Introduction
Embeddings are numerical representations of text, images, or other data types that capture semantic meaning. OpenAI provides powerful embedding models that can be used for applications such as search, clustering, recommendation systems, and more. In this article, we will explore how to generate embeddings using OpenAI’s API and implement them in a real-world use case.
What Are Embeddings?
Embeddings transform words, sentences, or documents into fixed-length vector representations in a high-dimensional space. These representations help in tasks such as:
- Semantic search – Finding similar documents based on meaning.
- Clustering – Grouping similar content together.
- Recommendation systems – Suggesting relevant items.
- Text classification – Categorizing text automatically.