analyst_agent = Agent(
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agent_name="Operational-Analyst",
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system_prompt=None,
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llm=model,
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max_loops=1,
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autosave=True,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="operational_analyst_agent.json",
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user_name="pe_firm",
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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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)
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# Initialize the SwarmRouter
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router = SequentialWorkflow(
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name="pe-document-analysis-swarm",
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description="Analyze documents for private equity due diligence and investment decision-making",
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max_loops=1,
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agents=[
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data_extractor_agent,
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summarizer_agent,
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financial_analyst_agent,
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market_analyst_agent,
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operational_analyst_agent,
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],
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output_type="all",
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)
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# Example usage
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if __name__ == "__main__":
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# Run a comprehensive private equity document analysis task
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result = router.run(
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"Where is the best place to find template term sheets for series A startups. Provide links and references"
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)
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print(result)
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