Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other
data types in a dense vector space, where semantically similar items are located closer to each other.
This representation enables more effective semantic searches, where the goal is to retrieve
information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually
relevant searches. For example, if a user searches for "financial reports," an embedding model can
understand that "quarterly earnings" is semantically related, even if the exact phrase does not
appear in the document. This capability greatly enhances the accuracy and relevance of search
results, making it a powerful tool for handling large and diverse datasets .
