Having established the foundational concepts of vector embeddings, database architectures, and Approximate Nearest Neighbor search algorithms, we now shift focus to practical application. This chapter bridges theory and implementation, demonstrating how to utilize specific vector database systems to build functional semantic search solutions.

You will work directly with client libraries for popular vector databases, including Pinecone, Weaviate, Milvus, and ChromaDB. We will cover the common workflows: connecting to the database, defining schemas or collections, indexing vector data alongside metadata, and executing similarity searches, often combined with metadata filters.

Furthermore, we address practical considerations such as selecting an appropriate database platform (managed vs. self-hosted), strategies for efficiently indexing large volumes of data, and the basics of monitoring system health and performance. The chapter culminates in a hands-on exercise where you integrate these components to construct a small, yet complete, semantic search application.