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180 changes: 180 additions & 0 deletions .gitignore
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# Created by https://www.toptal.com/developers/gitignore/api/python
# Edit at https://www.toptal.com/developers/gitignore?templates=python
\#*

scratch.py
notes.org

### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
.pybuilder/
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version

# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock

# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml

# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/

# pytype static type analyzer
.pytype/

# Cython debug symbols
cython_debug/

# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/

### Python Patch ###
# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
poetry.toml

# ruff
.ruff_cache/

# LSP config files
pyrightconfig.json

# End of https://www.toptal.com/developers/gitignore/api/python
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318 changes: 318 additions & 0 deletions poetry.lock

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28 changes: 28 additions & 0 deletions pyproject.toml
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[tool.poetry]
name = "sparql-fastapi"
version = "0.1.0"
description = ""
authors = ["Your Name <[email protected]>"]
readme = "README.md"

[tool.poetry.dependencies]
python = "^3.12"
sparqlwrapper = "^2.0.0"
toolz = "^0.12.1"
pydantic = "^2.8.2"


[tool.poetry.group.dev.dependencies]
ruff = "^0.5.4"
deptry = "^0.17.0"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"


[tool.ruff]
lint.ignore = ["F401"]

[tool.deptry.package_module_name_map]
sparqlwrapper = "SPARQLWrapper"
1 change: 1 addition & 0 deletions sparql_fastapi/__init__.py
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from sparql_fastapi.adapter import SPARQLModelAdapter
43 changes: 43 additions & 0 deletions sparql_fastapi/adapter.py
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"""SPARQLModelAdapter class for QueryResult to Pydantic model conversions."""

from collections.abc import Sequence

from SPARQLWrapper import JSON, QueryResult, SPARQLWrapper
from pydantic import BaseModel
from sparql_fastapi.utils._types import _TModelConstructorCallable
from sparql_fastapi.utils.utils import (
get_bindings_from_query_result,
instantiate_model_from_kwargs,
)


class SPARQLModelAdapter[ModelType: BaseModel]:
"""Adapter/Mapper for QueryResult to Pydantic model conversions."""

def __init__(self, sparql_wrapper: SPARQLWrapper) -> None:
self.sparql_wrapper = sparql_wrapper

if self.sparql_wrapper.returnFormat != "json":
self.sparql_wrapper.setReturnFormat(JSON)

def __call__(self, query: str, model_constructor) -> Sequence[ModelType]:
self.sparql_wrapper.setQuery(query)
query_result: QueryResult = self.sparql_wrapper.query()

if isinstance(model_constructor, type(BaseModel)):
bindings = get_bindings_from_query_result(query_result)
models: list[ModelType] = [
instantiate_model_from_kwargs(model_constructor, **binding)
for binding in bindings
]

elif isinstance(model_constructor, _TModelConstructorCallable):
models: list[ModelType] = model_constructor(query_result)

else:
raise TypeError(
"Argument 'model_constructor' must be a model class "
"or a model constructor callable."
)

return models
17 changes: 17 additions & 0 deletions sparql_fastapi/utils/_types.py
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"""Type definitions for sparql_fastapi."""

from typing import Annotated, Protocol, TypeVar, runtime_checkable

from pydantic import BaseModel


_TModelInstance: Annotated[TypeVar, "Type defintion for Pydantic model instances."] = (
TypeVar("_TModelInstance", bound=BaseModel)
)


@runtime_checkable
class _TModelConstructorCallable[ModelType: BaseModel](Protocol):
"""Callback protocol for model constructor callables."""

def __call__(self, **kwargs) -> ModelType: ...
66 changes: 66 additions & 0 deletions sparql_fastapi/utils/utils.py
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"""SPARQL/FastAPI utils."""

from collections.abc import Iterator, Mapping
from typing import cast

from SPARQLWrapper import QueryResult
from pydantic import BaseModel
from toolz import valmap


def get_bindings_from_query_result(query_result: QueryResult) -> Iterator[dict]:
"""Extract just the bindings from a SPARQLWrapper.QueryResult."""
query_json = cast(Mapping, query_result.convert())
bindings = map(
lambda binding: valmap(lambda v: v["value"], binding),
query_json["results"]["bindings"],
)

return bindings


def instantiate_model_from_kwargs[ModelType: BaseModel](
model: type[ModelType], **kwargs
) -> ModelType:
"""Instantiate a (potentially nested) model from (flat) kwargs.

Example:

class SimpleModel(BaseModel):
x: int
y: int


class NestedModel(BaseModel):
a: str
b: SimpleModel


class ComplexModel(BaseModel):
p: str
q: NestedModel


model = instantiate_model_from_kwargs(ComplexModel, x=1, y=2, a="a value", p="p value")
print(model) # p='p value' q=NestedModel(a='a value', b=SimpleModel(x=1, y=2))
"""

def _get_bindings(model: type[ModelType], **kwargs) -> dict:
"""Get the bindings needed for model instantation.

The function traverses model.model_fields
and constructs a bindings dict by either getting values from kwargs or field defaults.
For model fields the recursive clause runs.

Note: This needs exception handling and proper testing.
"""
return {
k: (
v.annotation(**_get_bindings(v.annotation, **kwargs))
if isinstance(v.annotation, type(BaseModel))
else kwargs.get(k, v.default)
)
for k, v in model.model_fields.items()
}

return model(**_get_bindings(model, **kwargs))
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