Buckle up for the first tutorial in our Vector Database 101 series and untangle the intricacy around Milvus with us every week. 

Vector Database 101

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Everything about unstructured data - what they are, how they differ from traditional data types, and the paradigm shift called for by the need to efficiently process them.

Introduction to Unstructured Data

Learn what a vector database is and how it can revolutionize your AI apps. Get started today!

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An introduction to the concepts related to vector database.

What is a Vector Database?

In this post, we provide a deep dive into better understanding vector databases and comparing them to vector search libraries and vector search plugins.

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Understanding Vector Databases: Compare Vector Databases, Vector Search Libraries, and Vector Search Plugins

Zilliz tells the story about building the world's very first open-source vector database, Milvus. The start, the pivotal, and the future.

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How we built the world's most popular open-source vector database.

Introduction to Milvus Vector Database

Milvus vector database supports two modes of deployments: standalone and cluster. 

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Getting started with Milvus - the world's most popular open-source vector database.

Milvus Quickstart: Install Milvus Vector Database in 5 Minutes

Learn what vector search is and the metrics pertinent to decide the distance (or similarity) between objects.

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Vector similarity search explained through a word embedding example.

Introduction to Vector Similarity Search

This tutorial analyzes the components of a modern indexer before going over two of the simplest and most basic indexing strategies - flat indexing and inverted file (IVF) indexes.

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Flat indexing and inverted file (IVF) indexes are two basic indexing strategies.

Vector Index Basics and the Inverted File Index

A hands-on dive into scalar quantization (integer quantization) and product quantization with Python.

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Learn about Scalar Quantization and Product Quantization

Vector Database 101 - Scalar Quantization and Product Quantization

Learn about the most commonly used primary algorithm today: HNSW, how it performs in terms of speed and accuracy.

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All about Hierarchical Navigable Small Worlds (HNSW)

Hierarchical Navigable Small Worlds (HNSW)

Explore the efficiency and accuracy of Annoy, the game-changing approximate nearest neighbors algorithm.  

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Annoy: A Deep Dive into Approximate Nearest Neighbors - Vector Database 101

Approximate Nearest Neighbors Oh Yeah (Annoy)

Understanding in-memory vector search algorithms, indexing strategies, and guidelines on choosing the right vector index for your project.

Index and Libraries

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Guidelines on choosing the right vector index for your project

Choosing the Right Vector Index for Your Project

In this tutorial, we'll dive into DiskANN, a graph-based vector index that enables large-scale storage, indexing, and search of vectors by persisting the bulk of the index on NVMe hard disks. We'll first cover Vamana, the core data structure behind DiskANN, before discussing how the on-disk portion of DiskANN utilizes a Vamana graph to perform queries efficiently. Like previous tutorials, we'll also develop our implementation of the Vamana algorithm in Python.

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DiskANN and the Vamana Algorithm

Learn how to use a vector database to build your own trademark image similarity search system that could save you from intellectual property lawsuits.

Applications of Vector Similarity Search

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Learn how to apply vector similarity search in the industry of IP protection.

Image-based Trademark Similarity Search System: A Smarter Solution to IP Protection

The HM-ANN algorithm was recently introduced in a collaborative research paper co-written by authors from Microsoft Research and the University of California. This novel algorithm for graph-based similarity search offers distinct advantages over existing state-of-the-art ANN search solutions.

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Discover the distinct advantages the novel HM-ANN algorithm offers.

HM-ANN Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory

Learn how to use a vector database to build an intelligent outfit planning app that can search for similar garments.

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A case of applying vector similarity search to the fashion industry.

How to Make Your Wardrobe Sustainable with Vector Similarity Search

Learn what PG-based ANNS is and how to optimize the algorithm to achieve a trade-off between search accuracy and efficiency.

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A research into PG-based ANNS algorithm, which can help us achieve more accurate results without having to compare and evaluate more vectors in the dataset.

Proximity Graph-based Approximate Nearest Neighbor Search

Learn how to build an intelligent image similarity search system for online shopping using a vector database.

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An Intelligent Image Similarity Search System for Online Shopping.

How to Make Online Shopping More Intelligent with Image Similarity Search?

Learn how to use a vector database to build your own intelligent similarity search system for design assets that could greatly help designers ramp up their creative production.


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Get more inspirations for your graphical design using vector similarity search.

An Intelligent Similarity Search System for Graphical Designers

Learn about three types of attribute filtering in vector similarity search and explore our optimized solution to improve the efficiency of similarity search.

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An easy and integrated filtering solution to make vector similarity search even faster and more accurate.

 How to Best Fit Filtering into Vector Similarity Search?

Learn how to use vector similarity search to build an automated solution for identifying and filtering out duplicate video content from archive storage.

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Learn how to use a vector database to build an intelligent video deduplication system.

Building an Intelligent Video Deduplication System Powered by Vector Similarity Search

Discover ways to extract useful information from unstructured data sources in a scalable manner by the use of “embeddings.”

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Easiest ways to perform image-to-image and text-to-image similarity search.

Powering Semantic Similarity Search in Computer Vision with State of the Art Embeddings

Building a Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus, the most advanced open-source vector database.

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Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus

Supercharged Semantic Similarity Search in Production

Without vector indexing, many modern applications of AI would be prohibitively slow. Discover how indexes dramatically accelerate vector similarity search, different types of indexes, and how to choose the right index for your next AI application.


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Discover how indexes dramatically accelerate vector similarity search, different index types, and how to choose the right index for your AI application.

Accelerating Similarity Search on Really Big Data with Vector Indexing (Part II)

This article is dedicated to going a bit more in-depth into embeddings/embedding vectors, along with how they are used in modern ML algorithms and pipelines.

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What are embeddings and how are they used in modern ML algorithms and pipelines

Understanding Neural Network Embeddings

We aim to supercharge the development of applications that require embeddings and other ML tasks.

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Making Machine Learning More Accessible for Application Developers

Introducing an Open Source Vector  Database Benchmark Tool for Choosing the Ideal Vector Database for Your Project.

Open Source Vector Database Benchmark Tool

Introducing an Open Source Vector Database Benchmark Tool for Choosing the Ideal Vector Database for Your Project

Learning Series Just for You

Vector Database 101: Everything You Need to Know

Exploring Vector Database Use Cases

How to Pick the Right Vector Database for Your Use Case

The Definitive Guide to Building RAG Apps with LlamaIndex

Natural Language Processing (NLP) Basics

Accelerated Vector Search

Natural Language Processing (NLP) Advanced Guide

Large Language Models (LLMs) 101

Easy to use, blazing fast open source vector database