Vector Databases Mastery Path¶

Mastery Path

BOOTCAMP

VINOD BHANUJA

A structured, module-wise Vector Databases training program that takes you from your first embedding to production-grade, master-level retrieval systems — with runnable code in every module and a hands-on project at the end of each level.

How the program is organized¶

Level	Focus	Modules
Level 1 · Entry	Embeddings & similarity search foundations	9 topics + 1 project
Level 2 · Intermediate	Production vector databases (Pinecone, Weaviate, Qdrant, pgvector)	9 topics + 1 project
Level 3 · Advanced	Retrieval quality, scaling & performance	9 topics + 1 project
Level 4 · Master	Production RAG systems & architecture	9 topics + 1 capstone

How to use this site¶

Work through each level in order — later modules assume earlier ones.
Every topic page has runnable code snippets — copy them into a local .py file or a REPL and experiment.
Each level ends with a project that combines everything learned in that level.
Use the search bar (top of the page) to jump straight to a topic.

Start here → Level 1 · Entry

🎥 Prefer video? Watch the Mastery Path video series on YouTube — Shorts and full walkthroughs of these lessons.

Why vector databases, and why now¶

Every retrieval-augmented generation (RAG) system, semantic search feature, and recommendation engine shipping today leans on the same underlying idea: turn content into vectors, then find the nearest ones fast. That's a different problem from anything a traditional database was built to solve efficiently at scale — which is why a whole category of purpose-built vector databases (Pinecone, Weaviate, Qdrant) and vector-native extensions (pgvector) has emerged. This course treats that as its own discipline: how embeddings actually work, how approximate nearest-neighbor search makes billion-vector queries return in milliseconds, and how to run all of it reliably in production.