SingleStore

Vectors play a key role in generative AI. Typically, vector embeddings are used to represent natural language words and phrases as vectors – essentially quantifying their linguistic meaning – then a vector-capable database is employed to store those embeddings and retrieve them via semantic search (which is to say, based on the meaning of the natural language they represent). Accordingly, SingleStore is well-equipped to both store and retrieve vector data. In particular, as a multi-model database, it can leverage vectors in conjunction with other kinds of data much more easily than its pure-play counterparts. This significantly increases the range of techniques available to its search functionality.

To elaborate, SingleStore allows you to store vector data in relational data tables, directly alongside other data types, enabling you to better access any attributes or metadata attached to your vector data during search queries. It provides built-in functions for performing vector similarity calculations that can be leveraged using SQL, and conversely, it allows you to leverage standard SQL capabilities – joins, filters, and so on – within your vector similarity searches. For example, this can be used to add metadata filtering to your vector searches, or to create hybrid searches that combine vector similarity searching with SQL queries over other data types. This includes unstructured data types, such as JSON, full text, time-series, and spatial data. These combined searches are actioned within a single SQL query, and work by performing a vector similarity search, followed by whatever other kind of search, then adjusting the results accordingly.

At the same time, you are not restricted to using SQL: SingleStore’s semantic searches can themselves be actioned using natural language. Its own natural language processing capability is used to interpret the meaning of your natural language query, which it subsequently uses to create a corresponding vector embedding. Now in vector form, it is used to drive a vector similarity search, as above.

SingleStore uses a nearest-neighbour methodology for its similarity searches. Both approximate and exact search are available; the latter is only possible because of the database’s high performance, while the former can be made even faster by creating clusters of similar data, each of which can be discarded in its entirety (without checking each individual vector) if it falls sufficiently far from the query vector. You can also use set-based searches to find multiple matches using a single query, rather than performing several separate queries. This is accomplished using joins, making it another beneficiary of SingleStore’s multi-model capabilities.

For performance, SingleStore offers a distributed architecture that features built-in parallelisation and vector processing. This allows it to similarity match on high dimensional vectors, or at a large scale, in a performant fashion. In fact, you can use it to query your vector data in real time, providing immediate insights. Moreover, real time updating can drive continuous learning for your LLM, reducing or even removing the need for periodic retraining.

As a vector database, SingleStore provides many relevant advantages: high performance and scalability, production readiness, robust data type support, and so on. Its core value proposition is still simplifying your data architecture and replacing a complex system of databases with a single one, preventing or ameliorating issues with redundant data, excessive data movement, data inconsistency, and more (not to mention additional licensing costs).

This is especially significant when employing it as a vector database: many organisations want vector storage to power generative AI, and will therefore need to choose between a multi-model solution like SingleStore and adding yet another component into their environment. Given the choice between simplifying and complicating your application architecture, our guess is that most companies will prefer the former. If you are already a SingleStore customer, the choice seems even more obvious.

Moreover, multi-model capabilities benefit vector databases in a unique way, due to the particular usefulness of hybrid search, metadata filtering and so on in adding additional context and nuance to your vector searches. While it is possible to implement at least some of these capabilities using a pure-play vector database, it is far more difficult, and will almost certainly be less efficient, to do so.

The bottom line

SingleStore is highly effective and competitive, both as a multi-model database in general and as a vector database in particular. More than that, its multi-model nature substantially benefits its vector capabilities. In summary, if you need vector support and are suffering from a proliferation of databases, SingleStore should be at the top of your shortlist.