Migrate from Solr to SAI for Accelerated Development and Performance: Part 3

Apache Cassandra 5.0 introduces Storage-attached indexing (SAI), which enhances query patterns, reduces coding requirements, and simplifies adding application functionality. With SAI, developers can modernize their applications with semantic search capabilities using large language models (LLMs) and generative AI to retrieve data based on contextual meaning rather than just text matching. Semantic search uses natural language processing (NLP) and machine learning algorithms to understand the underlying meaning of a user's query and deliver more accurate results. Vector embeddings are used to encode data in a database, allowing for mathematical operations to measure similarity between vectors and quickly locate relevant records. Astra DB, a vector database, enables storage of embeddings alongside movie data, facilitating semantic search capabilities. The introduction of SAI with semantic search capabilities aims to replace traditional full-text search solutions like Solr.

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