IntermediateИнженерия

OpenAI Embeddings: Complete Guide

Official OpenAI examples: classification, clustering, semantic search, Q&A, recommendations, code search, and reranking with cross-encoders. Real Python code from the OpenAI team.

4modules

8lessons

200 mintotal time

Developers building search systems with OpenAIaudience

Module 1

Embedding Fundamentals

What embeddings are, how to obtain them via the OpenAI API, and core patterns for working with vector representations.

Embeddings: Basic Usage

Obtain vector representations of text via the OpenAI API. We cover the text-embedding-3-small model, exponential backoff with tenacity, and best practices for batch processing.

25 min

Module 2

Search and Reranking

Semantic search over text and code, plus reranking with cross-encoders to improve result precision.

Semantic Text Search

Build a search engine over product reviews: embed query → cosine similarity against all documents → top-N results. Full working example on the Amazon Fine Food Reviews dataset.

25 min

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OpenAI Embeddings: Complete Guide

Embedding Fundamentals

Search and Reranking

Classification and Clustering

RAG and Recommendations