Vector Search in Cassandra 5

Patrick McFadin takes a deep dive on Vector Search in Apache Cassandra, and the future of semantic search with practical implementations.


Key Highlights:
Evolution of Search Technologies
From exact match to keyword search
Introduction to semantic search and vector embeddings
Real-world applications in modern search systems

Technical Deep Dive: JVector
Efficient compression techniques using product quantization
Memory management optimizations
Integration with Storage Attached Index (SAI)
JDK 21+ performance benefits

Implementation Details
New vector data type and syntax
Working with embedding models
Best practices for vector search implementation
Performance considerations and benchmarks

Real-World Success Stories
Physics Wallah's AI Guru implementation
Priceline's travel chatbot integration
Industry recognition in Forrester Wave™

Perfect for:
Database engineers exploring vector search
Teams implementing AI/ML search capabilities
Cassandra users looking to leverage new features
Developers interested in RAG applications with high scaling

Links from talk:
JVector Github - https://github.com/jbellis/jvector
DataStax Branch with latest JVector - https://github.com/datastax/cassandra

#ApacheCassandra #VectorSearch #AI #DatabaseEngineering