Introduction to Embeddings with the OpenAI API

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.

• What are Embeddings?
• Discover how embeddings models power many of the most exciting AI applications. Learn to use the OpenAI API to create embeddings and compute the semantic similarity between text.
• Embeddings for AI Applications
• Embeddings enable powerful AI applications, including semantic search engines, recommendation engines, and classification tasks like sentiment analysis. Learn how to use OpenAI's embeddings model to enable these exciting applications!
• Vector Databases
• To enable embedding applications in production, you'll need an efficient vector storage and querying solution: enter vector databases! You'll learn how vector databases can help scale embedding applications and begin creating and adding to your very own vector databases using Chroma.