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

Vector Database 101

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

Approximate Nearest Neighbor Oh Yeah (Annoy)

A guide to the new AI Stack - ChatGPT, your Vector Database, and Prompt as code

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OpenAI's ChatGPT and the new AI Stack - ChatGPT, your Vector Database, and Prompt as code

OpenAI's ChatGPT

The ever-increasing amount of unstructured data requires a paradigm shift and a new category of database management system - the vector database.

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An introduction to the concepts related to vector database.

What is a Vector Database?

Consensus-based replication is a widely-adopted strategy in many cloud-native, distributed databases. However, it is not a perfect solution. 


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Why consensus-based replication algorithm is not the silver bullet for achieving data consistency in distributed databases?

Raft or not? The Best Solution to Data Consistency in Cloud-native Databases

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.

Applications of Vector Similarity Search

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Making Machine Learning More Accessible for Application Developers

From Siri to chatbots, the applications of natural language processing are ubiquitous in our daily life. Learn about 7 models that represent the state of the art of NLP.


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Cheatsheet for BERT, XLNet, RoBERTa, ALBERT, StructBERT, T5, and ELECTRA.

7 Basic NLP Models to Empower Your ML Application

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)

NLP makes our lives much easier. Learn about all the basics of NLP, including its applications, algorithms, and more.

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From Siri to chatbots, from virtual support agents to knowledge graphs, the applications NLP are ubiquitous in our daily life.

Top 5 Real-World Applications for Natural Language Processing

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

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

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

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

Index and Libraries

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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 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

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 your own trademark image similarity search system that could save you from intellectual property lawsuits.

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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