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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.
Ensure you have:
  • A basic familiarity with Python
  • An OpenAI API key
  • The openai Python package installed

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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

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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

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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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