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Halaman ini menjelaskan cara membuat embedding multimodal menggunakan model multimodal Vertex AI yang didukung, multimodalembedding@001.
Anda dapat menggunakan model embedding multimodal Vertex AI yang dirujuk di Model yang didukung.
Halaman ini mengasumsikan bahwa Anda sudah memahami AlloyDB untuk PostgreSQL dan
konsep AI generatif. Untuk mengetahui informasi selengkapnya tentang embedding, lihat Apa itu embedding.
Sebelum memulai
Sebelum Anda menggunakan embedding multimodal, lakukan hal berikut:
Jika Anda mengalami masalah saat menjalankan perintah sebelumnya, atau jika ekstensi tidak diupdate ke versi 1.4.3 setelah Anda menjalankan perintah sebelumnya, hubungi dukungan AlloyDB.
Setelah Anda memastikan bahwa versi sudah yang terbaru, instal fungsi
pratinjau dengan menjalankan prosedur upgrade_to_preview_version:
Mengakses data di Cloud Storage untuk membuat sematan multimodal
Untuk membuat sematan multimodal, lihat konten di Cloud Storage menggunakan
URI gs://.
Akses konten Cloud Storage melalui agen layanan Vertex AI project Anda saat ini. Secara default, agen layanan Vertex AI sudah memiliki izin untuk mengakses bucket dalam project yang sama.
Untuk mengetahui informasi selengkapnya, lihat Indeks peran dan izin IAM.
Untuk mengakses data di bucket Cloud Storage di project Google Cloud lain, jalankan perintah gcloud CLI berikut untuk memberikan peran Storage Object Viewer (roles/storage.objectViewer) kepada agen layanan Vertex AI di project AlloyDB Anda.
[[["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 2025-09-04 UTC."],[],[],null,["# Generate multimodal embeddings\n\n| **Preview**\n|\n|\n| This feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n| **Note:** This experimental launch is a Pre-GA offering.\n\nThis page describes how to generate multimodal embeddings using the supported\nVertex AI multimodal model, `multimodalembedding@001`.\n\nYou can use the Vertex AI multimodal embedding models referred to\nin [Supported models](/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings#supported-models).\n\nThis page assumes that you're familiar with AlloyDB for PostgreSQL and\n[generative AI concepts](/docs/generative-ai). For more information about embeddings, see [What are embeddings](/alloydb/docs/ai/work-with-embeddings#what-are-embeddings).\n\nBefore you begin\n----------------\n\nBefore you use multimodal embeddings, do the following:\n\n- [Verify that the `google_ml_integration` extension is installed](/alloydb/docs/ai/configure-vertex-ai#verify-installed-extension).\n- [Verify that the `google_ml_integration.enable_model_support` flag is set to `on`](/alloydb/docs/instance-configure-database-flags).\n- [Integrate with Vertex AI](/alloydb/docs/ai/configure-vertex-ai).\n- [Access data in Cloud Storage to generate multimodal embeddings](#access-data-in-storage).\n\n### Integrate with Vertex AI and install the extension\n\n1. [Integrate with Vertex AI](/alloydb/docs/ai/configure-vertex-ai).\n2. Ensure that the latest version of `google_ml_integration` is installed.\n 1. To check the installed version, run the following\n command:\n\n ```bash\n SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';\n extversion\n ------------\n 1.4.3\n (1 row)\n \n ```\n 2. If the extension isn't installed or if the installed version is\n earlier than 1.4.3, update the extension by running the following commands:\n\n ```bash\n CREATE EXTENSION IF NOT EXISTS google_ml_integration;\n ALTER EXTENSION google_ml_integration UPDATE;\n \n ```\n\n If you experience issues when you run the preceding commands, or if the\n extension isn't updated to version 1.4.3 after you run the preceding\n commands, contact AlloyDB support.\n 3. After you ensure that the version is current, install the preview\n functionality by running the `upgrade_to_preview_version` procedure:\n\n ```bash\n CALL google_ml.upgrade_to_preview_version();\n SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';\n extversion\n ------------\n 1.4.4\n (1 row)\n \n ```\n | Note: The expected version is 1.4.4 or later.\n\n### Access data in Cloud Storage to generate multimodal embeddings\n\n- To generate multimodal embeddings, refer to content in Cloud Storage using a `gs://` URI.\n- Access Cloud Storage content through your current project's Vertex AI service agent. By default, the Vertex AI service agent already has permission to access the bucket in the same project. For more information, see [IAM roles and permissions index](/iam/docs/understanding-roles#aiplatform.serviceAgent).\n- To access data in a Cloud Storage bucket in another Google Cloud project,\n run the following gcloud CLI command to grant the [Storage Object Viewer role (`roles/storage.objectViewer`)](/storage/docs/access-control/iam-roles) to the Vertex AI service agent of your\n AlloyDB project.\n\n gcloud projects add-iam-policy-binding \u003cANOTHER_PROJECT_ID\u003e \\\n --member=\"serviceAccount:service-\u003cPROJECT_ID\u003e@gcp-sa-aiplatform.iam.gserviceaccount.com\" \\\n --role=\"roles/storage.objectViewer\"\n\n For more information, see [Set and manage IAM policies on buckets](/storage/docs/access-control/using-iam-permissions).\n\nTo generate multimodal embeddings, select one of the following schemas. \nai schema google_ml schema\n\nWhat's next\n-----------\n\n- [Register a model endpoint with model endpoint management](/alloydb/docs/ai/register-model-endpoint).\n\n- [Query using AI powered SQL operators](/alloydb/docs/ai/evaluate-semantic-queries-ai-operators)."]]
