As we outlined in our post on the Ultimate Tech Trends Defining 2025, technology continues to advance at an astonishing pace. From groundbreaking developments in artificial intelligence (AI), such as the widespread use of ChatGPT, and quantum computing, to sustainable energy innovations and transformative consumer tech, the landscape is rapidly evolving. At the center of all these innovations is data, which has become the foundation of most modern applications. With data now a top priority across industries, many data management companies are competing to revolutionize the industry. MongoDB is one of the most popular data management companies that is redefining the industry.
This year, CNBC reported that MongoDB shares closed up nearly 13% after the software company surpassed fiscal first-quarter earnings expectations and raised its outlook, citing growing confidence in its cloud-based database service. One reason that confidence is so high is how the company has elevated the semantic search through its own vector search and made it a game-changer.
What is a Vector Search
A vector search is a type of similarity search that is a function of vector databases. Instead of searching through data stored in the tables or columns of relational databases or the flexible data models of NoSQL databases, a vector search collates data that has been converted into a vector. After being entered into a vector embedding model, an algorithm transforms the data into a vector, which is a sequence of numbers that represent each component of the data. The aim of vector search is to search and retrieve items that are similar to a given query item. Instead of comparing each item with the query item, vector search algorithms use techniques to quickly identify a subset of candidate items that are contextually or semantically similar.
For example, for video retrieval, a vector search would find similar videos based on comparable features such as genre, director, or title. This is how video recommendation systems enable personalized search results.
How is MongoDB’s Vector Search a Game-Changer
Not a Traditional Vector Database
MongoDB’s vector search is a game-changer in that it is not part of a traditional vector database. Instead, the vector databases at MongoDB are integrated into the MongoDB Atlas platform. Compared to a traditional vector database, a MongoDB Vector Search stores vector embeddings alongside the original data and metadata in Atlas. This ensures any updates or additions to the vector data are instantly synchronized, streamlining the architecture and offering a unified developer experience.
Allows for a Full Text Vector Search
The Atlas Vector Search is a managed service with its own indexing and querying mechanisms, allowing for hybrid search scenarios that combine text-based and vector-based searches. MongoDB’s Full-Text Search allows developers to store and index vector data, such as embeddings, feature vectors, or other numerical representations, within MongoDB documents. They can then perform a vector search on this data using MongoDB’s query capabilities.
By offering a full-text vector search, MongoDB has combined the advantages of its document database capabilities with its vector search function.
Seamless Integration with AI Technologies
The Atlas Vector Search can be easily integrated with popular AI providers and LLMs through their standard connection methods and APIs. MongoDB allows developers to use different frameworks to store custom data in Atlas and implement features of Atlas Vector Search. One such framework is LangChainGo, which is a framework that simplifies the creation of LLM applications in Go. By integrating Atlas Vector Search with LangChain, developers can use Atlas as a vector database and use the Atlas Vector Search to retrieve semantically similar documents from their data. As per the MongoDB website, “it also integrates seamlessly with ecosystem partners such as Google Vertex AI, AWS, Azure, and Databricks, ensuring proprietary business data enhances the performance and accuracy of AI-powered applications”.
MongoDB is at the forefront of the data management industry, and its Atlas Vector Search is evolving the capabilities of modern databases. With the company’s vector search function being such a game-changer, MongoDB will continue to lead the progression of vector searches.