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d8d14f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | import os
from dotenv import load_dotenv
from swarms import Agent, SequentialWorkflow
from swarm_models import OpenAIChat
load_dotenv()
# Get the OpenAI API key from the environment variable
api_key = os.getenv("GROQ_API_KEY")
# Model
model = OpenAIChat(
openai_api_base="https://api.groq.com/openai/v1",
openai_api_key=api_key,
model_name="llama-3.1-70b-versatile",
temperature=0.1,
)
# Initialize specialized agents
data_extractor_agent = Agent(
agent_name="Data-Extractor",
system_prompt="""You are a data extraction specialist. Your role is to:
1. Extract key information, data points, and metrics from documents
2. Identify and pull out important facts, figures, and statistics
3. Structure extracted data in a clear, organized format
4. Flag any inconsistencies or missing data
5. Ensure accuracy in data extraction while maintaining context""",
llm=model,
max_loops=1,
autosave=True,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path="data_extractor_agent.json",
user_name="pe_firm",
retry_attempts=1,
context_length=200000,
output_type="string",
)
summarizer_agent = Agent(
agent_name="Document-Summarizer",
system_prompt="""You are a document summarization expert. Your role is to:
1. Create concise, comprehensive summaries of documents
2. Highlight key points and main takeaways
3. Maintain the essential meaning while reducing length
4. Structure summaries in a logical, readable format
5. Identify and emphasize critical insights""",
llm=model,
max_loops=1,
autosave=True,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path="summarizer_agent.json",
user_name="pe_firm",
retry_attempts=1,
context_length=200000,
output_type="string",
)
financial_analyst_agent = Agent(
agent_name="Financial-Analyst",
system_prompt="""You are a financial analysis expert. Your role is to:
1. Analyze financial statements and metrics
2. Evaluate company valuations and financial projections
3. Assess financial risks and opportunities
4. Provide insights on financial performance and health
5. Make recommendations based on financial analysis""",
llm=model,
max_loops=1,
autosave=True,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path="financial_analyst_agent.json",
user_name="pe_firm",
retry_attempts=1,
context_length=200000,
output_type="string",
)
market_analyst_agent = Agent(
agent_name="Market-Analyst",
system_prompt="""You are a market analysis expert. Your role is to:
1. Analyze market trends and dynamics
2. Evaluate competitive landscape and market positioning
3. Identify market opportunities and threats
4. Assess market size and growth potential
5. Provide strategic market insights and recommendations""",
llm=model,
max_loops=1,
autosave=True,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path="market_analyst_agent.json",
user_name="pe_firm",
retry_attempts=1,
context_length=200000,
output_type="string",
)
operational_analyst_agent = Agent(
agent_name="Operational-Analyst",
system_prompt="""You are an operational analysis expert. Your role is to:
1. Analyze business operations and processes
2. Evaluate operational efficiency and effectiveness
3. Identify operational risks and opportunities
4. Assess scalability and growth potential
5. Provide recommendations for operational improvements""",
llm=model,
max_loops=2,
autosave=True,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path="operational_analyst_agent.json",
user_name="pe_firm",
retry_attempts=1,
context_length=200000,
output_type="string",
)
# Initialize the SwarmRouter
router = SequentialWorkflow(
name="pe-document-analysis-swarm",
description="Analyze documents for private equity due diligence and investment decision-making",
max_loops=1,
agents=[
data_extractor_agent,
summarizer_agent,
financial_analyst_agent,
market_analyst_agent,
operational_analyst_agent,
],
output_type="all",
)
# Example usage
if __name__ == "__main__":
# Run a comprehensive private equity document analysis task
result = router.run(
"Where is the best place to find template term sheets for series A startups. Provide links and references",
no_use_clusterops=True,
)
print(result)
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