The Timescale Vector Python client library is a new library that enables Python developers to easily store, index, and query millions of vector embeddings using PostgreSQL. It provides an optimized schema for storing vectors and metadata, performant batch ingestion of vectors, and creates indexes on the vectors to speed up similarity search. The library also supports semantic search, hybrid search, ANN search with time-based filtering of vectors, and Retrieval Augmented Generation (RAG) with time-based context retrieval. It leverages TimescaleDB's hypertables for efficient querying on vectors by both similarity to a query vector and time. The library is designed to be easy to use, with a simple installation process and documentation that covers its key features and usage.