AI Vector Generator
AI vector generators convert text, image, or speech objects into a mathematically computed string of numbers representing the object across many dimensions. The string of numbers (or vectors) represents various attributes of an object. In vector space, similar objects are adjacent to each other. An AI-driven natural language vector search can be used to find related objects more accurately than traditional database queries.
In design applications like Adobe Firefly, AI-based text prompts allow users to generate editable vector graphic image files.
Why are AI Vector Generators Important?
AI generator technology in the design world allows prompt-based creation of vector graphics, without which users would have a steep learning curve to overcome. Vector graphic generators can also make converting existing pixel-based images into vector graphics easy.
An AI prompt-driven interface can be used to write SQL statements to simplify database exploration for less technical users. Queries that access large tables using parallel or vectorized query plans to shorten response times.
In the case of highly dimensional data, vector queries can be used to return objects with similar attributes faster than queries expressed using SQL.
Using AI to Help Write SQL Queries
Many Business Intelligence tools employ chatbots that use prompted conversations to construct queries with the user that prompt them for requirements, as in the following example:
- Firstly, prompt what entities need to be queried. These could be sales by region, for example.
- A prompt can request what attributes are of interest. These could be products.
- What needs to be known about products? Perhaps descriptions and sales volumes.
- Do they need to be grouped? Perhaps results are grouped by store in the western region.
- Ordering? By volume, descending, Top 10?
What Does Vector Processing Do For Database Queries?
Vector processing enables a single query to be run as multiple threads that operate concurrently on table subsets to accelerate query processing. In a single session multiplex protocol (SMP) server, you can run as many threads as you have CPU cores to process each query thread. This scenario would be called vertical scalability.
As workloads grow larger, vertical scalability can become expensive. Configuring a multi-node cluster comprising several lower-cost servers is often more cost efficient. For example, a cluster made up of 4 x 32-core server systems costs less than a single 128-core server. For this reason, horizontal scalability using massively parallel processor (MPP) clusters is attractive.
Actian Vector is a database system that scales vertically in a single server and horizontally across servers in a cluster. Actian Vector is a component of the Actian Data Platform that can run on a cluster such as Hadoop on-premise and cloud platforms, including Google, AWS and Azure. This provides massively parallel query processing benefits wherever your data resides.
Vector Search
A recent development in the database world is Vector Search, which uses a mathematical algorithm to represent a database object and its attributes as a string of numbers. These vector representations of an object in a multi-dimensional space cluster are based on the similarities of their attributes.
When a user asks Chatbot to make query request items that have similar attributes, this can result in faster results without the use of any database indexing. For example, if the user wants to know about all clothing with a Christmas theme in a medium size ordered by popularity, just scanning the vector value column will provide the result.
Adobe Firefly AI Vector Image Generation
Adobe text-to-vector AI image generator Firefly can generate images based on text descriptions via prompts. Firefly even supports AI-driven editing capabilities that can adjust various photo settings, including depth of field and motion blur.
The Actian Data Platform
The Actian Data Platform provides a unified experience for ingesting, transforming, analyzing, and storing data.
The Actian Data Platform includes data integration to construct and automate data preparation pipelines. The Vector Columnar Database uses a highly scalable architecture that will parallelize query processing to exploit CPUs and cache in single servers and clusters to deliver the fastest query processing in the industry. Try the 30-day free trial by signing up here.
