This PR adds a `zeta zeta2 predict` subcommand that takes an edit
prediction example markdown file as an argument, and performs zeta2's
prediction, showing the retrieved context and the predicted edit.
* [x] Apply uncommitted diff to get repo into the right state.
* [x] Apply edits in edit history
* [x] Display predicted edits as unified diff, regardless of model
output format
Release Notes:
- N/A
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Co-authored-by: Agus Zubiaga <agus@zed.dev>
Co-authored-by: Piotr Osiewicz <24362066+osiewicz@users.noreply.github.com>
Co-authored-by: Ben Kunkle <ben.kunkle@gmail.com>
Adds a `convert-example` subcommand to the zeta cli that converts eval
examples from/to `json`, `toml`, and `md` formats.
Release Notes:
- N/A
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Co-authored-by: Max Brunsfeld <maxbrunsfeld@gmail.com>
We've been considering removing workspace-hack for a couple reasons:
- Lukas ran into a situation where its build script seemed to be causing
spurious rebuilds. This seems more likely to be a cargo bug than an
issue with workspace-hack itself (given that it has an empty build
script), but we don't necessarily want to take the time to hunt that
down right now.
- Marshall mentioned hakari interacts poorly with automated crate
updates (in our case provided by rennovate) because you'd need to have
`cargo hakari generate && cargo hakari manage-deps` after their changes
and we prefer to not have actions that make commits.
Currently removing workspace-hack causes our workspace to grow from
~1700 to ~2000 crates being built (depending on platform), which is
mainly a problem when you're building the whole workspace or running
tests across the the normal and remote binaries (which is where
feature-unification nets us the most sharing). It doesn't impact
incremental times noticeably when you're just iterating on `-p zed`, and
we'll hopefully get these savings back in the future when
rust-lang/cargo#14774 (which re-implements the functionality of hakari)
is finished.
Release Notes:
- N/A
Retrieval stats will now use polars to build a big data frame for
references with the cartesian product of LSP declarations and retrieved
declaration candidates (with all their score components) and rebuilds
the stats summary on top of it.
This data frame is written to a `.parquet` file, which we can load into
advanced analytics tools (such as Metabase), so we can explore our
scoring distributions and find ways to improve retrieval, and then train
the decision tree.
Release Notes:
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Also skips indexing files that don't have a suffix that indicates a
known language, and skips when the language doesn't have an outline
grammar.
Release Notes:
- N/A
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Co-authored-by: Agus <agus@zed.dev>
Julia Ryan
