AWS Vector Databases – Part 2: Search, Filtering, and Chunking

This is Part 2 of the AWS vector database series. 👉 Missed Part 1? Start here: Embeddings, Dimensions, and Similarity Search In Part 1, we covered the fundamentals of embeddings and how similarity is measured. Now we move into how retrieval actually works in practice. In this part, we’ll look at search patterns (KNN vs ANN), hybrid search, metadata filtering, and chunking strategies — the building blocks of effective RAG systems. Vector Search Types The following section explains the vector search types Approach How it works When to use AWS services KNN — Exact Nearest Neighbor Search Check every single item, compare it to your query, return the best matches. Perfectly accurate, but slow. Small datasets (under 100K vectors) or situations where you absolutely cannot afford to miss a result — like medical diagnostics or legal compliance checks. All vector services support KNN as a fallback, but it's not practical at scale. ANN — Approximate Nearest Neighbor Search Uses a smart index str