Turn Pydantic defined Data Models into CLI Tools
Project description
Pydantic Commandline Tool Interface
Turn Pydantic defined Data Models into CLI Tools!
Features
- Schema driven interfaces built on top of Pydantic
- Validation is performed in a single location as defined by Pydantic's validation model
- CLI parsing is only structurally validating that the args or optional arguments are provided
- Clear interface between the CLI and your application code
- Easy to test (due to reasons defined above)
Quick Start
To create a commandline tool that takes an input file and max number of records to process as positional arguments:
my-tool /path/to/file.txt 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 theto_runner
function, or therun_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:]
, call can 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')
If the data model has default values, the commandline argument with be optional and the CLI arg will be prefixed with `--'.
For example:
from pydantic import BaseModel
from pydantic_cli import run_and_exit
class MinOptions(BaseModel):
input_file: str
max_records: int = 10
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')
Will create a tool with my-tool /path/to/input.txt --max_records 1234
my-tool /path/to/input.txt --max_records 1234
with --max_records
being optional to the commandline interface.
WARNING: Boolean values must be communicated explicitly (e.g., --run_training True
)
The --help
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
Config
of the Pydantic Data model. - The second "schema" method is to define the metadata in the
Schema
model in Pydantic
Quick Model for Customization
We're going to change the usage from my-tool /path/to/file.txt 1234
to my-tool /path/to/file.txt -m 1234
.
This only requires adding CLI_EXTRA_OPTIONS
to the Pydantic Config
.
from pydantic import BaseModel
class MinOptions(BaseModel):
class Config:
CLI_EXTRA_OPTIONS = {'max_records': ('-m', )}
input_file: str
max_records: int = 10
You can also override the "long" argument. However, note this is starting to add a new layer of indirection on top of the schema. (e.g., 'max_records' to '--max-records') that may or may not be useful.
from pydantic import BaseModel
class MinOptions(BaseModel):
class Config:
CLI_EXTRA_OPTIONS = {'max_records': ('-m', '--max-records')}
input_file: str
max_records: int = 10
Schema Approach
from pydantic import BaseModel, Schema
class Options(BaseModel):
class Config:
validate_all = True
validate_assignment = True
input_file: str = Schema(
..., # this implicitly means required=True
title="Input File",
description="Path to the input file",
required=True,
extras={"cli": ('-f', '--input-file')}
)
max_records: int = Schema(
123,
title="Max Records",
description="Max number of records to process",
gt=0,
extras={'cli': ('-m', '--max-records')}
)
Hooks into the CLI Execution
- exception handler
- epilogue handler
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_cli import run_and_exit
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)
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.
import sys
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
from pydantic.schema import UrlStr
from pydantic_cli.examples import ConfigDefaults
from pydantic_cli import run_sp_and_exit, SubParser
class AlphaOptions(BaseModel):
class Config(ConfigDefaults):
CLI_EXTRA_OPTIONS = {'max_records': ('-m', '--max-records')}
input_file: str
max_records: int = 10
class BetaOptions(BaseModel):
class Config(ConfigDefaults):
CLI_EXTRA_OPTIONS = {'url': ('-u', '--url'),
'num_retries': ('-n', '--num-retries')}
url: UrlStr
num_retries: int = 3
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')
Limitations
- Currently only support flat "simple" types (e.g., floats, ints, strings, boolean). There's no current support for
List[T]
or nested dicts. - Leverages argparse underneath the hood and argparse is a bit thorny of an API to build on top of.
To Improve
- Better type descriptions in help
- Better communication of required "options" in help
- Add load from JSON file
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