SemanticFinder now offers an easier way of pre-indexing files. Just like in all ML-flows the embedding calculation is the computation-intense part, so if you do this beforehand and load it in your browser, calculating cosine similarity is trivial (for a few thousand iterations).
It has the nice side-effect that this also allows for external computation of embeddings where you might have faster hardware than you're client device.
One could add so many features like adding full-text search by checking if the index contains certain keywords.
SemanticFinder now offers an easier way of pre-indexing files. Just like in all ML-flows the embedding calculation is the computation-intense part, so if you do this beforehand and load it in your browser, calculating cosine similarity is trivial (for a few thousand iterations).
It has the nice side-effect that this also allows for external computation of embeddings where you might have faster hardware than you're client device.
One could add so many features like adding full-text search by checking if the index contains certain keywords.
GitHub: https://github.com/do-me/SemanticFinder
In a way, your browser can become the vector DB.