Agent-RAG-System is a Retrieval Augmented Generation (RAG) based application that combines document retrieval with Large Language Models (LLMs) to generate accurate and context-aware responses.
This project focuses on a clean, modular architecture and demonstrates how agent-based workflows can be used to manage retrieval and generation effectively.
- Retrieval Augmented Generation (RAG) pipeline
- Vector-based document search
- Agent-driven response generation
- Context-aware question answering
- Streamlit-based user interface
- Secure API key management using environment variables
- Python
- LangChain
- OpenAI / LLM APIs
- Vector Databases
- Streamlit
git clone https://github.com/Rushi1696/Agent-RAG-System.git
cd Agent-RAG-System