As we enter a new age of technology, one of the most important issues that the modern world must face is the collection, storage, and sorting of new types of data. Research shows that every day, over 2.5 quintillion bytes of new data is collected from sources including social media, IoT devices, search engines, and apps.
As we outlined in a previous post, a recent study conducted by Radboud University found that thousands of websites include trackers that gather email addresses without consent, with some sites also logging every keystroke that you make. This staggering amount of data collection is why The Conversation listed data privacy as such an important topic in today’s digital society.
While there are valid concerns around data, it cannot be denied that the type of data we can collect and how it can be used is evolving just as rapidly as other groundbreaking innovations such as artificial intelligence (AI) and machine learning (ML). Vector databases are one such innovation that is being used by many businesses across multiple industries, and at the forefront of this new type of database is MongoDB through their Atlas Vector Search.
Who is MongoDB?
Created in 2007 by Dwight Merriman and Eliot Horowitz under the name 10gen, MongoDB has evolved to become one of the leading data management companies in the world. As per the most recent data, MongoDB commands between 6.21% and 6.7% of the total market share in the database management system category. MongoDB’s main customers include information technology and services, computer software, and the Internet. Financial services, marketing, advertising, higher education, human resources, retail, hospitals, and healthcare also use the company. Their biggest service is their multi-cloud database Mongo DB Atlas, which allows users to conduct vector searches on their dedicated Atlas Vector Search Index.
A Guide to the MongoDB Atlas Vector Search
What is a Vector Search?
A vector search is critical for vector databases because of its distinct data retrieval method.
Compared to traditional databases that rely on exact matches, a vector search looks for results based on similarity. A guide to MongoDB’s vector databases outlines how this database is used for storing and organizing unstructured data, such as videos, books, social media posts, PDFs, and audio files. Multiple data points from these sources are converted into vector embeddings, usually a list of numbers, and put into indexes. This allows a vector search to easily identify content based on semantic similarity to narrow down the desired results. It is this application that powers large language machines (LLMs), such as the recommendation system on the e-commerce platform Google Shopping. The platform recently introduced a tailored feed that displays a constant stream of items shoppers might find interesting.
The Atlas Vector Search Features
Atlas Vector Search is a fully integrated system that enables customers to build intelligent applications powered by semantic search and generative AI for any data type. Below are the specific features offered by the Atlas Vector Search:
Atlas Search Index
The Atlas Search Indexes are separate from other databases and are used to create vector searches. The information on the Atlas Search Index can support embeddings that are less than and equal to 4096 dimensions in length and allow the user to pre-filter their data by indexing any boolean, date, numeric, objectId, string, and UUID fields. This makes the Atlas Search Index one of the most comprehensive data management systems on the market and available for multiple use cases.
Supports Multiple Vector Search Queries
The Atlas Vector supports semantic searches and hybrid searches. A semantic search uses an approximate nearest neighbor (ANN) or an exact nearest neighbor (ENN) search to find information from the vector index. An ANN search is used to find data points closest to a given query point with a certain level of approximation, while an ENN guarantees retrieval of the absolute closest vectors to your query, eliminating the accuracy limitations inherent in ANN. A hybrid search combines results from both semantic searches and full-text search queries. Because the Atlas Vector Search supports both ANN and ENN, making it ideal for results from both similarity and exact matches, it is one of the most versatile databases available.
AI Integrations
The Atlas Vector Search, through vector databases, can power natural language processing (NLP), machine learning (ML), and generative AI applications. As the MongoDB site details, “You can use Atlas Vector Search with popular chat and embedding models from AI providers such as OpenAI, AWS, and Google. MongoDB and partners also provide specific product integrations to help you leverage Atlas Vector Search in your AI-powered applications”. Venture Beat reported this year that Atlas Vector Search is now also integrated with Amazon Bedrock. This allows developers to sync their foundation models and AI agents with the proprietary data stored within MongoDB. This results in a more relevant, accurate, and personalized response using Retrieval Augmented Generation (RAG).
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Lucas Brown 
Sarah Durrani 
