Build Simple Vector Search Applications Using Jupyter Notebook

Lab Description

In this lab, you will learn how to: Store and search text and image Embeddings using TiDB Cloud Starter to accelerate AI application development; Use Amazon models to generate Embeddings; Perform vector similarity search via SQL queries; Use Retrieval-Augmented Generation (RAG). Use Hybrid Search to query data, increase query channels, and improve retrieval performance; Use Graph Retrieval-Augmented Generation (GraphRAG) technology to increase information acquisition dimensions.