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
Technical Articles
Explore vector search concepts, retrieval research, Qdrant internals, and application patterns.
Read technical explanations, experiments, and engineering decisions from the Qdrant team.
Recently Published
The latest research and engineering explanations from the team.
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
Filtered Vector Search: What ACORN Fixes, and What Fixes ACORN
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
Vector Search Fundamentals
Understand how embeddings represent data, how vector similarity works, and why vector search uses dedicated databases.
Vector Embeddings Explained: How They Work in ML & Search
Explore the power of vector embeddings. Learn to use numerical machine learning representations to build a neural search service.
Sabrina Aquino
February 6, 2024
Vector Similarity: Going Beyond Full-Text Search
How vector similarity goes past search: dissimilarity, diversity, recommendation, and discovery.
Luis Cossío
August 8, 2023
Core Concepts
Start here to understand the building blocks of vector search. Learn what vector databases, embeddings, quantization, and retrieval-augmented generation are and how they fit together.
Why Vector Search Needs a Dedicated Database
Why add-on vector search looks good — until you actually use it.
Evgeniya Sukhodolskaya & Andrey Vasnetsov
February 17, 2025
What is a Vector Database?
What Is a Vector Database? Concepts, Architecture & Use Cases | Qdrant
Sabrina Aquino & Chadha Sridi
October 9, 2024
What is Vector Quantization?
What is Vector Quantization? Methods & Examples
Sabrina Aquino
September 25, 2024
Mastering Search
Go beyond the basics and master vector search with Qdrant. Learn how to combine filtering, hybrid retrieval, multivectors, and reranking to build high-quality search.
Relevance & Evaluation
Learn how to measure and improve the relevance of vector search results, from evaluation methods and relevance feedback to finding errors in your data.
Search Quality
Learn how to evaluate and improve the quality of your vector search. Explore relevance feedback, evaluation methodologies, and benchmarking techniques.
Predicting Weak Retrieval Without an LLM
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
Relevance Feedback in Qdrant
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
Relevance Feedback in Information Retrieval
A survey of relevance feedback methods from lexical and neural retrieval research, and why vector search engines lack them.
Evgeniya Sukhodolskaya
March 27, 2025
Production Ops
Operate Qdrant at scale. Learn how to optimize memory and resources, apply quantization, manage multitenancy and sharding, and secure access in production.
TurboQuant in Qdrant
TurboQuant ships in Qdrant 1.18
Ivan Pleshkov & Jonas Schulz
May 13, 2026
Binary Quantization: 40x Faster Vector Search
Binary Quantization is a newly introduced mechanism of reducing the memory footprint and increasing performance
Nirant Kasliwal
September 18, 2023
Product Quantization for Vector Search
Vector search with low memory? Try out our brand-new Product Quantization!
Kacper Łukawski
May 30, 2023
Qdrant Internals
Take a look under the hood of Qdrant’s high-performance vector search engine. Explore the architecture, components, and design principles the Qdrant Vector Search Engine is built on.
Filtered Vector Search: What ACORN Fixes, and What Fixes ACORN
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
Introducing Gridstore: Qdrant's Custom Key-Value Store
Why and how we built our own key-value store.
Luis Cossio, Arnaud Gourlay & David Myriel
February 5, 2025
Immutable Data Structures in Qdrant
Learn how immutable data structures improve vector search performance in Qdrant.
Andrey Vasnetsov
August 20, 2024
Embedding Research
Explore the research behind modern embeddings and neural retrieval. Dive into sparse neural models, late interaction, metric learning, and new baselines for hybrid search.
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
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
RAG & Agents
Build retrieval-augmented generation and agentic applications with Qdrant. Learn agentic RAG patterns, agent memory, semantic caching, and how agents access your data.
Is RAG Dead? Why Long Context Windows Don't Replace RAG
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
What is Agentic RAG? Building Agents with Qdrant
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
Semantic Caching for RAG: Cut LLM Cost and Latency
Semantic Cache for Best Results and Optimization.
Daniel Romero, David Myriel
May 7, 2024
Data Exploration
Learn how you can leverage vector similarity beyond just search. Reveal hidden patterns and insights in your data, provide recommendations, and navigate data space.
Demos & Tutorials
Learn by building. Follow hands-on tutorials and demos covering neural search, serverless deployments, search-as-you-type, and integrations with popular frameworks.
