Setting up Vector Search in Databricks (Step-by-Step Guide for Beginners)

Vector Search is one of the most powerful techniques behind modern AI applications such as semantic search, recommendation systems, and RAG pipelines. In this video, I show you how to build a complete Vector Search pipeline in Databricks step-by-step. We go from raw text data all the way to querying a vector index — all inside Databricks. This tutorial is designed for data engineers, ML engineers, and AI practitioners who want to understand how vector databases work inside the Databricks ecosystem. By the end of this video you will understand how to create embeddings, store them in Delta tables, create vector search endpoints, and query them for semantic search. ------------------------------------- What you will learn in this video: 1️⃣ Preparing text data for vector search 2️⃣ Choosing embedding strategy (Bring Your Own vs Databricks embeddings) 3️⃣ Ingesting data into Delta tables 4️⃣ Creating a Vector Search Endpoint 5️⃣ Building a Vector Search Index 6️⃣ Querying the index for semantic similarity search ------------------------------------- This tutorial covers: • Databricks Vector Search • Embedding models • Semantic search • Delta tables • AI powered search systems ------------------------------------- If you enjoy practical AI engineering tutorials, consider subscribing to the channel. I regularly post videos on: • AI Engineering • ML Systems • Databricks • Vector Databases • MLOps ------------------------------------- Channel: datageekrj #AIEngineering #Databricks #VectorSearch