In our eight-part video series, Unleashing AI with PostgreSQL, EDB Chief Architect for Analytics and AI Torsten Steinbach explores different facets of the AI and data landscape. We hope you have a chance to watch the videos and gain a deeper understanding of the essential elements of an AI data platform such as EDB Postgres AI.
Our previous blogs in this series explored the AI landscape, the definition and uses of a data lakehouse, and gave an overview of generative AI. Our discussion wouldn’t be complete without highlighting vector databases and the crucial role they play in generating AI and building AI applications. This week on our blog we're sharing a summary and insights from the fourth video in the series, The Path to Success with a Vector Database.
EDB Chief Architect for Analytics and AI, Torsten Steinbach explains the critical role vector databases play in AI implementations. Watch the video here.
What is a vector database?
As AI becomes mission-critical, vector databases also become mission-critical. So it’s important to understand how they work and what to look for when selecting a vector database.
Vector databases store vector embeddings (lists of numbers) representing the semantics of data, like documents or images, in a numeric space. The core function of a vector database is to enable vector searches to retrieve vectors that are semantically similar (numerically close) to a given query vector.
When working with high-dimensional data, especially in applications like recommendation engines, image search, and natural language processing, vector similarity search is a critical capability. Many AI applications involve finding similar items or recommendations based on user behavior or content similarity. The powerful Postgres pgvector extension is designed to perform vector similarity searches quickly and easily, making it the ideal choice for recommendation systems, content-based filtering, and similarity-based AI tasks.
Since vector searches often occur in interactive AI applications like chatbots or copilots, vector databases must perform fast, efficient vector searches to ensure responsive user experiences.
To achieve high-performance vector searches, vector databases rely on vector indexes. These indexes accelerate and streamline searches. However, the time it takes to build vector indexes must be taken into consideration.
What to consider when choosing a vector database
Beyond performance in vector searches, several other factors are crucial when selecting a mission-critical vector database:
- Accuracy: Vector indexes enable approximate nearest-neighbor searches, but the accuracy of results can vary based on the indexing technique employed.
- Dynamic data handling: As data changes, new vector embeddings need to be generated and ingested; the database must support real-time ingestion and transactional updates to vector data and indexes.
- Scalability: Mission-critical vector databases must be able to handle increasing volumes of vector data, searches, and concurrent workloads as the AI application grows.
- Security: Fine-grained access control mechanisms can ensure only authorized data is retrieved for specific users or applications.
- High availability: Robust backup, restore, and failover capabilities are necessary to maintain uninterrupted service for mission-critical AI applications.
- Hybrid search: The ability to combine vector similarity searches with structured data filters or operations enables more complex queries that leverage both vector and tabular data.
A mission-critical vector database must address all of these factors to support demanding AI workloads in an AI environment while ensuring data security, availability, and the flexibility to integrate with existing data sources and structured information.
Support vector data initiatives with EDB Postgres AI
Because Postgres supports vectors, developers and engineers no longer have to deal with complex data transfer methods in and out of Postgres. The benefit of having vector data in Postgres is that you can still use your domain knowledge and use vectors to enhance your search experience.
By using the pgvector extension to leverage EDB Postgres AI as a full-featured AI database, you can seamlessly store, query, and analyze vector embeddings (text, video, or images) without adding operational overhead. It couldn’t be easier to run enterprise-grade Postgres on any cloud, from edge to core. Reach out for a demo.
Read the white paper: Intelligent Data: Unleashing AI with PostgreSQL