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With the rise of generative AI, vector embeddings have become the key to enabling powerful applications like large language models. However, storing and searching vector data is not feasible option for traditional databases.

In this presentation, we will explore pgvector - a PostgreSQL extension that enables efficient storage and retrieval of vector data. We will cover:

• What are vector embeddings and why are they critical for generative AI models like LLMs? We'll look at how vector data is generated and structured.

• Managing vectors in PostgreSQL with pgvector - we'll demo with sample vector data and using pgvector's vector operators like distance and similarity search.

• Optimizing search with IVF and HNSW indexes - we'll compare two indexing approaches available in pgvector for fast approximate nearest neighbour search.

• Benchmarking vector query performance - we'll showcase pgvector's query speed on indexed vector data and discuss scaling considerations.

• Building a demo app with pgvector - we'll walk through a simple generative AI demo app powered by vectors stored and queried from PostgreSQL.

By the end of this session, you'll understand the role of vector data in modern AI and how PostgreSQL can be augmented with pgvector to manage vectors for productive generative AI applications. The demos will highlight pgvector's capabilities and how it enables PostgreSQL to support vector-based workloads.