
About the Embedding Models in Generative AI

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 or 1024 numbers, depending on the embedding
 model that you choose.
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. 
The following pretrained models are available for creating text embeddings in English and
 other languages:
cohere.embed-english-v3.0
cohere.embed-multilingual-v3.0
cohere.embed-english-light-v3.0
cohere.embed-multilingual-light-v3.0
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.
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.
Embedding Model Parameter
When using the embedding models, you can get a different output by changing the
 following parameter.
Truncate
Whether to truncate the start or end tokens in a sentence, when that sentence exceeds
 the maximum number of allowed tokens. For example, a sentence has 516 tokens, but the
 maximum token size is 512. If you select to truncate the end, the last 4 tokens of that
 sentence are cut off.
