Similarity Search Patterns
Skill Verified ActiveImplement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Implement efficient similarity search with vector databases by providing concrete examples and best practices for building semantic search, nearest neighbor queries, and optimizing retrieval performance.
Features
- Efficient similarity search implementation
- Support for Pinecone, Qdrant, pgvector, and Weaviate
- Explanation of distance metrics and index types
- Code templates for upserting and searching vectors
- Guidance on RAG, recommendation engines, and search optimization
Use Cases
- Building semantic search systems
- Implementing RAG retrieval
- Creating recommendation engines
- Optimizing search latency
- Scaling to millions of vectors
Non-Goals
- Implementing the vector databases themselves
- Providing generic LLM wrappers without vector search context
Practical Utility
- info:Edge casesThe skill documents core concepts like distance metrics and index types, implicitly covering some practical considerations, but does not explicitly list failure modes or recovery steps for each template.
Installation
First, add the marketplace
/plugin marketplace add wshobson/agents/plugin install llm-application-dev@claude-code-workflowsQuality Score
VerifiedTrust Signals
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