Scylladb Vector Search

| Guides ScyllaDB Cloud users through implementing and optimizing Vector Search for semantic similarity, RAG, and similar use cases. Use this skill when users need to store and query embeddings, build vector indexes (HNSW), run approximate nearest neighbour (ANN) queries, apply filtering (global/local secondary indexes), configure quantization, or integrate with LLM frameworks (e.g. LangChain). Also use when users mention "vector", "embeddings", "similarity search", "ANN", "nearest neighbor", "RAG", "semantic search", or "recommendation system" in the context of ScyllaDB.