Mastering Generative AI with OpenAI

Using Word Embeddings For Dynamic Context

Section Intro

Welcome to your guide on leveraging word embeddings to provide dynamic, relevant context for large language models. In this lesson, you will:

  • Understand what word embeddings are and why they’re crucial
  • Learn to perform similarity searches on embedding vectors
  • Augment prompts to GPT-3.5 Turbo using retrieved context

By the end of this tutorial, you’ll be able to integrate custom datasets into your chatbot workflows, enhancing accuracy and relevance.

Prerequisites

Ensure you have:

  • A basic familiarity with Python
  • An OpenAI API key
  • The openai Python package installed

What Are Word Embeddings?

Word embeddings map text tokens into numeric vectors where semantic similarity is preserved. Models like OpenAI Embeddings transform words, sentences, or documents into high-dimensional vectors.

  • Similar tokens lie close together in vector space
  • Enables efficient semantic search and clustering

Why Use Embeddings for Contextual Retrieval?

When working with large language models, embedding-based retrieval lets you:

  1. Maintain relevance—fetch only the most pertinent snippets
  2. Scale gracefully—index millions of documents
  3. Reduce prompt size—include concise context instead of entire texts

Lesson Objectives

StepDescription
1. Define EmbeddingsExplain embedding concepts and dimensionality
2. Perform Similarity SearchCompute cosine similarity to find nearest vectors
3. Augment GPT-3.5 Turbo PromptsDynamically insert retrieved context into your API requests

Ready to dive in? Let’s explore how to generate and query embeddings in Python.


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