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-v3.0cohere.embed-multilingual-v3.0cohere.embed-english-light-v3.0cohere.embed-multilingual-light-v3.0cohere.embed-english-light-v2.0
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 truncated.