User-Centric RAG Using LlamaIndex Multi-Agent System and Qdrant

Overview

This project provides a customizable retrieval-augmented generation (RAG) application that leverages LlamaIndex's multi-agent system and Qdrant vector database. It is designed for developers looking to enhance user experience by tailoring LLMs, embeddings, and search functionalities based on user preferences.