Dense vector search is excellent at capturing semantic intent, but it often struggles with queries that demand high keyword accuracy. To quantify this gap, we benchmarked a standard dense-only retriever against a hybrid RAG system that incorporates SPLADE sparse vectors.| AIMultiple
The effectiveness of any Retrieval-Augmented Generation (RAG) system depends on the precision of its retriever component.| AIMultiple
RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models with various chunk sizes to see what works best.| AIMultiple
Discover Qdrant Cloud, the cutting-edge managed cloud for scalable, high-performance AI applications. Manage and deploy your vector data with ease today.| qdrant.tech
Find out how the document model eliminates operational complexity while ensuring unmatched resilience, scalability, and enterprise-grade security through the Atlas cloud database.| MongoDB
Elasticsearch is the leading distributed, RESTful, open source search and analytics engine designed for speed, horizontal scalability, reliability, and easy management. Get started for free....| Elastic