OpenAI Embeddings API

Convert text into high-dimensional vectors for semantic search, clustering, and recommendation engines using models like text-embedding-3-small.

OpenAI Embeddings API translates natural language into numerical vectors (lists of floating-point numbers) that capture semantic meaning. By measuring the distance between these vectors, developers can perform complex tasks like retrieval-augmented generation (RAG) or anomaly detection. The latest text-embedding-3-large model supports up to 3072 dimensions, providing high accuracy for dense information retrieval at a cost of $0.00013 per 1M tokens. It integrates seamlessly with vector databases like Pinecone or Milvus to power production-grade AI applications.

https://platform.openai.com/docs/guides/embeddings