Beyond Vector Search: The Move to Tensor-Based Retrieval (thenewstack.io)

The story argues that vector-only retrieval is hitting practical limits: flat embeddings collapse sequence, position and modality structure, forcing brittle rerank pipelines and separate services for filtering, personalization and multimodal reasoning. Instead, the piece promotes tensor-based retrieval—reminding readers that vectors are just 1D tensors—and shows how multi-dimensional tensors preserve token/region/scene structure (e.g., text[token][embedding], image[frame][region][channel], video[scene][timestamp][feature]). That structure enables fine-grained matching (token- or region-level), context-aware cross-modal embeddings, and richer query interactions beyond scalar similarity, which are essential for ColBERT-style dense retrieval, temporal video search and real-time RAG applications. Technically, the authors call for a practical tensor framework for production: a minimal, composable set of tensor operations, unified handling of dense and sparse dimensions, and strong typing with named dimensions (product_id, timestamp, color_channel). Those choices simplify APIs, prevent dimension-mismatch bugs, and expose clearer computation graphs for vectorization, parallelism and memory reuse. The net effect is fewer brittle components, unified ranking that blends visual and attribute signals in real time, better explainability and easier optimization. Vespa’s strongly typed, production-ready tensor formalism is presented as an implementation path for teams wanting to move from retrieval that “finds” to retrieval that can reason.