Google Distributed Cloud (GDC) 氣隙式 Vertex AI 提供越來越多基礎生成式 AI 模型，可供您測試、部署及實作氣隙式應用程式。基礎模型經過微調，可因應特定用途，價格也各不相同。本頁面會概述 Google Cloud Generative AI API 中提供的模型系列，並根據用途指引您選擇合適的模型。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-04 (世界標準時間)。"],[],[],null,["# Available Generative AI models\n\n| **Important:** This content applies to version 1.14.4 and later.\n\nVertex AI on Google Distributed Cloud (GDC) air-gapped features a growing\nlist of foundation Generative AI models you can test, deploy, and implement\nfor your air-gapped applications. Foundation models are fine-tuned for specific\nuse cases and offered at different prices. This page summarizes the model\nfamilies available in the Generative AI APIs on GDC\nand guides you on which models to choose by use case.\n\nEmbeddings models\n-----------------\n\nEmbeddings convert textual data written in a natural language into numerical\nvectors. These vector representations are designed to capture the semantic\nmeaning and context of the words they represent. Text embedding models can\ngenerate optimized embeddings for various task types, such as document\nretrieval, questions and answers, classification, and fact verification. For\nEnglish text, use `text-embedding-004`. For multilingual text, use\n`text-multilingual-embedding-002`.\n\nThe following table summarizes the models available in the Embeddings API.\nFor more information on embeddings, see\n[Text embeddings](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/genai/text-embeddings-overview)."]]
