Advanced Vector Search | From Zero to AI Engineer | Similarity Search and ChromaDB | Session 11

This is Session 11 of the AI Engineering Course - a hands-on video course for software engineers getting into AI engineering. Session 10 gave us RAG foundations - vectors, embeddings, chunking, and the recall/precision tradeoff. This session is where we take embeddings off the whiteboard and into a real database. The first question an engineer asks is: once I have a million vectors, how do I find the right ones fast? This session answers that properly. The comparison between traditional and vector databases makes the architectural reasoning concrete for the need of specific DBs to store Vectors. In the last part, there is a builds hands-on with ChromaDB - an open-source embedded vector database with its core written in Rust. We cover its full architecture: collections, embedding storage, querying, and CRUD operations. We go into metadata filtering in working code - the mechanism that narrows your candidate pool before vector search even runs - covering both where and where_document filter types. #retrievalaugmentedgeneration #vectordb #aiagents #python #AI #aiengineering #aiagents ======================================================= Github repo :https://github.com/cloudlyai/Session-... ====================================================== 🔴 Subscribe to get more content like this :    / @cloudlyai