easy-calc-tool 0.1.4

A flexible calculation toolbox designed for LLM Function Calling

Easy Calc Tool

🚀 A flexible calculation toolbox designed specifically for LLM Function Calling

✨ Features

• 🎯 LLM-Optimized: Natural language parameter parsing, making it easier for LLMs to call
• 📊 All-in-One: Basic arithmetic, statistical analysis, time series, unit conversion, and more
• 🔌 LangChain Ready: Out-of-the-box LangChain tool integration
• 🛡️ Secure: Safe expression evaluation, preventing injection attacks
• 📦 Lightweight: Core dependencies only (pandas, numpy)
• 🚀 High Performance: Built-in caching, batch computation support
• 🌍 Multi-format Support: CSV, JSON, DataFrame, file paths

📦 Installation

# Basic installation
pip install easy-calc-tool

# With LangChain support
pip install easy-calc-tool[langchain]

# Full installation (includes development tools)
pip install easy-calc-tool[all]

🚀 Quick Start

Basic Usage

from easy_calc_tool import EasyCalc

calc = EasyCalc()

# 基础算术
result = calc.calculate("arithmetic", None, expression="100 * 2 + 5")
print(result["result"])  # 205

# 统计分析
data = [
    {"sales": 100, "profit": 20},
    {"sales": 200, "profit": 40},
    {"sales": 300, "profit": 60}
]
result = calc.calculate("statistics", data, columns="sales", operations=["sum", "mean"])
print(result["statistics"]["sales"]["sum"])  # 600

Natural Language Interface

# Natural language aggregation
result = calc.calculate("aggregate", data, 
                       what="group by profit and sum sales")
print(result["grouped_result"])

🛠️ LangChain Integration

from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from easy_calc_tool.langchain import get_tools

llm = ChatOpenAI(model="gpt-4")
tools = get_tools()  # 一键获取所有工具

agent = create_agent(model=llm, tools=tools)
response = agent.invoke({
    "messages": [{"role": "user", "content": "计算 12345 * 67890"}]
})

📚 Supported Scenarios

1. Basic Calculations

• Arithmetic operations (+, -, *, /, **, %)
• Trigonometric functions (sin, cos, tan)
• Logarithmic and exponential functions
• Percentage calculations
• Rounding operations

2. Statistical Analysis

• Descriptive statistics (mean, median, mode, std, var)

• Group statistics (GroupBy operations)

• Correlation analysis

• Regression analysis

• Hypothesis testing

3. Data Processing

• Aggregation (sum, mean, count, min, max)

• Data filtering

• Pivot tables

• Data transformation (normalization, standardization)

4. Time Series

• Moving averages

• Growth rates (MoM, YoY)

• Cumulative sums

• Lag/Difference operations

• Date arithmetic

5. Other Features

• Unit conversion (length, weight, temperature, currency)

• Date calculations (age, date differences)

• Financial calculations (loans, IRR, NPV)

• Matrix operations

🔧 Configuration

from easy_calc_tool import EasyCalc

# Custom configuration
calc = EasyCalc(config={
    "cache_enabled": True,      # Enable result caching
    "precision": 2,              # Floating point precision
    "timeout": 30,              # Computation timeout (seconds)
    "max_data_size": 1000000,    # Maximum data rows
    "safe_mode": True,          # Enable safe evaluation mode
})

📖 Documentation

Full documentation is available at https://easy-calc-tool.readthedocs.io

• Installation Guide

• Usage Examples

• API Reference

• Contributing Guide

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/easy-calc-tool/easy-calc-tool.git
cd easy-calc-tool

# Install development dependencies
make install-dev

# Run tests
make test

# Run linting
make lint

# Format code
make format

📊 Benchmarks

Operation	Data Size	Time
Basic arithmetic	N/A	< 0.1ms
Statistics (mean, sum)	1M rows	~50ms
Group by aggregation	1M rows, 10 groups	~100ms
Time series (moving average)	100K rows	~30ms

🗺️ Roadmap

• v0.2.0 - More statistical functions (ANOVA, chi-square)

• v0.3.0 - Machine learning predictions

• v0.4.0 - Visualization support

• v0.5.0 - GPU acceleration

• v1.0.0 - Stable release with full documentation

🙏 Acknowledgments

• Built with pandas and numpy

• Inspired by the needs of LLM function calling applications

• Thanks to all contributors and users

📄 License

MIT License - see LICENSE file for details.

📧 Contact

Issues: GitHub Issues

Email: [e-mail]

Discord: Join our Discord