OpenAI's improved embedding model with 1536 dimensions offering 5x price reduction compared to ada-002, supporting Matryoshka Representation Learning for flexible dimension sizing.

OpenAI's flagship text embedding model with up to 3,072 dimensions, offering best-in-class performance and accuracy for English tasks with adjustable output sizes to optimize storage costs.

An embedding function that utilizes the OpenAI API to compute vector embeddings, commonly used with vector databases.

A pre-trained model used for extracting embeddings from content like PDFs, videos, and transcripts, which are then stored in vector databases for faster search.

Comprehensive guide to choosing embedding models covering performance, cost, domain specialization, multilingual support, and trade-offs between general-purpose and specialized models.

Foundational IR textbook that includes content on vector‑space models and retrieval, providing essential background for understanding vector search and hybrid retrieval in modern vector databases.