Turning data into vectors using state of the art neural network models.


What are the benefits of vectorizing data?

Neural vectors embody semantics and concepts and enable better similarity comparisons of data. Such quality allows a large category of use cases including search, recommendations, predictions, etc.

How to vectorize data on Relevance AI's platform

Vectorizing is to use the state of the art data representation relying on the latest development in AI and machine learning. Learn more about vectors at What are vectors.
Relevance AI's platform provides you with a no-code workflow to vectorize your "text" and "image" data with a few clicks.

Once you have uploaded your data, select your dataset, click on Vectorize under Workflows and follow the instruction.


Relevance AI - Vectorize a text field

Keep in mind that you need to specify the data type.


Relevance AI - Specifying the correct data-type for vectorizing

At the step, where you are selecting a model, a list of available vectorizers is shown with a brief description of each model. In general, MPNet and CLIP are good models for processing text and image data types respectively.


Relevance AI - Available vectorizers

To vectorize images, your dataset must contain the URLs to the images. Select the field (e.g. image-url), specify the type as image and select your desired image model (e.g. CLIP).

Running the vectorizer will add the corresponding vector field ("descriptionmpnet_vector" in our example) to your dataset that can be seen under the Dataset -> Monitor -> Vectors.


Relevance AI - Monitor vector fields in the dataset

More details on the workflows are available at Experimentation workflow.

Next step: analyze your data using techniques such as clustering or vector search.