Datatypes integrations
Note
Note that the codecs defined by the tapir integrations are used only when the specific types (e.g. enumerations0 are used at the top level. Any nested usages (e.g. as part of a json body), need to be separately configured to work with the used json library.
Cats datatypes integration
The tapir-cats
module contains additional instances for some cats
datatypes as well as additional syntax:
"com.softwaremill.sttp.tapir" %% "tapir-cats" % "1.0.2"
import sttp.tapir.integ.cats.codec._
- brings schema, validator and codec instancesimport sttp.tapir.integ.cats.syntax._
- brings additional syntax fortapir
types
Refined integration
If you use refined, the tapir-refined
module will provide implicit codecs and
validators for T Refined P
as long as a codec for T
already exists:
"com.softwaremill.sttp.tapir" %% "tapir-refined" % "1.0.2"
You’ll need to extend the sttp.tapir.codec.refined.TapirCodecRefined
trait or import sttp.tapir.codec.refined._
to bring the implicit values into scope.
The refined codecs contain a validator which wrap/unwrap the value from/to its refined equivalent.
Some predicates will bind correctly to the vanilla tapir Validator, while others will bind to a custom validator that
might not be very clear when reading the generated documentation. Correctly bound predicates can be found in
integration/refined/src/main/scala/sttp/tapir/codec/refined/TapirCodecRefined.scala
.
If you are not satisfied with the validator generated by tapir-refined
, you can provide an implicit
ValidatorForPredicate[T, P]
in scope using `ValidatorForPredicate.fromPrimitiveValidator’ to build it (do not
hesitate to contribute your work!).
Enumeratum integration
The tapir-enumeratum
module provides schemas, validators and codecs for Enumeratum
enumerations. To use, add the following dependency:
"com.softwaremill.sttp.tapir" %% "tapir-enumeratum" % "1.0.2"
Then, import sttp.tapir.codec.enumeratum
, or extends the sttp.tapir.codec.enumeratum.TapirCodecEnumeratum
trait.
This will bring into scope implicit values for values extending *EnumEntry
.
Enumeration integration
There is no library for the use of the build in scala Enumeration
, but it can be implemented by hand.
The example code below will generate enums to the open-api documentation.
import sttp.tapir._
trait EnumHelper { e: Enumeration =>
import io.circe._
implicit val enumDecoder: Decoder[e.Value] = Decoder.decodeEnumeration(e)
implicit val enumEncoder: Encoder[e.Value] = Encoder.encodeEnumeration(e)
// needs to be a def or lazy val so that the enumeration values are available!
implicit def schemaForEnum: Schema[e.Value] = Schema.string.validate(Validator.enumeration(e.values.toList, v => Option(v)))
}
object Color extends Enumeration with EnumHelper {
type Color = Value
val Blue = Value("blue")
val Red = Value("red")
}
Tapir Schema
for any Enumeration.Value
can also be auto or semi-auto derived using import sttp.tapir.generic.auto._
or Schema.derivedEnumerationValue
.
NewType integration
If you use scala-newtype, the tapir-newtype
module will provide implicit codecs and
schemas for a types with a @newtype
and @newsubtype
annotations as long as a codec and schema for its underlying value already exists:
"com.softwaremill.sttp.tapir" %% "tapir-newtype" % "1.0.2"
Then, import sttp.tapir.codec.newtype._
, or extend the sttp.tapir.codec.enumeratum.TapirCodecNewType
trait to bring the implicit values into scope.
Derevo integration
The tapir-derevo
module provides a way to derive schema for your type using @derive
annotation.
For details refer to derevo documentation.
To use, add the following dependency:
"com.softwaremill.sttp.tapir" %% "tapir-derevo" % "1.0.2"
Then you can derive schema for your ADT along with other typeclasses besides ADT declaration itself:
import derevo.derive
import sttp.tapir.derevo.schema
@derive(schema)
case class Person(name: String, age: Int)
//or with custom description
@derive(schema("Type of currency in the country"))
sealed trait Currency
object Currency {case object CommunisticCurrency extends Currency
case class USD(amount: Long) extends Currency
}
The annotation will simply generate a Schema[T]
for your type T
and put it into companion object.
Generation rules are the same as in Schema.derived[T]
.
This will also work for newtypes — estatico or supertagged:
import derevo.derive
import sttp.tapir.derevo.schema
import io.estatico.newtype.macros.newtype
object types {
@derive(schema)
@newtype
case class Amount(i: Int)
}
Resulting schema will be equivalent to implicitly[Schema[Int]].map(i => Some(types.Amount(i)))
.
Note that due to limitations of the derevo
library one can’t provide custom description for generated schema.
Next
Read on about serving static content.