findrepo2-experiment

These are experiments toward a program like findrepo, but using text-embedding-ada-002 (OpenAI’s new general purpose text embedding model).

This is in contrast to the custom nonempty-substring frequency vectors (non-semantic vocabulary-unaware sparse lossless embeddings in an indefinitely high-dimensional vector space) that findrepo uses.

Note that the goal of this repository is not to build the alternative. Rather, these experiments are to figure out of it makes sense to build it, and whether (and it what ways) it might do a better job than findrepo.

License

0BSD. See LICENSE.

Setup

Using conda:

git clone https://github.com/EliahKagan/findrepo2-experiment.git
cd findrepo2-experiment
conda env create
conda activate findrepo2-experiment
conda develop .

Or using poetry:

git clone https://github.com/EliahKagan/findrepo2-experiment.git
cd findrepo2-experiment
poetry install

Usage

The best place to start is notebooks/main.ipynb.

Make sure to activate the findrepo2-experiment environment, or verify that your editor/IDE has done so. If using conda:

cd findrepo2-experiment  # When about to use the software.
conda activate findrepo2-experiment

Or if using poetry:

cd findrepo2-experiment  # When about to use the software.
poetry shell

Preliminary results

Based on the matches shown in notebooks/main.ipynb, this seems to…

• …not work as well as findrepo in the common case when the user’s guess has some parts of the name correct. I call this the common case because I usually mostly remember what I called repositories.
• …work better than findrepo when the user’s guess is semantically related to the actual name but shared none, or very little, of it. This is as expected, since the algorithm findrepo uses doesn’t know anything about meaning, except insofar as that meaning correlates with shared substrings.

So the tests here should not be directly translated into an improved version of findrepo, at this time.

Next steps

Some of the output in notebooks/tokens.ipynb suggests that the way the repository names are tokenized may contribute to poor quality.

It’s unclear if this really matters. Ordinarily I think it it wouldn’t, because context would fill in the meaning. But when embedding repository names, no context goes into the embedding computation. So I suspect that when tokenization splits the name at semantically irrelevant boundaries, in the cases that this prevents splitting at semantically relevant ones, it may decrease the quality of results from the model.

Patterns of possibly undesirable splitting

Two patterns of interest appear:

1. Hyphens stealing first letters from words

For example, datarace is tokenized as data -r ace.

This is the pattern that seems most compelling. Repositories shouldn’t be named with spaces, but if the name had been data race, then it would’ve been tokenized as data  race.

2. Non-initial camel-case words starting with very short tokens

For example, FmtSandbox is tokenized as Fmt S andbox.

This is less common. It happens because there are many words that form single tokens when preceded by a space, but not by themselves.

What to do about it

Even beyond those particular issues, it’s intuitive to think that having spaces between the words would improve the quality of matches.

I emphasize again that I don’t actually know whether the way the pieces of names are combined ever causes worse matches or, even if it does, whether it has a tendency to do so. Any purported solution should be compared to the current approach before being considered an improvement.

With that said, the idea to get more semantic tokenization, which might help even separately from that, is to transform the repository names into phrases whose words are separated by spaces before computing their embeddings. This could be done instead of, or alongside, computing embeddings of the unmodified names:

• Hyphens and underscores would be replaced by spaces. So data-race would become data race.
• Spaces would be inserted between words of camel-case names, so VidDraw would become Vid Draw. This is a bit more complicated than the first transformation, it’s necessary to avoid inserting spaces between successive letters of capitalized acronyms: SFINAE remains SFINAE.
• Possibly, case folding could be done on non-acronyms, either to sentence case (VidDraw becoming Vid draw) or all lower-case (VidDraw becoming vid draw).