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similarity-search-patterns

Implement efficient similarity search with vector databases

similarity-search-patterns is an agent skill from the wshobson/agents collection for implementing similarity search efficiently with vector databases. Similarity search finds the items closest to a query in a space of embeddings, and the skill covers the choices that determine how well that works in production. Its core concepts begin with distance metrics, the measures of closeness that decide which results count as similar, and it covers nearest neighbor queries, the lookups that return the closest matches. It addresses the trade-offs in scaling a search to millions of vectors, including the speed and memory cost of exact versus approximate search, and optimizing search latency so results come back quickly enough for an interactive use. The skill also covers combining semantic and keyword search. It applies when building semantic search systems, implementing retrieval for RAG applications, creating recommendation engines that suggest similar items, optimizing search latency, scaling to very large numbers of vectors, and combining semantic search with keyword matching. The choice of metric and index depends on the embeddings and the data, so the agent's configuration should be measured on a sample of real queries and compared with exact results to confirm that approximate search is not losing the matches that matter. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 10.5K installs and passing results from three security audits at the time of the scan.