Are you building AI applications that need a reliable, scalable vector store? This talk provides a comprehensive guide to configuring PostgreSQL as an optimal vector store for AI applications using the pgvector extension.

We'll begin with a brief introduction to vector stores and PostgreSQL fundamentals before diving into the pgvector extension and its capabilities. The presentation will explore essential vector operations and compare indexing approaches (HNSW vs. IVFFlat), offering practical guidance on hyperparameter tuning for your specific use cases.

We'll also cover advanced optimization techniques including:
- Reduced precision
- Quantization methods
- Various scaling strategies such as partitioning, sharding, and replication to handle growing data volumes

We'll also see a real-world example of a semantic search application using pgvector, demonstrating how to implement and optimize it for production.

Whether you're implementing semantic search, recommendation systems, or other vector-based AI applications, you'll leave with actionable knowledge to configure PostgreSQL as a high-performance, production-ready vector database.