AristaDBA's Oracle Blog....

vector embedding models quantify features or dimensions. As an example , we have the word LION. And we want to convert that into a vector to represent what that word means. So we use a vector embedding model to generate an embedding and store that as a vector in a vector type column. The models can be pretrained open source models. They can also be based on your own data set. Now, depending on the type of data, you can use different pretrained open source models as examples for textual data. There are sentence transformers which transform words, sentences or paragraphs into vector embeddings. For visual data, you can use residual network, also known as ResNet, to generate vector embeddings. For audio data, you can use the visual spectrogram representation of the audio data in order to fall back into the visual data case. Now, there are many different types of models. And depending on the model, they have different numbers of dimensions. As an example, Cohere’s embedding model has 1,024 dimensions. OpenAI’s embedding model, text-embedding-3-large, has 3,072 dimensions. And Hugging Face’s embedding model, all-MiniLM-L6-v2, has 384 dimensions. You can also create your own model that is trained with your own data set. Now we’re going to talk more on generating vector embeddings. You can do that either outside the Oracle database or within the Oracle database. In order to do that within the Oracle database, you can import a model, as long as it is in the ONNX format. Now ONNX stands for Open Neural Network Exchange. And it is a standard. Oracle Database implements an ONNX runtime directly within the database. This allows you to generate vector embeddings directly within the Oracle database using SQL.

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In this post, we shall see very quickly how to create a table with vector embeddings and select the same. Remember, vectors are an array of...