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‎README.md

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```
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This will work for just about any example in the entire documentation
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and any supported format:
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and any supported format, except where explicitly noted otherwise:
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```cpp
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rfl::bson::write(homer);
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5) Field named 'children' not found.
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```
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## JSON schema
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reflect-cpp also supports generating JSON schemata:
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```cpp
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struct Person {
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std::string first_name;
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std::string last_name;
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rfl::Description<"Must be a proper email in the form [email protected].",
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rfl::Email>
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email;
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rfl::Description<
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"The person's children. Pass an empty array for no children.",
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std::vector<Person>>
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children;
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float salary;
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};
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const std::string json_schema = rfl::json::to_schema<Person>();
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```
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The resulting JSON schema looks like this:
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```json
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{"$schema":"https://json-schema.org/draft/2020-12/schema","$ref":"#/definitions/Person","definitions":{"Person":{"type":"object","properties":{"children":{"type":"array","description":"The person's children. Pass an empty array for no children.","items":{"$ref":"#/definitions/Person"}},"email":{"type":"string","description":"Must be a proper email in the form [email protected].","pattern":"^[a-zA-Z0-9._%+\\-]+@[a-zA-Z0-9.\\-]+\\.[a-zA-Z]{2,}$"},"first_name":{"type":"string"},"last_name":{"type":"string"},"salary":{"type":"number"}},"required":["children","email","first_name","last_name","salary"]}}}
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```
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Note that this is currently supported for JSON only, since most other formats do not support schemata in the first place.
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## Enums
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reflect-cpp supports scoped enumerations:
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- `std::unordered_set`
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- `std::variant`
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- `std::vector`
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- `std::wstring`
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### Additional containers
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‎docs/README.md

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2.4) [Size validation](https://github.com/getml/reflect-cpp/blob/main/docs/size_validation.md) - For imposing size constraints on containers such as `std::vector` or `std::string`.
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2.5) [JSON schema](https://github.com/getml/reflect-cpp/blob/main/docs/json_schema.md) - For validating your schema before you even send it to your C++ backend.
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## 3) Custom classes
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3.1) [Custom classes](https://github.com/getml/reflect-cpp/blob/main/docs/custom_classes.md) - For custom classes with private fields.

‎docs/json_schema.md

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# JSON schema
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JSON schemata are a powerful tool for expressing the expected structure of your input. You can use it to validate your input before you even send it to your C++ backend, which will result in better UX.
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It can also be used for code generation. For instance, tools such as https://app.quicktype.io/ allow you to generate code in multiple programming languages from the JSON schema (even though the validations are usually omitted).
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If you are interacting with Python, we warmly recommend https://docs.pydantic.dev/latest/integrations/datamodel_code_generator/. This allows you to generate Pydantic dataclasses, including the validation, from the JSON schema.
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Note that this is only supported for JSON, not for other formats.
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## Basic idea
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Suppose you have a struct like this:
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```cpp
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struct Person {
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std::string first_name;
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std::string last_name;
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rfl::Email email;
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std::vector<Person> children;
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float salary;
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};
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```
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You can generate a JSON schema like this:
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```cpp
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const std::string json_schema = rfl::json::to_schema<Person>(rfl::json::pretty);
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```
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You do not have to pass `rfl::json::pretty`, but for the purposes of this documentation it is better to do so.
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This will result in the following JSON schema:
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```json
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{
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"$ref": "#/definitions/Person",
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"definitions": {
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"Person": {
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"type": "object",
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"properties": {
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"children": {
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"type": "array",
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"items": {
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"$ref": "#/definitions/Person"
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}
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},
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"email": {
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"type": "string",
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"pattern": "^[a-zA-Z0-9._%+\\-]+@[a-zA-Z0-9.\\-]+\\.[a-zA-Z]{2,}$"
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},
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"first_name": {
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"type": "string"
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},
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"last_name": {
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"type": "string"
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},
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"salary": {
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"type": "number"
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}
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},
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"required": [
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"children",
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"email",
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"first_name",
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"last_name",
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"salary"
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]
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}
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}
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}
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```
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You can insert this into the tools mentioned above and see the generated code.
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## Adding descriptions
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JSON schemata also often contain descriptions. reflect-cpp supports this as well.
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```cpp
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struct Person {
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std::string first_name;
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std::string last_name;
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rfl::Description<"Must be a proper email in the form [email protected].",
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rfl::Email>
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email;
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rfl::Description<
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"The person's children. Pass an empty array for no children.",
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std::vector<Person>>
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children;
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float salary;
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};
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```
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```cpp
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const std::string json_schema = rfl::json::to_schema<Person>(rfl::json::pretty);
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```
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```json
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{
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"$ref": "#/definitions/Person",
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"definitions": {
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"Person": {
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"type": "object",
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"properties": {
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"children": {
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"type": "array",
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"description": "The person's children. Pass an empty array for no children.",
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"items": {
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"$ref": "#/definitions/Person"
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}
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},
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"email": {
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"type": "string",
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"description": "Must be a proper email in the form [email protected].",
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"pattern": "^[a-zA-Z0-9._%+\\-]+@[a-zA-Z0-9.\\-]+\\.[a-zA-Z]{2,}$"
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},
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"first_name": {
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"type": "string"
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},
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"last_name": {
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"type": "string"
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},
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"salary": {
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"type": "number"
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}
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},
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"required": [
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"children",
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"email",
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"first_name",
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"last_name",
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"salary"
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]
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}
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}
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}
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```
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You also add a description to the entire JSON schema:
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```cpp
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const std::string json_schema = rfl::json::to_schema<
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rfl::Description<"JSON schema that describes the required "
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"attributes for the person class.",
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Person>>(rfl::json::pretty);
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```
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```json
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{
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"$ref": "#/definitions/Person",
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"description": "JSON schema that describes the required attributes for the person class.",
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"definitions": {
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"Person": {
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"type": "object",
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"properties": {
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"children": {
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"type": "array",
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"description": "The person's children. Pass an empty array for no children.",
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"items": {
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"$ref": "#/definitions/Person"
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}
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},
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"email": {
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"type": "string",
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"description": "Must be a proper email in the form [email protected].",
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"pattern": "^[a-zA-Z0-9._%+\\-]+@[a-zA-Z0-9.\\-]+\\.[a-zA-Z]{2,}$"
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},
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"first_name": {
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"type": "string"
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},
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"last_name": {
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"type": "string"
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},
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"salary": {
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"type": "number"
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}
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},
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"required": [
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"children",
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"email",
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"first_name",
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"last_name",
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"salary"
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]
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}
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}
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}
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```

‎include/rfl/NamedTuple.hpp

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/// Invokes a callable object once for each field in order.
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template <typename F>
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void apply(const F& _f) {
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void apply(F&& _f) {
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const auto apply_to_field =
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[&_f]<typename... AFields>(AFields&&... fields) {
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((_f(std::forward<AFields>(fields))), ...);
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/// Invokes a callable object once for each field in order.
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template <typename F>
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void apply(const F& _f) const {
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void apply(F&& _f) const {
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const auto apply_to_field = [&_f](const auto&... fields) {
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((_f(fields)), ...);
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};
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/// Does nothing at all.
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template <typename F>
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void apply(const F& _f) const {}
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void apply(F&& _f) const {}
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/// Returns an empty tuple.
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auto fields() const { return std::tuple(); }

‎tests/json/test_apply.cpp

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}
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});
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rfl::to_view(lisa).apply([](auto field) {
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rfl::to_view(lisa).apply([](auto field) mutable {
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if constexpr (decltype(field)::name() == "first_name") {
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*field.value() = "Bart";
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}

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