CSVQuery-RAG

A Retrieval Augmented Generation (RAG) system that allows you to query CSV data using natural language. Built with LangChain and Gradio.

Project Structure

.
├── backend/
│   ├── db/
│   │   └── MOCK_DATA.csv
│   └── rag_system.py
├── frontend/
│   └── app.py
├── requirements.txt
├── .env.example
└── run.py

Setup

1. Create a virtual environment (recommended):

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

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env

Edit .env and add your OpenAI API key.

Running the Application

Simply run:

python run.py

The application will be available at http://localhost:7860

Features

• Load and process CSV data for question answering
• Natural language querying of CSV data
• Conversation memory to maintain context
• Simple and intuitive Gradio interface
• Smart query validation to ensure responses stay within data context
• Comprehensive system prompting for accurate and relevant answers

Sample Data

The system comes with mock car sales data covering the period from January 2020 to December 2020. This dataset includes information about various car brands, their sales figures, and related metrics throughout the year.

Sample Queries

Here are some example queries you can try:

• "How many Toyota cars were sold in May 2020?"
• "How many units of Honda were sold in June and July 2020?"
• "Which car model had the lowest sales in 2020?"
• "How much revenue did BMW generate in Q3 2020?"
• "Did Ford or BMW sell more cars in December 2020?"
• "Which car model had the highest sales from January to May 2020?"

Query Guidelines

The system is designed to:

• Answer questions specifically about the data in the CSV file
• Provide statistical insights when appropriate
• Maintain data privacy and security
• Reject questions that are outside the scope of the data

Example valid queries:

• "What are the most common values in column X?"
• "Show me the distribution of values in the data"
• "Find records where column Y matches specific criteria"

The system will politely decline to answer questions about:

• Current events or real-time information
• Personal opinions or advice
• Topics not present in the CSV data
• Code generation or programming tasks

System Requirements

• Python 3.12 or higher
• OpenAI API key
• CSV data file (included in backend/db/MOCK_DATA.csv)