pydantic-cli-pydantic-v2-compatible 4.4.0

Description:

pydanticclipydanticv2compatible 4.4.0

Pydantic Commandline Tool Interface
Turn Pydantic defined Data Models into CLI Tools and enable loading values from JSON files
Requires Pydantic >=1.5.1.


Installation
pip install pydantic-cli

Features and Requirements

Thin Schema driven interfaces constructed from Pydantic defined data models
Validation is performed in a single location as defined by Pydantic's validation model and defined types
CLI parsing is only structurally validating that the args or optional arguments are provided
Enable loading config defined in JSON to override or set specific values
Clear interface between the CLI and your application code
Leverage the static analyzing tool mypy to catch type errors in your commandline tool
Easy to test (due to reasons defined above)

Motivating Usecases

Quick scrapy commandline tools for local development (e.g., webscraper CLI tool, or CLI application that runs a training algo)
Internal tools driven by a Pydantic data model/schema
Configuration heavy tools that are driven by either partial (i.e, "presets") or complete configuration files defined using JSON

Quick Start
To create a commandline tool that takes an input file and max number of records to process as arguments:
my-tool --input_file /path/to/file.txt --max_records 1234

This requires two components.

Create Pydantic Data Model of type T
write a function that takes an instance of T and returns the exit code (e.g., 0 for success, non-zero for failure).
pass the T into to the to_runner function, or the run_and_exit

Explicit example show below.
import sys

from pydantic import BaseModel
from pydantic_cli import run_and_exit, to_runner

class MinOptions(BaseModel):
input_file: str
max_records: int


def example_runner(opts: MinOptions) -> int:
print(f"Mock example running with options {opts}")
return 0

if __name__ == '__main__':
# to_runner will return a function that takes the args list to run and
# will return an integer exit code
sys.exit(to_runner(MinOptions, example_runner, version='0.1.0')(sys.argv[1:]))

Or to implicitly use sys.argv[1:], leverage run_and_exit (to_runner is also useful for testing).
if __name__ == '__main__':
run_and_exit(MinOptions, example_runner, description="My Tool Description", version='0.1.0')

Customizing Description and Commandline Flags
If the Pydantic data model fields are reasonable well named (e.g., 'min_score', or 'max_records'), this can yield a good enough description when --help is called.
Customizing the commandline flags or the description can be done by leveraging description keyword argument in Field from pydantic. See Field model in Pydantic more details.
Custom 'short' or 'long' forms of the commandline args can be provided by using a Tuple[str] or Tuple2[str, str]. For example, cli=('-m', '--max-records') or cli=('--max-records',).
Note, Pydantic interprets ... as a "required" value when used in Field.
from pydantic import BaseModel, Field
from pydantic_cli import run_and_exit


class MinOptions(BaseModel):
input_file: str = Field(..., description="Path to Input H5 file", cli=('-i', '--input-file'))
max_records: int = Field(..., description="Max records to process", cli=('-m', '--max-records'))
debug: bool = Field(False, description="Enable debugging mode", cli= ('-d', '--debug'))


def example_runner(opts: MinOptions) -> int:
print(f"Mock example running with options {opts}")
return 0


if __name__ == '__main__':
run_and_exit(MinOptions, example_runner, description="My Tool Description", version='0.1.0')

WARNING: Data models that have boolean values and generated CLI flags (e.g., --enable-filter or --disable-filter) require special attention. See the "Defining Boolean Flags" section for more details.
Leveraging Field is also useful for validating inputs.
from pydantic import BaseModel, Field


class MinOptions(BaseModel):
input_file: str = Field(..., description="Path to Input H5 file", cli=('-i', '--input-file'))
max_records: int = Field(..., gt=0, lte=1000, description="Max records to process", cli=('-m', '--max-records'))

Loading Configuration using JSON
Tools can also load entire models or partially defined Pydantic data models from JSON files.
For example, given the following Pydantic data model:
from pydantic import BaseModel
from pydantic_cli import run_and_exit, DefaultConfig

class Opts(BaseModel):
class Config(DefaultConfig):
CLI_JSON_ENABLE = True

hdf_file: str
max_records: int = 10
min_filter_score: float
alpha: float
beta: float

def runner(opts: Opts):
print(f"Running with opts:{opts}")
return 0

if __name__ == '__main__':
run_and_exit(Opts, runner, description="My Tool Description", version='0.1.0')

Can be run with a JSON file that defines all the (required) values.
{"hdf_file": "/path/to/file.hdf5", "max_records": 5, "min_filter_score": 1.5, "alpha": 1.0, "beta": 1.0}

The tool can be executed as shown below. Note, options required at the commandline as defined in the Opts model (e.g., 'hdf_file', 'min_filter_score', 'alpha' and 'beta') are NO longer required values supplied to the commandline tool.
my-tool --json-config /path/to/file.json

To override values in the JSON config file, or provide the missing required values, simply provide the values at the commandline.
These values will override values defined in the JSON config file. The provides a general mechanism of using configuration "preset" files.
my-tool --json-config /path/to/file.json --alpha -1.8 --max_records 100

Similarly, a partially described data model can be used combined with explict values provided at the commandline.
In this example, hdf_file and min_filter_score are still required values that need to be provided to the commandline tool.
{"max_records":10, "alpha":1.234, "beta":9.876}

my-tool --json-config /path/to/file.json --hdf_file /path/to/file.hdf5 --min_filter_score -12.34

Catching Type Errors with mypy
If you've used argparse, you've probably been bitten by an AttributeError exception raised on the Namespace instance returned from parsing the raw args.
For example,
import sys
from argparse import ArgumentParser


def to_parser() -> ArgumentParser:
p = ArgumentParser(description="Example")
f = p.add_argument

f('hdf5_file', type=str, help="Path to HDF5 records")
f("--num_records", required=True, type=int, help="Number of records to filter over")
f('-f', '-filter-score', required=True, type=float, default=1.234, help="Min filter score")
f('-g', '--enable-gamma-filter', action="store_true", help="Enable gamma filtering")
return p


def my_library_code(path: str, num_records: float, min_filter_score, enable_gamma=True) -> int:
print("Mock running of code")
return 0


def main(argv) -> int:
p = to_parser()
pargs = p.parse_args(argv)
return my_library_code(pargs.hdf5_file, pargs.num_record, pargs.min_filter_score, pargs.enable_gamma_filter)


if __name__ == '__main__':
sys.exit(main(sys.argv[1:]))

The first error found at runtime is show below.
Traceback (most recent call last):
File "junk.py", line 35, in <module>
sys.exit(main(sys.argv[1:]))
File "junk.py", line 31, in main
return my_library_code(pargs.hdf5_file, pargs.num_record, pargs.min_filter_score, pargs.enable_gamma_filter)
AttributeError: 'Namespace' object has no attribute 'num_record'

The errors in pargs.num_records and pargs.filter_score are inconsistent with what is defined in to_parser method. Each error will have to be manually hunted down.
With pydantic-cli, it's possible to catch these errors by running mypy. This also enables you to refactor your code with more confidence.
For example,
from pydantic import BaseModel

from pydantic_cli import run_and_exit


class Options(BaseModel):
input_file: str
max_records: int


def bad_func(n: int) -> int:
return 2 * n


def example_runner(opts: Options) -> int:
print(f"Mock example running with {opts}")
return 0


if __name__ == "__main__":
run_and_exit(Options, bad_func, version="0.1.0")

With mypy, it's possible to proactively catch this types of errors.
mypy pydantic_cli/examples/simple.py ✘ 1
pydantic_cli/examples/simple.py:36: error: Argument 2 to "run_and_exit" has incompatible type "Callable[[int], int]"; expected "Callable[[Options], int]"
Found 1 error in 1 file (checked 1 source file)

Defining Boolean Flags
There are a few common cases of boolean values:

x:bool = True|False A bool field with a default value
x:bool A required bool field
x:Optional[bool] or x:Optional[bool] = None An optional boolean with a default value of None
x:Optional[bool] = Field(...) a required boolean that can be set to None, True or False in Pydantic.

Case 1 is very common and the semantics of the custom CLI overrides (as a tuple) are different than the cases 2-4.
Case 4 has limitations. It isn't possible to set None from the commandline when the default is True or False.
Boolean Field with Default
As demonstrated in a previous example, the common case of defining a type as bool with a default value work as expected.
For example:
from pydantic import BaseModel


class MinOptions(BaseModel):
debug: bool = False

By default, when defining a model with a boolean flag, an "enable" or "disable" prefix will be added to create the commandline flag depending on the default value.
In this specific case, a commandline flag of --enable-debug which will set debug in the Pydantic model to True.
If the default was set to False, then a --disable-debug flag would be created and would set debug to False in the Pydantic data model.
The CLI flag can be customized and provided as a Tuple[str] or Tuple[str, str] as (long, ) or (short, long) flags (respectively) to negate the default value.
For example, running -d or --debug will set debug to True in the Pydantic data model.
from pydantic import BaseModel, Field


class MinOptions(BaseModel):
debug: bool = Field(False, description="Enable debug mode", cli=('-d', '--debug'))

If the default is True, running the example below with --disable-debug will set debug to False.
from pydantic import BaseModel, Field


class MinOptions(BaseModel):
debug: bool = Field(True, description="Disable debug mode", cli=('-d', '--disable-debug'))

Boolean Required Field
Required boolean fields are handled a bit different than cases where a boolean is provided with a default value.
Specifically, the custom flag Tuple[str, str] must be provided as a (--enable, --disable) format.
from pydantic import BaseModel, Field


class MinOptions(BaseModel):
debug: bool = Field(..., description="Enable/Disable debugging", cli= ('--enable-debug', '--disable-debug'))

Currently, supplying the short form of each "enable" and "disable" is not supported.
Optional Boolean Fields
Similar to the required boolean fields case, Optional[bool] cases have the same (--enable, --disable) semantics.
from typing import Optional
from pydantic import BaseModel, Field


class MinOptions(BaseModel):
a: Optional[bool]
b: Optional[bool] = None
c: Optional[bool] = Field(None, cli= ('--yes-c', '--no-c'))
d: Optional[bool] = Field(False, cli=('--enable-d', '--disable-d'))
e: Optional[bool] = Field(..., cli=('--enable-e', '--disable-e'))

Note, that x:Optional[bool], x:Optional[bool] = None, x:Optional[bool] = Field(None) semantically mean the same thing in Pydantic.
In each of the above cases, the custom CLI flags must be provided as (--enable, --disable) format.
Also, note it isn't possible to set None from the commandline for the Optional[bool] = False or Optional[bool] = Field(...) case.
Customizing default Enable/Disable Bool Prefix
The enable/disable prefix used for all bool options can be customized by setting the Tuple[str, str] of CLI_BOOL_PREFIX on Config to the (positive, negative) of prefix flag.
The default value of Config.CLI_BOOL_PREFIX is ('--enable-', '--disable').
from pydantic import BaseModel


class Options(BaseModel):
class Config:
CLI_BOOL_PREFIX = ('--yes-', '--no-')

debug: bool = False

This will generate an optional --yes-debug flag that will set debug from the default (False) to True in the Pydantic data model.
In many cases, it's best to customize the commandline boolean flags to avoid ambiguities or confusion.
Customization and Hooks
If the description is not defined and the Pydantic data model fields are tersely named (e.g., 'total', or 'n'), this can yield a call to --help that is quite minimal (due to the lack of metadata). However, verbosely named arguments can often be good enough to communicate the intent of the commandline interface.
For customization of the CLI args, such as max number of records is -m 1234 in the above example, there are two approaches.

The first is the quick method that is a minor change to the core Config of the Pydantic Data model.
The second method is use Pydantic's "Field" metadata model is to define richer set of metadata. See Field model in Pydantic more details.

Hooks into the CLI Execution
There are three core hooks into the customization of CLI execution.

exception handler (log or write to stderr and map specific exception classes to integer exit codes)
prologue handler (pre-execution hook)
epilogue handler (post-execution hook)

Both of these cases can be customized to by passing in a function to the running/execution method.
The exception handler should handle any logging or writing to stderr as well as mapping the specific exception to non-zero integer exit code.
For example:
import sys

from pydantic import BaseModel, Field
from pydantic_cli import run_and_exit


class MinOptions(BaseModel):
input_file: str = Field(..., cli=('-i',))
max_records: int = Field(10, cli=('-m', '--max-records'))


def example_runner(opts: MinOptions) -> int:
return 0


def custom_exception_handler(ex) -> int:
exception_map = dict(ValueError=3, IOError=7)
sys.stderr.write(str(ex))
exit_code = exception_map.get(ex.__class__, 1)
return exit_code


if __name__ == '__main__':
run_and_exit(MinOptions, example_runner, exception_handler=custom_exception_handler)

A general pre-execution hook can be called using the prologue_handler. This function is Callable[[T], None], where T is an instance of your Pydantic data model.
This setup hook will be called before the execution of your main function (e.g., example_runner).
import sys
import logging

def custom_prologue_handler(opts) -> None:
logging.basicConfig(level="DEBUG", stream=sys.stdout)

if __name__ == '__main__':
run_and_exit(MinOptions, example_runner, prolgue_handler=custom_prologue_handler)

Similarly, the post execution hook can be called. This function is Callable[[int, float], None] that is the exit code and program runtime in sec as input.
from pydantic_cli import run_and_exit


def custom_epilogue_handler(exit_code: int, run_time_sec:float):
m = "Success" if exit_code else "Failed"
msg = f"Completed running ({m}) in {run_time_sec:.2f} sec"
print(msg)


if __name__ == '__main__':
run_and_exit(MinOptions, example_runner, epilogue_handler=custom_epilogue_handler)

SubParsers
Defining a subparser to your commandline tool is enabled by creating a container SubParser dict and calling run_sp_and_exit
import typing as T
from pydantic import BaseModel, AnyUrl, Field


from pydantic_cli import run_sp_and_exit, SubParser


class AlphaOptions(BaseModel):
input_file: str = Field(..., cli=('-i',))
max_records: int = Field(10, cli=('-m', '--max-records'))


class BetaOptions(BaseModel):
url: AnyUrl = Field(..., cli=('-u', '--url'))
num_retries: int = Field(3, cli=('-n', '--num-retries'))


def printer_runner(opts: T.Any):
print(f"Mock example running with {opts}")
return 0


def to_runner(sx):
def example_runner(opts) -> int:
print(f"Mock {sx} example running with {opts}")
return 0
return example_runner


def to_subparser_example():

return {
'alpha': SubParser(AlphaOptions, to_runner("Alpha"), "Alpha SP Description"),
'beta': SubParser(BetaOptions, to_runner("Beta"), "Beta SP Description")}


if __name__ == "__main__":
run_sp_and_exit(to_subparser_example(), description=__doc__, version='0.1.0')

Configuration Details and Advanced Features
Pydantic-cli attempts to stylistically follow Pydantic's approach using a class style configuration. See `DefaultConfig in ``pydantic_cli' for more details.
import typing as T
from pydantic_cli import CustomOptsType

class DefaultConfig:
"""
Core Default Config "mixin" for CLI configuration.
"""

# value used to generate the CLI format --{key}
CLI_JSON_KEY: str = "json-config"
# Enable JSON config loading
CLI_JSON_ENABLE: bool = False

# Set the default ENV var for defining the JSON config path
CLI_JSON_CONFIG_ENV_VAR: str = "PCLI_JSON_CONFIG"
# Set the default Path for JSON config file
CLI_JSON_CONFIG_PATH: T.Optional[str] = None
# If a default path is provided or provided from the commandline
CLI_JSON_VALIDATE_PATH: bool = True

# Customize the default prefix that is generated
# if a boolean flag is provided. Boolean custom CLI
# MUST be provided as Tuple[str, str]
CLI_BOOL_PREFIX: T.Tuple[str, str] = ("--enable-", "--disable-")

# Add a flag that will emit the shell completion
# this requires 'shtab'
# https://github.com/iterative/shtab
CLI_SHELL_COMPLETION_ENABLE: bool = False
CLI_SHELL_COMPLETION_FLAG: str = "--emit-completion"

AutoComplete leveraging shtab
There is support for zsh and bash autocomplete generation using shtab
The optional dependency can be installed as follows.
pip install "pydantic-cli[shtab]"

To enable the emitting of bash/zsh autocomplete files from shtab, set CLI_SHELL_COMPLETION_ENABLE: bool = True in your data model Config.
Then use your executable (or .py file) emit the autocomplete file to the necessary output directory.
For example, using zsh and a script call my-tool.py, my-tool.py --emit-completion zsh > ~/.zsh/completions/_my-tool.py. By convention/default, the executable name must be prefixed with an underscore.
When using autocomplete it should looks similar to this.
> ./my-tool.py --emit-completion zsh > ~/.zsh/completions/_my-tool.py
Completed writing zsh shell output to stdout
> ./my-tool.py --max
-- option --
--max_filter_score -- (type:int default:1.0)
--max_length -- (type:int default:12)
--max_records -- (type:int default:123455)
--max_size -- (type:int default:13)

See shtab for more details.
Note, that due to the (typically) global zsh completions directory, this can create some friction points with different virtual (or conda) ENVS with the same executable name.
General Suggested Testing Model
At a high level, pydantic_cli is (hopefully) a thin bridge between your Options defined as a Pydantic model and your
main runner(opts: Options) func that has hooks into the startup, shutdown and error handling of the command line tool.
It also supports loading config files defined as JSON. By design, pydantic_cli explicitly doesn't expose, or leak the argparse instance
because it would add too much surface area and it would enable users' to start mucking with the argparse instance in all kinds of unexpected ways.
The use of argparse internally is an hidden implementation detail.
Testing can be done by leveraging the to_runner interface.

It's recommend trying to do the majority of testing via unit tests (independent of pydantic_cli) with your main function and different instances of your pydantic data model.
Once this test coverage is reasonable, it can be useful to add a few smoke tests at the integration level leveraging to_runner to make sure the tool is functional. Any bugs at this level are probably at the pydantic_cli level, not your library code.

Note, that to_runner(Opts, my_main) returns a Callable[[List[str]], int] that can be used with argv to return an integer exit code of your program. The to_runner layer will also catch any exceptions.
import unittest

from pydantic import BaseModel
from pydantic_cli import to_runner


class Options(BaseModel):
alpha: int


def main(opts: Options) -> int:
if opts.alpha < 0:
raise Exception(f"Got options {opts}. Forced raise for testing.")
return 0


class TestExample(unittest.TestCase):

def test_core(self):
# Note, this has nothing to do with pydantic_cli
# If possible, this is where the bulk of the testing should be
self.assertEqual(0, main(Options(alpha=1)))

def test_example(self):
f = to_runner(Options, main)
self.assertEqual(0, f(["--alpha", "100"]))

def test_expected_error(self):
f = to_runner(Options, main)
self.assertEqual(1, f(["--alpha", "-10"]))

For more scrappy, interactive local development, it can be useful to add ipdb or pdb and create a custom exception_handler.
import sys
from pydantic import BaseModel
from pydantic_cli import default_exception_handler, run_and_exit


class Options(BaseModel):
alpha: int


def exception_handler(ex: BaseException) -> int:
exit_code = default_exception_handler(ex)
import ipdb; ipdb.set_trace()
return exit_code


def main(opts: Options) -> int:
if opts.alpha < 0:
raise Exception(f"Got options {opts}. Forced raise for testing.")
return 0


if __name__ == "__main__":
run_and_exit(Options, main, exception_handler=exception_handler)

Alternatively, wrap your main function to call ipdb.
import sys

from pydantic import BaseModel
from pydantic_cli import run_and_exit


class Options(BaseModel):
alpha: int


def main(opts: Options) -> int:
if opts.alpha < 0:
raise Exception(f"Got options {opts}. Forced raise for testing.")
return 0


def main_with_ipd(opts: Options) -> int:
import ipdb; ipdb.set_trace()
return main(opts)


if __name__ == "__main__":
run_and_exit(Options, main_with_ipd)

The core design choice in pydantic_cli is leveraging composable functions f(g(x)) style providing a straight-forward mechanism to plug into.
More Examples
More examples are provided here and Testing Examples can be seen here.
The TestHarness might provide examples of how to test your CLI tool(s)
Limitations

Positional Arguments are not supported (See more info in the next subsection)
Using Pydantic BaseSettings to set values from dotenv or ENV variables is not supported. Loading dotenv or similar in Pydantic overlapped and competed too much with the "preset" JSON loading model in pydantic-cli.
Pydantic has a perhaps counterintuitive model that sets default values based on the Type signature. For Optional[T] with NO default assign, a default of None is assigned. This can sometimes yield surprising commandline args generated from the Pydantic data model.
Currently only support "simple" types (e.g., floats, ints, strings, boolean) and limited support for fields defined as List[T], Set[T] and simple Enums. There is no support for nested models.
Leverages argparse underneath the hood (argparse is a bit thorny of an API to build on top of).

Why are Positional Arguments not supported?
The core features of pydantic-cli are:

Define and validate models using Pydantic and use these schemas as an interface to the command line
Leverage mypy (or similar static analyzer) to enable validating/checking typesafe-ness prior to runtime
Load partial or complete models using JSON (these are essentially, partial or complete config or "preset" files)

Positional arguments create friction points when combined with loading model values from a JSON file. More specifically, (required) positional values of the model could be supplied in the JSON and are no longer required at the command line. This can fundamentally change the commandline interface and create ambiguities/bugs.
For example:
from pydantic import BaseModel
from pydantic_cli import DefaultConfig

class MinOptions(BaseModel):
class Config(DefaultConfig):
CLI_JSON_ENABLE = True

input_file: str
input_hdf: str
max_records: int = 100

And the vanilla case running from the command line works as expected.
my-tool /path/to/file.txt /path/to/file.h5 --max_records 200

However, when using the JSON "preset" feature, there are potential problems where the positional arguments of the tool are shifting around depending on what fields have been defined in the JSON preset.
For example, running with this preset.json, the input_file positional argument is no longer required.
{"input_file": "/system/config.txt", "max_records": 12345}

Vanilla case works as expected.
my-tool file.txt /path/to/file.h5 --json-config ./preset.json

However, this also works as well.
my-tool /path/to/file.h5 --json-config ./preset.json

In my experience, the changing of the semantic meaning of the command line tool's positional arguments depending on the contents of the preset.json created issues and bugs.
The simplest fix is to remove the positional arguments in favor of -i or similar which removed the issue.
from pydantic import BaseModel, Field
from pydantic_cli import run_and_exit, to_runner, DefaultConfig

class MinOptions(BaseModel):
class Config(DefaultConfig):
CLI_JSON_ENABLE = True

input_file: str = Field(..., cli=('-i', ))
input_hdf: str = Field(..., cli=('-d', '--hdf'))
max_records: int = Field(100, cli=('-m', '--max-records'))

Running with the preset.json defined above, works as expected.
my-tool --hdf /path/to/file.h5 --json-config ./preset.json

As well as overriding the -i.
my-tool -i file.txt --hdf /path/to/file.h5 --json-config ./preset.json

Or
my-tool --hdf /path/to/file.h5 -i file.txt --json-config ./preset.json

This consistency was the motivation for removing positional argument support in earlier versions of pydantic-cli.
Other Related Tools
Other tools that leverage type annotations to create CLI tools.

cyto Pydantic based model leveraging Pydantic's settings sources. Supports nested values. Optional TOML support. (Leverages: click, pydantic)
typer Typer is a library for building CLI applications that users will love using and developers will love creating. Based on Python 3.6+ type hints. (Leverages: click)
glacier Building Python CLI using docstrings and typehints (Leverages: click)
Typed-Settings Manage typed settings with attrs classes – for server processes as well as click applications (Leverages: attrs, click)
cliche Build a simple command-line interface from your functions. (Leverages: argparse and type annotations/hints)
SimpleParsing Simple, Elegant, Typed Argument Parsing with argparse. (Leverages: dataclasses, argparse)
recline This library helps you quickly implement an interactive command-based application in Python. (Leverages: argparse + type annotations/hints)
clippy Clippy crawls the abstract syntax tree (AST) of a Python file and generates a simple command-line interface.
clize Turn Python functions into command-line interfaces (Leverages: attrs)
plac Parsing the Command Line the Easy Way.
typedparse Parser for command-line options based on type hints (Leverages: argparse and type annotations/hints)
paiargparse Extension to the python argparser allowing to automatically generate a hierarchical argument list based on dataclasses. (Leverages: argparse + dataclasses)
piou A CLI tool to build beautiful command-line interfaces with type validation.
pyrallis A framework for simple dataclass-based configurations.
ConfigArgParse A drop-in replacement for argparse that allows options to also be set via config files and/or environment variables.
spock spock is a framework that helps manage complex parameter configurations during research and development of Python applications. (Leverages: argparse, attrs, and type annotations/hints)
oneFace Generating interfaces(CLI, Qt GUI, Dash web app) from a Python function.
configpile Overlay for argparse that takes additional parameters from environment variables and configuration files

Stats

Github Star Growth of pydantic-cli