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[CLEANUP]
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142 changes: 125 additions & 17 deletions README.md
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Expand Up @@ -29,29 +29,137 @@ pip install -U swarms-deploy
## Quick Start 🚀

```python
from swarms_deploy import SwarmDeploy
from swarms.workflows import SequentialWorkflow

# Initialize your agents
data_extractor_agent = YourDataExtractorAgent()
summarizer_agent = YourSummarizerAgent()
financial_analyst_agent = YourFinancialAnalystAgent()

# Create a workflow
workflow = SequentialWorkflow(
name="document-analysis-swarm",
description="Document analysis pipeline",
import os
from dotenv import load_dotenv
from swarms import Agent, SequentialWorkflow
from swarm_models import OpenAIChat
from swarm_deploy import SwarmDeploy

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=None,
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=None,
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=None,
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=None,
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=None,
llm=model,
max_loops=1,
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
financial_analyst_agent,
market_analyst_agent,
operational_analyst_agent,
],
output_type="all"
output_type="all",
)

# Advanced usage with configuration
swarm = SwarmDeploy(
router,
max_workers=4,
# cache_backend="redis"
)
swarm.start(
host="0.0.0.0",
port=8000,
workers=4,
# ssl_keyfile="key.pem",
# ssl_certfile="cert.pem"
)

# Deploy the swarm
swarm = SwarmDeploy(workflow)
swarm.start(host="0.0.0.0", port=8000)
# # Create a cluster
# instances = SwarmDeploy.create_cluster(
# your_callable,
# num_instances=3,
# start_port=8000
# )
```

## Advanced Usage 🔧
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131 changes: 131 additions & 0 deletions examples.py
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@@ -0,0 +1,131 @@
import os
from dotenv import load_dotenv
from swarms import Agent, SequentialWorkflow
from swarm_models import OpenAIChat
from swarm_deploy import SwarmDeploy

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=None,
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=None,
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=None,
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=None,
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=None,
llm=model,
max_loops=1,
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",
)

# Advanced usage with configuration
swarm = SwarmDeploy(
router,
max_workers=4,
# cache_backend="redis"
)
swarm.start(
host="0.0.0.0",
port=8000,
workers=4,
# ssl_keyfile="key.pem",
# ssl_certfile="cert.pem"
)

# # Create a cluster
# instances = SwarmDeploy.create_cluster(
# your_callable,
# num_instances=3,
# start_port=8000
# )

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