This session introduces the basics of vector indexes and vector databases and all the basic knowledge of how to use them. We will walk through the steps to setting up a RAG (Retrieval-Augmented Generation) data pipeline into Apache Pinot ( a real-time OLAP and vector store). We'll create embeddings from unstructured data using Flink and store them in Pinot. Then start asking questions about the data we just loaded.

Technical details covered:
- Vector Indexes
- Apache Pinot
- Creating embeddings using Flink
- RAG
- Learn how to create embeddings and perform similarity searches.

Takeaway:
How we can combine similarity search with real-time analytics.

Target audience:
This session is for anyone interested in RAG, AI, vector databases, and real-time processing.