Vector search is an integral part of new-age AI applications. You make "embeddings" of your data, compare those embeddings using standard vector distance algorithms, and with some elbow grease, you can build entire semantic search engines, recommendation systems, or a Q&A platform. And now with a plethora of embedding generations tools out there like [OpenAI's Embeddings API](https://platform.openai.com/docs/guides/embeddings), [HuggingFace's Inference API](https://huggingface.co/blog/getting-started-with-embeddings#1-embedding-a-dataset), [sentence-transformers](https://www.sbert.net/), or literally [dozens of other open source models](https://github.com/embeddings-benchmark/mteb#leaderboard) out there, it's never been easier make embeddings of your own data!