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.
Menghitung token untuk Gemini
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Contoh kode ini menunjukkan cara menggunakan Vertex AI Generative Models API untuk menghitung jumlah token dalam perintah dan membuat konten menggunakan model Gemini.
Contoh kode
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
[[["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"]],[],[],[],null,["# Count tokens for Gemini\n\nThe code sample demonstrates how to use the Vertex AI Generative Models API to count the number of tokens in a prompt and generate content using the Gemini model.\n\nCode sample\n-----------\n\n### Go\n\n\nBefore trying this sample, follow the Go setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Go API\nreference documentation](/go/docs/reference/cloud.google.com/go/aiplatform/latest/apiv1).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n import (\n \t\"context\"\n \t\"fmt\"\n \t\"io\"\n \t\"mime\"\n \t\"path/filepath\"\n\n \t\"cloud.google.com/go/vertexai/genai\"\n )\n\n // countTokensMultimodal finds the number of tokens for a multimodal prompt (video+text), and writes to w. Then,\n // it calls the model with the multimodal prompt and writes token counts from the response metadata to w.\n //\n // video is a Google Cloud Storage path starting with \"gs://\"\n func countTokensMultimodal(w io.Writer, projectID, location, modelName string) error {\n \t// location := \"us-central1\"\n \t// modelName := \"gemini-2.0-flash-001\"\n \tprompt := \"Provide a description of the video.\"\n \tvideo := \"gs://cloud-samples-data/generative-ai/video/pixel8.mp4\"\n\n \tctx := context.Background()\n\n \tclient, err := genai.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/go/latest/genai.html#cloud_google_com_go_vertexai_genai_Client_NewClient(ctx, projectID, location)\n \tif err != nil {\n \t\treturn fmt.Errorf(\"unable to create client: %w\", err)\n \t}\n \tdefer client.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/go/latest/genai.html#cloud_google_com_go_vertexai_genai_Client_Close()\n\n \tmodel := client.GenerativeModel(modelName)\n\n \tpart1 := genai.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/go/latest/genai.html#cloud_google_com_go_vertexai_genai_Text(prompt)\n\n \t// Given a video file URL, prepare video file as genai.Part\n \tpart2 := genai.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/go/latest/genai.html#cloud_google_com_go_vertexai_genai_FileData{\n \t\tMIMEType: mime.TypeByExtension(filepath.Ext(video)),\n \t\tFileURI: video,\n \t}\n\n \t// Finds the total number of tokens for the 2 parts (text, video) of the multimodal prompt,\n \t// before actually calling the model for inference.\n \tresp, err := model.CountTokens(ctx, part1, part2)\n \tif err != nil {\n \t\treturn err\n \t}\n\n \tfmt.Fprintf(w, \"Number of tokens for the multimodal video prompt: %d\\n\", resp.TotalTokens)\n\n \tres, err := model.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/go/latest/genai.html#cloud_google_com_go_vertexai_genai_GenerativeModel_GenerateContent(ctx, part1, part2)\n \tif err != nil {\n \t\treturn fmt.Errorf(\"unable to generate contents: %w\", err)\n \t}\n\n \t// The token counts are also provided in the model response metadata, after inference.\n \tfmt.Fprintln(w, \"\\nModel response\")\n \tmd := res.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/go/latest/genai.html#cloud_google_com_go_vertexai_genai_UsageMetadata\n \tfmt.Fprintf(w, \"Prompt Token Count: %d\\n\", md.PromptTokenCount)\n \tfmt.Fprintf(w, \"Candidates Token Count: %d\\n\", md.CandidatesTokenCount)\n \tfmt.Fprintf(w, \"Total Token Count: %d\\n\", md.TotalTokenCount)\n\n \treturn nil\n }\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=generativeaionvertexai)."]]