Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Mulai menggunakan rekomendasi kustom
Anda dapat dengan cepat membuat aplikasi rekomendasi kustom canggih berdasarkan data Anda sendiri yang dapat menyarankan konten serupa dengan konten yang sedang dilihat pengguna.
Tutorial ini menjelaskan cara membuat aplikasi rekomendasi kustom untuk data terstruktur. Dalam hal ini, data terstruktur berbentuk NDJSON yang diserap dari bucket Cloud Storage.
Sebelum mengikuti tutorial ini, pastikan Anda telah melakukan langkah-langkah di bagian Sebelum Anda memulai.
Untuk mengikuti panduan langkah demi langkah tugas ini langsung di
Google Cloud konsol, klik Pandu saya:
Sign in to your Google Cloud account. If you're new to
Google Cloud,
create an account to evaluate how our products perform in
real-world scenarios. New customers also get $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
Bucket Cloud Storage ini berisi file film berformat NDJSON yang disediakan oleh Kaggle.
Klik Lanjutkan.
Tetapkan properti utama sebagai berikut:
Nama kolom
Properti kunci
homepage
uri
overview
description
Kemudian, klik Lanjutkan.
Masukkan nama tampilan untuk penyimpanan data Anda, lalu klik Buat.
Klik nama penyimpanan data Anda.
Di halaman Data, buka tab Aktivitas untuk melihat
status penyerapan data Anda. Impor selesai ditampilkan di kolom
Status saat proses impor selesai. Untuk set data ini,
proses ini biasanya memerlukan waktu dua hingga tiga menit. Anda mungkin perlu mengklik
Muat ulang untuk melihat Impor selesai.
Klik tab Dokumen untuk melihat dokumen yang diimpor.
Buat aplikasi
Selanjutnya, Anda membuat aplikasi rekomendasi dan menautkan penyimpanan data yang Anda buat sebelumnya.
Buka halaman Aplikasi.
Klik Buat aplikasi.
Di halaman Create App, pada bagian Recommendations engine, klik Create.
Di kolom App name, masukkan nama untuk aplikasi Anda. ID aplikasi Anda akan muncul di bawah nama aplikasi.
Klik Lanjutkan.
Di daftar penyimpanan data, pilih penyimpanan data yang Anda buat sebelumnya.
Klik Buat.
Melihat pratinjau aplikasi Anda
Di menu navigasi, klik
Pratinjau
untuk menguji aplikasi.
Jika Anda melihat pesan "Anda akan dapat melihat pratinjau mesin rekomendasi di sini. Kami masih menyiapkan mesin Anda, harap periksa kembali nanti", tunggu dan muat ulang halaman secara berkala. Anda mungkin harus menunggu
beberapa jam atau hingga hari berikutnya untuk melihat pratinjau data.
Klik kolom ID Dokumen. Daftar ID dokumen akan muncul.
Klik ID dokumen untuk dokumen yang rekomendasinya Anda inginkan.
Atau, masukkan ID dokumen ke kolom ID Dokumen.
Klik Dapatkan rekomendasi. Daftar dokumen yang direkomendasikan akan muncul.
Klik dokumen untuk mendapatkan detail dokumen.
Men-deploy aplikasi Anda
Tidak ada widget rekomendasi untuk men-deploy aplikasi Anda. Untuk menguji aplikasi Anda sebelum deployment:
Buka halaman Data dan salin ID dokumen.
Buka halaman Integrasi. Halaman ini menyertakan contoh perintah untuk metode
servingConfigs.recommend di REST API.
Tempel ID dokumen yang Anda salin sebelumnya ke kolom Document ID.
Biarkan kolom ID Pseudo Pengguna apa adanya.
Salin contoh permintaan dan jalankan di Cloud Shell.
Hasilnya adalah ID dokumen yang direkomendasikan berdasarkan dokumen yang Anda pilih.
Untuk mendapatkan bantuan dalam mengintegrasikan aplikasi rekomendasi ke dalam aplikasi web Anda, lihat contoh kode untuk C#, Go, Java, Node.js, PHP, dan Ruby di Mendapatkan rekomendasi untuk aplikasi.
Pembersihan
Agar akun Google Cloud Anda tidak dikenai biaya untuk
resource yang digunakan pada halaman ini, ikuti langkah-langkah berikut.
Untuk menghindari biaya yang tidak perlu, gunakan
Google Cloud console untuk menghapus project Anda jika tidak lagi diperlukan. Google Cloud
Jika Anda membuat project baru untuk mempelajari Aplikasi AI dan Anda tidak lagi memerlukan project tersebut, hapus project tersebut.
Jika Anda menggunakan project Google Cloud yang sudah ada, hapus resource yang Anda buat untuk menghindari tagihan pada akun Anda. Untuk mengetahui informasi selengkapnya,
lihat Menghapus aplikasi.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-19 UTC."],[[["\u003cp\u003eThis tutorial guides you through building a generic recommendations app that suggests content similar to what users are currently viewing, utilizing structured data in NDJSON format from a Cloud Storage bucket.\u003c/p\u003e\n"],["\u003cp\u003eBefore starting, you must enable Vertex AI Agent Builder and follow the steps outlined in the "Before you begin" section.\u003c/p\u003e\n"],["\u003cp\u003eYou will learn to create a data store by importing structured data (JSONL) from a specified Cloud Storage bucket containing movie metadata, then configure key properties to map data fields.\u003c/p\u003e\n"],["\u003cp\u003eThe tutorial also covers the creation of a recommendations app, linking it to the previously created data store, and using the preview feature to test the recommendations engine.\u003c/p\u003e\n"],["\u003cp\u003eThe final steps involve demonstrating how to deploy your app, including using the REST API's \u003ccode\u003eservingConfigs.recommend\u003c/code\u003e method to get document recommendations, as well as cleaning up resources to avoid unnecessary charges.\u003c/p\u003e\n"]]],[],null,["# Get started with custom recommendations\n=======================================\n\n| **Note:** This feature is a Preview offering, subject to the \"Pre-GA Offerings Terms\" of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using this feature, you agree to the [Generative AI Preview terms and conditions](https://cloud.google.com/trustedtester/aitos) (\"Preview Terms\"). For this feature, you can process personal data as outlined in the [Cloud Data Processing Addendum](https://cloud.google.com/terms/data-processing-terms), subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n|\n| \u003cbr /\u003e\n|\nYou can quickly build a state-of-the-art custom recommendations app on your own\ndata that can suggest content similar to the content that the user is currently\nviewing.\n\nThis tutorial explains how to create a custom recommendations app for\nstructured data. In this case, the structured data is in the form of NDJSON\ningested from a Cloud Storage bucket.\n\nBefore following this tutorial, make sure you have done the steps in [Before you\nbegin](/generative-ai-app-builder/docs/before-you-begin).\n\n*** ** * ** ***\n\nTo follow step-by-step guidance for this task directly in the\nGoogle Cloud console, click **Guide me**:\n\n[Guide me](https://console.cloud.google.com/gen-app-builder/?tutorial=generative-ai-app-builder--genappbuilder-recommendations-intro)\n\n*** ** * ** ***\n\nBefore you begin\n----------------\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the AI Applications, Cloud Storage APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=discoveryengine.googleapis.com,storage.googleapis.com)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the AI Applications, Cloud Storage APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=discoveryengine.googleapis.com,storage.googleapis.com)\n\n\u003cbr /\u003e\n\nEnable AI Applications\n----------------------\n\n1. In the Google Cloud console, go to the **AI Applications** page.\n\n [AI Applications](https://console.cloud.google.com/gen-app-builder/start)\n2. Optional: Click **Allow Google to selectively sample model input and\n responses**.\n\n3. Click **Continue and activate the API**.\n\nCreate a data store\n-------------------\n\nThis procedure guides you through creating a data store and uploading sample\ndata provided.\n\n1. Go to the **Data Stores** page.\n\n2. Click **Create data store**.\n\n3. On the **Select a data source** page, select **Cloud Storage**.\n\n4. On the **Import data from Cloud Storage** page, select **Structured\n data (JSONL)**.\n\n5. Click **File**.\n\n6. In the **gs://** field, enter the following value:\n\n ```\n cloud-samples-data/gen-app-builder/search/kaggle_movies/movie_metadata.ndjson\n ```\n\n This Cloud Storage bucket contains an NDJSON-formatted file of movies\n made available by\n [Kaggle](https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset?select=movies_metadata.csv).\n7. Click **Continue**.\n\n8. Assign key properties as follows:\n\n And, click **Continue**.\n9. Enter a display name for your data store, and then click **Create**.\n\n10. Click the name of your data store.\n\n11. On the **Data** page, go to the **Activity** tab to see the\n status of your data ingestion. **Import completed** displays in the\n **Status** column when the import process is complete. For this dataset,\n this typically takes two to three minutes. You might need to click\n **Refresh** to see **Import completed**.\n\n12. Click the **Documents** tab to see the imported documents.\n\nCreate an app\n-------------\n\nNext, you create a recommendations app and link the data store you created previously.\n\n1. Go to the **Apps** page.\n\n2. Click **Create app**.\n\n3. On the **Create App** page, under **Recommendations engine** , click **Create**.\n\n4. In the **App name** field, enter a name for your app. Your app ID\n appears under the app name.\n\n5. Click **Continue**.\n\n6. In the list of data stores, select the data store that you created earlier.\n\n7. Click **Create**.\n\n### Preview your app\n\n1. In the navigation menu, click\n **Preview**\n to test the app.\n\n2. If you see the message \"You will be able to preview your recommendation\n engine here We are still preparing your engine, please check back\n later\", wait and periodically refresh the page. You might have to wait\n some hours or until the next day to preview your data.\n\n3. Click the **Document ID** field. A list of document IDs appears.\n\n4. Click the document ID for the document that you want recommendations for.\n Alternatively, enter a document ID into the **Document ID** field.\n\n5. Click **Get recommendations**. A list of recommended documents appears.\n\n6. Click a document to get document details.\n\n### Deploy your app\n\nThere is no recommendations widget for deploying your app. To test your app\nbefore deployment:\n\n1. Go to the **Data** page and copy a document **ID**.\n\n2. Go to the **Integration** page. This page includes a sample command for the\n [`servingConfigs.recommend`](/generative-ai-app-builder/docs/reference/rest/v1beta/projects.locations.dataStores.servingConfigs/recommend) method in the REST API.\n\n3. Paste the document ID you copied earlier into the **Document ID** field.\n\n4. Leave the **User Pseudo ID** field as is.\n\n5. Copy the example request and run it in Cloud Shell.\n\n The results are the IDs of documents recommended based on the document that you chose.\n\nFor help integrating the recommendations app into your web app,\nsee the code samples for C#, Go, Java, Node.js, PHP, and Ruby at\n[Get recommendations for an app](/generative-ai-app-builder/docs/preview-recommendations).\n\nClean up\n--------\n\n\nTo avoid incurring charges to your Google Cloud account for\nthe resources used on this page, follow these steps.\n\n1. To avoid unnecessary Google Cloud charges, use the [Google Cloud console](https://console.cloud.google.com/) to delete your project if you don't need it.\n2. If you created a new project to learn about AI Applications and you no longer need the project, [delete the project](https://console.cloud.google.com/cloud-resource-manager).\n3. If you used an existing Google Cloud project, delete the resources you created to avoid incurring charges to your account. For more information, see [Delete an app](/generative-ai-app-builder/docs/delete-engine).\n4. Follow the steps in [Turn off\n Vertex AI Search](/generative-ai-app-builder/docs/turn-off-enterprise-search).\n\nWhat's next\n-----------\n\n- [Introduction to Vertex AI Search](/generative-ai-app-builder/docs/enterprise-search-introduction)\n- [About apps and data stores](/generative-ai-app-builder/docs/create-datastore-ingest)"]]
