pgvector is a PostgreSQL extension designed for storing vectors, and facilitating vector similarity searches. This process, crucial in AI, semantic search, and Retrieval Augmented Generation (RAG) applications, involves finding the K nearest neighbors of a given vector, typically representing a text. However, vector search can become inefficient at scale, often resulting in a sequential scan of the database, which can slow down processes and create bottlenecks. To address this, pgvector utilizes Approximate Nearest Neighbor (ANN) algorithms, such as Hierarchical Navigable Small World (HNSW), enhancing search efficiency.

A challenge for developers is the time-consuming nature of building indexes for millions of rows. Since version 0.6.0, pgvector addresses this with a parallel index build for HNSW, significantly speeding up the process but also intensifying CPU and memory usage. To optimize efficiency, we have integrated pgvector with Neon's autoscaling feature, which dynamically allocates more CPU and memory based on pgvector's requirements.

In this talk, I will demonstrate how to use the parallel index build feature in pgvector and enhance its performance with Neon's autoscaling capabilities. The session will cover:

- The fundamentals of vector search.
- An overview of ANN algorithms.
- Implementing parallel index build in HNSW.
- Utilizing Neon's autoscaling feature for improved efficiency.

This talk is for beginner developers as well as experienced ones interested in optimizing their AI applications. It will cover the fundamentals of vector search, pgvector and autoscaling and as well as deep dives into ANNs and cloud-native PostgreSQL