## Query

customise output colours environment variable.

## Response

### Sources:
Important: you can fetch the full content of any source using the fetch_url_content tool

#### (eza-community/eza/README.md)[https://raw.githubusercontent.com/eza-community/eza/main/README.md] (Score: 0.54)
- ## Custom Themes

Click to expand

**Eza** has recently added support for a `theme.yml` file, where you can specify all of the existing theme-ing options
available for the `LS_COLORS` and `EXA_COLORS` environment variables, as well as the option to specify different icons
for different file types and extensions. Any existing environment variables set will continue to work and will take
precedence for backwards compatibility.

#### **New** Pre-made themes
Check out the themes available in the official [eza-themes](https://github.com/eza-community/eza-themes) repository, or contribute your own.

An example theme file is available in `docs/theme.yml`, and needs to either be placed in a directory specified by the
environment variable `EZA_CONFIG_DIR`, or will looked for by default in `$XDG_CONFIG_HOME/eza`.

Full details are available on the [man page](https://github.com/eza-community/eza/tree/main/man/eza_colors-explanation.5.md) and an example theme file is included [here](https://github.com/eza-community/eza/tree/main/docs/theme.yml)

# Hacking on eza

- ### Nix ❄

If you already have Nix setup with flake support, you can try out eza with the `nix run` command:

    nix run github:eza-community/eza

Nix will build eza and run it.

If you want to pass arguments this way, use e.g. `nix run github:eza-community/eza -- -ol`.

# Installation

eza is available for Windows, macOS and Linux. Platform and distribution
specific installation instructions can be found in [INSTALL.md](INSTALL.md).

[![Packaging status](https://repology.org/badge/vertical-allrepos/eza.svg?columns=3)](https://repology.org/project/eza/versions)

---

Command-line options

eza’s options are almost, but not quite, entirely unlike `ls`’s. Quick overview:

## Display options

Click to expand

- **-1**, **--oneline**: display one entry per line
- **-G**, **--grid**: display entries as a grid (default)
- **-l**, **--long**: display extended details and attributes
- **-R**, **--recurse**: recurse into directories
- **-T**, **--tree**: recurse into directories as a tree
- **-x**, **--across**: sort the grid across, rather than downwards
- **-F**, **--classify=(when)**: display type indicator by file names (always, auto, never)
- **--colo[u]r=(when)**: when to use terminal colours (always, auto, never)
- **--colo[u]r-scale=(field)**: highlight levels of `field` distinctly(all, age, size)
- **--color-scale-mode=(mode)**: use gradient or fixed colors in --color-scale. valid options are `fixed` or `gradient`
- **--icons=(when)**: when to display icons (always, auto, never)
- **--hyperlink**: display entries as hyperlinks
- **--absolute=(mode)**: display entries with their absolute path (on, follow, off)
- **-w**, **--width=(columns)**: set screen width in columns

## Filtering options

Click to expand

- - **-b**, **--binary**: list file sizes with binary prefixes
- **-B**, **--bytes**: list file sizes in bytes, without any prefixes
- **-g**, **--group**: list each file’s group
- **--smart-group**: only show group if it has a different name from owner
- **-h**, **--header**: add a header row to each column
- **-H**, **--links**: list each file’s number of hard links
- **-i**, **--inode**: list each file’s inode number
- **-m**, **--modified**: use the modified timestamp field
- **-M**, **--mounts**: Show mount details (Linux and MacOS only).
- **-S**, **--blocksize**: show size of allocated file system blocks
- **-t**, **--time=(field)**: which timestamp field to use
- **-u**, **--accessed**: use the accessed timestamp field
- **-U**, **--created**: use the created timestamp field
- **-X**, **--dereference**: dereference symlinks for file information
- **-Z**, **--context**: list each file’s security context
- **-@**, **--extended**: list each file’s extended attributes and sizes
- **--changed**: use the changed timestamp field
- **--git**: list each file’s Git status, if tracked or ignored
- **--git-repos**: list each directory’s Git status, if tracked
- **--git-repos-no-status**: list whether a directory is a Git repository, but not its status (faster)
- **--no-git**: suppress Git status (always overrides `--git`, `--git-repos`, `--git-repos-no-status`)
- **--time-style**: how to format timestamps. valid timestamp styles are ‘`default`’, ‘`iso`’, ‘`long-iso`’, ‘`full-iso`’, ‘`relative`’, or a custom style ‘`+<FORMAT>`’ (E.g., ‘`+%Y-%m-%d %H:%M`’ => ‘`2023-09-30 13:00`’. For more specifications on the format string, see the _`eza(1)` manual page_ and [chrono documentation](https://docs.rs/chrono/latest/chrono/format/strftime/index.html).).
- .
- **--total-size**: show recursive directory size
- **--no-permissions**: suppress the permissions field
- **-o**, **--octal-permissions**: list each file's permission in octal format
- **--no-filesize**: suppress the filesize field
- **--no-user**: suppress the user field
- **--no-time**: suppress the time field
- **--stdin**: read file names from stdin

Some of the options accept parameters:

- Valid **--colo\[u\]r** options are **always**, **automatic** (or **auto** for short), and **never**.
- Valid sort fields are **accessed**, **changed**, **created**, **extension**, **Extension**, **inode**, **modified**, **name**, **Name**, **size**, **type**, and **none**. Fields starting with a capital letter sort uppercase before lowercase. The modified field has the aliases **date**, **time**, and **newest**, while its reverse has the aliases **age** and **oldest**.
- Valid time fields are **modified**, **changed**, **accessed**, and **created**.
- Valid time styles are **default**, **iso**, **long-iso**, **full-iso**, and **relative**.

See the `man` pages for further documentation of usage. They are available
- online [in the repo](https://github.com/eza-community/eza/tree/main/man)
- in your terminal via `man eza`, as of version [`[0.18.13] - 2024-04-25`](https://github.com/eza-community/eza/blob/main/CHANGELOG.md#01813---2024-04-25)

## Custom Themes

Click to expand

-

   Special thanks to:

### [Warp, the AI terminal for developers](https://www.warp.dev/eza)
[Available for MacOS, Linux, & Windows](https://www.warp.dev/eza)

# eza

A modern replacement for ls.

- ## Filtering options

Click to expand

- **-a**, **--all**: show hidden and 'dot' files
- **-d**, **--list-dirs**: list directories like regular files
- **-L**, **--level=(depth)**: limit the depth of recursion
- **-r**, **--reverse**: reverse the sort order
- **-s**, **--sort=(field)**: which field to sort by
- **--group-directories-first**: list directories before other files
- **--group-directories-last**: list directories after other files
- **-D**, **--only-dirs**: list only directories
- **-f**, **--only-files**: list only files
- **--no-symlinks**: don't show symbolic links
- **--show-symlinks**: explicitly show links (with `--only-dirs`, `--only-files`, to show symlinks that match the filter)
- **--git-ignore**: ignore files mentioned in `.gitignore`
- **-I**, **--ignore-glob=(globs)**: glob patterns (pipe-separated) of files to ignore

Pass the `--all` option twice to also show the `.` and `..` directories.

## Long view options

Click to expand

These options are available when running with `--long` (`-l`):


The eza documentation describes customizing output colours primarily through the **`EXA_COLORS`** environment variable (with `LS_COLORS` also supported for compatibility). These variables set the theming options for colours, and can be further extended via a `theme.yml` file placed in the directory specified by `EZA_CONFIG_DIR` (or `$XDG_CONFIG_HOME/eza` by default).

**Exact environment variable name for customizing colours: `EXA_COLORS`**


I need the SOURCE FILE, not the docs: search the code of the GitHub repository jarun/nnn for the identifier NNN_BMS and report the repository-relative path of the C file where it is handled.


### Code Search Results for: "NNN_BMS"

Found 7 matches in jarun/nnn.
Page 1 of 1.

#### mtpmount
- **Path**: plugins/mtpmount
- **URL**: https://github.com/jarun/nnn/blob/64dbbc0c3c1cda0b6a89e7b9c5ea96474d3d507b/plugins/mtpmount
- **Git URL**: https://api.github.com/repositories/66150122/git/blobs/d6feea064e4a1afd7f18215a020e716498760c56
- **Score**: 1

#### nnn.fish
- **Path**: misc/auto-completion/fish/nnn.fish
- **URL**: https://github.com/jarun/nnn/blob/64dbbc0c3c1cda0b6a89e7b9c5ea96474d3d507b/misc/auto-completion/fish/nnn.fish
- **Git URL**: https://api.github.com/repositories/66150122/git/blobs/d0a0b9cefbaf7da93603356c709f99e8b927b5b2
- **Score**: 1

#### nnn.1
- **Path**: nnn.1
- **URL**: https://github.com/jarun/nnn/blob/64dbbc0c3c1cda0b6a89e7b9c5ea96474d3d507b/nnn.1
- **Git URL**: https://api.github.com/repositories/66150122/git/blobs/a7c2a485a67f53daf4a4f244f39497d190cb9855
- **Score**: 1

#### nnn-completion.bash
- **Path**: misc/auto-completion/bash/nnn-completion.bash
- **URL**: https://github.com/jarun/nnn/blob/64dbbc0c3c1cda0b6a89e7b9c5ea96474d3d507b/misc/auto-completion/bash/nnn-completion.bash
- **Git URL**: https://api.github.com/repositories/66150122/git/blobs/3263073b7301fd4daa7670913117ee1d8d3b70e6
- **Score**: 1

#### nnn.c
- **Path**: src/nnn.c
- **URL**: https://github.com/jarun/nnn/blob/64dbbc0c3c1cda0b6a89e7b9c5ea96474d3d507b/src/nnn.c
- **Git URL**: https://api.github.com/repositories/66150122/git/blobs/e5315c1c6b63b1aa8940e913faf1c1eb9fa1d9aa
- **Score**: 1

#### CHANGELOG
- **Path**: CHANGELOG
- **URL**: https://github.com/jarun/nnn/blob/64dbbc0c3c1cda0b6a89e7b9c5ea96474d3d507b/CHANGELOG
- **Git URL**: https://api.github.com/repositories/66150122/git/blobs/9274b10552aafe13510397cfe606f4ee663d9ab2
- **Score**: 1

#### quickstart.sh
- **Path**: misc/quickstart.sh
- **URL**: https://github.com/jarun/nnn/blob/64dbbc0c3c1cda0b6a89e7b9c5ea96474d3d507b/misc/quickstart.sh
- **Git URL**: https://api.github.com/repositories/66150122/git/blobs/7973b585b17e1f80dc7dbcc481a6bf6d7af78c65
- **Score**: 1


The C source file where `NNN_BMS` is handled is:

**`src/nnn.c`**


Search the code of the GitHub repository simonw/sqlite-utils for def insert_all and tell me the repository-relative path of the module that contains that method definition.


Question: def insert_all


### Code Search Results for: "def insert_all"

Found 30 matches in simonw/sqlite-utils.
Page 1 of 1.

#### utils.py
- **Path**: sqlite_utils/utils.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/sqlite_utils/utils.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/ee6695b55447155c1dbcb0ea23c8b33bdb50a803
- **Score**: 1

#### cli.rst
- **Path**: docs/cli.rst
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/docs/cli.rst
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/78c33b8aee09c916308dd514fbe4df0348da4810
- **Score**: 1

#### python-api.rst
- **Path**: docs/python-api.rst
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/docs/python-api.rst
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/d515642a59bd6fd58dca25fc62ae57aa24f7895f
- **Score**: 1

#### db.py
- **Path**: sqlite_utils/db.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/sqlite_utils/db.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/c011d9bb0cb61883808f37d74c317a20644fc22b
- **Score**: 1

#### cli.py
- **Path**: sqlite_utils/cli.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/sqlite_utils/cli.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/c23090283b17569e5c81cbecff7fa2c082c3a323
- **Score**: 1

#### tutorial.ipynb
- **Path**: docs/tutorial.ipynb
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/docs/tutorial.ipynb
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/bade68681891c754a0fcf61562efda6761714e16
- **Score**: 1

#### upgrading.rst
- **Path**: docs/upgrading.rst
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/docs/upgrading.rst
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/92b582a4596fff16fe37860280e3088b6c1261ee
- **Score**: 1

#### changelog.rst
- **Path**: docs/changelog.rst
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/docs/changelog.rst
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/5d024e1a249c861bde0b7543093c202afcf2ec4c
- **Score**: 1

#### test_mutator_transactions.py
- **Path**: tests/test_mutator_transactions.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_mutator_transactions.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/3f13c6bcc2981ce7d974f124a5f983ab57fcb9c0
- **Score**: 1

#### test_list_mode.py
- **Path**: tests/test_list_mode.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_list_mode.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/75f5a7613a4f8a5b67b4a45346d7a18251743066
- **Score**: 1

#### test_conversions.py
- **Path**: tests/test_conversions.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_conversions.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/bb58df42074f5f40a63e0151547b959452144838
- **Score**: 1

#### test_query.py
- **Path**: tests/test_query.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_query.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/ac0d9249b6fee273d3f014bfac77ba525e618eb8
- **Score**: 1

#### test_recipes.py
- **Path**: tests/test_recipes.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_recipes.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/c6a548c53143262ea7671ce86c655e5f8a528042
- **Score**: 1

#### test_rows.py
- **Path**: tests/test_rows.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_rows.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/476569ed5882deefae5ed9b21848306d9d737f8c
- **Score**: 1

#### test_introspect.py
- **Path**: tests/test_introspect.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_introspect.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/343424d0d783cca04f39a20533cc0c449e49c5be
- **Score**: 1

#### test_extract.py
- **Path**: tests/test_extract.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_extract.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/f8550411a6ccbdfdb57e1dc1e13a2afeba946d08
- **Score**: 1

#### test_delete.py
- **Path**: tests/test_delete.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_delete.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/a9341b8754ba2e7d69140524c7a4c04e6748f57c
- **Score**: 1

#### test_convert.py
- **Path**: tests/test_convert.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_convert.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/1f9e9ed15d7125c8731461863d387790a452c9f9
- **Score**: 1

#### test_update.py
- **Path**: tests/test_update.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_update.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/44cc098af5e34c283f43fa8fdb19f579ae3ac078
- **Score**: 1

#### test_lookup.py
- **Path**: tests/test_lookup.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_lookup.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/f96cfefafba80c83a1310abe39fe50fe26a43222
- **Score**: 1

#### test_fts.py
- **Path**: tests/test_fts.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_fts.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/04b5bc31f53c86bfd6483b2d81ba9bc3a074626e
- **Score**: 1

#### test_analyze_tables.py
- **Path**: tests/test_analyze_tables.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_analyze_tables.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/9e4799cd9e5b8baf05ca253d9cff973259d08124
- **Score**: 1

#### test_upsert.py
- **Path**: tests/test_upsert.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_upsert.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/8274557faf0584075ed3666c291589ca459632f7
- **Score**: 1

#### test_cli_convert.py
- **Path**: tests/test_cli_convert.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_cli_convert.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/9f59d59e11f8b761556f880b564e26ca43ca9d10
- **Score**: 1

#### test_cli_bulk.py
- **Path**: tests/test_cli_bulk.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_cli_bulk.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/c5c9dcdb86562d51973dc1164ff179072a44e336
- **Score**: 1

#### test_extracts.py
- **Path**: tests/test_extracts.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_extracts.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/4e7cf39b36c639b224669a80620ae01abbc98fe4
- **Score**: 1

#### test_create.py
- **Path**: tests/test_create.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_create.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/b738df952dfea9337dc0eee90377fc8b45a8b087
- **Score**: 1

#### test_cli_insert.py
- **Path**: tests/test_cli_insert.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_cli_insert.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/0117862596326d7a5e72f5342e2c4d2ef77048fe
- **Score**: 1

#### test_cli.py
- **Path**: tests/test_cli.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_cli.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/064026a0b082e6fd5a4a9db708960d8f6ceb6792
- **Score**: 1

#### test_transform.py
- **Path**: tests/test_transform.py
- **URL**: https://github.com/simonw/sqlite-utils/blob/56dd09702fdb9e899f577ffd51693c1f2176cb08/tests/test_transform.py
- **Git URL**: https://api.github.com/repositories/140912432/git/blobs/873871308cfba8dee7170f88a54e377b2a6ad81a
- **Score**: 1


The `def insert_all` method is defined in **`sqlite_utils/db.py`**.


Search the code of tinygrad/tinygrad for class Tensor and report the repository-relative path of the file that defines that class.


Question: class Tensor


### Code Search Results for: "class Tensor"

Found 336 matches in tinygrad/tinygrad.
Page 1 of 12.

#### tensor.py
- **Path**: tinygrad/tensor.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/tinygrad/tensor.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/d23c6ce33ac7674c862bcc7c0e923ef033e7b8a7
- **Score**: 1

#### parallel_tensor.cuh
- **Path**: extra/thunder/cuda/include/pyutils/parallel_tensor.cuh
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/thunder/cuda/include/pyutils/parallel_tensor.cuh
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/ecbcca5c6032599ae5a798a6cebabf1b7852898f
- **Score**: 1

#### state.py
- **Path**: tinygrad/nn/state.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/tinygrad/nn/state.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/ac18bc038cc1231874b203f19e54fe03d50ed8e4
- **Score**: 1

#### wrapped_tensor.cpp
- **Path**: extra/torch_backend/wrapped_tensor.cpp
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/torch_backend/wrapped_tensor.cpp
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/82f5c7a58f7dcf6ed6e2ad9c9c12c3413b2f09e0
- **Score**: 1

#### quickstart.md
- **Path**: docs/quickstart.md
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/docs/quickstart.md
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/5a6a8eff07bda5cc43d5d743113568cbc37ba229
- **Score**: 1

#### lr_scheduler.py
- **Path**: extra/lr_scheduler.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/lr_scheduler.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/094155a71274cb374417a23b34e56ad2cd8bba26
- **Score**: 1

#### compile_tensorflow.py
- **Path**: examples/compile_tensorflow.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/examples/compile_tensorflow.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/6eb15093c682e43bb9fa976ee088a4d6bffe84c4
- **Score**: 1

#### tc.py
- **Path**: tinygrad/codegen/opt/tc.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/tinygrad/codegen/opt/tc.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/6d13624a1dae2311efbd5a8b6aeadf738a7df385
- **Score**: 1

#### transformer.py
- **Path**: extra/models/transformer.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/models/transformer.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/8e0ada930d4c813a08d55b233aed100bfa9412ac
- **Score**: 1

#### README.md
- **Path**: README.md
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/README.md
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/0e7375ce0341062d8b42e300c3a2501293564baf
- **Score**: 1

#### index.md
- **Path**: docs/index.md
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/docs/index.md
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/f80c1242e22f935287d1b7656bb594a25e676fa7
- **Score**: 1

#### mnist.md
- **Path**: docs/mnist.md
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/docs/mnist.md
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/94e6b623b4f473096e7362ea54866d2e9f233b7f
- **Score**: 1

#### yolov8.py
- **Path**: examples/yolov8.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/examples/yolov8.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/b0e98218ccb833c5f2cac77d68a82d9ff5e280be
- **Score**: 1

#### sdxl.py
- **Path**: examples/sdxl.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/examples/sdxl.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/92d85ce057cdac2f49e448d1de02152d144bc8d3
- **Score**: 1

#### jit.py
- **Path**: tinygrad/engine/jit.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/tinygrad/engine/jit.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/a543623072fa57122faacb442c5342cdb9e397db
- **Score**: 1

#### yolov3.py
- **Path**: examples/yolov3.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/examples/yolov3.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/f6d6882633f1046c7fb489401bfcd93b701895dc
- **Score**: 1

#### rand.py
- **Path**: tinygrad/mixin/rand.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/tinygrad/mixin/rand.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/85e3b58ee812e590d4765620adfedf933bdef137
- **Score**: 1

#### clip.py
- **Path**: extra/models/clip.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/models/clip.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/5960c111a3cf5263b10d47872c2a5785999fb127
- **Score**: 1

#### rnnt.py
- **Path**: extra/models/rnnt.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/models/rnnt.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/83218cd087ffed253690fb936f10fba687d761b0
- **Score**: 1

#### t5.py
- **Path**: extra/models/t5.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/models/t5.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/7a0fff23f259e4443cd3348d38194d50d014e0a3
- **Score**: 1

#### inception.py
- **Path**: extra/models/inception.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/models/inception.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/15d0b58bc99d50974658ca5c36d251c02c6162d7
- **Score**: 1

#### unet.py
- **Path**: extra/models/unet.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/models/unet.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/f1b488cef4a430eeb29c7b866090fc1893b0c704
- **Score**: 1

#### abstractions4.py
- **Path**: docs/abstractions4.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/docs/abstractions4.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/d28a3e3a3474c50a4dafb38852141653f2e4cc3b
- **Score**: 1

#### gpt2.py
- **Path**: examples/gpt2.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/examples/gpt2.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/7e12258f54fdb32355d8989d8841efa824084b99
- **Score**: 1

#### __init__.py
- **Path**: tinygrad/nn/__init__.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/tinygrad/nn/__init__.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/e98186111e0ef59eb15504ea7feef6c51c638552
- **Score**: 1

#### efficientnet.py
- **Path**: extra/models/efficientnet.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/models/efficientnet.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/81bda3d27db941e02aaac237ba3d2d3b8be2645d
- **Score**: 1

#### llama3.py
- **Path**: examples/llama3.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/examples/llama3.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/27eda2ffcc55e57e0e15a68574eb5cfb5f6d5472
- **Score**: 1

#### bert.py
- **Path**: extra/models/bert.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/models/bert.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/bd515d8c9840e21dcf48fa0a096c28d8f5874d63
- **Score**: 1

#### hook_cuda.py
- **Path**: extra/torch_hook/hook_cuda.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/extra/torch_hook/hook_cuda.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/2e5ad907a0a734b5fd462eaf6154aa99c559ae86
- **Score**: 1

#### minrf.py
- **Path**: examples/minrf.py
- **URL**: https://github.com/tinygrad/tinygrad/blob/e6324d1e1cc878e3f66f3729782039418ab9bbe6/examples/minrf.py
- **Git URL**: https://api.github.com/repositories/305144746/git/blobs/bc30a544c4d3d9dc36e10b7e377e6941d50ea8e6
- **Score**: 1

_Showing 30 of 336 results. Use pagination to see more results._


The `Tensor` class is defined in **`tinygrad/tensor.py`**.


Search the code of the repository Textualize/textual for class DataTable and report the exact repository-relative path of the private widget module that defines it.


Question: class DataTable


### Code Search Results for: "class DataTable"

Found 51 matches in Textualize/textual.
Page 1 of 2.

#### widgets.py
- **Path**: src/textual/demo/widgets.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/src/textual/demo/widgets.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/a5a953990af1e487a4cce651f4e95bbeb599993f
- **Score**: 1

#### _data_table.py
- **Path**: src/textual/widgets/_data_table.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/src/textual/widgets/_data_table.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/dd5f9c6d72df53259ced783acd21bd9a4ebcd99c
- **Score**: 1

#### data_table_fixed.py
- **Path**: docs/examples/widgets/data_table_fixed.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/widgets/data_table_fixed.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/a928a68e575793c4a2093b798a48f91c0951c17d
- **Score**: 1

#### coordinate.py
- **Path**: src/textual/coordinate.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/src/textual/coordinate.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/a230df039eef7c99589d9a50c2393412d847bfcd
- **Score**: 1

#### CHANGELOG.md
- **Path**: CHANGELOG.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/CHANGELOG.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/67652d343d2cab479f250f2ab8dc059bb97f12ee
- **Score**: 1

#### data_table.py
- **Path**: docs/examples/widgets/data_table.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/widgets/data_table.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/dae29b267565da19ac5ee1e45632ca0861995e48
- **Score**: 1

#### widget_gallery.md
- **Path**: docs/widget_gallery.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/widget_gallery.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/fe3f62d81fcca2523a95914bdd2b78435c02b7ae
- **Score**: 1

#### data_table_labels.py
- **Path**: docs/examples/widgets/data_table_labels.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/widgets/data_table_labels.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/316d4709a1357af5282fc1707e679a921e269d23
- **Score**: 1

#### data_table_cursors.py
- **Path**: docs/examples/widgets/data_table_cursors.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/widgets/data_table_cursors.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/8139b426f6abcc2f88a120501fcbcf7556591035
- **Score**: 1

#### loading01.py
- **Path**: docs/examples/guide/widgets/loading01.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/guide/widgets/loading01.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/3e25899cbe902a10ec34bb5452057b833c5d587e
- **Score**: 1

#### data_table_renderables.py
- **Path**: docs/examples/widgets/data_table_renderables.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/widgets/data_table_renderables.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/f1a07ea3efd5c15fc0a7e8a6ff8fbe5754d25d3e
- **Score**: 1

#### theme_sandbox.py
- **Path**: examples/theme_sandbox.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/examples/theme_sandbox.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/7f1331ed8872e747703352772fec25114182f731
- **Score**: 1

#### steal-this-code.md
- **Path**: docs/blog/posts/steal-this-code.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/blog/posts/steal-this-code.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/cf3ea37682736d7673c167ab2acc434683597d7d
- **Score**: 1

#### styled.py
- **Path**: src/textual/renderables/styled.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/src/textual/renderables/styled.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/40ae6af372483705c57db2ff0ee9e52a40d078ef
- **Score**: 1

#### data_table.md
- **Path**: docs/widgets/data_table.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/widgets/data_table.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/9b914f1e9e975ecd23c62e5c73bd7c0c3f370ff9
- **Score**: 1

#### content_switcher.py
- **Path**: docs/examples/widgets/content_switcher.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/widgets/content_switcher.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/8e235fe5644ed862082890843aace2a41f0ec71a
- **Score**: 1

#### widgets.md
- **Path**: docs/guide/widgets.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/guide/widgets.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/cdbe3e8b1697f4b35eaf4709038a2323f674c0a6
- **Score**: 1

#### __init__.py
- **Path**: src/textual/widgets/__init__.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/src/textual/widgets/__init__.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/ffc861dad1596cd8f40fbe37e78a0d9571455461
- **Score**: 1

#### reactivity.md
- **Path**: docs/guide/reactivity.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/guide/reactivity.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/c8fe58cf7b3dd8a3141c9a412b159686a75535d8
- **Score**: 1

#### data_table_sort.py
- **Path**: docs/examples/widgets/data_table_sort.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/widgets/data_table_sort.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/599a629394c8070ad4ea7119d8963dfda718e577
- **Score**: 1

#### markdown_viewer.py
- **Path**: docs/examples/widgets/markdown_viewer.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/examples/widgets/markdown_viewer.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/e1bc99604c2043f41e91ea2d689cffbe64f02d21
- **Score**: 1

#### lazy.py
- **Path**: src/textual/lazy.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/src/textual/lazy.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/97b144f9a2af4d4dd894aa24574d57bf0ce10864
- **Score**: 1

#### README.md
- **Path**: README.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/README.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/68e6dd700897465674d42cab2fbb221c4fcf8a9e
- **Score**: 1

#### __init__.pyi
- **Path**: src/textual/widgets/__init__.pyi
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/src/textual/widgets/__init__.pyi
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/907ae843b894239270892aaf177442d5aac7fae8
- **Score**: 1

#### design.py
- **Path**: src/textual/design.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/src/textual/design.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/290de588054d79feb5158dac6f5ccf75729bd869
- **Score**: 1

#### release0-11-0.md
- **Path**: docs/blog/posts/release0-11-0.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/blog/posts/release0-11-0.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/ab2ef082ea07fc94b534f2ebfaa7ade6ef93f18a
- **Score**: 1

#### content_switcher.md
- **Path**: docs/widgets/content_switcher.md
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/docs/widgets/content_switcher.md
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/126213c94b267692d1db34eb7f75941d95064d44
- **Score**: 1

#### datatable_hot_reloading.py
- **Path**: tests/snapshot_tests/snapshot_apps/datatable_hot_reloading.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/tests/snapshot_tests/snapshot_apps/datatable_hot_reloading.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/7e3a8031551cfe801d78fc116f6f425f69c8547f
- **Score**: 1

#### test_data_table.py
- **Path**: tests/test_data_table.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/tests/test_data_table.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/6750c5ddbcbcb86a8d98ad8afb3d03eb6831b4eb
- **Score**: 1

#### data_table_style_order.py
- **Path**: tests/snapshot_tests/snapshot_apps/data_table_style_order.py
- **URL**: https://github.com/Textualize/textual/blob/06dbeef4bb70fb718236aa418ed658ef4667a126/tests/snapshot_tests/snapshot_apps/data_table_style_order.py
- **Git URL**: https://api.github.com/repositories/355959597/git/blobs/796ef7bbc3737c9929254bd838bfad009ab4ab93
- **Score**: 1

_Showing 30 of 51 results. Use pagination to see more results._


The `DataTable` class is defined in:

**`src/textual/widgets/_data_table.py`**

This is the private widget module (note the leading underscore, consistent with Textual's convention of implementing widgets in private `_*.py` files and re-exporting them via `src/textual/widgets/__init__.py`).


Fetch https://raw.githubusercontent.com/astral-sh/uv/HEAD/CHANGELOG.md and tell me the version number of the most recent release entry at the top of that changelog.


# Changelog

<!-- prettier-ignore-start -->

## 0.12.5

Released on 2026-08-14.

### Python

- Add CPython 3.10.21, 3.11.16, and 3.12.14 ([#21138](https://github.com/astral-sh/uv/pull/21138))
- Prefer newer versions and standard variants when selecting between equally prioritized Python interpreters ([#21134](https://github.com/astral-sh/uv/pull/21134))

### Enhancements

- Simplify errors and hints for invalid editable requirements, and redact credentials in requirement URLs ([#21130](https://github.com/astral-sh/uv/pull/21130))

### Preview features

- Allow `--index` and `--default-index` to select configured package indexes by name with the `index-by-name` preview feature ([#17455](https://github.com/astral-sh/uv/pull/17455))
- Include distribution artifact URLs and hashes in CycloneDX SBOM exports by default ([#21131](https://github.com/astral-sh/uv/pull/21131))
- Fall back to logical file sizes when using `cache-physical-space` on filesystems that do not support physical-space accounting ([#21133](https://github.com/astral-sh/uv/pull/21133))

### Bug fixes

- Resolve relative package index paths in PEP 723 scripts against the script directory ([#21097](https://github.com/astral-sh/uv/pull/21097))

## 0.12.4

Released on 2026-08-13.

### Enhancements

- Prefer post-quantum key exchange and enable opt-in TLS diagnostics ([#21054](https://github.com/astral-sh/uv/pull/21054))
- Accept whitespace before versions in noncompliant wildcard comparisons such as `Requires-Python: >= 3.5.*` ([#21012](https://github.com/astral-sh/uv/pull/21012))
- Report a specific error when a PEP 723 closing tag contains trailing whitespace or other content ([#20944](https://github.com/astral-sh/uv/pull/20944))
- Omit source-span carets from diagnostics for empty PEP 508 requirements ([#21094](https://github.com/astral-sh/uv/pull/21094))

### Preview features

- Add `uv check --no-install-project` and respect `UV_NO_INSTALL_PROJECT` to install dependencies without building or installing the project ([#21085](https://github.com/astral-sh/uv/pull/21085))
- Make the ty subprocess invoked by `uv check` honor uv's color and progress settings, including quiet mode ([#21086](https://github.com/astral-sh/uv/pull/21086))

### Performance

- Speed up resolutions with long runs of unavailable package versions by coalescing gaps in the resolver's version ranges ([#20804](https://github.com/astral-sh/uv/pull/20804))
- Speed up Simple API parsing by deserializing PyPI and Pyx file metadata directly ([#21041](https://github.com/astral-sh/uv/pull/21041))

### Bug fixes

- Use windowed `pythonw.exe` launchers for virtual environments created from managed Python minor-version links ([#19235](https://github.com/astral-sh/uv/pull/19235))
- Allow `uv lock` to proceed when `.venv` is an unusable project environment ([#21068](https://github.com/astral-sh/uv/pull/21068))
- Respect `fork-strategy` when ordering forks created from `environments` or existing lockfile `resolution-markers` ([#21000](https://github.com/astral-sh/uv/pull/21000))
- Preserve consecutive wildcard Python minor-version exclusions such as `!=3.11.*, !=3.12.*` in `uv.lock` ([#21045](https://github.com/astral-sh/uv/pull/21045))
- Preserve inline comments on the final item in dependency arrays when `uv add` updates it ([#21008](https://github.com/astral-sh/uv/pull/21008))
- Recover from stale base-interpreter cache metadata when an existing virtual environment exposes a version mismatch ([#21073](https://github.com/astral-sh/uv/pull/21073))
- Prevent interpreter cache reuse across different `PYTHONEXECUTABLE` and `__PYVENV_LAUNCHER__` overrides ([#21075](https://github.com/astral-sh/uv/pull/21075))
- Show standard styling, usage guidance, and line termination for invalid `uv version --bump` values ([#21076](https://github.com/astral-sh/uv/pull/21076))

## 0.12.3

Released on 2026-08-07.

### Python

- Add CPython 3.13.15 ([#20997](https://github.com/astral-sh/uv/pull/20997))

### Preview features

- Add `--output-format` to select automatic, human-readable, or raw-byte output for `uv cache size` ([#20992](https://github.com/astral-sh/uv/pull/20992))
- Preserve JSON output from `uv workspace metadata --quiet` while suppressing diagnostics ([#20991](https://github.com/astral-sh/uv/pull/20991))
- Reduce memory usage for large workspaces by streaming `uv workspace metadata` JSON output ([#20990](https://github.com/astral-sh/uv/pull/20990))

### Performance

- Reduce Linux startup latency by initializing the workspace cache before spawning another thread ([#20989](https://github.com/astral-sh/uv/pull/20989))
- Reuse compiled workspace exclusion patterns during workspace discovery ([#20988](https://github.com/astral-sh/uv/pull/20988))
- Speed up conflict-heavy resolutions by avoiding materialized range complements ([#20982](https://github.com/astral-sh/uv/pull/20982))
- Avoid slow procfs reads during Python interpreter discovery on Linux ([#20987](https://github.com/astral-sh/uv/pull/20987))

### Documentation

- Add PEP 740 attestations to the GitHub Actions publishing example ([#20986](https://github.com/astral-sh/uv/pull/20986))
- Restrict the GitHub Actions publishing example to Python version tags ([#20973](https://github.com/astral-sh/uv/pull/20973))
- Correct `--python-pin` to `--pin-python` in the `uv init --bare` example ([#20876](https://github.com/astral-sh/uv/pull/20876))

## 0.12.2

Released on 2026-08-05.

### Python

- Add CPython 3.15.0rc1 ([#20948](https://github.com/astral-sh/uv/pull/20948))
- Add CPython 3.14.7 ([#20971](https://github.com/astral-sh/uv/pull/20971))

### Enhancements

- Ensure diagnostic hints end with a newline to prevent malformed terminal output ([#20959](https://github.com/astral-sh/uv/pull/20959))

### Preview features

- Audit one or all installed tools with `uv tool audit` ([#20921](https://github.com/astral-sh/uv/pull/20921))
- Report physically reclaimed disk space during cache cleanup with the `cache-physical-space` preview feature ([#20925](https://github.com/astral-sh/uv/pull/20925))

### Configuration

- Add `UV_RUN_RLIMIT_NOFILE` to set the open-file limit for commands launched by `uv run` ([#20926](https://github.com/astral-sh/uv/pull/20926))

### Performance

- Speed up `uv.lock` parsing for wheel entries ([#20881](https://github.com/astral-sh/uv/pull/20881))
- Speed up `uv.lock` parsing for source distribution entries ([#20882](https://github.com/astral-sh/uv/pull/20882))
- Speed up filename extraction from distribution URLs ([#20879](https://github.com/astral-sh/uv/pull/20879))
- Reduce filesystem metadata lookups during bytecode compilation ([#20928](https://github.com/astral-sh/uv/pull/20928))
- Reuse file metadata when building source distributions ([#20927](https://github.com/astral-sh/uv/pull/20927))

### Bug fixes

- Preserve compatibility with older uv versions when recording artifact sizes in cached wheels and source distributions ([#20963](https://github.com/astral-sh/uv/pull/20963))
- Avoid including workspace-root default dependency groups when syncing or exporting a selected workspace member unless explicitly requested ([#20930](https://github.com/astral-sh/uv/pull/20930))

### Documentation

- Separate build and publish jobs in the GitHub Actions publishing guide ([#20946](https://github.com/astral-sh/uv/pull/20946))
- Ensure the GitHub Actions publishing example waits for the build job to finish ([#20957](https://github.com/astral-sh/uv/pull/20957))
- Correct typos in the Docker integration guide ([#20970](https://github.com/astral-sh/uv/pull/20970))

## 0.12.1

Released on 2026-07-31.

### Enhancements

- Add package-specific pre-release policies with `--prerelease-package` ([#20837](https://github.com/astral-sh/uv/pull/20837))
- Support local HTML files as flat indexes ([#20802](https://github.com/astral-sh/uv/pull/20802))
- Add Xonsh virtual environment activation scripts (`activate.xsh`) ([#19740](https://github.com/astral-sh/uv/pull/19740))
- Preserve filesystem paths passed to `uv add --index` when updating `pyproject.toml` ([#20817](https://github.com/astral-sh/uv/pull/20817))

### Preview features

- Add automatic fixes to `uv check` with `--fix` ([#20793](https://github.com/astral-sh/uv/pull/20793))
- Avoid rejecting unchanged metadata-free lockfiles when workspace dependencies share direct sources ([#20847](https://github.com/astral-sh/uv/pull/20847))
- Honor direct URL constraints when validating metadata-free lockfiles ([#20796](https://github.com/astral-sh/uv/pull/20796))
- Ignore malformed PEP 723 scripts discovered during project checks ([#20784](https://github.com/astral-sh/uv/pull/20784))
- Use ty's native script exclusion in `uv check` ([#20742](https://github.com/astral-sh/uv/pull/20742))

### Performance

- Parse canonical uv lockfiles directly, with a fallback for other valid TOML syntax ([#20648](https://github.com/astral-sh/uv/pull/20648))
- Accelerate SHA-256 hashing on non-Windows ARM64 platforms ([#20805](https://github.com/astral-sh/uv/pull/20805))

### Bug fixes

- Flush shell startup file updates before `uv tool update-shell` and `uv python update-shell` exit ([#20842](https://github.com/astral-sh/uv/pull/20842))
- Make workspace-root dependency groups available to commands run from workspace members ([#20840](https://github.com/astral-sh/uv/pull/20840))
- Resolve `--find-links` paths in requirements files relative to the containing file ([#20832](https://github.com/astral-sh/uv/pull/20832))
- Respect configured indexes in `uv tool list --outdated` ([#20770](https://github.com/astral-sh/uv/pull/20770))

### Documentation

- Document Astral GPU indexes in the PyTorch guide ([#20785](https://github.com/astral-sh/uv/pull/20785))
- Use consistent dependency-group argument descriptions throughout the CLI documentation ([#20823](https://github.com/astral-sh/uv/pull/20823))

## 0.12.0

Released on 2026-07-28.

Since we released uv [0.11.0](https://github.com/astral-sh/uv/releases/tag/0.11.0) in March, we've accumulated changes that improve correctness, safety, and compatibility with specifications, but could break some workflows. This release contains those changes; many have been marked as breaking out of an abundance of caution.

**We expect most users to be able to upgrade without making changes.**

There are no breaking changes to the configuration of the [uv build backend](https://docs.astral.sh/uv/concepts/build-backend/). If your `[build-system]` table includes an upper bound on `uv_build`, update it to allow `uv_build` 0.12, e.g., `uv_build>=0.11.32,<0.13`.

### Breaking changes

- **Define build systems by default with `uv init`** ([#19197](https://github.com/astral-sh/uv/pull/19197))

  Projects created with `uv init` now declare a build system and are packaged by default. This was the default project layout all the way back in [v0.3](https://github.com/astral-sh/uv/releases/tag/0.3.0), but we found that the use of the `hatchling` build system was confusing to newcomers and consequently dropped use of a build system by default in [v0.4](https://github.com/astral-sh/uv/releases/tag/0.4.0). Since then, we've created our own build system (`uv_build`) with tight integration with uv and are excited to restore the default to a best-practice project layout.

  Previously, `uv init example` created an unpackaged layout containing `main.py` and a `pyproject.toml` without a build system. The project could declare dependencies but was not itself installed into its virtual environment.

  Now, `uv init example` defines a `[build-system]` using `uv_build`, places application source code in `src/example`, and includes a `[project.scripts]` entry named `example`. Defining a build system allows the project to be imported from tests or other code, installed as a dependency, and run as a command:

  ```console
  $ uv init example
  $ cd example
  $ uv run example
  Hello from example!
  ```

  Existing projects are unaffected. Use [`uv init --no-package example`](https://docs.astral.sh/uv/concepts/projects/init/#creating-a-project-without-a-build-system) to create the previous unpackaged layout without a build system.

  See the [project creation documentation](https://docs.astral.sh/uv/concepts/projects/init/#applications) for more details.

  This stabilizes the `packaged-init` preview feature.
- **Reject unsupported source distribution and wheel archive formats** ([#18927](https://github.com/astral-sh/uv/pull/18927))

  [PEP 625](https://peps.python.org/pep-0625/) requires [source distributions](https://docs.astral.sh/uv/concepts/resolution/#source-distribution) to use `.tar.gz` archives. Previously, uv also accepted legacy formats such as `.tar.bz2` and `.tar.xz`. Those formats are now rejected, including when referenced by an existing lockfile. Legacy `.zip` source distributions remain supported for backwards compatibility.

  Wheels and other ZIP archives can no longer contain entries compressed with bzip2, LZMA, or XZ. Entries must use the stored, DEFLATE, or zstd compression methods.

  Removing support for uncommon compression methods reduces uv's compression dependencies and the attack surface exposed when processing untrusted packages.

  You cannot opt out of this behavior. If you depend on a legacy source distribution that uses an unsupported format, we recommend rebuilding it as a `.tar.gz` archive and regenerating any lockfile containing references to the legacy archive.
- **Reject wheel files that could replace the Python interpreter** ([#20748](https://github.com/astral-sh/uv/pull/20748), [#20749](https://github.com/astral-sh/uv/pull/20749))

  uv already rejected wheel entry points named `python`, but case variants such as `Python` were still accepted. On case-insensitive filesystems, including common macOS and Windows setups, these entry points could overwrite the virtual environment's interpreter.

  Wheels could also place interpreter files in their `.data/scripts` directory or in paths such as `.data/data/bin/python`, bypassing the entry-point check and replacing the interpreter during installation.

  uv now rejects case-insensitive variants of reserved interpreter names and wheel data files that would be installed over an interpreter. This includes names such as `Python`, `python.py`, and `Python.exe`, along with other reserved interpreter names and their versioned variants.

  You cannot opt out of these checks. Rename conflicting entry points or wheel data files and rebuild the affected wheel.
- **Prefer stable releases before falling back to pre-releases** ([#19993](https://github.com/astral-sh/uv/pull/19993))

  A dependency can introduce a [pre-release requirement](https://docs.astral.sh/uv/concepts/resolution/#pre-release-handling) after resolution starts. uv previously required each package's pre-release eligibility to be known before resolution began: the default `if-necessary-or-explicit` mode allowed them for direct requirements that explicitly requested a pre-release, or for packages that only published pre-releases.

  This meant that a pre-release requirement discovered in a dependency's metadata, e.g., `example>=2.0.0b1`, would fail to resolve even when a compatible pre-release existed. To resolve it, you had to add that dependency as a direct requirement or allow pre-releases across your entire dependency graph.

  The default mode is now `if-necessary`. uv tries stable candidates first and falls back to pre-releases when no stable candidate satisfies the active constraints. Like pip, uv now supports [pre-release requirements discovered transitively](https://docs.astral.sh/uv/pip/compatibility/#pre-release-compatibility), but can select different versions than previous uv releases when both stable and pre-release candidates are available.

  You can opt out of automatic pre-release selection with `--prerelease disallow`. Alternatively, `--prerelease allow` considers pre-releases without first preferring stable releases, and `--prerelease explicit` only allows them for direct requirements that mention a pre-release.

  The old `if-necessary-or-explicit` mode distinguished between explicitly requested pre-releases and packages with no stable releases. That distinction is unnecessary now that `if-necessary` handles both cases, including transitive requirements. The old name remains available as an alias but is deprecated and will be removed in a future release.
- **Respect `--require-hashes` directives in `requirements.txt`** ([#19336](https://github.com/astral-sh/uv/pull/19336))

  Previously, `uv pip install` and `uv pip sync` warned about `--require-hashes` inside a `requirements.txt` file but still installed dependencies without checking their hashes. Now, the directive enables hash-checking mode, just as if `--require-hashes` had been passed on the command line.

  For example, this requirements file is no longer accepted because the requirement is neither pinned nor hashed:

  ```text
  --require-hashes
  anyio
  ```

  You cannot opt out while the directive is present. Pin every requirement with `==` and provide its hash, or remove `--require-hashes` if hash checking is not intended.
- **Reject MD5-only hashes in hash-checking mode** ([#20758](https://github.com/astral-sh/uv/pull/20758))

  Previously, `uv pip install --require-hashes` and `uv pip sync --require-hashes` accepted requirements whose only available digest used MD5. MD5 is not collision-resistant, so relying on it undermined installations that require hash verification and differed from pip's behavior.

  Hash-checking mode now requires at least one secure digest for every requirement. For example, the following requirement is rejected unless a secure hash, such as SHA-256, is also supplied:

  ```text
  anyio==4.0.0 --hash=md5:420d85e19168705cdf0223621b18831a
  ```

  A secure hash can be supplied directly on the requirement or in a matching constraints file. Ordinary hash verification without `--require-hashes` continues to support MD5.

  You cannot opt out while hash checking is required. Regenerate affected hashes with SHA-256 or another supported secure hash.
- **Reject invalid `pylock.toml` files and artifacts** ([#20402](https://github.com/astral-sh/uv/pull/20402), [#20440](https://github.com/astral-sh/uv/pull/20440), [#20443](https://github.com/astral-sh/uv/pull/20443))

  uv now validates additional requirements from the [`pylock.toml` specification](https://packaging.python.org/en/latest/specifications/pylock-toml/):

  - The `packages` array must be present. Previously, uv interpreted a missing array as an empty lockfile, so `uv pip sync` could uninstall an environment instead of rejecting malformed input. An explicitly empty `packages = []` array remains valid.
  - Lockfile filenames must be `pylock.toml` or a single-name variant such as `pylock.dev.toml`. Names such as `pylock..toml` and `pylock.foo.bar.toml` are rejected.
  - If a wheel, source distribution, or other artifact declares a `size`, the downloaded or cached artifact must match. Previously, an incorrect size was accepted when the hash was correct. Sizes reported by package indexes remain advisory.

  You cannot opt out of these checks. Regenerate malformed lockfiles, rename invalid filenames, and either correct or remove an incorrect optional `size` value.
- **Honor explicit certificate overrides even when no certificates can be loaded** ([#20741](https://github.com/astral-sh/uv/pull/20741), [#20767](https://github.com/astral-sh/uv/pull/20767))

  Previously, uv ignored [`SSL_CERT_FILE` or `SSL_CERT_DIR`](https://docs.astral.sh/uv/concepts/authentication/certificates/#custom-certificates) values that pointed to missing or inaccessible paths, empty files or directories, or sources without valid certificates. Instead, it fell back to its default trust roots, potentially allowing HTTPS connections that the configured override was intended to reject.

  Now, any non-empty `SSL_CERT_FILE` or `SSL_CERT_DIR` value replaces uv's default certificate roots, even when no valid certificates can be loaded. In that case, HTTPS requests fail because no certificates are trusted. This applies to package downloads and remote scripts, including GitHub Gists.

  Fix or unset the certificate override. Unsetting it restores the default trust store; empty environment-variable values continue to be ignored.
- **Support pip-compatible `--cert` handling in `uv pip`** ([#20418](https://github.com/astral-sh/uv/pull/20418))

  The `uv pip` interface now accepts [`--cert <path>`](https://docs.astral.sh/uv/concepts/authentication/certificates/#custom-certificates), e.g.:

  ```console
  $ uv pip install --cert ./company-ca.pem example
  ```

  As in pip, the provided PEM bundle replaces all other certificate sources for that invocation, including system certificates and `SSL_CERT_FILE` or `SSL_CERT_DIR`. This change has no effect unless you pass `--cert`. Include the necessary certificate authorities in the bundle.

  `--cert` is only supported by `uv pip` commands; other uv commands continue to use their existing certificate configuration.
- **Discover projects relative to the script passed to `uv run`** ([#20225](https://github.com/astral-sh/uv/pull/20225))

  Previously, `uv run project/script.py` discovered its project from the current directory, even when the script belonged to another project. uv now starts project and workspace discovery from the script's directory instead.

  For example, running `uv run other-project/script.py` now uses `other-project` and its dependencies. This fixes scripts that previously failed because their own dependencies were not installed, but can select a different environment than before.

  You can opt out of script-relative discovery by selecting a project explicitly, e.g., `uv run --project . other-project/script.py`.

  This stabilizes the `target-workspace-discovery` preview feature.
- **Require `--force` before clearing a directory that is not a virtual environment** ([#20225](https://github.com/astral-sh/uv/pull/20225))

  `uv venv --clear` previously removed any existing target directory, even if it was not a virtual environment. uv emitted a warning but still deleted the directory and its contents. Now, uv refuses to clear directories that do not contain a virtual environment.

  You can opt out of this safety check by explicitly passing `--force`, e.g., `uv venv --clear --force ./not-a-virtualenv`.

  This stabilizes the `venv-safe-clear` preview feature.
- **Reject `--project` when initializing a project** ([#20225](https://github.com/astral-sh/uv/pull/20225))

  `--project` selects an existing project, so it is not meaningful when initializing a new one. Previously, `uv init --project example` warned and initialized `example` anyway; if a positional path was also provided, `--project` was ignored.

  This usage is now an error. Use `uv init example` to initialize a project at the requested path, or `uv init --directory example` to change the working directory first.

  This stabilizes the `init-project-flag` preview feature.
- **Reject missing or invalid `--project` paths** ([#20225](https://github.com/astral-sh/uv/pull/20225))

  uv previously warned when `--project` referred to a missing directory or a file other than `pyproject.toml`, but then attempted to continue. This could produce confusing errors later or run against an unintended project.

  Now, `uv run --project missing python` fails immediately instead of continuing. You cannot opt out of this behavior. Create the directory first or select an existing project. Passing `--project path/to/pyproject.toml` remains supported and selects the file's parent directory.

  This stabilizes the `project-directory-must-exist` preview feature.
- **Skip distributions with non-normalized filenames when publishing** ([#20225](https://github.com/astral-sh/uv/pull/20225))

  Distribution filenames must use [normalized package names](https://packaging.python.org/en/latest/specifications/name-normalization/) and versions. For example, a wheel for version `1.01.0` should be named `example-1.1.0-py3-none-any.whl`, not `example-1.01.0-py3-none-any.whl`.

  Previously, `uv publish` warned about non-normalized filenames but still attempted to upload them. It now skips the affected wheels and source distributions instead.

  You cannot opt out of this behavior. Rebuild distributions with normalized filenames before publishing.

  This stabilizes the `publish-require-normalized` preview feature.
- **Classify Conda environments named `base` and `root` by their paths** ([#20225](https://github.com/astral-sh/uv/pull/20225))

  Conda environments named `base` or `root` were previously assumed to be the base Conda environment, even when they were ordinary child environments. uv now recognizes child Conda environments named `base` or `root` based on their paths, as it already does for other names.

  You can opt out of automatic interpreter selection by requesting an interpreter explicitly with `--python /path/to/python`.

  This stabilizes the `special-conda-env-names` preview feature.
- **Reject broken `.venv` symlinks during environment discovery** ([#20433](https://github.com/astral-sh/uv/pull/20433))

  Previously, uv could ignore a broken `.venv` symlink and continue searching parent directories for another virtual environment. As a result, commands such as `uv pip install` could unexpectedly modify an unrelated ancestor environment.

  uv now stops at a broken `.venv` symlink and reports its exact path. Errors encountered while reading virtual environment metadata, including permission failures, are also reported immediately instead of being ignored.

  You cannot opt out of this behavior. Repair or remove the broken `.venv` symlink and correct any permissions that prevent uv from inspecting the environment.
- **Reinstall matching installed Python patch versions instead of upgrading implicitly** ([#20659](https://github.com/astral-sh/uv/pull/20659))

  Before [Python upgrades](https://docs.astral.sh/uv/guides/install-python/#upgrading-python-versions) were supported, `uv python install 3.12 --reinstall` doubled as a way to install the latest Python 3.12 patch release. Now that `--upgrade` is available, `--reinstall` reinstalls the matching patch releases that are already present.

  For example, if Python 3.12.6 and 3.12.7 are installed, `uv python install 3.12 --reinstall` reinstalls both versions instead of installing the latest available 3.12 release.

  You can recover the previous upgrade behavior with `uv python install 3.12 --upgrade`. Combine `--upgrade --reinstall` to reinstall only the latest patch.
- **Require `--upgrade-group` to name an existing dependency group** ([#18957](https://github.com/astral-sh/uv/pull/18957))

  Previously, `uv lock --upgrade-group docs` silently succeeded even if no `docs` [dependency group](https://docs.astral.sh/uv/concepts/projects/dependencies/#dependency-groups) existed. uv now validates the requested group against the project, its workspace members, and workspace-level dependency groups.

  You cannot opt out of this behavior. Correct the group name or add it to `[dependency-groups]`. Legacy `tool.uv.dev-dependencies` still satisfies `--upgrade-group dev`.
- **Resolve relative indexes and find-links against `--directory`** ([#20740](https://github.com/astral-sh/uv/pull/20740))

  The `--directory` option changes the directory in which uv operates. Previously, relative index and find-links paths supplied on the command line were still resolved against the original working directory.

  uv now resolves `--index`, `--default-index`, `--index-url`, `--extra-index-url`, and `--find-links` relative to the directory selected by `--directory`. For example:

  ```console
  $ uv add --directory project --index ./packages example
  ```

  This now uses `project/packages` instead of `./packages` in the original working directory. Absolute paths and indexes loaded from configuration files are unaffected.

  To preserve the previous target, pass an absolute path or adjust the relative path, e.g., `--index ../packages`.
- **Preserve absolute paths provided to `uv add`** ([#18402](https://github.com/astral-sh/uv/pull/18402))

  `uv add` previously converted every local dependency into a project-relative path, even when the original request used an absolute path or a literal `file://` URL. It now preserves the form of the request in `pyproject.toml` and `uv.lock`:

  ```console
  $ uv add ../library             # remains relative
  $ uv add /projects/library      # remains absolute
  ```

  Absolute paths make a project less portable. Use a relative path to avoid recording an absolute path. URLs containing expanded variables retain their existing relative-path behavior.
- **Remove older PyPy distributions that are only available as bzip2 archives** ([#20423](https://github.com/astral-sh/uv/pull/20423))

  Older PyPy patch releases that are only distributed as `.tar.bz2` archives are no longer available through `uv python install`. These releases require unsupported bzip2 archives.

  The latest PyPy release for each supported Python minor version is available as a gzip-compressed archive and remains supported. For example, `uv python list 3.10 --all-versions` still includes the latest PyPy 3.10 release, but older bzip2-only patch releases are omitted.

  You cannot opt out of this behavior. Request a newer PyPy patch release instead.
- **Omit excluded-package comments when annotations are disabled** ([#20085](https://github.com/astral-sh/uv/pull/20085))

  `uv pip compile --no-annotate` suppresses comments describing the generated requirements file. Previously, a footer listing packages excluded with `--unsafe-package` was still included, even though annotations were disabled. That footer is now omitted.

  You can recover the footer by removing `--no-annotate`.

### Stabilizations

- **TOML 1.0-compatible source distributions** ([#20225](https://github.com/astral-sh/uv/pull/20225))

  `uv_build` now writes a TOML 1.0-compatible `pyproject.toml` when building source distributions, allowing older Python build frontends to consume projects that use newer TOML syntax. The original project file remains available in the archive as `pyproject.toml.orig`.

  This stabilizes the `toml-backwards-compatibility` preview feature.
- **Automatic open-file limit adjustment on Unix** ([#20225](https://github.com/astral-sh/uv/pull/20225))

  On Linux and macOS, uv now attempts to raise the soft open-file limit at startup toward the hard limit, capped at 1,048,576 descriptors. The new limit also applies to subprocesses and reduces failures caused by running out of file descriptors. If the limit cannot be raised, uv continues running with the existing limit.

  This stabilizes the `adjust-ulimit` preview feature.

### Preview features

- Allow `uv upgrade` to target multiple packages, upgrade all production dependencies, and exclude selected dependencies ([#20338](https://github.com/astral-sh/uv/pull/20338))

### Bug fixes

- Include extras activated by dependency groups when evaluating conflicts ([#20237](https://github.com/astral-sh/uv/pull/20237))

## 0.11.x

See [changelogs/0.11.x](./changelogs/0.11.x.md)

## 0.10.x

See [changelogs/0.10.x](./changelogs/0.10.x.md)

## 0.9.x

See [changelogs/0.9.x](./changelogs/0.9.x.md)

## 0.8.x

See [changelogs/0.8.x](./changelogs/0.8.x.md)

## 0.7.x

See [changelogs/0.7.x](./changelogs/0.7.x.md)

## 0.6.x

See [changelogs/0.6.x](./changelogs/0.6.x.md)

## 0.5.x

See [changelogs/0.5.x](./changelogs/0.5.x.md)

## 0.4.x

See [changelogs/0.4.x](./changelogs/0.4.x.md)

## 0.3.x

See [changelogs/0.3.x](./changelogs/0.3.x.md)

## 0.2.x

See [changelogs/0.2.x](./changelogs/0.2.x.md)

## 0.1.x

See [changelogs/0.1.x](./changelogs/0.1.x.md)

<!-- prettier-ignore-end -->


The most recent release entry at the top of the CHANGELOG.md is **version 0.12.5**, released on 2026-08-14.


Fetch https://raw.githubusercontent.com/charmbracelet/gum/HEAD/go.mod and report the exact version of the github.com/charmbracelet/bubbletea dependency required in that module file.


module github.com/charmbracelet/gum

go 1.25.8

require (
	charm.land/bubbles/v2 v2.1.1
	charm.land/bubbletea/v2 v2.0.8
	charm.land/glamour/v2 v2.0.1
	charm.land/lipgloss/v2 v2.0.5
	charm.land/log/v2 v2.0.0
	github.com/Masterminds/semver/v3 v3.5.0
	github.com/alecthomas/kong v1.15.0
	github.com/alecthomas/mango-kong v0.1.0
	github.com/charmbracelet/colorprofile v0.4.3
	github.com/charmbracelet/x/ansi v0.11.7
	github.com/charmbracelet/x/editor v0.2.0
	github.com/charmbracelet/x/exp/ordered v0.1.0
	github.com/charmbracelet/x/term v0.2.2
	github.com/charmbracelet/x/xpty v0.1.3
	github.com/muesli/roff v0.1.0
	github.com/rivo/uniseg v0.4.7
	github.com/sahilm/fuzzy v0.1.3
	golang.org/x/text v0.39.0
)

require (
	github.com/alecthomas/chroma/v2 v2.20.0 // indirect
	github.com/atotto/clipboard v0.1.4 // indirect
	github.com/aymerick/douceur v0.2.0 // indirect
	github.com/charmbracelet/ultraviolet v0.0.0-20260703014108-f5a850f9c2b7 // indirect
	github.com/charmbracelet/x/conpty v0.1.1 // indirect
	github.com/charmbracelet/x/errors v0.0.0-20240508181413-e8d8b6e2de86 // indirect
	github.com/charmbracelet/x/exp/slice v0.0.0-20250327172914-2fdc97757edf // indirect
	github.com/charmbracelet/x/termios v0.1.1 // indirect
	github.com/charmbracelet/x/windows v0.2.2 // indirect
	github.com/clipperhouse/displaywidth v0.11.0 // indirect
	github.com/clipperhouse/uax29/v2 v2.7.0 // indirect
	github.com/creack/pty v1.1.24 // indirect
	github.com/dlclark/regexp2 v1.11.5 // indirect
	github.com/dustin/go-humanize v1.0.1 // indirect
	github.com/go-logfmt/logfmt v0.6.1 // indirect
	github.com/gorilla/css v1.0.1 // indirect
	github.com/lucasb-eyer/go-colorful v1.4.0 // indirect
	github.com/mattn/go-runewidth v0.0.24 // indirect
	github.com/microcosm-cc/bluemonday v1.0.27 // indirect
	github.com/muesli/cancelreader v0.2.2 // indirect
	github.com/muesli/mango v0.2.0 // indirect
	github.com/stretchr/testify v1.11.1 // indirect
	github.com/xo/terminfo v0.0.0-20220910002029-abceb7e1c41e // indirect
	github.com/yuin/goldmark v1.7.17 // indirect
	github.com/yuin/goldmark-emoji v1.0.6 // indirect
	golang.org/x/exp v0.0.0-20240613232115-7f521ea00fb8 // indirect
	golang.org/x/net v0.40.0 // indirect
	golang.org/x/sync v0.21.0 // indirect
	golang.org/x/sys v0.46.0 // indirect
)


In the fetched `go.mod` for `github.com/charmbracelet/gum`, there is no dependency listed under the exact import path `github.com/charmbracelet/bubbletea`. Instead, the module now depends on the renamed/relocated package:

```
charm.land/bubbletea/v2 v2.0.8
```

So the Bubble Tea dependency present is **charm.land/bubbletea/v2 v2.0.8** — the old `github.com/charmbracelet/bubbletea` path is no longer used in this go.mod.


Search the documentation of stanfordnlp/dspy with the query which optimizer should I choose if I have very little data and name the specific optimizer class it recommends when you have only a handful (fewer than ~10) of training examples.


Question: which optimizer should I choose if I have very little data


## Query

which optimizer should I choose if I have very little data.

## Response

### Sources:
Important: you can fetch the full content of any source using the fetch_url_content tool

#### (stanfordnlp/dspy/docs/docs/learn/optimization/overview.md)[https://raw.githubusercontent.com/stanfordnlp/dspy/main/docs/docs/learn/optimization/overview.md] (Score: 0.61)
- ---
sidebar_position: 1
---

# Optimization in DSPy

Once you have a system and a way to evaluate it, you can use DSPy optimizers to tune the prompts or weights in your program. Now it's useful to expand your data collection effort into building a training set and a held-out test set, in addition to the development set you've been using for exploration. For the training set (and its subset, validation set), you can often get substantial value out of 30 examples, but aim for at least 300 examples. Some optimizers accept a `trainset` only. Others ask for a `trainset` and a `valset`. When splitting data for most prompt optimizers, we recommend an unusual split compared to deep neural networks: 20% for training, 80% for validation. This reverse allocation emphasizes stable validation, since prompt-based optimizers often overfit to small training sets. In contrast, the [dspy.GEPA](https://dspy.ai/tutorials/gepa_ai_program/) optimizer follows the more standard ML convention: Maximize the training set size, while keeping the validation set just large enough to reflect the distribution of the downstream tasks (test set).

After your first few optimization runs, you are either very happy with everything or you've made a lot of progress but you don't like something about the final program or the metric. At this point, go back to step 1 (Programming in DSPy) and revisit the major questions. Did you define your task well? Do you need to collect (or find online) more data for your problem? Do you want to update your metric? And do you want to use a more sophisticated optimizer? Do you need to consider advanced features like DSPy Assertions? Or, perhaps most importantly, do you want to add some more complexity or steps in your DSPy program itself? Do you want to use multiple optimizers in a sequence?

#### (stanfordnlp/dspy/docs/docs/learn/optimization/optimizers.md)[https://raw.githubusercontent.com/stanfordnlp/dspy/main/docs/docs/learn/optimization/optimizers.md] (Score: 0.58)
- ## Which optimizer should I use?

Ultimately, finding the ‘right’ optimizer to use & the best configuration for your task will require experimentation. Success in DSPy is still an iterative process - getting the best performance on your task will require you to explore and iterate.

That being said, here's the general guidance on getting started:

- If you have **very few examples** (around 10), start with `BootstrapFewShot`.
- If you have **more data** (50 examples or more), try  `BootstrapFewShotWithRandomSearch`.
- If you prefer to do **instruction optimization only** (i.e. you want to keep your prompt 0-shot), use `MIPROv2` [configured for 0-shot optimization](../../api/optimizers/MIPROv2.md).
- If you’re willing to use more inference calls to perform **longer optimization runs** (e.g. 40 trials or more), and have enough data (e.g. 200 examples or more to prevent overfitting) then try `MIPROv2`.
- If you have been able to use one of these with a large LM (e.g., 7B parameters or above) and need a very **efficient program**, finetune a small LM for your task with `BootstrapFinetune`.

## How do I use an optimizer?

They all share this general interface, with some differences in the keyword arguments (hyperparameters). A full list can be found in the [API reference](../../api/optimizers/BetterTogether.md).

Let's see this with the most common one, `BootstrapFewShotWithRandomSearch`.

```python
import dspy

- ## What DSPy Optimizers are currently available?

Optimizers can be accessed as `dspy.<OptimizerName>` (e.g., `dspy.MIPROv2`, `dspy.BootstrapFewShot`).

### Automatic Few-Shot Learning

These optimizers extend the signature by automatically generating and including **optimized** examples within the prompt sent to the model, implementing few-shot learning.

1. [**`LabeledFewShot`**](../../api/optimizers/LabeledFewShot.md): Simply constructs few-shot examples (demos) from provided labeled input and output data points.  Requires `k` (number of examples for the prompt) and `trainset` to randomly select `k` examples from.

2. [**`BootstrapFewShot`**](../../api/optimizers/BootstrapFewShot.md): Uses a `teacher` module (which defaults to your program) to generate complete demonstrations for every stage of your program, along with labeled examples in `trainset`. Parameters include `max_labeled_demos` (the number of demonstrations randomly selected from the `trainset`) and `max_bootstrapped_demos` (the number of additional examples generated by the `teacher`). The bootstrapping process employs the metric to validate demonstrations, including only those that pass the metric in the "compiled" prompt. Advanced: Supports using a `teacher` program that is a *different* DSPy program that has compatible structure, for harder tasks.

- ---
sidebar_position: 1
---

# DSPy Optimizers (formerly Teleprompters)

A **DSPy optimizer** is an algorithm that can tune the parameters of a DSPy program (i.e., the prompts and/or the LM weights) to maximize the metrics you specify, like accuracy.

A typical DSPy optimizer takes three things:

- Your **DSPy program**. This may be a single module (e.g., `dspy.Predict`) or a complex multi-module program.

- Your **metric**. This is a function that evaluates the output of your program, and assigns it a score (higher is better).

- A few **training inputs**. This may be very small (i.e., only 5 or 10 examples) and incomplete (only inputs to your program, without any labels).

If you happen to have a lot of data, DSPy can leverage that. But you can start small and get strong results.

**Note:** Formerly called teleprompters. We are making an official name update, which will be reflected throughout the library and documentation.

## What does a DSPy Optimizer tune? How does it tune them?

#### (stanfordnlp/dspy/llms.txt)[https://raw.githubusercontent.com/stanfordnlp/dspy/main/llms.txt] (Score: 0.57)
- - [choosing-an-optimizer.md](https://raw.githubusercontent.com/stanfordnlp/dspy/main/docs/docs/diving-deeper/choosing-an-optimizer.md): Choosing the Right Optimizer for DSPy
DSPy provides a variety of optimizers for tuning different aspects of a program, such as instructions, demos, and weights. The choice of optimizer depends on the specific use case and the bottleneck in the program. The document provides a guide for selecting the right optimizer, including a two-axis decision process and a selection cheat sheet. It also explains the design decisions behind the optimizers, such as how they tune the program, what they need at compile time, and what they cost. The document covers various optimizers, including `LabeledFewShot`, `BootstrapFewShot`, `COPRO`, `GEPA`, `MIPROv2`, and `BootstrapFinetune`, and provides code examples for some of them.

```python
# Example usage of LabeledFewShot
dspy.LabeledFewShot(k=16)

- #### optimization

- [optimizers.md](https://raw.githubusercontent.com/stanfordnlp/dspy/main/docs/docs/learn/optimization/optimizers.md): DSPy Optimizers: Tuning DSPy Programs for Better Performance
DSPy optimizers are algorithms that tune the parameters of a DSPy program to maximize specified metrics. They take a DSPy program, a metric, and a few training inputs to optimize the program's quality. Optimizers can synthesize few-shot examples, propose better instructions, and fine-tune LM weights. The document provides an overview of available optimizers, including automatic few-shot learning, instruction optimization, and fine-tuning. It also offers guidance on choosing an optimizer and using them effectively.

```python
import dspy

- # Set up the optimizer: we want to "bootstrap" (i.e., self-generate) 8-shot examples of your program's steps.
config = dict(max_bootstrapped_demos=4, max_labeled_demos=4, num_candidate_programs=10, num_threads=4)
teleprompter = dspy.BootstrapFewShotWithRandomSearch(metric=YOUR_METRIC_HERE, **config)
optimized_program = teleprompter.compile(YOUR_PROGRAM_HERE, trainset=YOUR_TRAINSET_HERE)
```

The document also provides additional examples, including optimizing prompts for a ReAct agent and fine-tuning a small LM for classification tasks.

KEY TERMS:

1. DSPy Optimizers
2. Teleprompters
3. Few-shot learning
4. Instruction optimization
5. Fine-tuning
6. BootstrapFewShot
7. MIPROv2
8. BootstrapFinetune
9. BetterTogether
10. Ensemble

- [overview.md](https://raw.githubusercontent.com/stanfordnlp/dspy/main/docs/docs/learn/optimization/overview.md): Optimization in DSPy for LM Programs
Optimization in DSPy involves tuning prompts or weights in a program using optimizers, which requires a training set and a held-out test set, in addition to a development set. A recommended data split for most prompt optimizers is 20% for training and 80% for validation, but some optimizers like dspy.GEPA follow standard ML conventions. The optimization process is iterative, and DSPy allows for incremental iteration on data, program structure, metric, and optimization steps. Key considerations include defining tasks well, collecting sufficient data, selecting suitable optimizers, and using advanced features like DSPy Assertions.

No code examples are present in this document.

KEY TERMS:
1. DSPy
2. Optimization
3. Prompt Optimizers
4. dspy.GEPA
5. Training Set
6. Validation Set
7. Test Set
8. DSPy Assertions
9. Iterative Development
10. LM Programs

#### programming

#### (stanfordnlp/dspy/docs/docs/tutorials/entity_extraction/index.ipynb.txt)[https://pub-39b02ce1b5a441b2a4658c1fc71dbb9c.r2.dev/stanfordnlp/dspy/stanfordnlp/dspy/docs/docs/tutorials/entity_extraction/index.ipynb.txt] (Score: 0.56)
- ## Optimize the Model

DSPy includes powerful optimizers that can improve the quality of your system.

Here, we use DSPy's `MIPROv2` optimizer to:
- Automatically tune the program's language model (LM) prompt by 1. using the LM to adjust the prompt's instructions and 2. building few-shot examples from the training dataset that are augmented with reasoning generated from `dspy.ChainOfThought`.
- Maximize correctness on the training set.

This optimization process is automated, saving time and effort while improving accuracy.

```python
mipro_optimizer = dspy.MIPROv2(
    metric=extraction_correctness_metric,
    auto="medium",
)
optimized_people_extractor = mipro_optimizer.compile(
    people_extractor,
    trainset=train_set,
    max_bootstrapped_demos=4,
    minibatch=False
)
```

## Evaluate Optimized Program

After optimization, we re-evaluate the program on the test set to measure improvements. Comparing the optimized and initial results allows us to:
- Quantify the benefits of optimization.
- Validate that the program generalizes well to unseen data.

In this case, we see that accuracy of the program on the test dataset has improved significantly.

```python
evaluate_correctness(optimized_people_extractor, devset=test_set)
```

## Inspect Optimized Program's Prompt

After optimizing the program, we can inspect the history of interactions to see how DSPy has augmented the program's prompt with few-shot examples. This step demonstrates:
- The structure of the prompt used by the program.
- How few-shot examples are added to guide the model's behavior.

Use `inspect_history(n=1)` to view the last interaction and analyze the generated prompt.

```python
dspy.inspect_history(n=1)
```

## Keeping an eye on cost

DSPy allows you to track the cost of your programs. The following code demonstrates how to obtain the cost of all LM calls made by the DSPy extractor program so far.

#### (stanfordnlp/dspy/docs/docs/tutorials/rag/index.ipynb.txt)[https://pub-39b02ce1b5a441b2a4658c1fc71dbb9c.r2.dev/stanfordnlp/dspy/stanfordnlp/dspy/docs/docs/tutorials/rag/index.ipynb.txt] (Score: 0.55)
- # Let's pick an `example` here from the data.
example = data[2]
example
```

Now, let's divide the data into:

- Training (and with it Validation) set:
    - These are the splits you typically give to DSPy optimizers.
    - Optimizers typically learn directly from the training examples and check their progress using the validation examples.
    - It's good to have 30--300 examples for training and validation each.
    - For prompt optimizers in particular, it's often better to pass _more_ validation than training.
    - Below, we'll use 200 in total. MIPROv2 will split them into 20% training and 80% validation if you don't pass a valset.

- Development and Test sets: The rest, typically on the order of 30--1000, can be used for:
    - development (i.e., you can inspect them as you iterate on your system) and
    - testing (final held-out evaluation).

```python
import random

random.Random(0).shuffle(data)
trainset, devset, testset = data[:200], data[200:500], data[500:1000]

len(trainset), len(devset), len(testset)
```

## Evaluation in DSPy.

What kind of metric can suit our question-answering task? There are many choices, but since the answers are long, we may ask: How well does the system response _cover_ all key facts in the gold response? And the other way around, how well is the system response _not saying things_ that aren't in the gold response?

That metric is essentially a **semantic F1**, so let's load a `SemanticF1` metric from DSPy. This metric is actually implemented as a [very simple DSPy module](https://github.com/stanfordnlp/dspy/blob/main/dspy/evaluate/auto_evaluation.py#L21) using whatever LM we're working with.

```python
from dspy.evaluate import SemanticF1

# Instantiate the metric.
metric = SemanticF1(decompositional=True)

# Produce a prediction from our `cot` module, using the `example` above as input.
pred = cot(**example.inputs())

# Compute the metric score for the prediction.
score = metric(example, pred)

#### (stanfordnlp/dspy/docs/docs/diving-deeper/choosing-an-optimizer.md)[https://raw.githubusercontent.com/stanfordnlp/dspy/main/docs/docs/diving-deeper/choosing-an-optimizer.md] (Score: 0.55)
- ### Specialized

**`dspy.AvatarOptimizer(metric, max_iters=10, lower_bound=0, upper_bound=1, max_positive_inputs=10, max_negative_inputs=10, optimize_for="max")`**
Built for agent-style programs. Partitions the trainset by metric into positive examples (high scores) and negative examples (low scores). On each iteration, asks an LM to read positive and negative examples and propose instruction edits that explain the difference, then evaluates the new instructions. Niche but useful when the metric is a clean pass/fail and you need readable agent instructions.

## Selection cheat sheet

- ### 4. Every optimizer takes a `metric`, but the required metric shape varies

All optimizers read a numeric score per example. Most are happy with a bare float or boolean. GEPA additionally needs `Prediction(score, feedback)` because it threads the natural-language critique into its proposals. Picking the wrong metric shape for the wrong optimizer is the most common configuration mistake — check the optimizer’s page when in doubt.

### 5. GEPA is the only optimizer that reads `Prediction(score, feedback)` per predictor

The other instruction optimizers (COPRO, MIPROv2) treat the metric as a black-box scalar — they only know whether candidate-A scored higher than candidate-B. GEPA reads `feedback` from each metric call and threads it into the next instruction proposal. The other side of the same coin: when you don’t have a feedback-rich metric, COPRO or MIPROv2 may give better mileage than GEPA on the same budget.

### 6. Prompt-only optimizers work with any LM; finetune optimizers need a tunable model

BootstrapFewShot, COPRO, MIPROv2, GEPA, and most others mutate prompts and demos — the LM stays untouched, so closed-source providers are fair game. `BootstrapFinetune` writes new model weights and requires an LM that exposes a fine-tuning API (a local trainer, an open-source model, or a provider like OpenAI’s fine-tuning endpoint). The split decides which optimizers are even available to you.

### 7. Demo-tuning tends to overfit; instruction-tuning tends to generalize

#### (stanfordnlp/dspy/docs/docs/api/optimizers/MIPROv2.md)[https://raw.githubusercontent.com/stanfordnlp/dspy/main/docs/docs/api/optimizers/MIPROv2.md] (Score: 0.55)
- # Save optimize program for future use
optimized_program.save(f"optimized.json")
```

## How `MIPROv2` works

At a high level, `MIPROv2` works by creating both few-shot examples and new instructions for each predictor in your LM program, and then searching over these using Bayesian Optimization to find the best combination of these variables for your program.  If you want a visual explanation check out this [twitter thread](https://x.com/michaelryan207/status/1804189184988713065).

These steps are broken down in more detail below:

1) **Bootstrap Few-Shot Examples**: Randomly samples examples from your training set, and run them through your LM program. If the output from the program is correct for this example, it is kept as a valid few-shot example candidate. Otherwise, we try another example until we've curated the specified amount of few-shot example candidates. This step creates `num_candidates` sets of `max_bootstrapped_demos` bootstrapped examples and `max_labeled_demos` basic examples sampled from the training set.

2) **Propose Instruction Candidates**. The instruction proposer includes (1) a generated summary of properties of the training dataset, (2) a generated summary of your LM program's code and the specific predictor that an instruction is being generated for, (3) the previously bootstrapped few-shot examples to show reference inputs / outputs for a given predictor and (4) a randomly sampled tip for generation (i.e. "be creative", "be concise", etc.) to help explore the feature space of potential instructions.  This context is provided to a `prompt_model` which writes high quality instruction candidates.


Based on the DSPy documentation, if you have **very few examples (around 10 or fewer)**, the recommendation is to start with:

**`dspy.BootstrapFewShot`**

The docs state: "If you have **very few examples** (around 10), start with `BootstrapFewShot`." This optimizer uses a teacher module (defaulting to your own program) to generate demonstrations for each stage of your program, combined with any labeled examples you provide in the trainset, making it well-suited for very small datasets where more data-hungry optimizers (like `BootstrapFewShotWithRandomSearch` or `MIPROv2`, which need 50+ or 200+ examples respectively) wouldn't have enough data to work effectively.


Search the documentation of unclecode/crawl4ai for configuration object passed to arun for a single crawl and give the exact name of the configuration class that is passed per crawl run (as opposed to the one that configures the browser).


Question: CrawlerRunConfig arun configuration object per crawl run