Course
Introduction to Embeddings with the OpenAI API
IntermediateSkill Level
Updated 03/2026
OpenAIArtificial Intelligence3 hr11 videos37 Exercises3,000 XP18,931Statement of Accomplishment
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Enable Powerful AI Applications
Embeddings allow us to represent text numerically, capturing the context and intent behind the text. You'll learn about how these abilities can enable semantic search engines, that can search based on meaning, more relevant recommendation engines, and perform classification tasks like sentiment analysis.Create Embeddings Using the OpenAI API
The OpenAI API not only has endpoints for accessing its GPT and Whisper models, but also for models for creating embeddings from text inputs. You'll create embeddings using OpenAI's state-of-the-art embeddings models to capture the semantic meaning of text.Build Semantic Search and Recommendation Engines
Traditional search engines relied on keyword matching to return the most relevant results to users, but more modern techniques use embeddings, as they can capture the semantic meaning of the text. You'll learn to create a semantic search engine for a online retail platform using OpenAI's embeddings model, so users can more easily find the most relevant products. You'll also learn how to create a product recommendation system, which are built on the same principles as semantic search.Utilize Vector Databases
AI applications in production that rely on embeddings often use a vector database to store and query the embedded text in a more efficient and reproducible way. In this course, you’ll learn to use ChromaDB, an open-source, self-managed vector database solution, to create and store embeddings on your local system.Prerequisites
Working with the OpenAI APIPython Toolbox1
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.
2
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!
3
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.
Introduction to Embeddings with the OpenAI API
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FAQs
Which tools and libraries are used in this course?
You will use the OpenAI Embeddings API to generate embeddings, scikit-learn for t-SNE visualization, SciPy for cosine similarity, and ChromaDB as the vector database.
What kind of applications can I build after this course?
You will be able to build semantic search engines, recommendation systems, and text classifiers powered by embeddings, with ChromaDB handling storage and retrieval at scale.
Is this course suitable for beginners?
This course assumes you are comfortable with Python and working with APIs. The first chapter starts with creating embeddings through the OpenAI API, so no prior embeddings knowledge is required.
Who is this course for?
Python developers, data scientists, and machine learning engineers who want to build AI applications such as semantic search, recommenders, or classifiers using OpenAI embeddings and vector databases.
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