schema is a library for validating Python data structures, such as those obtained from config-files, forms, external services or command-line parsing, converted from JSON/YAML (or something else) to Python data-types.
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Here is a quick example to get a feeling of schema, validating a list of entries with personal information:
.. code:: python
from schema import Schema, And, Use, Optional, SchemaError
schema = Schema(
[
{
"name": And(str, len),
"age": And(Use(int), lambda n: 18 <= n <= 99),
Optional("gender"): And(
str,
Use(str.lower),
lambda s: s in ("squid", "kid"),
),
}
]
)
data = [
{"name": "Sue", "age": "28", "gender": "Squid"},
{"name": "Sam", "age": "42"},
{"name": "Sacha", "age": "20", "gender": "KID"},
]
validated = schema.validate(data)
assert validated == [
{"name": "Sue", "age": 28, "gender": "squid"},
{"name": "Sam", "age": 42},
{"name": "Sacha", "age": 20, "gender": "kid"},
]If data is valid, Schema.validate will return the validated data
(optionally converted with Use calls, see below).
If data is invalid, Schema will raise SchemaError exception.
If you just want to check that the data is valid, schema.is_valid(data) will
return True or False.
Use pip <http://pip-installer.org>_ or easy_install::
pip install schemaAlternatively, you can just drop schema.py file into your project—it is
self-contained.
semantic versioning <http://semver.org>_.Schema validates dataTypes
If ``Schema(...)`` encounters a type (such as ``int``, ``str``, ``object``,
etc.), it will check if the corresponding piece of data is an instance of that type,
otherwise it will raise ``SchemaError``.
.. code:: python
>>> from schema import Schema
>>> Schema(int).validate(123)
123
>>> Schema(int).validate('123')
Traceback (most recent call last):
...
schema.SchemaUnexpectedTypeError: '123' should be instance of 'int'
>>> Schema(object).validate('hai')
'hai'
CallablesIf Schema(...) encounters a callable (function, class, or object with
__call__ method) it will call it, and if its return value evaluates to
True it will continue validating, else—it will raise SchemaError.
.. code:: python
>>> import os
>>> Schema(os.path.exists).validate('./')
'./'
>>> Schema(os.path.exists).validate('./non-existent/')
Traceback (most recent call last):
...
schema.SchemaError: exists('./non-existent/') should evaluate to True
>>> Schema(lambda n: n > 0).validate(123)
123
>>> Schema(lambda n: n > 0).validate(-12)
Traceback (most recent call last):
...
schema.SchemaError: <lambda>(-12) should evaluate to True"Validatables"
If ``Schema(...)`` encounters an object with method ``validate`` it will run
this method on corresponding data as ``data = obj.validate(data)``. This method
may raise ``SchemaError`` exception, which will tell ``Schema`` that that piece
of data is invalid, otherwise—it will continue validating.
An example of "validatable" is ``Regex``, that tries to match a string or a
buffer with the given regular expression (itself as a string, buffer or
compiled regex ``SRE_Pattern``):
.. code:: python
>>> from schema import Regex
>>> import re
>>> Regex(r'^foo').validate('foobar')
'foobar'
>>> Regex(r'^[A-Z]+$', flags=re.I).validate('those-dashes-dont-match')
Traceback (most recent call last):
...
schema.SchemaError: Regex('^[A-Z]+$', flags=re.IGNORECASE) does not match 'those-dashes-dont-match'
For a more general case, you can use ``Use`` for creating such objects.
``Use`` helps to use a function or type to convert a value while validating it:
.. code:: python
>>> from schema import Use
>>> Schema(Use(int)).validate('123')
123
>>> Schema(Use(lambda f: open(f, 'a'))).validate('LICENSE-MIT')
<_io.TextIOWrapper name='LICENSE-MIT' mode='a' encoding='UTF-8'>
Dropping the details, ``Use`` is basically:
.. code:: python
class Use(object):
def __init__(self, callable_):
self._callable = callable_
def validate(self, data):
try:
return self._callable(data)
except Exception as e:
raise SchemaError('%r raised %r' % (self._callable.__name__, e))
Sometimes you need to transform and validate part of data, but keep original data unchanged.
``Const`` helps to keep your data safe:
.. code:: python
>> from schema import Use, Const, And, Schema
>> from datetime import datetime
>> is_future = lambda date: datetime.now() > date
>> to_json = lambda v: {"timestamp": v}
>> Schema(And(Const(And(Use(datetime.fromtimestamp), is_future)), Use(to_json))).validate(1234567890)
{"timestamp": 1234567890}
Now you can write your own validation-aware classes and data types.
Lists, similar containersIf Schema(...) encounters an instance of list, tuple, set
or frozenset, it will validate contents of corresponding data container
against all schemas listed inside that container and aggregate all errors:
.. code:: python
>>> Schema([1, 0]).validate([1, 1, 0, 1])
[1, 1, 0, 1]
>>> Schema((int, float)).validate((5, 7, 8, 'not int or float here'))
Traceback (most recent call last):
...
schema.SchemaError: Or(<class 'int'>, <class 'float'>) did not validate 'not int or float here'
'not int or float here' should be instance of 'int'
'not int or float here' should be instance of 'float'Dictionaries
If ``Schema(...)`` encounters an instance of ``dict``, it will validate data
key-value pairs:
.. code:: python
>>> d = Schema(
... {"name": str, "age": lambda n: 18 <= n <= 99}
... ).validate(
... {"name": "Sue", "age": 28}
... )
>>> assert d == {'name': 'Sue', 'age': 28}
You can specify keys as schemas too:
.. code:: python
>>> schema = Schema({
... str: int, # string keys should have integer values
... int: None, # int keys should be always None
... })
>>> data = schema.validate({
... "key1": 1,
... "key2": 2,
... 10: None,
... 20: None,
... })
>>> schema.validate({
... "key1": 1,
... 10: "not None here",
... })
Traceback (most recent call last):
...
schema.SchemaError: Key '10' error:
None does not match 'not None here'
This is useful if you want to check certain key-values, but don't care
about others:
.. code:: python
>>> schema = Schema({
... "<id>": int,
... "<file>": Use(open),
... str: object, # don't care about other str keys
... })
>>> data = schema.validate({
... "<id>": 10,
... "<file>": "README.rst",
... "--verbose": True,
... })
You can mark a key as optional as follows:
.. code:: python
>>> Schema({
... "name": str,
... Optional("occupation"): str,
... }).validate({"name": "Sam"})
{'name': 'Sam'}
``Optional`` keys can also carry a ``default``, to be used when no key in the
data matches:
.. code:: python
>>> Schema({
... Optional("color", default="blue"): str,
... str: str,
... }).validate({"texture": "furry"}) == {
... "color": "blue",
... "texture": "furry",
... }
True
Defaults are used verbatim, not passed through any validators specified in the
value.
default can also be a callable:
.. code:: python
>>> from schema import Schema, Optional
>>> Schema({Optional('data', default=dict): {}}).validate({}) == {'data': {}}
True
Also, a caveat: If you specify types, **schema** won't validate the empty dict:
.. code:: python
>>> Schema({int:int}).is_valid({})
False
To do that, you need ``Schema(Or({int:int}, {}))``. This is unlike what happens with
lists, where ``Schema([int]).is_valid([])`` will return True.
**schema** has classes ``And`` and ``Or`` that help validating several schemas
for the same data:
.. code:: python
>>> from schema import And, Or
>>> Schema({'age': And(int, lambda n: 0 < n < 99)}).validate({'age': 7})
{'age': 7}
>>> Schema({'password': And(str, lambda s: len(s) > 6)}).validate({'password': 'hai'})
Traceback (most recent call last):
...
schema.SchemaError: Key 'password' error:
<lambda>('hai') should evaluate to True
>>> Schema(And(Or(int, float), lambda x: x > 0)).validate(3.1415)
3.1415
In a dictionary, you can also combine two keys in a "one or the other" manner. To do
so, use the `Or` class as a key:
.. code:: python
>>> from schema import Or, Schema
>>> schema = Schema({
... Or("key1", "key2", only_one=True): str
... })
>>> schema.validate({"key1": "test"}) # Ok
{'key1': 'test'}
>>> schema.validate({"key1": "test", "key2": "test"}) # SchemaError
Traceback (most recent call last):
...
schema.SchemaOnlyOneAllowedError: There are multiple keys present from the Or('key1', 'key2') condition
HooksYou can define hooks which are functions that are executed whenever a valid key:value is found.
The Forbidden class is an example of this.
You can mark a key as forbidden as follows:
.. code:: python
>>> from schema import Forbidden
>>> Schema({Forbidden('age'): object}).validate({'age': 50})
Traceback (most recent call last):
...
schema.SchemaForbiddenKeyError: Forbidden key encountered: 'age' in {'age': 50}A few things are worth noting. First, the value paired with the forbidden key determines whether it will be rejected:
.. code:: python
>>> Schema({Forbidden('age'): str, 'age': int}).validate({'age': 50})
{'age': 50}Note: if we hadn't supplied the 'age' key here, the call would have failed too, but with SchemaWrongKeyError, not SchemaForbiddenKeyError.
Second, Forbidden has a higher priority than standard keys, and consequently than Optional. This means we can do that:
.. code:: python
>>> Schema({Forbidden('age'): object, Optional(str): object}).validate({'age': 50})
Traceback (most recent call last):
...
schema.SchemaForbiddenKeyError: Forbidden key encountered: 'age' in {'age': 50}You can also define your own hooks. The following hook will call _my_function if key is encountered.
.. code:: python
from schema import Hook
def _my_function(key, scope, error):
print(key, scope, error)
Hook("key", handler=_my_function)Here's an example where a Deprecated class is added to log warnings whenever a key is encountered:
.. code:: python
from schema import Hook, Schema
class Deprecated(Hook):
def __init__(self, *args, **kwargs):
kwargs["handler"] = lambda key, *args: logging.warn(f"`{key}` is deprecated. " + (self._error or ""))
super(Deprecated, self).__init__(*args, **kwargs)
Schema({Deprecated("test", "custom error message."): object}, ignore_extra_keys=True).validate({"test": "value"})
...
WARNING: `test` is deprecated. custom error message.Extra Keys
The ``Schema(...)`` parameter ``ignore_extra_keys`` causes validation to ignore extra keys in a dictionary, and also to not return them after validating.
.. code:: python
>>> schema = Schema({'name': str}, ignore_extra_keys=True)
>>> schema.validate({'name': 'Sam', 'age': '42'})
{'name': 'Sam'}
If you would like any extra keys returned, use ``object: object`` as one of the key/value pairs, which will match any key and any value.
Otherwise, extra keys will raise a ``SchemaError``.
Customized ValidationThe Schema.validate method accepts additional keyword arguments. The
keyword arguments will be propagated to the validate method of any
child validatables (including any ad-hoc Schema objects), or the default
value callable (if a callable is specified) for Optional keys.
This feature can be used together with inheritance of the Schema class
for customized validation.
Here is an example where a "post-validation" hook that runs after validation against a sub-schema in a larger schema:
.. code:: python
class EventSchema(schema.Schema):
def validate(self, data, _is_event_schema=True):
data = super(EventSchema, self).validate(data, _is_event_schema=False)
if _is_event_schema and data.get("minimum", None) is None:
data["minimum"] = data["capacity"]
return data
events_schema = schema.Schema(
{
str: EventSchema({
"capacity": int,
schema.Optional("minimum"): int, # default to capacity
})
}
)
data = {'event1': {'capacity': 1}, 'event2': {'capacity': 2, 'minimum': 3}}
events = events_schema.validate(data)
assert events['event1']['minimum'] == 1 # == capacity
assert events['event2']['minimum'] == 3Note that the additional keyword argument _is_event_schema is necessary to
limit the customized behavior to the EventSchema object itself so that it
won't affect any recursive invoke of the self.__class__.validate for the
child schemas (e.g., the call to Schema("capacity").validate("capacity")).
You can pass a keyword argument error to any of validatable classes
(such as Schema, And, Or, Regex, Use) to report this error
instead of a built-in one.
.. code:: python
>>> Schema(Use(int, error='Invalid year')).validate('XVII')
Traceback (most recent call last):
...
schema.SchemaError: Invalid yearYou can see all errors that occurred by accessing exception's exc.autos
for auto-generated error messages, and exc.errors for errors
which had error text passed to them.
You can exit with sys.exit(exc.code) if you want to show the messages
to the user without traceback. error messages are given precedence in that
case.
Here is a quick example: validation of
create a gist <http://developer.github.com/v3/gists/>_
request from github API.
.. code:: python
>>> gist = '''{"description": "the description for this gist",
... "public": true,
... "files": {
... "file1.txt": {"content": "String file contents"},
... "other.txt": {"content": "Another file contents"}}}'''
>>> from schema import Schema, And, Use, Optional
>>> import json
>>> gist_schema = Schema(
... And(
... Use(json.loads), # first convert from JSON
... # use str since json returns unicode
... {
... Optional("description"): str,
... "public": bool,
... "files": {str: {"content": str}},
... },
... )
... )
>>> gist = gist_schema.validate(gist)
# gist:
{u'description': u'the description for this gist',
u'files': {u'file1.txt': {u'content': u'String file contents'},
u'other.txt': {u'content': u'Another file contents'}},
u'public': True}docopt <http://github.com/docopt/docopt>_Assume you are using docopt with the following usage-pattern:
Usage: my_program.py [--count=N] <path> <files>...and you would like to validate that <files> are readable, and that
<path> exists, and that --count is either integer from 0 to 5, or
None.
Assuming docopt returns the following dict:
.. code:: python
>>> args = {
... "<files>": ["LICENSE-MIT", "setup.py"],
... "<path>": "../",
... "--count": "3",
... }this is how you validate it using schema:
.. code:: python
>>> from schema import Schema, And, Or, Use
>>> import os
>>> s = Schema({
... "<files>": [Use(open)],
... "<path>": os.path.exists,
... "--count": Or(None, And(Use(int), lambda n: 0 < n < 5)),
... })
>>> args = s.validate(args)
>>> args['<files>']
[<_io.TextIOWrapper name='LICENSE-MIT' ...>, <_io.TextIOWrapper name='setup.py' ...]
>>> args['<path>']
'../'
>>> args['--count']
3As you can see, schema validated data successfully, opened files and
converted '3' to int.
You can also generate standard draft-07 JSON schema <https://json-schema.org/>_ from a dict Schema.
This can be used to add word completion, validation, and documentation directly in code editors.
The output schema can also be used with JSON schema compatible libraries.
JSON: Generating
Just define your schema normally and call ``.json_schema()`` on it. The output is a Python dict, you need to dump it to JSON.
.. code:: python
>>> from schema import Optional, Schema
>>> import json
>>> s = Schema({
... "test": str,
... "nested": {Optional("other"): str},
... })
>>> json_schema = json.dumps(s.json_schema("https://example.com/my-schema.json"))
# json_schema
{
"type":"object",
"properties": {
"test": {"type": "string"},
"nested": {
"type":"object",
"properties": {
"other": {"type": "string"}
},
"required": [],
"additionalProperties": false
}
},
"required":[
"test",
"nested"
],
"additionalProperties":false,
"$id":"https://example.com/my-schema.json",
"$schema":"http://json-schema.org/draft-07/schema#"
}
You can add descriptions for the schema elements using the ``Literal`` object instead of a string. The main schema can also have a description.
These will appear in IDEs to help your users write a configuration.
.. code:: python
>>> from schema import Literal, Schema
>>> import json
>>> s = Schema(
... {Literal("project_name", description="Names must be unique"): str},
... description="Project schema",
... )
>>> json_schema = json.dumps(s.json_schema("https://example.com/my-schema.json"), indent=4)
# json_schema
{
"type": "object",
"properties": {
"project_name": {
"description": "Names must be unique",
"type": "string"
}
},
"required": [
"project_name"
],
"additionalProperties": false,
"$id": "https://example.com/my-schema.json",
"$schema": "http://json-schema.org/draft-07/schema#",
"description": "Project schema"
}
JSON: Supported validationsThe resulting JSON schema is not guaranteed to accept the same objects as the library would accept, since some validations are not implemented or
have no JSON schema equivalent. This is the case of the Use and Hook objects for example.
Implemented '''''''''''
Object properties <https://json-schema.org/understanding-json-schema/reference/object.html#properties>_
Use a dict literal. The dict keys are the JSON schema properties.
Example:
``Schema({"test": str})``
becomes
``{'type': 'object', 'properties': {'test': {'type': 'string'}}, 'required': ['test'], 'additionalProperties': False}``.
Please note that attributes are required by default. To create optional attributes use ``Optional``, like so:
``Schema({Optional("test"): str})``
becomes
``{'type': 'object', 'properties': {'test': {'type': 'string'}}, 'required': [], 'additionalProperties': False}``
additionalProperties is set to true when at least one of the conditions is met:
- ignore_extra_keys is True
- at least one key is `str` or `object`
For example:
``Schema({str: str})`` and ``Schema({}, ignore_extra_keys=True)``
both becomes
``{'type': 'object', 'properties' : {}, 'required': [], 'additionalProperties': True}``
and
``Schema({})``
becomes
``{'type': 'object', 'properties' : {}, 'required': [], 'additionalProperties': False}``Types Use the Python type name directly. It will be converted to the JSON name:
- ``str`` -> `string <https://json-schema.org/understanding-json-schema/reference/string.html>`_
- ``int`` -> `integer <https://json-schema.org/understanding-json-schema/reference/numeric.html#integer>`_
- ``float`` -> `number <https://json-schema.org/understanding-json-schema/reference/numeric.html#number>`_
- ``bool`` -> `boolean <https://json-schema.org/understanding-json-schema/reference/boolean.html>`_
- ``list`` -> `array <https://json-schema.org/understanding-json-schema/reference/array.html>`_
- ``dict`` -> `object <https://json-schema.org/understanding-json-schema/reference/object.html>`_
Example:
``Schema(float)``
becomes
``{"type": "number"}``Array items <https://json-schema.org/understanding-json-schema/reference/array.html#items>_
Surround a schema with [].
Example:
``Schema([str])`` means an array of string and becomes:
``{'type': 'array', 'items': {'type': 'string'}}``Enumerated values <https://json-schema.org/understanding-json-schema/reference/generic.html#enumerated-values>_
Use Or.
Example:
``Schema(Or(1, 2, 3))`` becomes
``{"enum": [1, 2, 3]}``Constant values <https://json-schema.org/understanding-json-schema/reference/generic.html#constant-values>_
Use the value itself.
Example:
``Schema("name")`` becomes
``{"const": "name"}``Regular expressions <https://json-schema.org/understanding-json-schema/reference/regular_expressions.html>_
Use Regex.
Example:
``Schema(Regex("^v\d+"))`` becomes
``{'type': 'string', 'pattern': '^v\\d+'}``Annotations (title and description) <https://json-schema.org/understanding-json-schema/reference/generic.html#annotations>_
You can use the name and description parameters of the Schema object init method.
To add description to keys, replace a str with a ``Literal`` object.
Example:
``Schema({Literal("test", description="A description"): str})``
is equivalent to
``Schema({"test": str})``
with the description added to the resulting JSON schema.Combining schemas with allOf <https://json-schema.org/understanding-json-schema/reference/combining.html#allof>_
Use And
Example:
``Schema(And(str, "value"))``
becomes
``{"allOf": [{"type": "string"}, {"const": "value"}]}``
Note that this example is not really useful in the real world, since ``const`` already implies the type.Combining schemas with anyOf <https://json-schema.org/understanding-json-schema/reference/combining.html#anyof>_
Use Or
Example:
``Schema(Or(str, int))``
becomes
``{"anyOf": [{"type": "string"}, {"type": "integer"}]}``Not implemented '''''''''''''''
The following JSON schema validations cannot be generated from this library.
String length <https://json-schema.org/understanding-json-schema/reference/string.html#length>_
However, those can be implemented using RegexString format <https://json-schema.org/understanding-json-schema/reference/string.html#format>_
However, those can be implemented using RegexObject dependencies <https://json-schema.org/understanding-json-schema/reference/object.html#dependencies>_Array length <https://json-schema.org/understanding-json-schema/reference/array.html#length>_Array uniqueness <https://json-schema.org/understanding-json-schema/reference/array.html#uniqueness>_Numeric multiples <https://json-schema.org/understanding-json-schema/reference/numeric.html#multiples>_Numeric ranges <https://json-schema.org/understanding-json-schema/reference/numeric.html#range>_Property Names <https://json-schema.org/understanding-json-schema/reference/object.html#property-names>_
Not implemented. We suggest listing the possible keys instead. As a tip, you can use Or as a dict key.
Example:
Schema({Or("name1", "name2"): str})
Annotations (default and examples) <https://json-schema.org/understanding-json-schema/reference/generic.html#annotations>_Combining schemas with oneOf <https://json-schema.org/understanding-json-schema/reference/combining.html#oneof>_Not <https://json-schema.org/understanding-json-schema/reference/combining.html#not>_Object size <https://json-schema.org/understanding-json-schema/reference/object.html#size>_additionalProperties having a different schema (true and false is supported)JSON: Minimizing output size
Explicit Reuse
''''''''''''''
If your JSON schema is big and has a lot of repetition, it can be made simpler and smaller by defining Schema objects as reference.
These references will be placed in a "definitions" section in the main schema.
`You can look at the JSON schema documentation for more information <https://json-schema.org/understanding-json-schema/structuring.html#reuse>`_
.. code:: python
>>> from schema import Optional, Schema
>>> import json
>>> s = Schema({
... "test": str,
... "nested": Schema({Optional("other"): str}, name="nested", as_reference=True)
... })
>>> json_schema = json.dumps(s.json_schema("https://example.com/my-schema.json"), indent=4)
# json_schema
{
"type": "object",
"properties": {
"test": {
"type": "string"
},
"nested": {
"$ref": "#/definitions/nested"
}
},
"required": [
"test",
"nested"
],
"additionalProperties": false,
"$id": "https://example.com/my-schema.json",
"$schema": "http://json-schema.org/draft-07/schema#",
"definitions": {
"nested": {
"type": "object",
"properties": {
"other": {
"type": "string"
}
},
"required": [],
"additionalProperties": false
}
}
}
This becomes really useful when using the same object several times
.. code:: python
>>> from schema import Optional, Or, Schema
>>> import json
>>> language_configuration = Schema(
... {"autocomplete": bool, "stop_words": [str]},
... name="language",
... as_reference=True,
... )
>>> s = Schema({Or("ar", "cs", "de", "el", "eu", "en", "es", "fr"): language_configuration})
>>> json_schema = json.dumps(s.json_schema("https://example.com/my-schema.json"), indent=4)
# json_schema
{
"type": "object",
"properties": {
"ar": {
"$ref": "#/definitions/language"
},
"cs": {
"$ref": "#/definitions/language"
},
"de": {
"$ref": "#/definitions/language"
},
"el": {
"$ref": "#/definitions/language"
},
"eu": {
"$ref": "#/definitions/language"
},
"en": {
"$ref": "#/definitions/language"
},
"es": {
"$ref": "#/definitions/language"
},
"fr": {
"$ref": "#/definitions/language"
}
},
"required": [],
"additionalProperties": false,
"$id": "https://example.com/my-schema.json",
"$schema": "http://json-schema.org/draft-07/schema#",
"definitions": {
"language": {
"type": "object",
"properties": {
"autocomplete": {
"type": "boolean"
},
"stop_words": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"autocomplete",
"stop_words"
],
"additionalProperties": false
}
}
}
Automatic reuse
'''''''''''''''
If you want to minimize the output size without using names explicitly, you can have the library generate hashes of parts of the output JSON
schema and use them as references throughout.
Enable this behaviour by providing the parameter ``use_refs`` to the json_schema method.
Be aware that this method is less often compatible with IDEs and JSON schema libraries.
It produces a JSON schema that is more difficult to read by humans.
.. code:: python
>>> from schema import Optional, Or, Schema
>>> import json
>>> language_configuration = Schema({"autocomplete": bool, "stop_words": [str]})
>>> s = Schema({Or("ar", "cs", "de", "el", "eu", "en", "es", "fr"): language_configuration})
>>> json_schema = json.dumps(s.json_schema("https://example.com/my-schema.json", use_refs=True), indent=4)
# json_schema
{
"type": "object",
"properties": {
"ar": {
"type": "object",
"properties": {
"autocomplete": {
"type": "boolean",
"$id": "#6456104181059880193"
},
"stop_words": {
"type": "array",
"items": {
"type": "string",
"$id": "#1856069563381977338"
}
}
},
"required": [
"autocomplete",
"stop_words"
],
"additionalProperties": false
},
"cs": {
"type": "object",
"properties": {
"autocomplete": {
"$ref": "#6456104181059880193"
},
"stop_words": {
"type": "array",
"items": {
"$ref": "#1856069563381977338"
},
"$id": "#-5377945144312515805"
}
},
"required": [
"autocomplete",
"stop_words"
],
"additionalProperties": false
},
"de": {
"type": "object",
"properties": {
"autocomplete": {
"$ref": "#6456104181059880193"
},
"stop_words": {
"$ref": "#-5377945144312515805"
}
},
"required": [
"autocomplete",
"stop_words"
],
"additionalProperties": false,
"$id": "#-8142886105174600858"
},
"el": {
"$ref": "#-8142886105174600858"
},
"eu": {
"$ref": "#-8142886105174600858"
},
"en": {
"$ref": "#-8142886105174600858"
},
"es": {
"$ref": "#-8142886105174600858"
},
"fr": {
"$ref": "#-8142886105174600858"
}
},
"required": [],
"additionalProperties": false,
"$id": "https://example.com/my-schema.json",
"$schema": "http://json-schema.org/draft-07/schema#"
}