RAGApplication

RAGApplication is a project designed to demonstrate Retrieval-Augmented Generation (RAG) techniques, integrating language models with external knowledge sources to provide more accurate, context-aware responses. This repository provides the core logic and examples for building RAG-based applications, suitable for experimentation, prototyping, or production use.

Features

• Retrieval-Augmented Generation: Integrates retrieval from external knowledge bases with generative AI models.
• Modular Architecture: Easily extend or adapt for custom data sources, vector stores, or model backends.
• Example Workflows: Includes scripts and notebooks for running typical RAG pipelines.
• Language Model Integration: Plug in your favorite LLM (OpenAI, HuggingFace, etc.) with minimal configuration.

Getting Started

Prerequisites

• Python 3.8+
• (Optional) Docker for containerized deployment
• Recommended: Access credentials for your target LLM provider (OpenAI, HuggingFace, etc.)

Installation

1. Clone the repository:

   git clone https://github.com/Programmer-DN-AI/RAGApplication.git
   cd RAGApplication