The Flutter / Dart and Python binding of our database now enable “vector types”. In both languages these are more commonly referred to as “lists” and now you are able to efficiently store lists of numeric types, i.e. integers and floating point numbers (aka “vectors / vector embeddings”). Native support for that is crucial for data intensive applications, especially in the field of AI.
What are Vector embeddings? Multi-dimensional vectors are a central building block for AI applications. And accordingly, the ability to store vectors to add long-term memory to your AI applications (e.g. via vector databases) is gaining importance. This is what the ObjectBox database now supports natively.
Dart example code
Let’s assume some shapes that use a palette of RGB colors. This allows the shape to reference colors by their index. An entity for this might look like this:
| 1 2 3 4 5 6 7 8 | @Entity() class Shape { @Id() int id = 0; // An array of RGB color values that are used by this shape. Int32List? palette; } |
Python example code
Python is the number one programming language for AI. So let’s assume having an Image class that contains an URL to point to the content (e.g. JPEG/PNG images) and additionally a vector embedding. The latter are supplied by a ML model and contain a list of 32-bit floating points.
| 1 2 3 4 5 6 7 8 9 | @Entity(id=1, uid=1) class ImageEmbedding: id = Id(id=1, uid=1001) # Link to the actual image, e.g. on Cloud storage url = Property(str, id=2, uid=1002) # The vector embedding for this image (created via some ML model) embedding = Property(list, type=PropertyType.floatVector, id=3, uid=1003) |
There is more…
The support for vector types is not the only new feature. E.g. ObjectBox Flutter database comes with several fixes and our Python database binding now also supports date types. For details, please check the changelog for Dart DB vector release or Python DB vector release.