Timescale Vector is an enhancement to PostgreSQL designed to optimize vector data storage and retrieval, especially for AI applications that utilize large language models (LLMs). It builds upon the pgvector extension with improved features such as faster Approximate Nearest Neighbor (ANN) search using a DiskANN-inspired index, optimized time-based filtering, and streamlined data handling that integrates vector, relational, and time-series data. Timescale Vector aims to simplify the AI application stack by reducing the operational complexity associated with managing multiple databases. Developers are encouraged to explore the platform during its early access phase, which offers extended trial periods and free usage, to refine the product through feedback. The solution leverages PostgreSQL's reliability and extensibility, positioning it as a versatile choice for developers navigating the evolving landscape of AI-driven applications.