aifiles 0.1.0

A Python library for downloading sample files in various formats for AI experimentation

Aifiles

A Python library for instantly downloading up to 10 real, meaningful sample files in any format — perfect for generative AI, agentic AI, RAG pipelines, multimodal model testing, document parsing, prompt engineering, and general AI/ML experimentation.

✨ Features

• 40+ file formats supported across documents, data, images, audio, video, code, and more
• Smart sourcing: Downloads real files from public repositories, falls back to synthetic generation
• AI-optimized: Formats supported by LangChain, LlamaIndex, OpenAI Vision, and other AI tools
• Developer-friendly: Simple API, rich CLI, comprehensive error handling
• Secure: MIME validation, path sanitization, no hardcoded credentials

🚀 Installation

pip install aifiles

# For audio generation (optional)
pip install aifiles[audio]

# For all optional dependencies
pip install aifiles[all]

📖 Quick Start

Python API

from aifiles import get_files, list_formats, info, preview

# Get 5 PDF samples for RAG pipeline testing
files = get_files("pdf", count=5, output_dir="./rag_data")
print(files)
# ['/rag_data/sample_1.pdf', '/rag_data/sample_2.pdf', ...]

# Get 3 CSV files with sales data variant
files = get_files("csv", count=3, variant="sales")
# ['/samples/sales_1.csv', '/samples/sales_2.csv', ...]

# Get 10 WAV files for speech model testing
files = get_files("wav", count=10, output_dir="./audio_test")

# List all supported formats
formats = list_formats()
print(formats["documents"])  # ['pdf', 'docx', 'txt', 'md', ...]

# Get format information
meta = info("json")
print(meta)
# {
#   "mime_type": "application/json",
#   "category": "structured",
#   "use_cases": ["agent logs", "API responses", "chat history"],
#   "supported_by": ["OpenAI", "LangChain", "LlamaIndex", ...]
# }

# Preview a downloaded file
preview("./samples/sample1.csv")

Command Line

# Get 3 PDF sample files
aifiles get pdf --count 3

# Get 5 CSV files with sales variant
aifiles get csv --count 5 --variant "sales data" --output ./my_samples

# List all supported formats
aifiles list-formats

# Show format info
aifiles info json

# Preview a file
aifiles preview ./samples/sample1.csv

📋 Supported Formats

📄 Documents

• PDF - Multi-page documents, invoices, research papers
• DOCX - Word documents, resumes, letters
• TXT - Plain text, prompts, logs, poetry
• MD - Markdown docs, README files, notes
• RTF - Rich text with formatting
• ODT - OpenDocument text files

📊 Structured / Data

• CSV - Tabular datasets, sales data, sensor readings
• TSV - Tab-separated data
• JSON - API responses, agent logs, chat histories
• YAML - Configurations, workflows, agent definitions
• XML - Structured markup, RSS feeds, SOAP data
• XLSX - Excel spreadsheets with charts
• PARQUET - Columnar data for ML pipelines
• SQLITE - Embedded database for agent memory

🖼️ Images

• PNG - Charts, diagrams, screenshots
• JPG - Photographs, real-world scenes
• WEBP - Modern compressed images
• TIFF - High-quality scanning/OCR
• GIF - Animated images for UI testing
• SVG - Vector graphics, logos, icons

🎵 Audio

• WAV - Raw speech audio for STT models
• MP3 - Compressed speech or music
• FLAC - Lossless audio for high-fidelity testing
• OGG - Open-source compressed audio

🎥 Video

• MP4 - General-purpose video for multimodal models
• MOV - Apple QuickTime video
• AVI - Legacy video format
• MKV - High-quality video container
• WEBM - Web-friendly open video format

💻 Code & Notebooks

• PY - Python scripts
• JS - JavaScript/Node.js scripts
• TS - TypeScript files
• IPYNB - Jupyter Notebooks with AI/ML examples
• HTML - Web pages for scraping/parsing
• CSS - Stylesheets
• SQL - Database queries, DDL scripts

📧 Email & Communication

• EML - Email messages with attachments
• MSG - Outlook email format
• ICS - Calendar events (iCalendar)
• VCF - Contact cards

🗜️ Archives

• ZIP - Compressed archive with mixed files
• TAR - Unix archive
• GZ - Gzipped content

🔬 Scientific / ML

• HDF5 - Hierarchical scientific data
• ARROW - Apache Arrow columnar data
• FEATHER - Fast columnar data storage
• NPY - NumPy array binary format
• PKL - Python pickle (serialized objects)

🏗️ 3D / Spatial

• OBJ - 3D object mesh
• STL - 3D printing / mesh format
• GLTF - 3D scene for multimodal/spatial AI

🔐 Config / Infra

• ENV - Environment config files
• TOML - Project config (like pyproject.toml)
• INI - Legacy configuration files
• DOCKERFILE - Docker build files

🛠️ API Reference

get_files(format, count=1, output_dir="./samples", variant=None)

Download sample files in the specified format.

Parameters:

• format (str): File format/extension (e.g., "pdf", "csv", "png")
• count (int): Number of files to fetch (1-10)
• output_dir (str): Directory to save files
• variant (str, optional): Content variant hint

Returns: List of absolute file paths

Raises:

• InvalidCountError: Count not between 1-10
• FormatNotSupportedError: Format not supported
• FormatNotAvailableError: Cannot fetch or generate files

list_formats()

Returns: Dictionary of categorized formats

info(format)

Returns: Dictionary with format metadata or None

preview(filepath)

Prints file preview or metadata to console.

🤝 Contributing

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

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

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

🙏 Acknowledgments

• Public sample file repositories for real file sources
• Open source libraries: requests, faker, Pillow, fpdf2, etc.
• AI community for inspiration and use cases

👤 Author

• Prajyot Birajdar
• Email: [e-mail]
• GitHub: https://github.com/itsbilyatt
• LinkedIn: https://www.linkedin.com/in/prajyot-birajdar-1b09a1173
• Portfolio: https://prajyotb.netlify.app/

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