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similarity-search-patterns

combined

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

When to use

• Building semantic search systems
• Implementing RAG retrieval
• Creating recommendation engines
• Optimizing search latency
• Scaling to millions of vectors
• Combining semantic and keyword search

When not to use

• The task is unrelated to similarity search patterns
• You need a different domain or tool outside this scope
• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The first copy is the only one that contains the actual procedure: distance metrics, index type trade-offs, and concrete Pinecone/Qdrant implementation templates. The other two copies are thin wrappers that omit the technical content and instead point to a resources/implementation-playbook.md file that is not present in the provided text. Because the first copy has material steps the others lack, and the others add a resource pointer and explicit limitations the first lacks, crowning one would hide a real alternative structure.