by wshobson
vector-index-tuning helps tune vector search indexes for latency, recall, and memory. Use it to choose index types, adjust HNSW settings, and compare quantization options for RAG workflows.
by wshobson
similarity-search-patterns helps you choose distance metrics, index types, and hybrid retrieval patterns for semantic search and RAG workflows. Use it to plan production vector search tradeoffs around recall, latency, and scale.
by wshobson
rag-implementation is a practical skill for planning RAG systems with vector databases, embeddings, retrieval patterns, and grounded-answer workflows. Use it to compare stack options, shape architecture decisions, and guide install and usage for document Q&A, knowledge assistants, and semantic search.
by wshobson
The hybrid-search-implementation skill shows how to combine vector and keyword retrieval with RRF, linear fusion, reranking, and cascade patterns for RAG and search systems.
by wshobson
embedding-strategies helps you choose and optimize embedding models for semantic search and RAG workflows, with practical guidance on chunking, model tradeoffs, multilingual content, and retrieval evaluation.
