Mulai 29 April 2025, model Gemini 1.5 Pro dan Gemini 1.5 Flash tidak tersedia di project yang belum pernah menggunakan model ini, termasuk project baru. Untuk mengetahui detailnya, lihat Versi dan siklus proses model.
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Dokumen ini menjelaskan cara membuat embedding teks menggunakan
Vertex AI
Text embeddings API.
Vertex AI text embeddings API menggunakan representasi vektor padat: gemini-embedding-001, misalnya, menggunakan vektor 3072 dimensi. Model penyematan vektor padat menggunakan metode deep learning yang serupa dengan yang digunakan oleh model bahasa besar. Tidak seperti vektor renggang, yang cenderung memetakan kata secara langsung ke angka, vektor padat dirancang untuk merepresentasikan makna sebuah teks dengan lebih baik. Manfaat menggunakan sematan vektor padat dalam AI generatif adalah, alih-alih menelusuri kecocokan kata atau sintaksis langsung, Anda dapat menelusuri bagian yang sesuai dengan arti kueri dengan lebih baik, meskipun bagian tersebut tidak menggunakan bahasa yang sama.
Vektor dinormalisasi, sehingga Anda dapat menggunakan kesamaan kosinus, perkalian titik, atau jarak Euclidean untuk memberikan peringkat kesamaan yang sama.
Untuk mempelajari model embedding teks, lihat Embedding teks.
Untuk mengetahui informasi tentang bahasa yang didukung oleh setiap model embedding, lihat Bahasa teks yang didukung.
Sebelum memulai
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.
Anda bisa mendapatkan embedding teks dengan menggunakan model berikut:
Nama model
Deskripsi
Dimensi Output
Panjang urutan maksimum
Bahasa teks yang didukung
gemini-embedding-001
Performa canggih untuk tugas bahasa Inggris, multibahasa, dan kode. Model ini menyatukan model khusus yang sebelumnya seperti text-embedding-005 dan text-multilingual-embedding-002 serta mencapai performa yang lebih baik di masing-masing domainnya. Baca Laporan Teknologi kami untuk mengetahui detail selengkapnya.
Untuk kualitas embedding yang lebih baik, gemini-embedding-001 adalah model besar kami yang dirancang untuk memberikan performa tertinggi. Perhatikan bahwa
gemini-embedding-001 mendukung satu instance per permintaan.
Mendapatkan embedding teks untuk cuplikan teks
Anda bisa mendapatkan embedding teks untuk cuplikan teks dengan menggunakan Vertex AI API atau
Vertex AI SDK untuk Python.
Batas API
Untuk setiap permintaan, Anda dibatasi hingga 250 teks input.
API memiliki batas token input maksimum 20.000.
Input yang melebihi batas ini akan menghasilkan error 400. Setiap teks input individual
dibatasi lebih lanjut hingga 2.048 token; kelebihan token akan dipangkas tanpa pemberitahuan. Anda juga
dapat menonaktifkan pemotongan senyap dengan menyetel autoTruncate ke false.
Semua model menghasilkan vektor embedding panjang penuh secara default. Untuk gemini-embedding-001,
vektor ini memiliki 3.072 dimensi, dan model lain menghasilkan vektor 768 dimensi. Namun, dengan
menggunakan parameter output_dimensionality, pengguna dapat mengontrol ukuran vektor embedding output.
Memilih dimensi output yang lebih kecil dapat menghemat ruang penyimpanan dan meningkatkan efisiensi komputasi untuk aplikasi hilir, sekaligus sedikit mengorbankan kualitas.
Contoh berikut menggunakan model gemini-embedding-001.
Tetapkan variabel lingkungan untuk menggunakan Gen AI SDK dengan Vertex AI:
# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values# with appropriate values for your project.exportGOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECTexportGOOGLE_CLOUD_LOCATION=globalexportGOOGLE_GENAI_USE_VERTEXAI=True
fromgoogleimportgenaifromgoogle.genai.typesimportEmbedContentConfigclient=genai.Client()response=client.models.embed_content(model="gemini-embedding-001",contents=["How do I get a driver's license/learner's permit?","How long is my driver's license valid for?","Driver's knowledge test study guide",],config=EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT",# Optionaloutput_dimensionality=3072,# Optionaltitle="Driver's License",# Optional),)print(response)# Example response:# embeddings=[ContentEmbedding(values=[-0.06302902102470398, 0.00928034819662571, 0.014716853387653828, -0.028747491538524628, ... ],# statistics=ContentEmbeddingStatistics(truncated=False, token_count=13.0))]# metadata=EmbedContentMetadata(billable_character_count=112)
Menambahkan embedding ke database vektor
Setelah membuat embedding, Anda dapat menambahkan embedding ke database vektor, seperti Vector Search. Hal ini memungkinkan pengambilan dengan latensi rendah, dan sangat penting seiring bertambahnya ukuran data 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,["# Get text embeddings\n\nThis document describes how to create a text embedding using the\nVertex AI\n[Text embeddings API](/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api).\n\nVertex AI text embeddings API uses dense vector representations: [gemini-embedding-001](#supported-models),\nfor example, uses 3072-dimensional vectors. Dense vector embedding models use deep-learning methods similar to the ones used by large language models. Unlike sparse vectors, which tend to directly map words to numbers, dense vectors are designed to better represent the meaning of a piece of text. The benefit of using dense vector\nembeddings in generative AI is that instead of searching for direct word or\nsyntax matches, you can better search for passages that align to the meaning of\nthe query, even if the passages don't use the same language.\n\nThe vectors are normalized, so you can use cosine similarity, dot product, or\nEuclidean distance to provide the same similarity rankings.\n\n- To learn more about embeddings, see the [embeddings APIs overview](/vertex-ai/generative-ai/docs/embeddings).\n- To learn about text embedding models, see [Text embeddings](/vertex-ai/generative-ai/docs/model-reference/text-embeddings).\n- For information about which languages each embeddings model supports, see [Supported text languages](/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#supported_text_languages).\n\n| To see an example of getting text embeddings,\n| run the \"Getting Started with Text Embeddings + Vertex AI Vector Search\" notebook in one of the following\n| environments:\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/embeddings/intro-textemb-vectorsearch.ipynb)\n|\n|\n| \\|\n|\n| [Open in Colab Enterprise](https://console.cloud.google.com/vertex-ai/colab/import/https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fgenerative-ai%2Fmain%2Fembeddings%2Fintro-textemb-vectorsearch.ipynb)\n|\n|\n| \\|\n|\n| [Open\n| in Vertex AI Workbench](https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fgenerative-ai%2Fmain%2Fembeddings%2Fintro-textemb-vectorsearch.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/intro-textemb-vectorsearch.ipynb)\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\n\n Enable the Vertex AI API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.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\n\n Enable the Vertex AI API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com)\n\n1. [Choose a task type](/vertex-ai/generative-ai/docs/embeddings/task-types) for your embeddings job.\n\n\u003cbr /\u003e\n\nSupported models\n----------------\n\nYou can get text embeddings by using the following models:\n\nFor superior embedding quality, `gemini-embedding-001` is our large\nmodel designed to provide the highest performance. Note that\n`gemini-embedding-001` supports one instance per request.\n| **Note:** Only use the models as listed in the supported models table. Don't specify a model without the `@version` suffix or `@latest`. These model names are not considered valid.\n\nGet text embeddings for a snippet of text\n-----------------------------------------\n\nYou can get text embeddings for a snippet of text by using the Vertex AI API or\nthe Vertex AI SDK for Python.\n\n### API limits\n\nFor each request, you're limited to 250 input texts.\nThe API has a maximum input token limit of 20,000.\nInputs exceeding this limit result in a 400 error. Each individual input text\nis further limited to 2048 tokens; any excess is silently truncated. You can\nalso disable silent truncation by setting `autoTruncate` to `false`.\n\nFor more information, see [Text embedding limits](/vertex-ai/docs/quotas#text-embedding-limits).\n\n### Choose an embedding dimension\n\nAll models produce a full-length embedding vector by default. For `gemini-embedding-001`,\nthis vector has 3072 dimensions, and other models produce 768-dimensional vectors. However, by\nusing the `output_dimensionality` parameter, users can control the size of the output embedding vector.\nSelecting a smaller output dimensionality can save storage space and\nincrease computational efficiency for downstream applications, while sacrificing\nlittle in terms of quality.\n\nThe following examples use the `gemini-embedding-001` model. \n\n### Python\n\n#### Install\n\n```\npip install --upgrade google-genai\n```\n\n\nTo learn more, see the\n[SDK reference documentation](https://googleapis.github.io/python-genai/).\n\n\nSet environment variables to use the Gen AI SDK with Vertex AI:\n\n```bash\n# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values\n# with appropriate values for your project.\nexport GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT\nexport GOOGLE_CLOUD_LOCATION=global\nexport GOOGLE_GENAI_USE_VERTEXAI=True\n```\n\n\u003cbr /\u003e\n\n from google import genai\n from google.genai.types import EmbedContentConfig\n\n client = genai.Client()\n response = client.models.embed_content(\n model=\"gemini-embedding-001\",\n contents=[\n \"How do I get a driver's license/learner's permit?\",\n \"How long is my driver's license valid for?\",\n \"Driver's knowledge test study guide\",\n ],\n config=EmbedContentConfig(\n task_type=\"RETRIEVAL_DOCUMENT\", # Optional\n output_dimensionality=3072, # Optional\n title=\"Driver's License\", # Optional\n ),\n )\n print(response)\n # Example response:\n # embeddings=[ContentEmbedding(values=[-0.06302902102470398, 0.00928034819662571, 0.014716853387653828, -0.028747491538524628, ... ],\n # statistics=ContentEmbeddingStatistics(truncated=False, token_count=13.0))]\n # metadata=EmbedContentMetadata(billable_character_count=112)\n\n\u003cbr /\u003e\n\nAdd an embedding to a vector database\n-------------------------------------\n\nAfter you've generated your embedding you can add embeddings to a vector\ndatabase, like Vector Search. This enables low-latency retrieval,\nand is critical as the size of your data increases.\n\nTo learn more about Vector Search,\nsee [Overview of Vector Search](/vertex-ai/docs/vector-search/overview).\n\nWhat's next\n-----------\n\n- To learn more about rate limits, see [Generative AI on Vertex AI rate limits](/vertex-ai/generative-ai/docs/quotas).\n- To get batch predictions for embeddings, see [Get batch text embeddings predictions](/vertex-ai/generative-ai/docs/embeddings/batch-prediction-genai-embeddings)\n- To learn more about multimodal embeddings, see [Get multimodal embeddings](/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings)\n- To tune an embedding, see [Tune text embeddings](/vertex-ai/generative-ai/docs/models/tune-embeddings)\n- To learn more about the research behind `text-embedding-005` and `text-multilingual-embedding-002`, see the research paper [Gecko: Versatile Text Embeddings Distilled from Large Language Models](https://arxiv.org/abs/2403.20327)."]]
