Unlock more advanced AI applications, like semantic search and recommendation engines, using OpenAI's embedding model!
Text embedding models create numerical representations from text inputs. This ability to encode text and capture its semantic meaning means that embedding models underpin many types of AI applications, like semantic search engines and recommendation engines. In this course, you'll learn how to harness OpenAI's Embeddings model via the OpenAI API to create embeddings from text datasets and begin developing real-world applications. You'll also take a big step towards creating production-ready applications by learning about vector databases to efficiently store and query embedded texts.
Text embedding models create numerical representations from text inputs. This ability to encode text and capture its semantic meaning means that embedding models underpin many types of AI applications, like semantic search engines and recommendation engines. In this course, you'll learn how to harness OpenAI's Embeddings model via the OpenAI API to create embeddings from text datasets and begin developing real-world applications. You'll also take a big step towards creating production-ready applications by learning about vector databases to efficiently store and query embedded texts.
