RAG with MongoDB

Implemented a Retrieval-Augmented Generation (RAG) pipeline by integrating MongoDB as the vector-backed document store for context retrieval. Leveraged embedding generation to index and query relevant data efficiently, enabling grounded, real-time responses from language models. This setup supports scalable, memory-efficient retrieval for dynamic knowledge bases.