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28
README.md
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@ -582,8 +582,7 @@ import os
from dotenv import load_dotenv from dotenv import load_dotenv
from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import AutoModelForCausalLM, AutoTokenizer
from pydantic import BaseModel
# Import the models, structs, and telemetry modules
from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent
# Load the environment variables # Load the environment variables
@ -596,15 +595,22 @@ gemini_api_key = os.getenv("GEMINI_API_KEY")
# Tool Agent # Tool Agent
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b")
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b") tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b")
json_schema = {
"type": "object", # Initialize the schema for the person's information
"properties": { class Schema(BaseModel):
"name": {"type": "string"}, name: str = Field(..., title="Name of the person")
"age": {"type": "number"}, agent: int = Field(..., title="Age of the person")
"is_student": {"type": "boolean"}, is_student: bool = Field(
"courses": {"type": "array", "items": {"type": "string"}}, ..., title="Whether the person is a student"
}, )
} courses: list[str] = Field(
..., title="List of courses the person is taking"
)
# Convert the schema to a JSON string
json_schema = base_model_to_json(Schema)
toolagent = ToolAgent(model=model, tokenizer=tokenizer, json_schema=json_schema) toolagent = ToolAgent(model=model, tokenizer=tokenizer, json_schema=json_schema)
# Blocks List which enables you to build custom swarms by adding classes or functions # Blocks List which enables you to build custom swarms by adding classes or functions

66
docs/corporate/monthly_formula.py
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@ -0,0 +1,66 @@
def calculate_monthly_charge(
development_time_hours: float,
hourly_rate: float,
amortization_months: int,
api_calls_per_month: int,
cost_per_api_call: float,
monthly_maintenance: float,
additional_monthly_costs: float,
profit_margin_percentage: float,
) -> float:
"""
Calculate the monthly charge for a service based on various cost factors.
Parameters:
- development_time_hours (float): The total number of hours spent on development and setup.
- hourly_rate (float): The rate per hour for development and setup.
- amortization_months (int): The number of months over which to amortize the development and setup costs.
- api_calls_per_month (int): The number of API calls made per month.
- cost_per_api_call (float): The cost per API call.
- monthly_maintenance (float): The monthly maintenance cost.
- additional_monthly_costs (float): Any additional monthly costs.
- profit_margin_percentage (float): The desired profit margin as a percentage.
Returns:
- monthly_charge (float): The calculated monthly charge for the service.
"""
# Calculate Development and Setup Costs (amortized monthly)
development_and_setup_costs_monthly = (
development_time_hours * hourly_rate
) / amortization_months
# Calculate Operational Costs per Month
operational_costs_monthly = (
(api_calls_per_month * cost_per_api_call)
+ monthly_maintenance
+ additional_monthly_costs
)
# Calculate Total Monthly Costs
total_monthly_costs = (
development_and_setup_costs_monthly
+ operational_costs_monthly
)
# Calculate Pricing with Profit Margin
monthly_charge = total_monthly_costs * (
1 + profit_margin_percentage / 100
)
return monthly_charge
# Example usage:
monthly_charge = calculate_monthly_charge(
development_time_hours=100,
hourly_rate=500,
amortization_months=12,
api_calls_per_month=500000,
cost_per_api_call=0.002,
monthly_maintenance=1000,
additional_monthly_costs=300,
profit_margin_percentage=10000,
)
print(f"Monthly Charge: ${monthly_charge:.2f}")

1
swarms/structs/auto_swarm.py
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@ -25,6 +25,7 @@ class SequentialAccountingSwarm(AbstractSwarm):
Run the swarm simulation. Run the swarm simulation.
""" """
def __init__( def __init__(
self, self,
name: Optional[str] = "kyegomez/sequential-accounting-swarm", name: Optional[str] = "kyegomez/sequential-accounting-swarm",

0
swarms/structs/rearrange.py
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