Creating intelligent assistants that query large datasets doesn’t have to be expensive. In this session, you’ll learn how to build a LangChain-powered agent that queries an Amazon S3 Vectors store built from CSV product review data, providing scalable, low-cost semantic search for e-commerce insights.

We’ll cover:

1. Vectorizing content for semantic search using S3 Vectors as the low-cost vector database.
2. Building a LangChain agent backed by OpenAI models to reason over query results.
3. Implementing role-based access control with metadata filtering, ensuring users see only what they’re allowed to access.
4. Query decomposition and rephrasing to improve retrieval accuracy and relevance.

By the end of the session, attendees will understand how to design a cost-efficient retrieval pipeline, combine LangChain agents with S3 vectors, and implement secure, relevant search over structured CSV data.