# Simple RAG Agent

This repository contains a simple Retrieval-Augmented Generation (RAG) agent implemented with **LangChain** and **OpenAI** embeddings. It demonstrates how to build a question‑answering system that retrieves relevant documents from a local vector store and generates answers using OpenAI’s GPT model.

## Features

- **Document ingestion**: Load text files and create embeddings.
- **Vector store**: Uses FAISS as the local vector store.
- **Retriever**: Retrieves top‑k relevant documents for a user query.
- **LLM wrapper**: Uses OpenAI GPT‑3.5‑Turbo or GPT‑4.
- **Prompt template**: Combines retrieved context with the user question.
- **Simple CLI**: Interact with the agent from the command line.

## Installation

```bash
pip install -r requirements.txt
```

## Usage

```bash
python rag_agent.py --data_dir path/to/documents --query "What is the capital of France?"
```

The agent will print the generated answer.

## Dependencies

All required packages are listed in `requirements.txt`.