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

pip install -r requirements.txt

Usage

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.