Enable Powerful AI ApplicationsEmbeddings 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 APIThe 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 EnginesTraditional 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 DatabasesAI 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.
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.The wonderful world of embeddings!50 xpWhat are embeddings?50 xpEmbeddings applications100 xpCreating embeddings100 xpDigging into the embeddings response100 xpInvestigating the vector space50 xpEmbedding product descriptions100 xpVisualizing the embedded descriptions100 xpText similarity50 xpComputing cosine distances50 xpMore repeatable embeddings100 xpFinding the most similar product100 xp
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!Semantic search and enriched embeddings50 xpEnriching embeddings100 xpSorting by similarity100 xpSemantic search for products100 xpRecommendation systems50 xpProduct recommendation system100 xpAdding user history to the recommendation engine100 xpEmbeddings for classification tasks50 xpEmbedding restaurant reviews100 xpClassifying review sentiment100 xpEmbedding more detailed descriptions100 xp
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.Vector databases for embedding systems50 xpTo metadata or not to metadata?100 xpChoosing a vector database solution50 xpCreating vector databases with ChromaDB50 xpGetting started with ChromaDB100 xpEstimating embedding costs with tiktoken100 xpAdding data to the collection100 xpQuerying and updating the database50 xpQuerying the Netflix collection100 xpUpdating and deleting items from a collection100 xpMultiple queries and filtering50 xpQuerying with multiple texts100 xpFiltering using metadata100 xpCongratulations!50 xp
In the following tracksOpenAI Fundamentals
Audio Recorded By
Emmanuel PireSee More
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.
James ChapmanSee More
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.
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