CSVAlchemy-sdk 0.1.2

A lightweight Python client SDK for the CSValchemy API

CSVAlchemy

A Python SDK for processing and analyzing spreadsheets using LLMs.

Installation

pip install CSVAlchemy-sdk

Overview

CSVAlchemy provides Excel processing capabilities. The library helps you efficiently process, analyze, and extract insights from spreadsheet data.

Features

• Spreadsheet Processing - Handle Excel and CSV files with advanced features
• Data Extraction - Extract structured data from complex spreadsheets
• Formula Extraction - Extract all the formula's from you spreadsheets
• Pivot Table Support - Process and analyze pivot tables
• Chart Analysis - Extract and encode chart data
• Conditional Formatting - Capture conditional formatting rules
• API Client - Easy-to-use client for the CSVAlchemy API

CSVAlchemy Feature Comparison

Feature	Support
Excel (.xlsx) Processing	✓
Formula Extraction	✓
Merged Cell Detection	✓
Header Detection	✓ (configurable)
Structural Anchor Extraction	✓
Python SDK	✓
Customizable Configuration	✓
Error Handling Modes	✓ (configurable)
Parallel Processing	✓ (configurable)
Memory Optimization	✓ (chunking configurable)
Comprehensive Logging	✓
Open Source	✓ (MIT License)

Usage

from CSValchemy_sdk.core import encode_workbook
from CSValchemy_sdk.utils import load_workbook_from_file
from CSValchemy_sdk.config import get_config

# Process spreadsheets
workbook = load_workbook_from_file("Company_Financial_Data_For_PE.xlsx")
config = get_config()
encoded_data = encode_workbook(workbook, config)

# Now use the encoded data in your application
print("encoded_sheets ",encoded_data)
print(f"Processed {len(encoded_data['sheets'])} sheets")

API Documentation

Full API documentation is available at:

• Interactive API docs: /docs
• ReDoc API docs: /redoc

Contributing to CSVAlchemy

We welcome contributions from the community! This guide will help you set up your local development environment and understand our contribution process.

Local Development Setup

1. Fork and Clone the Repository

   git clone https://github.com/Unsiloed-ai/spreadsheet-llm.git
   cd spreadsheet-llm
   

2. Set Up a Virtual Environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install Development Dependencies

   pip install -e ".[dev]"
   

4. Run Tests to Verify Setup

   pytest
   

Development Workflow

1. Create a Feature Branch

   git checkout -b feature/your-feature-name
   

2. Make Your Changes

   • Write code following our style guidelines
   • Add tests for new functionality
   • Update documentation as needed

3. Run Tests and Linting

   # Run tests
   pytest
   
   # Run linting
   flake8
   
   # Run formatter
   black .
   

4. Commit Your Changes

   git add .
   git commit -m "Add a descriptive commit message"
   

5. Push to Your Fork

   git push origin feature/your-feature-name
   

6. Create a Pull Request

   Go to the original repository and create a pull request from your fork.

Code Style Guidelines

• Follow PEP 8 standards
• Use Black for code formatting
• Write docstrings for all functions, classes, and methods
• Include type hints according to PEP 484

Pull Request Process

1. Ensure all tests pass and code is properly formatted
2. Update documentation if necessary
3. Link any related issues in your PR description
4. Wait for review from maintainers
5. Address any requested changes
6. Once approved, your PR will be merged

Running the API Locally

To run the API server locally during development:

python run.py

The API will be available at http://localhost:8000 with interactive documentation at http://localhost:8000/docs.

Building the Package Locally

To build the package locally:

python -m build

Contact

If you have questions or need help, please:

• Open an issue on GitHub
• Contact the maintainers at [e-mail]

Thank you for contributing to CSVAlchemy!

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Developed by Unsiloed AI