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.
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