ml-kit-genai-prompt-api

This skill provides step-by-step guidance for integrating and optimizing the ML Kit GenAI Prompt API in Android apps.

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

To optimize prompts for use with the ML Kit Prompt API, follow the prompt optimization guide.

2. Prefix caching optimization

If the prompt is more than 200 words, implement the prefix caching API.

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:

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

  
  

4. Structured output

When implementing or refactoring a prompt to use structured output, follow these rules:

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:

   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
   }
   

   
   

   This is the example code after using Structured Output API:

   @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
   }