🚀 CSV Worker

Supercharge your CSV files with AI-powered data processing

CSV Worker is a powerful command-line tool that leverages OpenRouter's AI models to process and enrich your CSV data. Whether you need to add new columns with AI-generated content or transform existing data, CSV Worker makes it simple and efficient.

✨ Features

• 🤖 AI-Powered Processing: Uses OpenRouter API with Google Gemini 2.5 Flash Lite for fast, intelligent data processing
• 📊 Flexible Operations: Add new columns or edit existing ones with custom prompts
• 🔄 Batch Processing: Group rows by columns for efficient batch operations
• ⚡ Real-time Updates: See your CSV file update as each row is processed
• 🛡️ Safe Operations: Optional output file preserves your original data

🛠️ Installation

From Source

git clone https://github.com/yourusername/csv-worker.git
cd csv-worker
pip install -e .

Dependencies

• Python 3.7+
• requests
• python-dotenv

🚀 Quick Start

1. Set up your OpenRouter API key

   export OPENROUTER_API_KEY="your-api-key-here"
   # or create a .env file with: OPENROUTER_API_KEY=your-api-key-here

2. Process your first CSV

   csv-worker data.csv --add-column "sentiment" --prompt "Analyze the sentiment of this text"

📖 Usage

Basic Syntax

csv-worker <csv_file> [OPTIONS] --prompt "<your prompt>"

Options

Option	Description
--add-column COLUMN	Add a new column with the specified name
--edit-column COLUMN	Edit an existing column
--prompt PROMPT	Your prompt for the AI (required)
--system-prompt PROMPT	Custom system prompt (default: helpful assistant)
--group-by COLUMNS	Comma-separated columns to group by for batch processing
--output, -o FILE	Output file path (preserves original if specified)

Examples

Add a sentiment analysis column

csv-worker reviews.csv --add-column "sentiment" --prompt "Classify the sentiment as positive, negative, or neutral"

Extract entities from text

csv-worker articles.csv --add-column "entities" --prompt "Extract named entities from this text"

Batch process by category

csv-worker products.csv --add-column "description" --group-by "category" --prompt "Generate a compelling product description"

Edit existing column

csv-worker emails.csv --edit-column "subject" --prompt "Make this subject line more engaging"

Preserve original file

csv-worker data.csv --add-column "processed" --prompt "Process this data" --output "processed_data.csv"

🎯 Use Cases

• Data Enrichment: Add AI-generated insights to existing datasets
• Text Analysis: Perform sentiment analysis, entity extraction, or classification
• Content Generation: Create descriptions, summaries, or transformations
• Data Cleaning: Standardize and normalize text data
• Batch Processing: Efficiently process large datasets by grouping

🔧 Configuration

Create a .env file in your project directory:

OPENROUTER_API_KEY=your_api_key_here

📝 Tips

• Use specific, clear prompts for better results
• Group similar rows together with --group-by for more consistent batch processing
• Always test with a small sample first using the --output option
• The tool processes rows sequentially and saves after each operation for data safety

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Made with ❤️ for data enthusiasts