Vector Search and Embeddings with LanceDB

In the age of generative AI and unstructured data, traditional keyword search is no longer enough. Vector search allows you to find information based on meaning and semantic similarity. This text-based course guides you from the fundamental concepts of embeddings to implementing high-performance search pipelines. You will gain a solid understanding of how to manage vector data, run semantic queries, and solve real-world record-matching challenges using LanceDB. What you will learn: 1. Understand the core terminology of vector spaces and similarity metrics. 2. Configure LanceDB to store, index, and manage vector data. 3. Query vector databases using semantic and hybrid search techniques. 4. Apply vector embeddings to solve entity resolution and record-matching problems. 5. Practice optimizing search performance with modern indexing strategies. You will start with the essential theory of vector embeddings before moving on to hands-on configuration, querying, and practical entity resolution workflows. This course is designed for beginner developers, data analysts, and software engineers who want to learn vector databases without needing prior experience in machine learning. Start reading today to master the fundamentals of vector search with LanceDB.