Neon, in collaboration with LangChain, has introduced the PGEmbedding integration for vector similarity search in Postgres, aimed at enhancing query execution speed and scalability for large language model (LLM) applications. While PGVector, another vector store in LangChain, offers 100% accuracy through exact similarity searches, it becomes costly at scale. PGEmbedding addresses this by utilizing the Hierarchical Navigable Small World (HNSW) index graph-based approach, which constructs a layered hierarchy of graphs, leading to significantly faster search times—20 times faster with 99% accuracy compared to PGVector. The choice between PGEmbedding and PGVector should be guided by specific application needs, as PGEmbedding generally offers higher accuracy and speed, albeit with greater memory usage due to its graph structure. Users are encouraged to explore both options to determine the best fit for their projects.