alexpovel

For fun, I had put together a GitHub bot for this purpose a while ago. It indexes all existing issues in a repo, creates embeddings and stores them in a vector DB. When a new issue is created, the bot comments with the 3 most similar, existing issues, from vector similarity.

In theory, that empowers users to close their own issues without maintainer intervention, provided an existing & solved issue covers their use case. In practice, the project never made it past PoC.

The mechanism works okay, but I've found available (cheap) embedding models to not be powerful enough. For GitHub, technology-wise, it should be easy to implement though.

     cat file.py | srgn --py def --py identifiers 'database' 'db'

An input like

    import database
    import pytest


    @pytest.fixture()
    def test_a(database):
        return database


    def test_b(database):
        return database


    database = "database"


    class database:
        pass

    import database
    import pytest


    @pytest.fixture()
    def test_a(db):
        return db


    def test_b(db):
        return db


    database = "database"


    class database:
        pass

Note: I just wrote this, and version 0.13.2 is required for the above to work.

It allows you to grep inside source code, but limit the search to e.g. “only docstrings inside class definitions”, among other things. That is, it allows nesting and is syntax aware. That example is for Python, but the tool speaks more languages (thanks to treesitter).