Gemini で PDF ファイルを処理する
コレクションでコンテンツを整理
必要に応じて、コンテンツの保存と分類を行います。
このサンプルでは、Gemini を使用して PDF ドキュメントを処理する方法を示します。
もっと見る
このコードサンプルを含む詳細なドキュメントについては、以下をご覧ください。
コードサンプル
特に記載のない限り、このページのコンテンツはクリエイティブ・コモンズの表示 4.0 ライセンスにより使用許諾されます。コードサンプルは Apache 2.0 ライセンスにより使用許諾されます。詳しくは、Google Developers サイトのポリシーをご覧ください。Java は Oracle および関連会社の登録商標です。
[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],[],[],[],null,["# Process a PDF file with Gemini\n\nThis sample shows you how to process a PDF document using Gemini.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Document understanding](/vertex-ai/generative-ai/docs/multimodal/document-understanding)\n\nCode sample\n-----------\n\n### Python\n\n\nBefore trying this sample, follow the Python 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 Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\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 from google import genai\n from google.genai.types import HttpOptions, Part\n\n client = genai.Client(http_options=HttpOptions(api_version=\"v1\"))\n model_id = \"gemini-2.5-flash\"\n\n prompt = \"\"\"\n You are a highly skilled document summarization specialist.\n Your task is to provide a concise executive summary of no more than 300 words.\n Please summarize the given document for a general audience.\n \"\"\"\n\n pdf_file = Part.from_uri(\n file_uri=\"gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf\",\n mime_type=\"application/pdf\",\n )\n\n response = client.models.generate_content(\n model=model_id,\n contents=[pdf_file, prompt],\n )\n\n print(response.text)\n # Example response:\n # Here is a summary of the document in 300 words.\n #\n # The paper introduces the Transformer, a novel neural network architecture for\n # sequence transduction tasks like machine translation. Unlike existing models that rely on recurrent or\n # convolutional layers, the Transformer is based entirely on attention mechanisms.\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=googlegenaisdk)."]]