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
openaiPython 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:
- Maintain relevance—fetch only the most pertinent snippets
- Scale gracefully—index millions of documents
- Reduce prompt size—include concise context instead of entire texts
Lesson Objectives
| Step | Description |
|---|---|
| 1. Define Embeddings | Explain embedding concepts and dimensionality |
| 2. Perform Similarity Search | Compute cosine similarity to find nearest vectors |
| 3. Augment GPT-3.5 Turbo Prompts | Dynamically insert retrieved context into your API requests |
Ready to dive in? Let’s explore how to generate and query embeddings in Python.
Links and References
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