The previous chapter built RAG from scratch with numpy arrays. In practice, you use a vector database to store and search embeddings efficiently. This chapter explores ChromaDB — a popular open-source vector database — applied to real-world datasets.
You’ll see how the same retrieve-augment-generate pattern scales from a few documents to hundreds, and how ChromaDB handles the embedding storage, indexing, and similarity search automatically.