Overview
Ragify was developed to supplement my science research project. Many at the Academy of Science and Medicine (at my school) simply did not understand the concept of Retrieval Augmented Generation. However, with Ragify, I was able to create a simple and easy-to-use interface that allowed for the easy generation of text. This allows anyone to play with the amazing technology of RAG! This project really taught me a lot about neural networks, especially large language models.
Key Features
RAG Pipeline
Complete Retrieval Augmented Generation workflow with document indexing and retrieval.
LangChain Integration
Leverages LangChain for seamless LLM orchestration and prompt management.
User-Friendly Interface
Simple, intuitive UI makes complex AI technology accessible to everyone.
Custom Document Upload
Upload your own documents to create personalized knowledge bases.
Challenges & Learning
Building Ragify taught me invaluable lessons about: • Understanding how neural networks process and generate text • Implementing vector databases for efficient semantic search • Optimizing prompt engineering for better RAG outputs • Balancing model capability with response latency • Making complex AI concepts accessible to non-technical users This project deepened my understanding of modern NLP and gave me hands-on experience with cutting-edge AI technologies.