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.
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
Why add-on vector search looks good — until you actually use it.
Evgeniya Sukhodolskaya & Andrey Vasnetsov
February 17, 2025
Discover what a vector database is, its core functionalities, and real-world applications.
Sabrina Aquino
October 09, 2024
In this article, we'll teach you about compression methods like Scalar, Product, and Binary Quantization. Learn how to choose the best method for your specific application.
Sabrina Aquino
September 25, 2024
Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.
Sabrina Aquino
March 19, 2024
Uncover the necessity of vector databases for RAG and learn how Qdrant's vector database empowers enterprise AI with unmatched accuracy and cost-effectiveness.
David Myriel
February 27, 2024
Discover the power of vector embeddings. Learn how to harness the potential of numerical machine learning representations to create a personalized Neural Search Service with FastEmbed.
Sabrina Aquino
February 06, 2024
Learn what sparse vectors are, how they work, and their importance in modern data processing. Explore methods like SPLADE for creating and leveraging sparse vectors efficiently.
Nirant Kasliwal
December 09, 2023
Why vector search requires a dedicated service.
Andrey Vasnetsov
November 30, 2023
