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.

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MUVERA: Making Multivectors More Performant

Multi-vector representations are superior to single-vector embeddings in many benchmarks. MUVERA embeddings aim to solve the problem of slow multi-vector search by creating a single-vector representation that approximates the multi-vector representation. This single vector can be used for fast initial retrieval using traditional vector search methods, and then the multi-vector representation can be used for reranking the top results.

Kacper Łukawski

September 05, 2025

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

Vector Search in Production

We gathered our most recommended tips and tricks to make your production deployment run smoothly.

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.

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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.

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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.

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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

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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.

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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