Skip to main content
HomeArtificial IntelligenceIntroduction to Embeddings with the OpenAI API

Introduction to Embeddings with the OpenAI API

Unlock more advanced AI applications, like semantic search and recommendation engines, using OpenAI's embedding model!

Start Course for Free
3 Hours11 Videos37 Exercises
3,308 LearnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
GroupTraining 2 or more people?Try DataCamp For Business

Loved by learners at thousands of companies


Course Description

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.
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp for BusinessFor a bespoke solution book a demo.

In the following Tracks

Developing AI Applications

Go To Track

OpenAI Fundamentals

Go To Track
  1. 1

    What are Embeddings?

    Free

    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.

    Play Chapter Now
    The wonderful world of embeddings!
    50 xp
    What are embeddings?
    50 xp
    Embeddings applications
    100 xp
    Creating embeddings
    100 xp
    Digging into the embeddings response
    100 xp
    Investigating the vector space
    50 xp
    Embedding product descriptions
    100 xp
    Visualizing the embedded descriptions
    100 xp
    Text similarity
    50 xp
    Computing cosine distances
    50 xp
    More repeatable embeddings
    100 xp
    Finding the most similar product
    100 xp
  2. 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!

    Play Chapter Now
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Developing AI Applications

Go To Track

OpenAI Fundamentals

Go To Track

Datasets

Online Retail ProductsNetflix Titles (Full)Netflix Titles (First 1000)

Collaborators

Collaborator's avatar
Chris Harper

Audio Recorded By

Emmanuel Pire's avatar
Emmanuel Pire
Emmanuel Pire HeadshotEmmanuel Pire

Senior Software Engineer, DataCamp

Emmanuel Pire is a senior software engineer at DataCamp, where he has been working since 2019. With over 15 years of experience as a self-taught web developer, Emmanuel brings a wealth of knowledge to the table. Based in Brussels, he has been actively exploring and experimenting with Large Language Models (LLMs) since the release of GPT-3. Passionate about the advancements in the field, Emmanuel closely follows developments in LLMs and has hands-on experience with prompt chains and agents.
See More
James Chapman HeadshotJames Chapman

Curriculum Manager, DataCamp

James is a Curriculum Manager at DataCamp, where he collaborates with experts from industry and academia to create courses on AI, data science, and analytics. He has led nine DataCamp courses on diverse topics in Python, R, AI developer tooling, and Google Sheets. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in high-redshift quasar detection. In his spare time, he enjoys restoring retro toys and electronics.

Follow James on LinkedIn
See More

What do other learners have to say?

Join over 14 million learners and start Introduction to Embeddings with the OpenAI API today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.