isage-agentic-tooluse 0.1.0.2

Tool retrieval and ranking algorithms for LLM agents

SAGE Tool Use

Tool retrieval and ranking algorithms for LLM agents

🎯 Overview

sage-agentic-tooluse provides a comprehensive suite of tool selection and ranking algorithms for LLM agents:

• Keyword Selector: Fast matching based on keyword overlap
• Embedding Selector: Semantic similarity using embeddings
• Hybrid Selector: Combines keyword and embedding approaches
• DFS-DT Selector: Decision tree-based tool selection
• Gorilla Selector: Gorilla-style tool retrieval

📦 Installation

# Basic installation
pip install isage-agentic-tooluse

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

🚀 Quick Start

Keyword-based Tool Selection

from sage_libs.sage_agentic_tooluse import KeywordSelector, ToolSelectionQuery

# Create selector
selector = KeywordSelector.from_config(config=keyword_config, resources=resources)

# Select tools for a query
selected = selector.select(
    query=ToolSelectionQuery(
        sample_id="q1",
        instruction="Get current weather in New York",
        candidate_tools=["weather_api", "search_api"],
    ),
    top_k=5
)

for tool in selected:
    print(f"Tool: {tool.tool_id}, Score: {tool.score}")

Embedding-based Tool Selection

from sage_libs.sage_agentic_tooluse import EmbeddingSelector, ToolSelectionQuery

# Create selector with embedding model
selector = EmbeddingSelector.from_config(config=embedding_config, resources=resources)

# Select tools based on semantic similarity
selected = selector.select(
    query=ToolSelectionQuery(
        sample_id="q2",
        instruction="What's the weather like?",
        candidate_tools=["weather_api", "search_api"],
    ),
    top_k=5
)

Hybrid Tool Selection

from sage_libs.sage_agentic_tooluse import HybridSelector, ToolSelectionQuery

# Combine keyword and embedding approaches
selector = HybridSelector.from_config(config=hybrid_config, resources=resources)

selected = selector.select(
    query=ToolSelectionQuery(
        sample_id="q3",
        instruction="Find tools for weather updates",
        candidate_tools=["weather_api", "search_api", "calendar_api"],
    ),
    top_k=5,
)

📚 Key Components

Selectors

• KeywordSelector: Fast keyword-based matching
• EmbeddingSelector: Semantic similarity using embeddings
• HybridSelector: Weighted combination of multiple selectors
• DFSDTSelector: Decision tree-based selection
• GorillaSelector: Gorilla-style API-centric retrieval

Base Classes

• BaseToolSelector: Abstract base for all selectors
• SelectorRegistry: Central registry for selector implementations

Schemas

• ToolSelectionQuery: Query payload for selector input
• ToolPrediction: Selection result with score and metadata
• SelectorConfig: Base selector configuration schema

🏗️ Architecture

sage_libs.sage_agentic_tooluse/
├── __init__.py              # Public API exports
├── base.py                  # Base selector interface
├── keyword_selector.py      # Keyword-based selection
├── embedding_selector.py    # Embedding-based selection
├── hybrid_selector.py       # Hybrid selection strategy
├── dfsdt_selector.py        # Decision tree selector
├── gorilla_selector.py      # Gorilla-style retrieval
├── registry.py              # Selector registry
├── schemas.py               # Data schemas
└── retriever/               # Retrieval utilities

🎓 Use Cases

1. Agent Tool Selection: Help agents choose the right tools
2. API Discovery: Find relevant APIs for a task
3. Function Calling: Select appropriate functions for LLMs
4. Tool Recommendation: Recommend tools to users
5. Multi-step Planning: Select tool sequences for complex tasks

🔗 Integration with SAGE

This package is part of the SAGE ecosystem and can be used with SAGE agents:

# Standalone usage
from sage_libs.sage_agentic_tooluse import HybridSelector

from sage_libs.sage_agentic_tooluse import create_selector

selector = create_selector({"name": "hybrid", "top_k": 5}, resources)

📖 Documentation

• Repository: https://github.com/intellistream/sage-agentic-tooluse
• SAGE Documentation: https://intellistream.github.io/SAGE-Pub/
• Issues: https://github.com/intellistream/sage-agentic-tooluse/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-agentic package, now maintained as an independent repository for focused development and research.

📧 Contact

• Team: IntelliStream Team
• Email: [e-mail]
• GitHub: https://github.com/intellistream

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Part of the SAGE ecosystem - Stream Analytics for Generative AI Engines