Amazon S3 Vectorsis is a new service designed to efficiently store and query vector embeddings, which are essential for AI and semantic search applications like Retrieval Augmented Generation (RAG). RAG enhances large language models by integrating external knowledge, but scaling S3 Vectors requires complex setups. Bodo, an open-source high-performance DataFrame library for Python, simplifies this process by providing Pandas-compatible APIs that automatically parallelize workloads. This allows users to handle extensive AI tasks using familiar Pandas code without needing to migrate data or configure distributed systems manually. Bodo's integration with S3 Vectors involves creating an S3 vector bucket and index, ensuring proper AWS credentials, and using Bodo's methods to store and query vector data. The system leverages MPI-based technology and a JIT compiler to optimize performance, making it significantly faster than Spark or Dask. Bodo's capabilities are accessible from version 2025.8 and are available for installation via pip. Users can explore these features further through Bodo's documentation and GitHub repository.