Mastering Vector Databases: A Complete Guide to Building High-Performance RAG Applications with Pinecone and Milvus
Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto pattern for building knowledge‑aware language‑model applications. At its core, RAG couples a large language model (LLM) with a vector store that holds dense embeddings of documents, passages, or other pieces of knowledge. When a user asks a question, the system first retrieves the most relevant vectors, converts them back into text, and then generates an answer that is grounded in the retrieved material. ...