The rapid spread of artificial intelligence technologies is also transforming data management approaches. One of the technologies that particularly stands out in this transformation is vector databases. Playing a critical role in areas such as semantic search, recommendation systems, and natural language processing, these systems have now become the focus of interest for traditional relational databases as well.
What is a Vector Database?
Vector databases convert unstructured data (text, images, audio, etc.) into numerical vectors and operate on these vectors similarity-based search ensures it is done. Moving beyond classic keyword searches, to the semantic similarity delivers data-driven results.
Where is it used?
- Chatbots: Creating semantic context in question-answering systems
- Search engines: Searching for similarity in text or visual content
- Recommendation systems: Personalized recommendations based on user behavior
- Anomaly detection and sentiment analysis
Requirements for a Healthy Vector Database
- High-performance Approximate Nearest Neighbor (ANN) search
- Advanced indexing methods such as HNSW or IVFFlat
- Ability to efficiently store high-dimensional vectors
- Ability to work compatibly with SQL and AI systems
- Security, scalability and easy integration
Why Is It So Important?
AI models, especially embedding-based in operating systems, it processes text or images by converting them into multi-dimensional vectors. The efficient storage and retrieval of these vectors directly affect the success of the model. Therefore vector database infrastructure, has become one of the fundamental components of modern AI applications.
How Are Popular RDBMS Databases Approaching This Trend?
PostgreSQL – Flexible and Open Source Solution with pgvector
- pgvector it offers vector search support with 3 different distance metrics (L2, cosine, inner product) via its extension (source: https://github.com/pgvector/pgvector)
- IVFFlat or HNSW High-performance searches can be performed thanks to its indexes (source: https://aws.amazon.com/blogs/database/accelerate-hnsw-indexing-and-searching-with-pgvector/)
- It is the most widely used vector solution in the open-source ecosystem.
- It is supported on major cloud platforms like AWS, Azure, GCP.
- Profelis provides consultancy services on PostgreSQL and pgvector installation, configuration, and enterprise support.
EnterpriseDB (EDB) – Optimized for Generative AI with Postgres AI
- In addition to the pgvector integration Pipelines offers in-database embedding support with the extension (source: https://www.enterprisedb.com/blog/path-vector-database-success)
- Ready-to-use structure for RAG (Retrieval-Augmented Generation) scenarios
- It is a high-performance and scalable enterprise PostgreSQL solution.
- Profelis provides licensing, support, and technical integration services as an authorized partner of EDB at Turkiye.
MariaDB – Directly Integrated Vector Type with MariaDB Vector
- comes with MariaDB 11.7 VECTOR it offers data type and HNSW index support (source: https://mariadb.com/docs/server/data-types/vector/)
- Available in open source and SkySQL cloud environments
- It is aimed at scenarios such as recommendation systems, similarity search, and embedding stores.
- Profelis specializes in MariaDB installation, migration, and high-availability configurations.
Result
Vector databases are now on the agenda not only of technology companies, but of organizations from every sector. A properly configured vector database both makes your existing data more meaningful and enables you to directly integrate artificial intelligence applications into your business processes.
The fact that common databases such as PostgreSQL, EDB, and MariaDB are keeping up with developments in this area and producing solutions shows that this transformation is permanent.
Future artificial intelligence applications will rely not only on smart algorithms, but also on feeding those algorithms to semantic data needs. Therefore, the transition to vector databases is not just a technology choice, strategic decision.
As Profelis, provides a wide range of services from open-source database solutions to enterprise system integrations, and we strengthen the data infrastructure of your artificial intelligence projects. With our experience in PostgreSQL, EDB, and MariaDB platforms, we can help you determine and implement the vector database solutions suited to your projects.
