Spring AI Ollama tools is not compatible with a json shema containing an array in a "type" field (kotlin)

Bug description When generating a json schema for a function callback input type in Kotlin, if a property is nullable in the Kotlin language like this field1 :

    class MyFunction() : java.util.function.Function<MyFunction.Request, String?> {
        @JvmRecord
        data class Request(
            val field1: Int?)

        override fun apply(r: Request): String {
            return "OK"
        }
    }

    @Bean
    fun myFunctionBean(): FunctionCallback =
        FunctionCallback.builder()
            .function("myFunctionBean", MyFunction())
            .inputType(MyFunction.Request::class.java)
            .description("description of my function")
            .build()

the json schema generated will be like :

{
  "$schema" : "https://json-schema.org/draft/2020-12/schema",
  "type" : "object",
  "properties" : {
    "field1" : {
      "type" : [ "integer", "null" ],
      "format" : "int32",
    }
  }
}

But all Ollama calls will fail in a HTTP 400 error with this message : [400] Bad Request - {"error":"json: cannot unmarshal array into Go struct field .tools.function.parameters.properties.type of type string"}

I also tried to add @field:NotNull annotation, or set a default value val field1: Int? = null without success.

Environment Spring AI version : 1.0.0-M5 Kotlin version : 1.9.25

Steps to reproduce

see bug description

Expected behavior

The json schema should look like below.:

{
  "$schema" : "https://json-schema.org/draft/2020-12/schema",
  "type" : "object",
  "properties" : {
    "field1" : {
      "type" : "integer",
      "format" : "int32",
    }
  }
}

More generally, providing easy customization of json schema generation could be great... But there's quite lot of work.

Comment From: ThomasVitale

@PheelTi thanks for reporting this! Could you try to see if it's still a problem in the latest snapshot version? We have introduced new APIs for tool calling and improved several aspects, including the JSON Schema generation.

For example, when building a FunctionToolCallback, you can provide a custom JSON schema:

ToolCallback toolCallback = FunctionToolCallback
    .builder("myFunctionBean", new MyFunction())
    .description("description of my function")
    .inputType(MyFunction.Request.class)
    .inputSchema(JsonSchemaGenerator.generateForType(MyFunction.Request.class))
    .build();

Here's the new documentation about JSON Schema for tools: https://docs.spring.io/spring-ai/reference/api/tools.html#_json_schema