Having explored vector embeddings, database structures, and the mechanics of Approximate Nearest Neighbor search, we now focus on assembling these elements into functional semantic search systems. This chapter transitions from understanding the components to constructing the end-to-end pipelines that power intelligent search applications.

You will learn how to:

• Design the architecture of a semantic search pipeline, from data ingestion to result presentation.
• Apply strategies for preparing and chunking data effectively for vectorization.
• Process user queries, generate appropriate embeddings, and manage search execution.
• Implement methods for ranking and re-ranking search results to improve relevance.
• Combine semantic search with traditional keyword techniques (hybrid search).
• Evaluate the performance and quality of your semantic search system using established metrics.

We will examine the practical steps involved in building systems that retrieve information based on meaning, contrasting this with conventional keyword matching. The hands-on sections will guide you through designing the core logic for processing search requests and returning relevant results from a vector index.