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As organizations generate and store ever-increasing amounts of data, the need for more advanced search and retrieval methods grows. Traditional keyword-based search methods have their uses, but as data complexity increases, businesses now need to shift their strategies in order to empower the teams to access and gain insights from data and better meet customer expectations. This is where vector search comes into play as an important new part of MongoDB data strategy for enterprises.
What is vector search?
Vector search is a method of search based on similarities in a dataset based on numerical representations (vectors). This makes them different to traditional keyword-based searches, which are limited to looking for specific keywords or exact matches.
In a typical vector search workflow, each item – whether it’s a document, product, or image – is embedded into a vector. Vector search, especially in the context of approximate nearest neighbor search, allows the searching of vectors – numerical representations of data – to identify items that are most similar to the query.
Vector search and MongoDB: A seamless integration
While traditional databases like relational databases store structured data in tables, vector search and No SQL databases offer a new approach. It uses an entirely different architecture, one that focuses on managing and searching through a list of numbers (vectors) efficiently.
This is particularly useful for querying the varied data stored in No SQL databases and all cases where exact matches are not required, but similarity matters.
MongoDB, with its flexible document-based architecture, is well-suited to integrate vector search.
By storing and searching vectors as embedded data points, MongoDB Vector Search users can easily perform queries based on similarity rather than equality.
There are also tools, including Superlinkd and Studio 3T, that help make the interaction with Mongo DB seamless. Whether it’s building high-performing Generative AI applications or simplifying the management and retrieval of enterprise data.
Users are already finding they can get more from data with embeddings for MongoDB Vector Search and so proving an increasingly popular part of MongoDB data strategy for enterprises.
Why add vector search to your data strategy
The main reason vector search needs to be part of your data strategy is that it is the prominent way to effectively search in terms of similarity and not equality or identity. This will be critical as we move toward a world driven by artificial intelligence and machine learning.
Unlike SQL databases, where queries are typically based on exact identities (e.g., “where the price is greater than 20”), vector search allows for flexibility. It can find results that are “close enough” to what the user is searching for, which is essential as the complexity of data increases.
Vector search is the dominant method for reducing context sizes and improving efficiency. In GenAI operations, where users pay for tokens processed by large models, vector search enables the narrowing of context before querying, reducing unnecessary processing costs. It’s not just cost savings on offer, it can also help to mitigate the errors that even the most sophisticated LLMs have when the context becomes too large.
The consequences of inaction
No business wants to leave money on the table, but failing to implement vector search can result in exactly that.
With the rise of AI and large language models, users have greater expectations for search than they did just a few years ago. Many now expect search to handle complex, nuanced queries that go beyond simple keyword matching.
The need to stay on top of the technology is clear – in a world where users expect instant results, those who do not find what they want quickly may leave your site and never return. Vector search’s ability to help you deliver more relevant and faster results can capture attention, leading to increased engagement and higher conversion rates.
Who benefits from vector search?
Any industry with large amounts of complex data and low latency needs could benefit from vector search. Organizations in the healthcare and finance sectors find vector search improves search and retrieval of medical records, financial transactions, and more.
A bank could use it to detect fraudulent transactions by analyzing customer behavior and transaction patterns using vector embeddings, while hospitals could use it to find similar patient cases from its databases of medical images, X-rays, or MRI scans.
Other obvious beneficiaries of vector search are e-commerce and the travel industry. For example, when searching for accommodation, users often look for properties with certain attributes: price, location, amenities, and customer ratings.
Traditional search engines might struggle to deliver relevant results when the products are complex and vary significantly in their attributes. Vector search can effectively combine these attributes to provide highly relevant search results in real time.
Organizations always have a choice of how to arrange items or products on a page before somebody starts searching and traditionally users might see the most popular products. Vector search can allow them to personalize the page before the user starts to search. This means they can be fed items they are more likely to be interested in. For example, items popular with customers who also bought items they previously purchased.
There are some impressive real-world use cases of this action. By implementing a recommender system based on vector search, a UK-based flash-sale e-commerce platform achieved a 77% higher conversion rate, 68% increase in average order value and 60% growth in revenue per user.
The system predicted which products users were most likely to purchase, even before they started searching. This personalization led to a significant uptick in conversion rates, average order value and revenue per user as shoppers were shown the most relevant items right away.
Whatever the use case, the ability to quickly search vast amounts of data is great news for both decision-making and improving customer experiences.
Overcoming misconceptions and measuring success
Common concerns around adopting vector search include its perceived complexity and high cost. However, many vector database providers offer free tiers, allowing businesses to experiment with the technology before committing to full-scale implementation.
Tools like Studio 3T and Superlinked can simplify the process of working with vector data and embeddings, simplifying MongoDB complexity even for those without deep technical expertise.
Here’s how this could be brought to life: You use an Import Wizard to easily bring in vector data from various sources (CSV, JSON, SQL databases, etc.) and use a vector embedding solution. You use a query builder (with both Visual and JSON modes) to simplify the construction of complex $vectorSearch queries, and then you inspect and verify embeddings stored in MongoDB efficiently thanks to both Table and Tree views.
Now you might think “this all sounds great, but how much does it cost?”. It’s a fair question, as vector databases tend to be more expensive than traditional relational databases due to their more complex architecture and the large volumes of numerical data that need to be stored and processed. However, the key to justifying these costs lies in the efficiency and ROI they provide.
Measuring success is a key part of the process. While benchmarks in academia can be useful for a general sense of performance, they may not reflect real-world use cases. For instance, a system might score highly on a benchmark but underperform in actual business applications. Therefore, the true measure of success must be linked to desired business outcomes.
The most relevant metric? Revenue. As seen in a case study with a flash-sale e-commerce platform, companies that have implemented personalized solutions based on vector search have seen a significant increase in revenue. In this case, the use of vector search enabled the platform to predict which products users were most likely to buy, delivering a more personalized experience that directly boosted conversions. Running A/B tests to compare the effectiveness of vector search-based solutions versus non-implemented solutions is a clear way to measure success. If the solution generates more revenue, the investment in vector search is validated.
What’s next for organizations?
Vector search is poised for continued growth. Efficiency will be a key driver in this space, with companies focusing on driving costs down while improving performance.
An emerging trend in vector search is the embedding of not just text, but other types of data, including numerical data and more complex attributes. By doing this businesses are able to harness the full power of their complex data, delivering high-quality applications that allows them to reap the benefits of Generative AI.
As the technology evolves, specialized embedding models will become more refined, allowing for better handling of diverse data types. This evolution is essential for industries that deal with multi-modal data and need to combine different data points into a single cohesive search experience.
Time to make vector search part of your strategy
Vector search is a potentially game-changing technology that will form a key part of MongoDB data strategy for enterprises across multiple industries. By enabling search based on similarity rather than exact matches, you’re opening the door to more personalized customer service, greater efficiencies, and cost-savings.
As data becomes increasingly complex, businesses that adopt vector search early will be better positioned to stay ahead of the curve.
