RAG Application

Overview

This application uses a Retrieval-Augmented Generation (RAG) approach to provide insights from World Bank data and GEM reports. It leverages Hugging Face's embeddings and language models, and integrates with Streamlit for user interaction.

Features

• Fetches World Bank data for Brazil and global statistics on poverty, internet usage, and unemployment.
• Retrieves and processes GEM reports in PDF format.
• Uses Hugging Face's embeddings and language models for text-based queries.
• Provides a web interface for querying and interacting with the processed data.

Requirements

• Python 3.7 or higher
• requests - For fetching data from APIs
• pypdf - For extracting text from PDF reports
• pandas - For data handling (optional, if needed)
• streamlit - For the web interface
• langchain - For handling text and embeddings
• huggingface_hub - For interacting with Hugging Face models

Usage

1. Clone this repository:

   git clone https://github.com/yourusername/RAG.git
   cd RAG
   

2. Install the required packages

    pip install -r requirements.txt
   

3. Set up your Hugging Face API token:

Replace HUGGINGFACEHUB_API_TOKEN in app.py with your actual API token.

4. Fetch World Bank data and GEM report:

   python datafetcher.py
   

5. Run the Streamlit app.

   streamlit run app.py