About the Embedding Models in Generative AI

The OCI Generative AI embedding models transforms each phrase, sentence, or paragraph that you input, into an array with 384 (light models) or 1024 numbers, depending on the embedding model that you select.

You can use these embeddings for finding similarity in phrases that are similar in context or category. Embeddings are typically stored in a vector database. Embeddings are mostly used for semantic searches where the search function focuses on the meaning of the text that it's searching through rather than finding results based on keywords.

Available Models

The following pretrained models are available for creating text embeddings:

• cohere.embed-english-v3.0
• cohere.embed-multilingual-v3.0
• cohere.embed-english-light-v3.0
• cohere.embed-multilingual-light-v3.0

Choosing a Model

• Use the Cohere Embed English models to generate text embeddings from English documents.
• Use the Cohere Embed Multilingual models when:
  • Instead of English, the documents are written in one of the supported languages.
  • The documents are written in more than one language and those languages are one of the supported languages.

Visualizing the Embeddings

To visualize the outputs with embeddings, output vectors are projected into two dimensions and plotted as points in the Oracle Cloud Console. Points that are close together correspond to phrases that the model considers similar. Click Export output to get an array of 1024 vectors for each embedding saved in a JSON file.

Use Cases

The following uses cases are ideal for text embeddings.

• Semantic search: Search through call transcripts, internal knowledge sources, and so on.

• Text classification: Classify intent in customer chat logs and support tickets.
• Text clustering: Identify salient topics in customer reviews or new data.
• Recommendation systems: Represent podcast descriptions, for example, as a numerical feature to use in a recommendation model.