LlamaIndex is a popular data framework for connecting custom data sources to large language models (LLMs). Timescale Vector has a native LlamaIndex integration that supports all the features of pgvector and Timescale Vector. It enables you to use Timescale Vector as a vector store and leverage all its capabilities in your applications built with LlamaIndex.

Here are resources about using Timescale Vector with LlamaIndex:

• Getting started with LlamaIndex and Timescale Vector: You'll learn how to use Timescale Vector for (1) similarity search, (2) time-based vector search, (3) faster search with indexes, and (4) retrieval and query engine.
• Time-based retrieval: Learn how to power RAG applications with time-based retrieval.
• Llama Pack: Auto Retrieval with time-based search: This pack demonstrates performing auto-retrieval for hybrid search based on both similarity and time, using the timescale-vector (PostgreSQL) vector store.
• Learn more about Timescale Vector and LlamaIndex : How Timescale Vector is a better PostgreSQL for AI applications.