SAGE Tool Use SIAS (Sample-Importance-Aware Selection)

Tool selection algorithm using sample-importance-aware selection for agent training and tool curation

🎯 Overview

sage-agentic-tooluse-sias provides Sample-Importance-Aware Selection algorithms specifically designed for:

• Tool Selection: Select important tools for agent use
• Agent Training: Select important trajectories for fine-tuning
• Continual Learning: Efficient sample selection for continual/lifelong learning
• Tool/Trajectory Curation: Curate representative samples for agent development

📦 Installation

# Basic installation
pip install isage-agentic-tooluse-sias

# Development installation
pip install isage-agentic-tooluse-sias[dev]

🚀 Quick Start

Continual Learning

from sage_sias import OnlineContinualLearner, SIASSample

# Create continual learner
learner = OnlineContinualLearner(
    buffer_size=1000,
    replay_ratio=0.3,
)

# Add samples
new_samples = [
    SIASSample(sample_id="s1", text="tool call trace A"),
    SIASSample(sample_id="s2", text="tool call trace B"),
]
training_batch = learner.update_buffer(new_samples)

# Inspect replay buffer
important_samples = learner.buffer_snapshot()

Coreset Selection

from sage_sias import CoresetSelector

# Create coreset selector
selector = CoresetSelector(
    strategy="hybrid",
)

# Select representative samples
coreset = selector.select(samples=dataset, target_size=100)

📚 Key Components

1. Continual Learner (continual_learner.py)

Manages sample selection for continual learning:

• Buffer management with importance-based eviction
• Multiple selection strategies (loss_topk, diversity, hybrid, random)
• Support for experience replay

2. Coreset Selector (coreset_selector.py)

Selects representative subsets:

• Loss/diversity/hybrid/random selection
• Diversity-aware sampling
• Importance scoring
• Support for large-scale datasets

3. Core Types (core_types.py)

Common data types and protocols:

• Sample representation
• Sample protocol
• Selection summary

🔧 Architecture

sage_sias/
├── continual_learner.py    # Continual learning with buffer management
├── coreset_selector.py      # Coreset selection algorithms
├── core_types.py            # Common types and protocols
└── __init__.py              # Public API exports

🎓 Use Cases

1. Agent Training: Select important trajectories for fine-tuning
2. Data Pruning: Reduce dataset size while maintaining performance
3. Active Learning: Query most informative samples
4. Memory Management: Maintain representative samples in limited buffers
5. Transfer Learning: Select relevant samples for adaptation

🔗 Integration with SAGE

This package is part of the SAGE ecosystem but can be used independently:

# Standalone usage
from sage_sias import OnlineContinualLearner, CoresetSelector, SIASSample

learner = OnlineContinualLearner(buffer_size=2048, replay_ratio=0.25)
selector = CoresetSelector(strategy="hybrid")

samples = [
    SIASSample(sample_id="a", text="query weather"),
    SIASSample(sample_id="b", text="query calendar"),
]
selected = selector.select(samples=samples, target_size=1)
batch = learner.update_buffer(selected)

📖 Documentation

• Repository: https://github.com/intellistream/sage-agentic-tooluse-sias
• SAGE Documentation: https://intellistream.github.io/SAGE-Pub/
• Issues: https://github.com/intellistream/sage-agentic-tooluse-sias/issues

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Originally part of the SAGE framework, now maintained as an independent package for broader community use.

📧 Contact

• Team: IntelliStream Team
• Email: shuhao_zhang@hust.edu.cn
• GitHub: https://github.com/intellistream