In the previous article on (RAG with Elasticsearch), you detailed the full pipeline—ingestion, BM25/hybrid retrieval, and LLM integration—showing how Elasticsearch excels as a dedicated search engine for RAG apps. This follow-up assumes that foundation, shifting focus to MongoDB’s streamlined alternative via Atlas Vector Search, with a side-by-side comparison and thoughts on RAG’s evolution.​ RAG in MongoDB: Quick Overview MongoDB Atlas integrates RAG seamlessly through its Vector Search feature, storing documents with embeddings, metadata, and application data in a single collection for hybrid semantic and keyword queries. This simplifies ingestion (chunking data, generating embeddings) and retrieval, feeding relevant context directly to LLMs without separate storage layers.​