vector-db-rag-expert
De la autor
High-performance Vector DBs, Deep RAG, pgvector HNSW, and re-ranking / Vector DB performa tinggi, Deep RAG, pgvector HNSW, dan re-ranking.
Instalare
npx skills add roedyrustam/vibes-plug --skill vector-db-rag-expert -a claude-code
Se instalează în .claude/skills/vector-db-rag-expert/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 83
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- vect-searchtonone-ai/tononeDesign a vector search or RAG system — retrieval strategy, reranking, and database selection. Use when asked to "build a RAG system", "design vector search", or "which vector database should we use".Date★ 76
- vector-db-patternsvibeeval/vibecosystemEmbedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.Altele★ 541
- vector-memorya5c-ai/babysitterHNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.Productivitate★ 1.9K
- vector-database-engineertranhieutt/software_development_departmentProvides vector database and semantic search patterns for Pinecone, Weaviate, Qdrant, Milvus, and pgvector in RAG and recommendation systems. Use when implementing vector search or when the user mentions vector database, semantic search, embeddings, or similarity search.Date★ 74
- vector-dbericrisco/rsc-harnessUse when operating a vector store as a data layer — choosing or migrating between Pinecone, Qdrant, Weaviate and pgvector; designing a collection or index (distance metric, dimensions, HNSW parameters, named vectors); filtering on metadata; hybrid dense-plus-sparse search; and quantization to cut RAM and cost. Covers garbage results, silently ignored filters, low recall, slow queries, and filtered queries returning fewer than k rows. NOT producing, chunking or judging embeddings (that is `embeddings-search`).Altele★ 297
- vector-database-opsBagelHole/DevOps-Security-Agent-SkillsDeploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.Date★ 1.2K
Note și recenzii
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