Gemini로 PDF 파일 처리
컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
이 샘플은 Gemini를 사용하여 PDF 문서를 처리하는 방법을 보여줍니다.
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코드 샘플
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","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)."]]