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Introduction to Embeddings with the OpenAI API

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

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Descripción del curso

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.
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En las siguientes pistas

Desarrollo de aplicaciones de IA

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Fundamentos de OpenAI

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  1. 1

    What are Embeddings?

    Gratuito

    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.

    Reproducir Capítulo Ahora
    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!

    Reproducir Capítulo Ahora
Empresas

Group¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

En las siguientes pistas

Desarrollo de aplicaciones de IA

Ir a la pista

Fundamentos de OpenAI

Ir a la pista

Sets De Datos

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

Colaboradores

Collaborator's avatar
Chris Harper

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Emmanuel Pire's avatar
Emmanuel Pire
Emmanuel Pire HeadshotEmmanuel Pire

Senior Software Engineer, DataCamp

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