TurboQuant in Qdrant
TurboQuant — a new rotation-based vector quantization algorithm from Google Research — now ships in Qdrant 1.18, with extensions that make it work on real embeddings.
Ivan Pleshkov & Jonas Schulz
May 13, 2026
Latest Articles
The most recent publications from the Qdrant team.
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 5: From Research to Product
Part 5 of the sparse embeddings series. We packaged the entire training pipeline from Parts 1-4 into an open-source CLI and web dashboard that fine-tunes SPLADE models for any product catalog in minutes.
Thierry Damiba
March 09, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 4: Specialization vs Generalization
Part 4 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Test cross-domain generalization, train a multi-domain model, and decide when to specialize vs generalize.
Thierry Damiba
March 09, 2026
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.
How to choose an embedding model
Building proper search requires selecting the right embedding model for your specific use case. This guide helps you navigate the selection process based on performance, cost, and other practical considerations.
Kacper Łukawski
July 15, 2025
Built for Vector Search
Why add-on vector search looks good — until you actually use it.
Evgeniya Sukhodolskaya & Andrey Vasnetsov
February 17, 2025
What is a Vector Database?
Discover what a vector database is, its core functionalities, and real-world applications.
Sabrina Aquino
October 09, 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.
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 5: From Research to Product
Part 5 of the sparse embeddings series. We packaged the entire training pipeline from Parts 1-4 into an open-source CLI and web dashboard that fine-tunes SPLADE models for any product catalog in minutes.
Thierry Damiba
March 09, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 4: Specialization vs Generalization
Part 4 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Test cross-domain generalization, train a multi-domain model, and decide when to specialize vs generalize.
Thierry Damiba
March 09, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 3: Evaluation and Hard Negatives
Part 3 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Index products in Qdrant, run retrieval benchmarks, and implement ANCE-inspired hard negative mining for a 28% improvement over BM25.
Thierry Damiba
March 09, 2026
Search Quality
Learn how to evaluate and improve the quality of your vector search. Explore relevance feedback, evaluation methodologies, and benchmarking techniques.
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 Informational Retrieval
Relerance feedback: from ancient history to LLMs. Why relevance feedback techniques are good on paper but not popular in neural search, and what we can do about it.
Evgeniya Sukhodolskaya
March 27, 2025
Optimizing RAG Through an Evaluation-Based Methodology
Learn how Qdrant-powered RAG applications can be tested and iteratively improved using LLM evaluation tools like Quotient.
Atita Arora
June 12, 2024
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 — a new rotation-based vector quantization algorithm from Google Research — now ships in Qdrant 1.18, with extensions that make it work on real embeddings.
Ivan Pleshkov & Jonas Schulz
May 13, 2026
Vector Search in Production
We gathered our most recommended tips and tricks to make your production deployment run smoothly.
David Myriel
April 30, 2025
Optimizing Memory for Bulk Uploads
Efficient memory management is key when handling large-scale vector data. Learn how to optimize memory consumption during bulk uploads in Qdrant and keep your deployments performant under heavy load.
Sabrina Aquino
February 13, 2025
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.
Introducing Gridstore: Qdrant's Custom Key-Value Store
Why and how we built our own key-value store. A short technical report on our procedure and results.
Luis Cossio, Arnaud Gourlay & David Myriel
February 05, 2025
Qdrant Internals: Immutable Data Structures
Learn how immutable data structures improve vector search performance in Qdrant.
Andrey Vasnetsov
August 20, 2024
Qdrant under the hood: io_uring
Slow disk decelerating your Qdrant deployment? Get on top of IO overhead with this one trick!
Andre Bogus
June 21, 2023
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: on the Road to Usable Sparse Neural Retrieval
Introducing miniCOIL, a lightweight sparse neural retriever capable of generalization.
Evgeniya Sukhodolskaya
May 13, 2025
Modern Sparse Neural Retrieval: From Theory to Practice
A comprehensive guide to modern sparse neural retrievers: COIL, TILDEv2, SPLADE, and more. Find out how they work and learn how to use them effectively.
Evgeniya Sukhodolskaya
October 23, 2024
Any* Embedding Model Can Become a Late Interaction Model... If You Give It a Chance!
We recently discovered that embedding models can become late interaction models & can perform surprisingly well in some scenarios. See what we learned here.
Kacper Łukawski
August 14, 2024
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.
Building Performant, Scaled Agentic Vector Search with Qdrant
Learn how to build performant, scalable AI agents with efficient vector retrieval, hybrid dense-sparse search, real-time memory, multimodal context integration, and optimized architectures for low-latency, high-accuracy execution in production environments.
Thierry Damiba
October 26, 2025
What is Agentic RAG? Building Agents with Qdrant
Agents are a new paradigm in AI, and they are changing how we build RAG systems. Learn how to build agents with Qdrant and which framework to choose.
Kacper Łukawski
November 22, 2024
Semantic Cache: Accelerating AI with Lightning-Fast Data Retrieval
Semantic cache is reshaping AI applications by enabling rapid data retrieval. Discover how its implementation benefits your RAG setup.
Daniel Romero, David Myriel
May 07, 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.
Distance-based data exploration
Explore your data under a new angle with Qdrant's tools for dimensionality reduction, clusterization, and visualization.
Andrey Vasnetsov
March 11, 2025
Discovery needs context
Discovery Search, an innovative way to constrain the vector space in which a search is performed, relying only on vectors.
Luis Cossío
January 31, 2024
Food Discovery Demo
Feeling hungry? Find the perfect meal with Qdrant's multimodal semantic search.
Kacper Łukawski
September 05, 2023
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.
FastEmbed: Qdrant's Efficient Python Library for Embedding Generation
Learn how to accurately and efficiently create text embeddings with FastEmbed.
Nirant Kasliwal
October 18, 2023
Semantic Search As You Type
To show off Qdrant's performance, we show how to do a quick search-as-you-type that will come back within a few milliseconds.
Andre Bogus
August 14, 2023
Serverless Semantic Search
Create a serverless semantic search engine using nothing but Qdrant and free cloud services.
Andre Bogus
July 12, 2023
