You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
232 lines
9.1 KiB
232 lines
9.1 KiB
# SpreadSheetSwarm
|
|
|
|
*Structured approach to data management and operations in spreadsheet-like format*
|
|
|
|
**Swarm Type**: `SpreadSheetSwarm`
|
|
|
|
## Overview
|
|
|
|
The SpreadSheetSwarm provides a structured approach to data management and operations, ideal for tasks involving data analysis, transformation, and systematic processing in a spreadsheet-like structure. This architecture organizes agents to work on data in a tabular format with clear rows, columns, and processing workflows.
|
|
|
|
Key features:
|
|
- **Structured Data Processing**: Organizes work in spreadsheet-like rows and columns
|
|
- **Systematic Operations**: Sequential and methodical data handling
|
|
- **Data Transformation**: Efficient processing of structured datasets
|
|
- **Collaborative Analysis**: Multiple agents working on different data aspects
|
|
|
|
## Use Cases
|
|
|
|
- Financial data analysis and reporting
|
|
- Customer data processing and segmentation
|
|
- Inventory management and tracking
|
|
- Research data compilation and analysis
|
|
|
|
## API Usage
|
|
|
|
### Basic SpreadSheetSwarm Example
|
|
|
|
=== "Shell (curl)"
|
|
```bash
|
|
curl -X POST "https://api.swarms.world/v1/swarm/completions" \
|
|
-H "x-api-key: $SWARMS_API_KEY" \
|
|
-H "Content-Type: application/json" \
|
|
-d '{
|
|
"name": "Financial Analysis Spreadsheet",
|
|
"description": "Systematic financial data analysis using spreadsheet structure",
|
|
"swarm_type": "SpreadSheetSwarm",
|
|
"task": "Analyze quarterly financial performance data for a retail company with multiple product lines and create comprehensive insights",
|
|
"agents": [
|
|
{
|
|
"agent_name": "Data Validator",
|
|
"description": "Validates and cleans financial data",
|
|
"system_prompt": "You are a data validation specialist. Clean, validate, and structure financial data ensuring accuracy and consistency.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.2
|
|
},
|
|
{
|
|
"agent_name": "Revenue Analyst",
|
|
"description": "Analyzes revenue trends and patterns",
|
|
"system_prompt": "You are a revenue analyst. Focus on revenue trends, growth patterns, and seasonal variations across product lines.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.3
|
|
},
|
|
{
|
|
"agent_name": "Cost Analyst",
|
|
"description": "Analyzes cost structures and margins",
|
|
"system_prompt": "You are a cost analyst. Examine cost structures, margin analysis, and expense categorization.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.3
|
|
},
|
|
{
|
|
"agent_name": "Performance Calculator",
|
|
"description": "Calculates KPIs and financial metrics",
|
|
"system_prompt": "You are a financial metrics specialist. Calculate KPIs, ratios, and performance indicators from the analyzed data.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.1
|
|
},
|
|
{
|
|
"agent_name": "Report Generator",
|
|
"description": "Creates structured financial reports",
|
|
"system_prompt": "You are a report generator. Create comprehensive, well-structured financial reports with insights and recommendations.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.4
|
|
}
|
|
],
|
|
"max_loops": 1
|
|
}'
|
|
```
|
|
|
|
=== "Python (requests)"
|
|
```python
|
|
import requests
|
|
import json
|
|
|
|
API_BASE_URL = "https://api.swarms.world"
|
|
API_KEY = "your_api_key_here"
|
|
|
|
headers = {
|
|
"x-api-key": API_KEY,
|
|
"Content-Type": "application/json"
|
|
}
|
|
|
|
swarm_config = {
|
|
"name": "Financial Analysis Spreadsheet",
|
|
"description": "Systematic financial data analysis using spreadsheet structure",
|
|
"swarm_type": "SpreadSheetSwarm",
|
|
"task": "Analyze quarterly financial performance data for a retail company with multiple product lines and create comprehensive insights",
|
|
"agents": [
|
|
{
|
|
"agent_name": "Data Validator",
|
|
"description": "Validates and cleans financial data",
|
|
"system_prompt": "You are a data validation specialist. Clean, validate, and structure financial data ensuring accuracy and consistency.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.2
|
|
},
|
|
{
|
|
"agent_name": "Revenue Analyst",
|
|
"description": "Analyzes revenue trends and patterns",
|
|
"system_prompt": "You are a revenue analyst. Focus on revenue trends, growth patterns, and seasonal variations across product lines.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.3
|
|
},
|
|
{
|
|
"agent_name": "Cost Analyst",
|
|
"description": "Analyzes cost structures and margins",
|
|
"system_prompt": "You are a cost analyst. Examine cost structures, margin analysis, and expense categorization.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.3
|
|
},
|
|
{
|
|
"agent_name": "Performance Calculator",
|
|
"description": "Calculates KPIs and financial metrics",
|
|
"system_prompt": "You are a financial metrics specialist. Calculate KPIs, ratios, and performance indicators from the analyzed data.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.1
|
|
},
|
|
{
|
|
"agent_name": "Report Generator",
|
|
"description": "Creates structured financial reports",
|
|
"system_prompt": "You are a report generator. Create comprehensive, well-structured financial reports with insights and recommendations.",
|
|
"model_name": "gpt-4o",
|
|
"max_loops": 1,
|
|
"temperature": 0.4
|
|
}
|
|
],
|
|
"max_loops": 1
|
|
}
|
|
|
|
response = requests.post(
|
|
f"{API_BASE_URL}/v1/swarm/completions",
|
|
headers=headers,
|
|
json=swarm_config
|
|
)
|
|
|
|
if response.status_code == 200:
|
|
result = response.json()
|
|
print("SpreadSheetSwarm completed successfully!")
|
|
print(f"Cost: ${result['metadata']['billing_info']['total_cost']}")
|
|
print(f"Execution time: {result['metadata']['execution_time_seconds']} seconds")
|
|
print(f"Structured analysis: {result['output']}")
|
|
else:
|
|
print(f"Error: {response.status_code} - {response.text}")
|
|
```
|
|
|
|
**Example Response**:
|
|
```json
|
|
{
|
|
"status": "success",
|
|
"swarm_name": "financial-analysis-spreadsheet",
|
|
"swarm_type": "SpreadSheetSwarm",
|
|
"task": "Analyze quarterly financial performance data for a retail company with multiple product lines and create comprehensive insights",
|
|
"output": {
|
|
"data_validation": {
|
|
"data_quality": "95% accuracy after cleaning",
|
|
"missing_values": "Identified and filled 3% missing entries",
|
|
"data_structure": "Standardized format across all product lines"
|
|
},
|
|
"revenue_analysis": {
|
|
"q4_revenue": "$2.4M total revenue",
|
|
"growth_rate": "12% quarter-over-quarter growth",
|
|
"top_performers": ["Product Line A: +18%", "Product Line C: +15%"],
|
|
"seasonal_trends": "Strong holiday season performance"
|
|
},
|
|
"cost_analysis": {
|
|
"total_costs": "$1.8M operational costs",
|
|
"cost_breakdown": "60% COGS, 25% Marketing, 15% Operations",
|
|
"margin_analysis": "25% gross margin, 15% net margin",
|
|
"cost_optimization": "Identified 8% potential savings in supply chain"
|
|
},
|
|
"performance_metrics": {
|
|
"roi": "22% return on investment",
|
|
"customer_acquisition_cost": "$45 per customer",
|
|
"lifetime_value": "$320 average CLV",
|
|
"inventory_turnover": "6.2x annual turnover"
|
|
},
|
|
"comprehensive_report": {
|
|
"executive_summary": "Strong Q4 performance with 12% growth...",
|
|
"recommendations": ["Expand Product Line A", "Optimize supply chain", "Increase marketing for underperformers"],
|
|
"forecast": "Projected 15% growth for Q1 based on trends"
|
|
}
|
|
},
|
|
"metadata": {
|
|
"processing_structure": {
|
|
"rows_processed": 1250,
|
|
"columns_analyzed": 18,
|
|
"calculations_performed": 47
|
|
},
|
|
"data_pipeline": [
|
|
"Data Validation",
|
|
"Revenue Analysis",
|
|
"Cost Analysis",
|
|
"Performance Calculation",
|
|
"Report Generation"
|
|
],
|
|
"execution_time_seconds": 34.2,
|
|
"billing_info": {
|
|
"total_cost": 0.078
|
|
}
|
|
}
|
|
}
|
|
```
|
|
|
|
## Best Practices
|
|
|
|
- Structure data in clear, logical formats before processing
|
|
- Use systematic, step-by-step analysis approaches
|
|
- Ideal for quantitative analysis and reporting tasks
|
|
- Ensure data validation before proceeding with analysis
|
|
|
|
## Related Swarm Types
|
|
|
|
- [SequentialWorkflow](sequential_workflow.md) - For ordered data processing
|
|
- [ConcurrentWorkflow](concurrent_workflow.md) - For parallel data analysis
|
|
- [HierarchicalSwarm](hierarchical_swarm.md) - For complex data projects |