How I Turned My Company’s Docs into a Searchable Database with OpenAI

For the past six months, I’ve been working at series A startup Voxel51, a and creator of the open source computer vision toolkit FiftyOne. As a machine learning engineer and developer evangelist, my job is to listen to our open source community and bring them what they need — new features, integrations, tutorials, workshops, you name it. A few weeks ago, we added native support for vector search engines and text similarity queries to FiftyOne, so that users can find the most relevant images in their (often massive — containing millions or tens of millions of samples) datasets, via simple natural language queries. This put us in a curious position: it was now possible for people using open source FiftyOne to readily search datasets with natural language queries, but using our documentation still required traditional keyword search. We have a lot of documentation, which has its pros and cons. As a user myself, I sometimes find that given the sheer quantity of documentation, finding precisely what I’m looking for requires more time than I’d like. <a href="https://towardsdatascience.com/how-i-turned-my-companys-docs-into-a-searchable-database-with-openai-4f2d34bd8736">Visit Now</a>