RAG at Scale: Why Tensors Outperform Vectors in Real-World AI

As AI applications advance, the limitations of vector databases, such as their inability to fully capture complex relationships across various data modalities, are becoming evident, which is where tensors provide a more robust solution. While vector databases excel at fast retrieval through approximate nearest neighbor search, they often lack full-text search capabilities, integration with structured data, and support for custom ranking, leading to inefficiencies in applications requiring personalization and real-time updates. Tensors, as multi-dimensional numerical representations, preserve context and relationships, enabling more precise and explainable retrieval tasks, such as hybrid logic and multimodal understanding. Vespa's tensor system offers a scalable, expressive framework that supports both dense and sparse data dimensions, with strong typing and a minimal set of operations, allowing for seamless integration of symbolic and semantic search. This approach not only enhances the ability to reason with data but also provides a scalable, high-performance platform suitable for real-time AI applications.