
Universal I/O in Qdrant
How Qdrant talks to different storage backends through a unified I/O interface
Luis Cossío, Clelia Bertelli
October 12, 2026
Explore vector search concepts, retrieval research, Qdrant internals, and application patterns.
Read technical explanations, experiments, and engineering decisions from the Qdrant team.
The latest research and engineering explanations from the team.

How Qdrant talks to different storage backends through a unified I/O interface
Luis Cossío, Clelia Bertelli
October 12, 2026

miniCOIL goes bilingual: concept-based exact matching for English and Spanish, research preview.
Juan Pablo Sotelo and Evgeniya Sukhodolskaya
October 8, 2026

An early look at hierarchical data in curved space, and how to search it in Qdrant.
Matin Mahmood and John Kupchanko
September 8, 2026
Understand vector search concepts, embeddings, and the trade-offs behind retrieval systems.

Why add-on vector search looks good — until you actually use it.
Evgeniya Sukhodolskaya & Andrey Vasnetsov
February 17, 2025

What Is a Vector Database? Concepts, Architecture & Use Cases | Qdrant
Sabrina Aquino & Chadha Sridi
October 9, 2024

Explore the power of vector embeddings. Learn to use numerical machine learning representations to build a neural search service.
Sabrina Aquino
February 6, 2024
Explore embedding models, sparse retrieval, late interaction, and metric learning.

miniCOIL goes bilingual: concept-based exact matching for English and Spanish, research preview.
Juan Pablo Sotelo and Evgeniya Sukhodolskaya
October 8, 2026

An early look at hierarchical data in curved space, and how to search it in Qdrant.
Matin Mahmood and John Kupchanko
September 8, 2026

Making multi-vector retrieval more efficient by approximating it with single-vector search
Kacper Łukawski
September 5, 2025
Understand relevance feedback, discovery, evaluation, and dataset quality.

Cheap signals flag weak retrieval before you pay for a rerank or LLM judge. The one that works depends on how your search fails.
Dylan Couzon
June 24, 2026

The story behind the vector search-native relevance feedback feature, available since 1.17.0, which increases the relevance of search results universally, cheaply, and at scale.
Evgeniya Sukhodolskaya
February 20, 2026

A survey of relevance feedback methods from lexical and neural retrieval research, and why vector search engines lack them.
Evgeniya Sukhodolskaya
March 27, 2025
Explore filterable HNSW, quantization, memory use, and Qdrant storage mechanisms.

How Qdrant talks to different storage backends through a unified I/O interface
Luis Cossío, Clelia Bertelli
October 12, 2026

ACORN repairs filtered HNSW search at query time, extra edges at index time. We benchmarked both in Qdrant on one million points.
Dylan Couzon & Meina Ghafouri
August 7, 2026

TurboQuant ships in Qdrant 1.18
Ivan Pleshkov & Jonas Schulz
May 13, 2026
Understand retrieval in language-model applications and agent systems.

What published measurements show about long context windows versus retrieval: quality, cost, latency, and when each approach fits.
David Myriel & Chadha Sridi
August 4, 2026

Will agentic RAG replace linear RAG? Learn how to build agents with Qdrant and which framework is best for your use case.
Kacper Łukawski
November 22, 2024

What is RAG?
Sabrina Aquino
March 19, 2024