SelfMoASeq Example¶

The SelfMoASeq (Self-Mixture of Agents Sequential) is an ensemble method that generates multiple outputs from a single high-performing model and aggregates them sequentially using a sliding window approach. This addresses context length constraints while maintaining the effectiveness of in-model diversity.

How It Works¶

Sample Generation: Generates multiple diverse samples from a proposer model
Sliding Window Aggregation: Processes samples in windows, aggregating progressively
Sequential Synthesis: Uses a sliding window to combine outputs while managing context
Bias Toward Best: Maintains the best output so far and uses it to guide aggregation

This architecture is ideal for: - Tasks requiring high-quality outputs with context constraints - Generating diverse perspectives and aggregating them - Complex problem-solving with limited context windows - Quality improvement through ensemble methods

Installation¶

Install the swarms package using pip:

pip install -U swarms

Basic Setup¶

First, set up your environment variables:

WORKSPACE_DIR="agent_workspace"
OPENAI_API_KEY="your-api-key"

Step-by-Step Example¶

Step 1: Import Required Modules¶

from swarms import SelfMoASeq

Step 2: Create SelfMoASeq¶

self_moa = SelfMoASeq(
    model_name="gpt-4o-mini",
    temperature=0.7,
    window_size=6,
    reserved_slots=3,
    num_samples=30,
    max_loops=10,
    verbose=True,
)

Step 3: Run a Task¶

task = "Write a comprehensive analysis of the benefits and challenges of renewable energy"

result = self_moa.run(task=task)

print(result)

Understanding the Parameters¶

model_name: The model to use for generation
temperature: Controls randomness (0.0-2.0)
window_size: Number of samples to process in each aggregation window
reserved_slots: Number of slots reserved for the best output in each window
num_samples: Total number of samples to generate
max_loops: Maximum number of aggregation iterations
verbose: Whether to show detailed progress

Output Structure¶

The result is a dictionary containing:

{
    "final_output": "The synthesized best output",
    "all_samples": ["sample1", "sample2", ...],
    "aggregation_steps": 5,
    "metrics": {
        "total_samples_generated": 30,
        "total_aggregations": 5,
        "execution_time_seconds": 45.2
    },
    "task": "Your original task",
    "timestamp": "2024-01-01T12:00:00"
}

Custom Configuration¶

Adjust Sample Count¶

self_moa = SelfMoASeq(
    num_samples=50,  # Generate more samples for better quality
    window_size=8,
    reserved_slots=3,
)

Control Aggregation Window¶

self_moa = SelfMoASeq(
    window_size=10,  # Larger window for more context
    reserved_slots=4,  # More slots for best output
    num_samples=40,
)

Use Different Models¶

self_moa = SelfMoASeq(
    proposer_model_name="gpt-4o",  # Better model for generation
    aggregator_model_name="gpt-4o-mini",  # Efficient model for aggregation
    num_samples=30,
)

Example Use Cases¶

Complex Analysis¶

self_moa = SelfMoASeq(
    num_samples=30,
    window_size=6,
    max_loops=10,
)

result = self_moa.run("""
Analyze the impact of artificial intelligence on:
1. Employment and job markets
2. Education systems
3. Healthcare delivery
4. Economic growth

Provide a comprehensive analysis with multiple perspectives.
""")

print(result)

Creative Writing¶

self_moa = SelfMoASeq(
    temperature=0.8,  # Higher temperature for creativity
    num_samples=25,
    window_size=5,
)

result = self_moa.run("Write a short story about a future where AI and humans collaborate to solve climate change")

print(result)

Technical Documentation¶

self_moa = SelfMoASeq(
    temperature=0.3,  # Lower temperature for precision
    num_samples=20,
    window_size=6,
)

result = self_moa.run("""
Write comprehensive documentation for a REST API that includes:
- Authentication methods
- Endpoint descriptions
- Request/response examples
- Error handling
""")

print(result)

Accessing Metrics¶

result = self_moa.run("Your task here")

# Access execution metrics
metrics = result["metrics"]
print(f"Samples generated: {metrics['total_samples_generated']}")
print(f"Aggregations: {metrics['total_aggregations']}")
print(f"Execution time: {metrics['execution_time_seconds']:.2f}s")

# Access the final output
final_output = result["final_output"]
print(final_output)

Retry Configuration¶

SelfMoASeq includes automatic retry logic:

self_moa = SelfMoASeq(
    max_retries=5,  # Number of retry attempts
    retry_delay=2.0,  # Initial delay between retries
    retry_backoff_multiplier=2.0,  # Exponential backoff
    retry_max_delay=60.0,  # Maximum delay
)

Support and Community¶

If you're facing issues or want to learn more, check out the following resources:

Platform	Link	Description
📚 Documentation	docs.swarms.world	Official documentation and guides
💬 Discord	Join Discord	Live chat and community support
🐦 Twitter	@swarms_corp	Latest news and announcements