In this post, I’ll guide you through a practical example of how to integrate GPT (Generative Pre-trained Transformer) into a Kotlin application to create a basic chatbot. This chatbot will be able to respond to user queries naturally and efficiently.
Setting Up the Project
Step 1: Set Up Dependencies
First, make sure you have the necessary dependencies in your project. We’ll use OkHttp to handle HTTP requests and org.json to work with JSON.
Add the following dependencies to your build.gradle.kts file:
implementation(“com.squareup.okhttp3:okhttp:4.9.1”)
implementation(“org.json:json:20210307”)
}
Step 2: Configure the GPT Request
Create a function to send requests to the GPT API and receive responses. You’ll need an API key from OpenAI, which you can obtain by signing up on their platform.
import org.json.JSONObject
val client = OkHttpClient()
fun getGPTResponse(prompt: String): String? {
val requestBody = JSONObject()
.put(“model”, “text-davinci-003”)
.put(“prompt”, prompt)
.put(“max_tokens”, 100)
.toString()
val request = Request.Builder()
.url(“https://api.openai.com/v1/completions”)
.post(RequestBody.create(MediaType.parse(“application/json”), requestBody))
.addHeader(“Authorization”, “Bearer YOUR_API_KEY”)
.build()
client.newCall(request).execute().use { response ->
return if (!response.isSuccessful) null else JSONObject(response.body()?.string()).getJSONArray(“choices”).getJSONObject(0).getString(“text”)
}
}
Step 3: Handle Exceptions
It’s important to handle exceptions properly to ensure your application is robust.
return try {
getGPTResponse(prompt) ?: “Error: No response from GPT.”
} catch (e: Exception) {
“Exception: ${e.message}”
}
}
Using Coroutines for Asynchronous Calls
To improve the efficiency and responsiveness of your application, use Kotlin coroutines to handle GPT API calls asynchronously.
fun main() = runBlocking {
val userInput = “Describe the advantages of Kotlin.”
val gptResponse = async { getGPTResponseSafely(userInput) }
println(gptResponse.await())
}
Implementing the Chatbot
Step 1: Create a Simple Interface
For this example, we’ll use a basic console interface to demonstrate interaction with the chatbot.
while (true) {
print(“You: “)
val userInput = readLine()
if (userInput.isNullOrEmpty()) break
val gptResponse = async { getGPTResponseSafely(userInput) }
println(“Chatbot: ${gptResponse.await()}”)
}
}
Step 2: Handle Conversation Context
To improve the user experience, you can store the conversation history and provide it to GPT to maintain context.
fun main() = runBlocking {
while (true) {
print(“You: “)
val userInput = readLine()
if (userInput.isNullOrEmpty()) break
conversationHistory.add(“You: $userInput”)
val context = conversationHistory.joinToString(“n”)
val gptResponse = async { getGPTResponseSafely(“Context: $contextnResponse:”) }
val response = gptResponse.await()
println(“Chatbot: $response”)
conversationHistory.add(“Chatbot: $response”)
}
}
Conclusion
Integrating GPT into a Kotlin application to create a basic chatbot is an excellent way to enhance user interaction. This example provides a solid foundation upon which you can build and add more features as needed. Explore and experiment with these tools to discover their full potential!
