Menambahkan respons AI generatif ke aplikasi contoh SociaLite
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Aplikasi contoh SociaLite menunjukkan cara menggunakan API platform
Android untuk mengimplementasikan fitur yang biasa di-deploy di aplikasi jaringan sosial
dan komunikasi. Kami telah mengintegrasikan Gemini API menggunakan Firebase AI Logic SDK untuk menunjukkan cara kemampuan chatbot dapat diterapkan di aplikasi Android Anda sendiri.
Kode contoh ini menggunakan Gemini Flash yang cepat dan hemat biaya.
Pelajari model Gemini lebih lanjut. Untuk menerapkan chatbot berbasis AI dalam
demo Socialite, kami menggunakan fungsi petunjuk sistem
Gemini API untuk mengubah perilaku model. Dalam
hal ini, kita menggunakan perintah "Harap balas percakapan chat ini seperti
kucing yang ramah". SociaLite versi Gemini ini juga menggunakan kemampuan
multimodal model untuk memungkinkan chatbot bereaksi terhadap gambar.
Mengimplementasikan Gemini API
Penerapan chatbot terutama terletak di class ChatRepository.
Class GenerativeModel memungkinkan Anda berinteraksi dengan Gemini API, yang
dibuat instance-nya sebagai berikut:
valgenerativeModel=GenerativeModel(// Set the model name to the latest Gemini model.modelName="gemini-2.0-flash-lite-001",// Set a system instruction to set the behavior of the model.systemInstruction=content{text("Please respond to this chat conversation like a friendly cat.")},)
Dalam cakupan coroutine, mulai chat dengan meneruskan pastMessages ke startChat()
untuk memastikan model memiliki akses ke histori percakapan. Hal ini memberi chatbot Anda kemampuan untuk mempertahankan konteks dan menghasilkan respons yang koheren yang didasarkan pada percakapan sebelumnya.
Konten dan contoh kode di halaman ini tunduk kepada lisensi yang dijelaskan dalam Lisensi Konten. Java dan OpenJDK adalah merek dagang atau merek dagang terdaftar dari Oracle dan/atau afiliasinya.
Terakhir diperbarui pada 2025-07-27 UTC.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Informasi yang saya butuhkan tidak ada","missingTheInformationINeed","thumb-down"],["Terlalu rumit/langkahnya terlalu banyak","tooComplicatedTooManySteps","thumb-down"],["Sudah usang","outOfDate","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Masalah kode / contoh","samplesCodeIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-07-27 UTC."],[],[],null,["# Add generative AI responses the SociaLite sample app\n\nThe [SociaLite sample app](https://github.com/android/socialite) demonstrates how to use Android\nplatform APIs to implement features that are commonly deployed in social network\nand communications apps. We have integrated the Gemini API using the Firebase AI\nLogic SDK to demonstrate how chatbot capabilities can be implemented in your\nown Android apps.\n\nThis sample code uses Gemini Flash which fast and cost-effective.\n[Learn more about the Gemini models](https://firebase.google.com/docs/ai-logic/models). To implement an AI-driven chatbot in\nthe Socialite demo, we used the [*system instructions*](https://firebase.google.com/docs/ai-logic/system-instructions)\nfunctionality of the Gemini API to modify the behavior of the model. In this\ncase, we use the prompt \"Please respond to this chat conversation like a\nfriendly cat\". This Gemini-infused version of SociaLite also uses the multimodal\ncapabilities of the model to let the chatbot react to images.\n\nImplement the Gemini API\n------------------------\n\nThe chatbot implementation is primarily located in the `ChatRepository` class.\nThe `GenerativeModel` class lets you interact with the Gemini API, which is\ninstantiated as follows: \n\n val generativeModel = GenerativeModel(\n // Set the model name to the latest Gemini model.\n modelName = \"gemini-2.0-flash-lite-001\",\n // Set a system instruction to set the behavior of the model.\n systemInstruction = content {\n text(\"Please respond to this chat conversation like a friendly cat.\")\n },\n )\n\nIn a coroutine scope, initiate a chat by passing `pastMessages` to `startChat()`\nto ensure that the model has access to conversation history. This gives your\nchatbot the ability to maintain context and generate coherent responses that\nbuild on previous exchanges. \n\n val pastMessages = getMessageHistory(chatId)\n val chat = generativeModel.startChat(\n history = pastMessages,\n )\n\nUse the `sendMessage()` method to pass messages to the model.\n\nTest the AI chatbot\n-------------------\n\nYou can test it yourself by following these steps:\n\n1. Check out the code for the [SociaLite sample app](https://github.com/android/socialite) and open it in Android Studio.\n2. Set up a Firebase Project, connect your app to the *Gemini Developer API* by following [these steps](https://firebase.google.com/docs/ai-logic/get-started?platform=android&api=dev),\n3. Replace google-services.json with your own \\& Run `app` configuration,\n4. Sync and run your app.\n5. In the SociaLite app, tap **Settings** and then tap **AI Chatbot** so that the button label reads \"*AI Chatbot: enabled*\".\n\nYou are now ready to chat!\n\nAdditional resources\n--------------------\n\n[Learn more about the Firebase AI Logic SDK](/ai/gemini)."]]
