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Ask natural language questions about your CSV data using AI

Project description

QueryMind

Ask natural language questions about your CSV data using AI. This package intelligently translates your questions into pandas code using Large Language Models (LLMs) and provides clear, concise answers.

Features

  • 🤖 Natural language to pandas code translation
  • 🔒 Safe code execution in a controlled environment
  • 📊 Optional result summarization in natural language
  • 🔌 Support for multiple LLM providers (OpenAI and Anthropic)
  • 🛠️ Clean, maintainable, and extensible design

Installation

pip install querymind

Quick Start

from querymind import AIQuery

# Initialize with your CSV file and preferred LLM provider
ai = AIQuery(
    csv_path="your_data.csv",
    llm_provider="openai",  # or "anthropic"
    api_key="your-api-key"
)

# Ask questions in natural language
result = ai.ask("What is the average age of customers who made purchases over $100?")
print(result)

# Get raw results without summarization
raw_result = ai.ask("Show me the top 5 products by sales", summarize=False)
print(raw_result)

Supported LLM Providers

OpenAI

  • Uses GPT-4 for code generation and summarization
  • Requires an OpenAI API key

Anthropic

  • Uses Claude 3 Opus for code generation and summarization
  • Requires an Anthropic API key

Example Questions

Here are some example questions you can ask:

  • "What is the average salary by department?"
  • "Show me the top 10 customers by total purchase amount"
  • "How many products are out of stock?"
  • "What is the correlation between age and purchase amount?"
  • "List all transactions from last month"

Security

The package executes generated code in a controlled environment with limited access to:

  • The pandas DataFrame (df)
  • The pandas library (pd)
  • No access to other Python built-ins or system resources

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

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