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

Code Search via Embeddings

Index Python functions from the openai-python repository and search them with plain English queries. We cover function-level AST parsing, embedding code, and semantic code retrieval.

25 min

Reranking with Cross-Encoders

Two-stage search: bi-encoder for fast candidate retrieval + GPT as a cross-encoder for precise reranking. We cover the logit_bias trick for binary Yes/No classification.

25 min

Module 3

Classification and Clustering

Using embeddings for text classification with ML models and clustering transactions with auto-generated labels via GPT.

Text Classification via Embeddings

Predict a review's star rating (1-5) from its embedding using Random Forest. Full pipeline: embed → train_test_split → RandomForestClassifier → classification_report.

25 min

Transaction Clustering with Auto-generated Labels

K-Means clustering of financial transactions by embeddings + GPT generates human-readable cluster names. Visualization via t-SNE.

25 min

Module 4

RAG and Recommendations

Question Answering via knowledge base search and recommendation systems based on nearest-neighbor search.

Q&A via Embeddings-based Search

Search-Ask architecture: embed → rank Wikipedia sections → insert into GPT prompt → receive accurate answer. We compare this with fine-tuning and explain why RAG is more reliable.

25 min

Recommendations via Nearest Neighbor Search

Build a recommendation system for news articles: embed descriptions → cosine distances → k nearest neighbors. Cache embeddings in pickle to save API calls.

25 min