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.
Memproses file PDF dengan Gemini
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Contoh ini menunjukkan cara memproses dokumen PDF menggunakan Gemini.
Mempelajari lebih lanjut
Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:
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,["# 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)."]]