As industries across the globe continue to evolve rapidly through advancements in AI and ML, a surge in large language models, and a need to automate repetitive database management processes, more companies are investing in vector databases.
Such is the rapid expansion and demand of this sector that MarketsandMarkets forecasts that the global Vector Database Market size is expected to grow from $1.5 billion in 2023 to $4.3 billion by 2028 at a CAGR of 23.3%. One of the key players in the Vector Database Market is MongoDB. MongoDB is a global leader in the software industry. It was founded in 2007 by the American software company 10gen as a planned platform-as-a-service product before being developed as an open-source development model in 2009. Today, MongoDB has a market cap of over $18 billion with a mission to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. At the forefront of their services is the Atlas Vector Search.
What is a Vector Database
A vector database is a different way of storing and searching for data than a traditional search. A traditional search works well if you have specific details and are looking for an exact result. In contrast, a vector search works better for identifying and retrieving information that is not just identical but similar to the request. A vector in maths and physics is a quantity with both magnitude (or size) and direction that can be broken down into many different components. In terms of a database, a vector can represent any data mode, from text and images to videos and audio. The vector database then converts the data into numbers where each data point is represented by a vector in high-dimensional space. This then allows for searches in image and video recognition, natural language processing, text search, recommendation systems, and emerging applications.
MongoDB’s Atlas Vector Search
How it Differs to a Regular Vector Search
The Atlas Vector Search is the latest addition to the MongoDB developer data platform. The main function of MongoDB’s Atlas Vector Search is to enable customers to “build intelligent applications powered by semantic search and generative AI over any type of data.” Unlike other vector databases, which required the developer to choose a bolt-on vector database, which would add another tool to the tech stack, or juggle a mix of search tools and open-source solutions, Atlas Vector Search simplifies designing applications through semantic searching and generative AI. This means that even if the user doesn’t know what they are looking for, the Atlas Vector Search can return applicable results based on the meaning of the request. The example MongoDB gives is how “a search for “ice cream” would return “sundae,” even if the user didn’t know sundaes existed.” This Medium Rofl Facts Guide to using the Atlas Vector Search for a pizza chain demonstrates how the database works. They leverage the Atlas Vector Search features “to cut down on the time it takes to manually search for every type of cheese by just entering “cheese,” which will automatically return all of the varieties because they are semantically related.”
Improved Search Experiences
Using MongoDB’s Atlas Vector Search, users can create search experiences that address use cases that traditional search tools cannot. These include semantic searches – which are context-driven searches; enhanced recommendations – to suggest related items to the initial search; diverse media searches – the ability to search for specific details in images and audio files; hybrid searches – combining the strengths of vector search with traditional full-text searching, and long-term memory for large language models – providing proprietary business data context to large language models.
Integration With AI-Models
MongoDB’s Atlas Vector Search uses generative AI for any data type as AI becomes further integrated across the economy. This has been achieved by integrating seamlessly with ecosystem partners such as Google Vertex AI, AWS, Azure, and Databricks. This ensures that the proprietary business data enhances the performance and accuracy of AI-powered applications. Earlier this year, MongoDB Atlas Vector Search integrated with Amazon Bedrock, which is a fully managed library of generative AI foundation models. According to the report, “Bedrock is a service from Amazon Web Services Inc. that provides access to foundation models from numerous providers, including AI21 Labs, Amazon.com Inc., Anthropic PBC, Cohere Inc., Meta Platforms Inc., Mistral AI, and Stability AI Ltd”. By combining Amazon Bedrock with Atlas Vector Search, users are now able to “build large language models using company data converted into vectors for customization without the need for building a new model.” This will allow many organizations to create generative AI specific to their needs.
