Astra DB now supports hybrid search, which combines vector search and BM25 keyword search to improve the accuracy of search results by up to 45%. Hybrid search uses a reranking model to combine the results from both searches and returns the most relevant results. Astra DB can perform hybrid search using its built-in NVIDIA NeMo Retriever reranking microservices, which allows developers to create vector embeddings for their unstructured content and store them in a database. The hybrid search process involves creating a collection with settings that define how to create vectors and handle keyword searches. Developers can ingest data into the collection and perform vector and hybrid searches against it. Hybrid search improves relevance by combining the strengths of both vector and keyword search, making it useful for retrieval-augmented generation (RAG) applications. By using hybrid search, developers can improve the accuracy of their RAG application's output, reducing the likelihood of inaccurate or hallucinated responses.