A compact and efficient pre-trained sentence embedding model, widely used for generating vector representations of text. It's a popular choice for applications requiring fast and accurate semantic search, often integrated with vector databases.

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.

A collection of examples and guides from OpenAI, including best practices for working with embeddings, which are fundamental to vector search and vector database applications.

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

NVIDIA's generalist embedding model achieving record 69.32 score on MTEB benchmark. Fine-tuned from Llama architecture with improved techniques for training LLMs as embedding models.