RAG-App 🤖

A Retrieval-Augmented Generation (RAG) application that allows you to query your own documents using Large Language Models (LLMs).

🚀 Overview

This project implements a classic RAG pipeline:

1. Load: Import documents (PDFs/Text).
2. Chunk: Split text into manageable pieces.
3. Embed: Convert text into mathematical vectors.
4. Store: Save vectors in a local vector database.
5. Retrieve & Generate: Find the best context to answer user questions.

🛠️ Tech Stack

• Language: Python
• LLM: [e.g., OpenAI GPT or local Ollama]
• Orchestration: [e.g., LangChain or LlamaIndex]
• Vector Store: [e.g., FAISS or ChromaDB]

📋 Setup Instructions

1. Clone the Repo

git clone [https://github.com/YOUR_GITHUB_USERNAME/rag-app.git](https://github.com/YOUR_GITHUB_USERNAME/rag-app.git)
cd rag-app
### 2. Install Dependencies
pip install -r requirements.txt
3. Environment Configuration
Create a .env file in the root directory and add your API keys:
API_KEY=your_secret_key_here
🖥️ Usage
Place your source files in the data/ directory and run:
python main.py