Title

AWS re:Invent 2023 - Best practices for querying vector data for gen AI apps in PostgreSQL (DAT407)

Summary

Jonathan Katz discusses vector search and retrieval in databases, focusing on generative AI applications.
The talk is a deep dive (400 level) into the use of PostgreSQL as a vector store, leveraging the open-source PG vector extension and features in Amazon Aurora for vector queries.
Katz explains foundational models, trained on vast data, that can be queried for human-like answers and augmented with private data for applications.
Amazon Bedrock is highlighted as a service that allows the use of foundational models securely within a user's VPC.
Retrieval Augmented Generation (RAG) is a technique for augmenting foundational model responses with private database data.
Vectors are the mathematical representation of data used in generative AI for similarity search.
Katz discusses the importance of efficient vector storage and querying, considering the size and dimensionality of vectors.
He compares two indexing methods for vectors in PostgreSQL: IVF-Flat (K-means based) and HNSW (graph-based), and provides best practices for each.
Katz also addresses the challenges of filtering when performing vector searches and suggests techniques like partial indexing and partitioning.
Amazon Aurora's optimized reads and newer hardware like R7Gs are recommended for better performance in vector workloads.
Future developments for PG vector include parallel builds for HNSW, enhanced filtering, support for more data types, and parallel query support.

The need for vector search and retrieval is driven by the rise of generative AI applications that require augmenting foundational model responses with private data.
PostgreSQL is a suitable vector store due to its extensibility, robustness, and ability to co-locate AI/ML data with transactional data.
PG vector is an open-source extension that enables vector search and retrieval in PostgreSQL, supporting both exact and approximate nearest neighbor searches.
The choice between IVF-Flat and HNSW indexing methods depends on the specific needs of the application, such as the importance of recall, query performance, and ease of management.
Filtering can significantly impact the performance of vector searches, and developers must carefully consider how to implement filtering to avoid slow queries or unexpected results.
Amazon Aurora's optimized reads feature can significantly improve the performance of vector workloads by leveraging NVMe cache for faster data retrieval.
The selection of hardware, such as choosing between R6Gs and R7Gs instances, can have a substantial impact on the performance of vector workloads.
The vector search and retrieval space is rapidly evolving, and developers should stay informed about new developments and best practices to ensure their applications remain efficient and scalable.