Add README

README.md

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+# 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`.