{"skill":{"slug":"pgvector","displayName":"pgvector","summary":"PostgreSQL vector database skill with pgvector extension. Enables vector similarity search, embeddings storage, RAG (Retrieval-Augmented Generation) pipeline...","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":431,"installsAllTime":2,"installsCurrent":2,"stars":0,"versions":1},"createdAt":1772934681294,"updatedAt":1777525723438},"latestVersion":{"version":"1.0.0","createdAt":1772934681294,"changelog":"Initial release of the pgvector skill, enabling PostgreSQL-based vector search:\n\n- Integrates PostgreSQL with the pgvector extension for vector similarity search and embedding storage.\n- Supports creation and indexing of vector tables (HNSW, IVFFlat) optimized for fast and flexible searches.\n- Provides SQL examples for inserting embeddings, running vector and hybrid (vector + keyword) searches, and supporting RAG (Retrieval-Augmented Generation) workflows.\n- Includes guides for table management, monitoring, updating, deleting, and batch-inserting embeddings via Python.\n- Summarizes key use cases: semantic search, RAG pipelines, recommendations, anomaly detection, and image/video search.","license":null},"metadata":{"os":null,"systems":null},"owner":{"handle":"damiencronw","userId":"s17c60c9m3bq316p0h0gym6y1183fdqg","displayName":"damienCronw","image":"https://avatars.githubusercontent.com/u/128512183?v=4"},"moderation":null}