Tensor-based retrieval is the next frontier after vector search, and it's reshaping how we think about data stores, indexes, and workloads. The shift is laid out in Beyond Vector Search: The Move to Tensor-Based Retrieval [1], and it’s already spurring real-world tooling that blends traditional databases with tensor-friendly capabilities.
What Tensor-Based Retrieval Means
It's a broader approach that expands on vector math, exploring how high-dimensional tensors are stored, retrieved, and transformed during queries. The idea pushes databases to rethink storage layouts, latency budgets, and how similarity signals are shaped across large datasets. This shift is driving real-world tooling like Qdrant and the SQLite vector extensions [1].
Current tooling in practice
• Qdrant — a high-performance vector database and vector search engine in Rust [2]
• SQLite vector extension family — the sqlite-vector project (not open source) and the open-source sqlite-vec (dual-licensed Apache and MIT) [3]
Tensor-based retrieval isn’t hype—it's forcing fresh thoughts on database design, indexing, and performance as tensors become first-class citizens in real apps. Watch this space as tooling migrates from theory to production.
References
Beyond Vector Search: The Move to Tensor-Based Retrieval
Discusses moving from vector search to tensor-based retrieval in data systems and implications for databases and search performance.
View sourceQdrant: High-Performance Vector Database and Vector Search Engine in Rust
Overview of Qdrant, a high-performance vector database and vector search engine implemented in Rust, designed for efficient similarity search scalability.
View sourceUltra efficient vector extension for SQLite
Discussion of sqlite-vector extension, licensing concerns, comparisons to sqlite-vec, DuckDB, and HNSW, with brute-force search performance.
View source