Retrieval-Augmented Generation (RAG) is a powerful AI design pattern that combines the strengths of large language models (LLMs) with external knowledge retrieval systems. Unlike traditional language models that rely solely on pre-trained data, RAG dynamically fetches relevant information from external sources at query time, then generates responses grounded in both retrieved data and learned knowledge. This makes RAG especially effective for up-to-date, accurate, and context-aware applications Core Components of RAG Indexing (Offline Step): Data from various sources (documents, APIs, databases) is first loaded and split into manageable chunks. These chunks are then converted into vector embeddings using an embedding model and stored in a vector database optimized for fast similarity search. Retrieval (Online Step): When a user query comes in, it is transformed into a vector. The system searches the vector database to find the most relevant pieces of informatio...