Timescale Vector x LlamaIndex: Making PostgreSQL a Better Vector Database

Timescale Vector is an integration that enables LlamaIndex developers to build better AI applications with PostgreSQL as their vector database, providing faster vector similarity search, efficient time-based search filtering, and operational simplicity. It enhances pgvector, the open-source extension for vector data on PostgreSQL, by introducing a new search index inspired by the DiskANN algorithm, achieving 3x faster search speed at ~99% recall than specialized databases. Timescale Vector optimizes time-based vector search queries, leveraging automatic time-based partitioning and indexing of hypertables to efficiently find recent embeddings and constrain vector search by a time range or document age. It simplifies AI infra stack by combining vector embeddings, relational data, and time-series data in one PostgreSQL database, eliminating operational complexity. The integration also provides robust, production-ready cloud PostgreSQL platform with flexible pricing, enterprise-grade security, and free expert support.

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