similarity-search-patterns
Implement efficient similarity search with vector databases
by wshobsonFree
Description
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.…
Highlights
- Distance metrics for similarity
- Nearest neighbor queries
- Scaling to millions of vectors
- Search latency optimization
- Combining semantic and keyword search
- Installs with the skills CLI
Ratings & Reviews
No ratings yet
Be the first to rate similarity-search-patterns.
People also visit
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications
rag-architect
Designs and implements production-grade RAG systems by chunking documents
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support
hybrid-search-implementation
Combine vector and keyword search for improved retrieval
vector-index-tuning
Optimize vector index performance for latency, recall, and memory
similarity-search-patterns FAQ
What is similarity search?
Finding the items closest to a query in a space of embeddings, such as the most related documents or products.
What is a distance metric?
The measure of closeness that decides which results count as similar. The skill covers the main choices.
Does it cover large datasets?
Yes. Scaling to millions of vectors is one of its topics.
Can it support recommendations?
Yes. Building recommendation engines is one of the listed uses.
How do I install it?
Run npx skills add https://github.com/wshobson/agents --skill similarity-search-patterns.
What security audits does it have?
skills.sh lists three audits: Gen Agent Trust Hub Pass, Socket Pass and Snyk Pass.
What are the best similarity-search-patterns alternatives?
Popular alternatives to similarity-search-patterns include embedding-strategies, rag-architect and embeddings. See all similarity-search-patterns alternatives