similarity-search-patterns

Similarity search in a vector database retrieves nearest neighbors using distance metrics like cosine or L2. This Skill provides reusable patterns and templates for Pinecone, Qdrant, pgvector, and Weaviate to build efficient RAG pipelines.

Optimizing nearest neighbor search requires scalable indexing strategies, batching, and reranking. This Skill provides patterns to tune distance metrics and indexing approaches to improve retrieval latency and recall across large-scale vector stores.

Yes, this Skill provides ready-made templates and examples for pgvector and Weaviate, along with Pinecone and Qdrant, to help implement vector indexing and semantic search without starting from scratch.

Yes, these similarity search patterns support hybrid search and large-scale recommendations across diverse domains. They include guidance on batching and reranking to retrieve top-k similar items efficiently.

Cosine similarity measures angular distance, ideal for text semantic search, while L2 distance measures straight-line vector magnitude. This Skill guides choosing the right distance metric and indexing strategy for your vector database workload.

Poor similarity search results often stem from mismatched distance metrics or inefficient vector indexing. This Skill helps resolve retrieval issues by providing patterns for proper metric selection, batching, and reranking.