ml-kit-genai-prompt-api

Prerequisites

• Android API level must be 26 or higher. If

  minSdk

  is below 26, update it to 26.
• Add the ML Kit GenAI Prompt API dependency (

  com.google.mlkit:genai-prompt

  ) to the app-level

  build.gradle

  file, with version at least

  1.0.0-beta4

  .
• If

  com.google.mlkit:genai-schema-compiler

  dependency is used and KSP plugin version is below 2.3.6, update it to 2.3.6.

Detailed steps

1. Prompt optimization

2. Prefix caching optimization

3. Lifecycle and best practices

• The model must be fully downloaded and available before calling the first inference. Follow the guide on implementing a generative model to check that the

  FeatureStatus

  of a model is

  AVAILABLE

  before making an inference.
• Release ML Kit instances by calling

  close()

  when an

  Activity

  ,

  Fragment

  , or

  ViewModel

  is destroyed. Example:
  kotlin

  // Instantiating model in activity, fragment, or ViewModel
      val generativeModel = Generation.getClient()
  
  // When activity, fragment, or ViewModel is destroyed
      generativeModel.close()

  <br />

4. Structured output

1. Check for API availability: Verify Structured Output feature is available on the device with

   isStructuredOutputFeatureAvailable()

   before using it. Refer to the Structured Output API guide for full instructions.
2. Return type: Return the

   @Generable

   typed object from the function signature instead of a

   String

   or JSON string.
   For example:

   fun parseEmail(email: String): String {
       ... 
   }

   should be refactored to:

   fun parseEmail(email: String): ParsedEmail? {
       ...
   }

3. Example:
   This is the example code before refactoring:
   kotlin

   suspend fun parseEmail(email: String): String {
       val parseEmailPrompt = "Parse this email and return the sender, title, and short summary of the email less than 10 words: "
   
       val parsedEmail = generativeModel.generateContent(parseEmailPrompt + email)
   
       return parsedEmail.candidates[0].text
   }

   <br />
   This is the example code after using Structured Output API:
   kotlin

   @Generable
   data class ParsedEmail(
       @Guide(description = "Sender of the email")
       var sender: String = "",
   
       @Guide(description = "Title of the email")
       var title: String = "",
   
       @Guide(description = "Summary of the email less than 10 words")
       var summary: String = ""
   )
   
   suspend fun parseEmail(email: String): ParsedEmail? {
       val parseEmailPrompt =
           "Parse this email: $email"
   
       val baseRequest = GenerateContentRequest.Builder(TextPart(parseEmailPrompt)).build()
       val typedRequest = generateTypedContentRequest(baseRequest, ParsedEmail::class)
       val typedResponse = generativeModel.generateContent(typedRequest)
       return typedResponse.candidates[0].response
   }

   <br />