timescale/pg-aiguide

Designs PostgreSQL tables with best practices, focusing on normalization, data types, constraints, indexing, and schema optimization strategies.

Enables semantic search in PostgreSQL by storing and efficiently querying vector embeddings using pgvector, supporting RAG, similarity search, and AI-powered content retrieval.

Configures and optimizes TimescaleDB hypertables for high-performance time-series, IoT, and metrics data storage with automatic partitioning and compression.

Converts identified PostgreSQL tables to TimescaleDB hypertables with optimal configuration, migration planning, and performance validation.

Identifies potential hypertables in PostgreSQL databases by analyzing table characteristics and recommending TimescaleDB conversion candidates.

Enables hybrid text search by combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF) in PostgreSQL.

Comprehensive PostgreSQL documentation and best practices through semantic search and curated skills, including ecosystem tools like TimescaleDB and Tiger Cloud

Enhances AI coding tools with intelligent, version-aware PostgreSQL code generation, providing semantic search and best practice recommendations for database schemas and queries.