Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

DataCamp

Introduction to Embeddings with the OpenAI API

via DataCamp

Overview

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

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.

Syllabus

  • 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.

Taught by

Emmanuel Pire and James Chapman

Reviews

Start your review of Introduction to Embeddings with the OpenAI API

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.