This project intends to build a search engine for local files that have been lost somewhere within a directory.

The Seach engine works well for small directories with minimal subdirectories. However, because it is all written in python, it is extremely slow for larger directories. I am in the process of rewriting much of the python scripts into Go to take advantage of the speed of go routines.

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The way this project works is as follows:

1.) Use a sentence embedding model to encode contents of all files within a directory (and its subdirectories) into vector embeddings

2.) Gather a semantic description of the lost file wanting to be found

3.) Encode this semantic description into a vector embedding

4.) Use a simmilarity function to find the file within the directory that most resembles the search

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