Vector Search Skill

Implement semantic search and similarity-based ranking using vector embeddings.

What This Skill Provides

• Similarity Metrics: Cosine similarity, Euclidean distance, dot product
• Range Understanding: Mathematical domains for each metric
• Threshold Selection: Setting appropriate minScore/maxDistance values
• Implementation Patterns: Database queries, filtering, ranking

When to Use

• Implementing semantic search (text, images, audio)
• Ranking results by similarity to query
• Recommendation systems (find similar items)
• Clustering or classification based on embeddings
• When user mentions: "vector search", "embeddings", "similarity", "semantic search"