pydantic-cli 9.1.0

Turn Pydantic defined Data Models into CLI Tools

Pydantic Commandline Tool Interface

Turn Pydantic defined Data Models into CLI Tools and enable loading values from JSON files

Requires Pydantic >=2.8.2.

Installation

pip install pydantic-cli

Features and Requirements

1. Thin Schema driven interfaces constructed from Pydantic defined data models
2. Validation is performed in a single location as defined by Pydantic's validation model and defined types
3. The CLI parsing level is only structurally validating the args or optional arguments/flags are provided
4. Enable loading config defined in JSON to override or set specific values (e.g. mytool -i in.csv --json-conf config.json)
5. Clear interface between the CLI and your application code
6. Leverage the static analyzing tool mypy to catch type errors in your commandline tool
7. Easy to test (due to reasons defined above)

Motivating Use cases

• 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

Note: Newer version of Pydantic-settings has support for commandline functionality. It allows mixing of "sources", such as ENV, YAML, JSON and might satisfy your requirements.

https://docs.pydantic.dev/2.8/concepts/pydantic_settings/#settings-management

Pydantic-cli predates the CLI component of pydantic-settings and has a few different requirements and design approach.

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_cli import run_and_exit, to_runner, Cmd


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

    def run(self) -> None:
        print(f"Mock example running with {self}")


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, 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, 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.

https://docs.pydantic.dev/latest/concepts/models/#required-fields

from pydantic import Field
from pydantic_cli import run_and_exit, Cmd


class MinOptions(Cmd):
    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 run(self) -> None:
        print(f"Mock example running with options {self}")


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

Running

$> mytool -i input.hdf5 --max-records 100 --debug y
Mock example running with options MinOptions(input_file="input.hdf5", max_records=100, debug=True)

Leveraging Field is also useful for validating inputs using the standard Pydantic for validation.

from pydantic import Field
from pydantic_cli import Cmd


class MinOptions(Cmd):
    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'))

    def run(self) -> None:
        print(f"Mock example running with options {self}")

See Pydantic docs for more details.

Loading Configuration using JSON

User created commandline tools using pydantic-cli can also load entire models or partially defined Pydantic data models from JSON files.

For example, given the following Pydantic data model with the cli_json_enable = True in CliConfig.

The cli_json_key will define the commandline argument (e.g., config will translate to --config). The default value is json-config (--json-config).

from pydantic_cli import CliConfig, run_and_exit, Cmd

class Opts(Cmd):
    model_config = CliConfig(
        frozen=True, cli_json_key="json-training", cli_json_enable=True
    )

    hdf_file: str
    max_records: int = 10
    min_filter_score: float
    alpha: float
    beta: float
    
    def run(self) -> None:
        print(f"Running with opts:{self}")

if __name__ == '__main__':
    run_and_exit(Opts, 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-training /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-training /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-training /path/to/file.json --hdf_file /path/to/file.hdf5 --min_filter_score -12.34

Note: The mixing and matching of a config/preset JSON file and commandline args is the fundamental design requirement of pydantic-cli.

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_cli import run_and_exit, Cmd


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

    def run(self) -> None:
        print(f"Mock example running with {self.max_score}")


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

With mypy, it's possible to proactively catch these types of errors.

Using Boolean Flags

There's an ergonomic tradeoff to lean on Pydantic and avoid some friction points at CLI level. This yields an explicit model, but slight added verboseness.

Summary:

• xs:bool can be set from commandline as --xs true or --xs false. Or using Pydantic's casting, --xs yes or --xs y.
• xs:Optional[bool] can be set from commandline as --xs true, --xs false, or --xs none

For the None case, you can configure your Pydantic model to handle the casting/coercing/validation. Similarly, the bool casting should be configured in Pydantic.

Consider a basic model:

from typing import Optional
from pydantic import Field
from pydantic_cli import run_and_exit, Cmd

class Options(Cmd):
    input_file: str
    max_records: int = Field(100, cli=('-m', '--max-records'))
    dry_run: bool = Field(default=False, description="Enable dry run mode", cli=('-d', '--dry-run'))
    filtering: Optional[bool]
    
    def run(self) -> None:
        print(f"Mock example running with {self}")
    

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

In this case,

• dry_run is an optional value with a default and can be set as --dry-run yes or --dry-run no
• filtering is a required value and can be set --filtering true, --filtering False, and --filtering None

See the Pydantic docs for more details on boolean casting.

https://docs.pydantic.dev/2.8/api/standard_library_types/#booleans

Customization and Hooks

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 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 Field
from pydantic_cli import run_and_exit, Cmd


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

    def run(self) -> None:
        # example/mock error raised. Will be mapped to exit code 3 
        raise ValueError(f"No records found in input file {self.input_file}")


def custom_exception_handler(ex: Exception) -> 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, 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, 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) -> None:
    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, epilogue_handler=custom_epilogue_handler)

SubParsers

Defining a subcommand to your commandline tool is enabled by creating a container of dict[str, Cmd] (with str is the subcommand name) into run_and_exit (or to_runner).

"""Example Subcommand Tool"""
from pydantic import AnyUrl, Field
from pydantic_cli import run_and_exit, Cmd


class AlphaOptions(Cmd):
    input_file: str = Field(..., cli=('-i',))
    max_records: int = Field(10, cli=('-m', '--max-records'))
    
    def run(self) -> None:
        print(f"Running alpha with {self}")


class BetaOptions(Cmd):
    """Beta command for testing. Description of tool"""
    url: AnyUrl = Field(..., cli=('-u', '--url'))
    num_retries: int = Field(3, cli=('-n', '--num-retries'))
    
    def run(self) -> None:
        print(f"Running beta with {self}")


if __name__ == "__main__":
    run_and_exit({"alpha": AlphaOptions, "beta": BetaOptions}, 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 import ConfigDict


class CliConfig(ConfigDict, total=False):
    # value used to generate the CLI format --{key}
    cli_json_key: str
    # Enable JSON config loading
    cli_json_enable: bool

    # Set the default ENV var for defining the JSON config path
    cli_json_config_env_var: str
    # Set the default Path for JSON config file
    cli_json_config_path: T.Optional[str]
    # If a default path is provided or provided from the commandline
    cli_json_validate_path: bool

    # Add a flag that will emit the shell completion
    # this requires 'shtab'
    # https://github.com/iterative/shtab
    cli_shell_completion_enable: bool
    cli_shell_completion_flag: str

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 CliConfig(cli_shell_completion_enable=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 look 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 Cmd.run() -> None method 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 does not expose, or leak the argparse instance or implementation details. Argparse is a bit thorny and was written in a different era of Python. Exposing these implementation details would add too much surface area and would enable users' to start mucking with the argparse instance in all kinds of unexpected ways.

Testing can be done by leveraging the to_runner interface.

1. 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.
2. 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) returns a Callable[[List[str]], int] that can be used with sys.argv[1:] to return an integer exit code of your program. The to_runner layer will also catch any exceptions.

import unittest

from pydantic_cli import to_runner, Cmd


class Options(Cmd):
    alpha: int
    
    def run(self) -> None:
        if self.alpha < 0:
            raise Exception(f"Got options {self}. Forced raise for testing.")



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
        # You code should raise exceptions here or return None on success
        self.assertTrue(Options(alpha=1).run() is None)

    def test_example(self):
        # This is intended to mimic end-to-end testing 
        # from argv[1:]. The exception handler will map exceptions to int exit codes.   
        f = to_runner(Options)
        self.assertEqual(1, f(["--alpha", "100"]))

    def test_expected_error(self):
        f = to_runner(Options)
        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.

from pydantic_cli import default_exception_handler, run_and_exit, Cmd


class Options(Cmd):
    alpha: int
    
    def run(self) -> None:
        if self.alpha < 0:
            raise Exception(f"Got options {self}. Forced raise for testing.")

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


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

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.
• 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. Pydantic-settings might be a better fit for these cases.
• 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_cli import CliConfig, Cmd


class MinOptions(Cmd):
    model_config = CliConfig(cli_json_enable=True)
    
    input_file: str
    input_hdf: str
    max_records: int = 100

    def run(self) -> None:
        print(f"Running with mock {self}")

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 Field
from pydantic_cli import CliConfig, Cmd

class MinOptions(Cmd):
    model_config = CliConfig(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'))

    def run(self) -> None:
        print(f"Running {self}")

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.

• pydantic-settings Pydantic >= 2.8.2 supports CLI as a settings "source".
• 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