Similarity Search Patterns

If you're building semantic search or RAG systems, this skill gives you production-ready patterns for vector similarity search. It covers the practical stuff: optimizing nearest neighbor queries, scaling to millions of vectors, and combining semantic with keyword search when you need both. The implementation playbook has detailed examples for common scenarios like recommendation engines and retrieval pipelines. It's focused on performance and scale, so it's most useful when latency and throughput actually matter to your use case, not when you're just prototyping with a few hundred embeddings.