Embedding at the Speed of Light
Static embedding models in FastEmbed: how they work, how fast they run, and the retrieval tradeoffs.
Clelia Bertelli, Minish
November 4, 2026
Embeddings & Machine Learning
Explore embedding models, sparse retrieval, late interaction, and metric learning.
miniCOIL EN-ES: Sparse Neural Retrieval Across the Language Barrier
miniCOIL goes bilingual: concept-based exact matching for English and Spanish, research preview.
Juan Pablo Sotelo and Evgeniya Sukhodolskaya
October 8, 2026
Why Hyperbolic Embeddings Matter for Hierarchical Data
An early look at hierarchical data in curved space, and how to search it in Qdrant.
Matin Mahmood and John Kupchanko
September 8, 2026
MUVERA: Making Multivectors More Performant
Making multi-vector retrieval more efficient by approximating it with single-vector search
Kacper Łukawski
September 5, 2025
miniCOIL: on the Road to Usable Sparse Neural Retrieval
Our attempt to learn from drawbacks of modern sparse neural retrievers
Evgeniya Sukhodolskaya
May 13, 2025
Data Exploration with Qdrant's Distance Matrix API
Efficient visualization and clusterization of high-dimensional data with Qdrant
Andrey Vasnetsov
March 11, 2025
Modern Sparse Neural Retrieval: From DeepCT to SPLADE++
How sparse neural retrievers evolved from DeepCT to SPLADE++, and when to choose them over BM25.
Evgeniya Sukhodolskaya
October 23, 2024
Late Interaction Retrieval with Dense Token Embeddings
Standard dense embedding models perform surprisingly well in late interaction scenarios.
Kacper Łukawski
August 14, 2024
BM42: Attention-Based Sparse Embeddings for Hybrid Search
An experimental attention-based sparse embedding approach for lexical search.
Andrey Vasnetsov
July 1, 2024
Understanding SPLADE and Sparse Vectors
Discover sparse vectors, their function, and significance in modern data processing, including methods like SPLADE for efficient use.
Nirant Kasliwal
December 9, 2023
Advanced Introduction to Triplet Loss
What are the advantages of Triplet Loss and how to efficiently implement it?
Yusuf Sarıgöz
March 24, 2022
Metric Learning Tips & Tricks
How to train an object matching model and serve it in production.
Andrei Vasnetsov
May 15, 2021