Hyperbolic Embeddings in Qdrant
Why hierarchical data suits a curved space, and what that buys you.
Matin Mahmood and John Kupchanko
September 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.
Configure Qdrant's Optimizer for Predictable Search Latency
Best practices for configuring Qdrant's optimizers, backed by search latency benchmarks.
Clelia Bertelli
August 25, 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
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.
Is RAG Dead? Why Long Context Windows Don't Replace RAG
Learn how Qdrant enhances enterprise AI with superior accuracy and cost-effectiveness.
David Myriel & Chadha Sridi
August 4, 2026
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
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
One command to fine-tune SPLADE for your catalog. No ML pipeline assembly required.
Thierry Damiba
March 9, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 4: Specialization vs Generalization
When to fine-tune sparse embeddings and how far to specialize before generalization suffers.
Thierry Damiba
March 9, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 3: Evaluation and Hard Negatives
Evaluate fine-tuned SPLADE with Qdrant and boost results with hard negative mining.
Thierry Damiba
March 9, 2026
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 Informational Retrieval
Incorporating relevance feedback into discovery search, an overview of a research field.
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.
Configure Qdrant's Optimizer for Predictable Search Latency
Best practices for configuring Qdrant's optimizers, backed by search latency benchmarks.
Clelia Bertelli
August 25, 2026
TurboQuant in Qdrant
TurboQuant ships in Qdrant 1.18
Ivan Pleshkov & Jonas Schulz
May 13, 2026
Vector Search in Production: Scaling, HA & Tuning Guide
A comprehensive guide to running vector search in production environments
David Myriel
April 30, 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.
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.
Hyperbolic Embeddings in Qdrant
Why hierarchical data suits a curved space, and what that buys you.
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
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
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
Discover how to architect scalable, high-performance AI agents with vector search, real-time memory, and multimodal retrieval.
Thierry Damiba
October 26, 2025
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.
Data Exploration with Qdrant's Distance Matrix API
Efficient visualization and clusterization of high-dimensional data with Qdrant
Andrey Vasnetsov
March 11, 2025
Discovery Search in Qdrant
Discover points by constraining the vector space.
Luis Cossío
January 31, 2024
Multimodal Food Search Demo
Feeling hungry? Find the perfect meal with Qdrant's multimodal semantic search.
Kacper Łukawski
September 5, 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.
Question Answering with LangChain and Qdrant
Build a retrieval-augmented question answering pipeline with just a few lines of code.
Kacper Łukawski and Manas Chopra
July 30, 2026
FastEmbed: Qdrant's Efficient Python Library for Embedding Generation
FastEmbed: Quantized Embedding models for fast CPU Generation
Nirant Kasliwal and Manas Chopra
July 30, 2026
Semantic Search As You Type
Instant search using Qdrant
Andre Bogus
August 14, 2023