This project is a simple Retrieval-Augmented Generation (RAG) system that can load documents, index them, and answer user questions based on their content. It supports multiple document formats, generates embeddings, stores them in a vector database, and retrieves the most relevant context for each query. The assistant then uses an LLM to produce accurate, context-aware answers.
This is my first core project in the certification, where I applied key concepts from Module 1 including:
We will implement a complete RAG system that can:
data/ directoryThis project is subdivided in 7 main steps:
By completing this project, we are able now:
Our implementation is complete since: