Utils

spectree.utils.default_after_handler(req: Any, resp: Any, resp_validation_error: ValidationError, instance: Any)

default handler called after the response validation

Parameters:

• req – request provided by the web framework

• resp – response from the endpoint function (if there is no validation error) or response validation error

• resp_validation_error – response validation error

• instance – class instance if the endpoint function is a class method

spectree.utils.default_before_handler(req: Any, resp: Any, req_validation_error: ValidationError, instance: Any)

default handler called before the endpoint function after the request validation

Parameters:

• req – request provided by the web framework

• resp – response generated by SpecTree that will be returned if the validation error is not None

• req_validation_error – request validation error

• instance – class instance if the endpoint function is a class method

spectree.utils.flask_response_unpack(resp: Any) → Tuple[Any, int, Dict[str, Any]]

Parse Flask response object into a tuple of (payload, status_code, headers).

spectree.utils.gen_list_model(model: Type[BaseModel]) → Type[BaseModel]

generate the corresponding list[model] class for a given model class

spectree.utils.get_model_key(model: Type[BaseModelSubclassType]) → str

generate model name suffixed by short hashed path (instead of its path to avoid code-structure leaking)

Parameters:

model – pydantic.BaseModel query, json, headers or cookies from request or response

spectree.utils.get_model_schema(model: ~typing.Type[~spectree._types.BaseModelSubclassType], naming_strategy: ~typing.Callable[[~typing.Type[~spectree._types.BaseModelSubclassType]], str] = <function get_model_key>, nested_naming_strategy: ~typing.Callable[[str, str], str] = <function get_nested_key>)

return a dictionary representing the model as JSON Schema with a hashed infix in ref to ensure name uniqueness

Parameters:

model – pydantic.BaseModel query, json, headers or cookies from request or response

spectree.utils.get_multidict_items(multidict: MultiDict, model: Type[BaseModelSubclassType] | None = None) → Dict[str, None | str | List[str]]

return the items of a werkzeug.datastructures.ImmutableMultiDict

spectree.utils.get_nested_key(parent: str, child: str) → str

generate nested model reference name suffixed by parent model name

Parameters:

• parent – string of parent name

• child – string of child name

spectree.utils.get_security(security: None | Mapping | Sequence[Any]) → List[Any]

return the correct format of security

spectree.utils.has_model(func: Any) → bool

return True if this function have pydantic.BaseModel

spectree.utils.hash_module_path(module_path: str)

generate short hash for module path to avoid the same name object defined in different Python files

Parameters:

module_path – str module path

spectree.utils.is_list_item(key: str, model: Type[BaseModelSubclassType] | None) → bool

Check if this key is a list item in the model.

spectree.utils.parse_code(http_code: str) → str

get the code of this HTTP status

Parameters:

http_code (str) – format like HTTP_200

spectree.utils.parse_comments(func: Callable[[...], Any]) → Tuple[str | None, str | None]

Parse function docstring into a summary and description string.

The first few lines of the docstring up to the first empty line will be extracted as the summary, and the rest of the docstring, following the empty line will become the description.

If the function’s docstring also contains parameter documentation, you can avoid parsing it as part of the summary or description by prefixing it with the “\f” form feed character. Everything after the “\f” character will be ignored in the docstring.

Parameters:

func – The callable whose docstring should be parsed.

Returns:

A two element tuple with the summary and the description strings.

spectree.utils.parse_name(func: Callable[[...], Any]) → str

the func can be

• undecorated functions

• decorated functions

• decorated class methods

spectree.utils.parse_params(func: Callable[[...], Any], params: List[Mapping[str, Any]], models: Mapping[str, Any]) → List[Mapping[str, Any]]

get spec for (query, headers, cookies)

spectree.utils.parse_request(func: Any) → Dict[str, Any]

get json spec

spectree.utils.parse_resp(func: ~typing.Any, naming_strategy: ~typing.Callable[[~typing.Type[~spectree._types.BaseModelSubclassType]], str] = <function get_model_key>)

get the response spec

If this function does not have explicit resp but have other models, a 422 Validation Error will be appended to the response spec, since this may be triggered in the validation step.

spectree.utils.werkzeug_parse_rule(rule: str) → Iterator[Tuple[str | None, str | None, str]]

A copy of werkzeug.parse_rule which is now removed.

Parse a rule and return it as generator. Each iteration yields tuples in the form (converter, arguments, variable). If the converter is None it’s a static url part, otherwise it’s a dynamic one.