top-left-radius:0;border-bottom-left-radius:0}.input-group:not(.has-validation)>.custom-file:not(:last-child) .custom-file-label,.input-group:not(.has-validation)>.custom-file:not(:last-child) .custom-file-label::after,.input-group:not(.has-validation)>.custom-select:not(:last-child),.input-group:not(.has-validation)>.form-control:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.input-group.has-validation>.custom-file:nth-last-child(n+3) .custom-file-label,.input-group.ha...

All the same process that applied for path parameters also applies for query parameters:

* Editor support (obviously)
* Data <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr>
* Data validation
* Automatic documentation

## Defaults { #defaults }

* A `skip` query parameter that is an `int`, with a default of `0`.
* A `limit` query parameter that is an `int`, with a default of `100`.

In both cases the data will be converted, validated, documented on the OpenAPI schema, etc.

## Use it { #use-it }

Now you can declare your dependency using this class.

{* ../../docs_src/dependencies/tutorial002_an_py310.py hl[19] *}

	if err = sys.LDAPConfig.LDAP.LookupBind(conn); err != nil {
		return []string{}, err
	}

	// We map keys that correspond to LDAP DNs and validate that they exist in
	// the LDAP server.
	dnValidator := sys.LDAPConfig.GetValidatedUserDN
	if isGroup {
		dnValidator = sys.LDAPConfig.GetValidatedGroupDN
	}

	// map of normalized DN keys to original keys.
	normalizedDNKeysMap := make(map[string][]string)

* Valida **estruturas complexas**:
    * Use modelos hierárquicos do Pydantic, `List` e `Dict` do `typing` do Python, etc.
    * Validadores permitem que esquemas de dados complexos sejam limpos e facilmente definidos, conferidos e documentados como JSON Schema.
    * Você pode ter **JSONs aninhados** profundamente e tê-los todos validados e anotados.
* **Extensível**:

    //                                                                       Validation
    //                                                                           =========
    /**
     * Validates document fields according to index field requirements.
     *
     * @param doc the document to validate
     * @param throwError consumer to handle validation errors
     */

///

### Read Heroes with `HeroPublic` { #read-heroes-with-heropublic }

We can do the same as before to **read** `Hero`s, again, we use `response_model=list[HeroPublic]` to ensure that the data is validated and serialized correctly.

{* ../../docs_src/sql_databases/tutorial002_an_py310.py ln[65:72] hl[65] *}

### Read One Hero with `HeroPublic` { #read-one-hero-with-heropublic }

We can **read** a single hero:

border-bottom-left-radius: 0;\n}\n\n.input-group:not(.has-validation) > .form-control:not(:last-child),\n.input-group:not(.has-validation) > .custom-select:not(:last-child),\n.input-group:not(.has-validation) > .custom-file:not(:last-child) .custom-file-label,\n.input-group:not(.has-validation) > .custom-file:not(:last-child) .custom-file-label::after {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n\n.input-group.has-validation > .form-control:nth-last-child(n + 3),\n.input-group.has-validation...

type contentLengthRange struct {
	Min   int64
	Max   int64
	Valid bool // If content-length-range was part of policy
}

// PostPolicyForm provides strict static type conversion and validation for Amazon S3's POST policy JSON string.
type PostPolicyForm struct {
	Expiration time.Time // Expiration date and time of the POST policy.
	Conditions struct {  // Conditional policy structure.
		Policies []struct {

////

/// info

In Pydantic version 1 the method to parse and validate an object was `Item.parse_obj()`, in Pydantic version 2, the method is called `Item.model_validate()`.

///

/// tip

Here we reuse the same Pydantic model.

But the same way, we could have validated it in some other way.