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.

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.

A course focused on mastering multimodal Retrieval Augmented Generation (RAG) and embeddings, which are fundamental components often stored and managed by vector databases.

A curated list of vector database solutions, libraries, and resources tailored for AI applications. Categorizes items by license and type, providing a valuable directory for those seeking vector database technologies.