Vector Search in Production
We gathered our most recommended tips and tricks to make your production deployment run smoothly.
David Myriel
April 30, 2025
Vector Search Manuals
Take full control of your vector data with Qdrant. Learn how to easily store, organize, and optimize vectors for high-performance similarity search.
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
Vector Search Resource Optimization Guide
Learn how to get the most from Qdrant's optimization features. Discover key tricks and best practices to boost vector search performance and reduce Qdrant's resource usage.
David Myriel
February 09, 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.
Built for Vector Search
Why add-on vector search looks good — until you actually use it.
Evgeniya Sukhodolskaya & Andrey Vasnetsov
February 17, 2025
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
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
Vector Similarity: Going Beyond Full-Text Search | Qdrant
Discover how vector similarity expands data exploration beyond full-text search. Explore diversity sampling and more for enhanced data discovery!
Luis Cossío
August 08, 2023
Machine Learning
Explore Machine Learning principles and practices which make modern semantic similarity search possible. Apply Qdrant and vector search capabilities to your ML projects.
miniCOIL: on the Road to Usable Sparse Neural Retrieval
Introducing miniCOIL, a lightweight sparse neural retriever capable of generalization.
Evgeniya Sukhodolskaya
May 13, 2025
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
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 & GenAI
Leverage Qdrant for Retrieval-Augmented Generation (RAG) and build AI Agents
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
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
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
Practical Examples
Building blocks and reference implementations to help you get started with Qdrant. Learn how to use Qdrant to solve real-world problems and build the next generation of AI applications.
Optimizing OpenAI Embeddings: Enhance Efficiency with Qdrant's Binary Quantization
Explore how Qdrant's Binary Quantization can significantly improve the efficiency and performance of OpenAI's Ada-003 embeddings. Learn best practices for real-time search applications.
Nirant Kasliwal
February 21, 2024
Food Discovery Demo
Feeling hungry? Find the perfect meal with Qdrant's multimodal semantic search.
Kacper Łukawski
September 05, 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
Ecosystem
Tools, libraries and integrations around Qdrant vector search engine.
Qdrant Summer of Code 2024 - WASM based Dimension Reduction
My journey as a Qdrant Summer of Code 2024 participant working on enhancing vector visualization using WebAssembly (WASM) based dimension reduction.
Jishan Bhattacharya
August 31, 2024
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
Google Summer of Code 2023 - Web UI for Visualization and Exploration
My journey as a Google Summer of Code 2023 student working on the "Web UI for Visualization and Exploration" project for Qdrant.
Kartik Gupta
August 28, 2023