This article argues that MongoDB is often chosen by developers unfamiliar with the capabilities of PostgreSQL, and that PostgreSQL is generally a superior database solution due to its robustness, data integrity features, and performance. It details specific PostgreSQL features that address common MongoDB use cases.
This article details the process of building a fast vector search system for a large legal dataset (Australian High Court decisions). It covers choosing embedding providers, performance benchmarks, using USearch and Isaacus embeddings, and the importance of API terms of service. It focuses on achieving speed and scalability while maintaining reasonable accuracy.
