Text aus einem Bild generieren
Mit Sammlungen den Überblick behalten
Sie können Inhalte basierend auf Ihren Einstellungen speichern und kategorisieren.
In diesem Beispiel wird gezeigt, wie Sie mit dem Gemini-Modell Text aus einem Bild generieren.
Weitere Informationen
Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:
Codebeispiel
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[[["Leicht verständlich","easyToUnderstand","thumb-up"],["Mein Problem wurde gelöst","solvedMyProblem","thumb-up"],["Sonstiges","otherUp","thumb-up"]],[["Schwer verständlich","hardToUnderstand","thumb-down"],["Informationen oder Beispielcode falsch","incorrectInformationOrSampleCode","thumb-down"],["Benötigte Informationen/Beispiele nicht gefunden","missingTheInformationSamplesINeed","thumb-down"],["Problem mit der Übersetzung","translationIssue","thumb-down"],["Sonstiges","otherDown","thumb-down"]],[],[],[],null,["# Generate text from an image\n\nThis sample demonstrates how to use the Gemini model to generate text from an image.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Quickstart: Generate text using the Vertex AI Gemini API](/vertex-ai/generative-ai/docs/start/quickstarts/quickstart-multimodal)\n- [Vertex AI client libraries](/vertex-ai/generative-ai/docs/reference/libraries)\n\nCode sample\n-----------\n\n### C++\n\n\nBefore trying this sample, follow the C++ 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 C++ API\nreference documentation](/cpp/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 namespace vertex_ai = ::google::cloud::aiplatform_v1;\n namespace vertex_ai_proto = ::google::cloud::aiplatform::v1;\n [](std::string const& project_id, std::string const& location_id,\n std::string const& model, std::string const& prompt,\n std::string const& mime_type, std::string const& file_uri) {\n google::cloud::Location location(project_id, location_id);\n auto client = vertex_ai::PredictionServiceClient(\n vertex_ai::MakePredictionServiceConnection(location.location_id()));\n\n vertex_ai_proto::GenerateContentRequest request;\n request.set_model(location.FullName() + \"/publishers/google/models/\" +\n model);\n auto generation_config = request.mutable_generation_config();\n generation_config-\u003eset_temperature(0.4f);\n generation_config-\u003eset_top_k(32);\n generation_config-\u003eset_top_p(1);\n generation_config-\u003eset_max_output_tokens(2048);\n\n auto contents = request.add_contents();\n contents-\u003eset_role(\"user\");\n contents-\u003eadd_parts()-\u003eset_text(prompt);\n auto image_part = contents-\u003eadd_parts();\n image_part-\u003emutable_file_data()-\u003eset_file_uri(file_uri);\n image_part-\u003emutable_file_data()-\u003eset_mime_type(mime_type);\n\n auto response = client.GenerateContent(request);\n if (!response) throw std::move(response).status();\n\n for (auto const& candidate : response-\u003ecandidates()) {\n for (auto const& p : candidate.content().parts()) {\n std::cout \u003c\u003c p.text() \u003c\u003c \"\\n\";\n }\n }\n }\n\n### Java\n\n\nBefore trying this sample, follow the Java 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 Java API\nreference documentation](/java/docs/reference/google-cloud-aiplatform/latest/com.google.cloud.aiplatform.v1).\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 com.google.cloud.vertexai.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.VertexAI.html;\n import com.google.cloud.vertexai.api.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.api.GenerateContentResponse.html;\n import com.google.cloud.vertexai.generativeai.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.generativeai.ContentMaker.html;\n import com.google.cloud.vertexai.generativeai.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.generativeai.GenerativeModel.html;\n import com.google.cloud.vertexai.generativeai.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.generativeai.PartMaker.html;\n import java.io.IOException;\n\n public class Quickstart {\n\n public static void main(String[] args) throws IOException {\n // TODO(developer): Replace these variables before running the sample.\n String projectId = \"your-google-cloud-project-id\";\n String location = \"us-central1\";\n String modelName = \"gemini-2.0-flash-001\";\n\n String output = quickstart(projectId, location, modelName);\n System.out.println(output);\n }\n\n // Analyzes the provided Multimodal input.\n public static String quickstart(String projectId, String location, String modelName)\n throws IOException {\n // Initialize client that will be used to send requests. This client only needs\n // to be created once, and can be reused for multiple requests.\n try (https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.VertexAI.html vertexAI = new https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.VertexAI.html(projectId, location)) {\n String imageUri = \"gs://generativeai-downloads/images/scones.jpg\";\n\n https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.generativeai.GenerativeModel.html model = new https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.generativeai.GenerativeModel.html(modelName, vertexAI);\n https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.api.GenerateContentResponse.html response = model.https://cloud.google.com/vertex-ai/generative-ai/docs/reference/java/latest/com.google.cloud.vertexai.generativeai.GenerativeModel.html#com_google_cloud_vertexai_generativeai_GenerativeModel_generateContent_com_google_cloud_vertexai_api_Content_(ContentMaker.fromMultiModalData(\n PartMaker.fromMimeTypeAndData(\"image/png\", imageUri),\n \"What's in this photo\"\n ));\n\n return response.toString();\n }\n }\n }\n\n### Node.js\n\n\nBefore trying this sample, follow the Node.js 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 Node.js API\nreference documentation](/nodejs/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 const {VertexAI} = require('https://cloud.google.com/nodejs/docs/reference/vertexai/latest/overview.html');\n\n /**\n * TODO(developer): Update these variables before running the sample.\n */\n async function createNonStreamingMultipartContent(\n projectId = 'PROJECT_ID',\n location = 'us-central1',\n model = 'gemini-2.0-flash-001',\n image = 'gs://generativeai-downloads/images/scones.jpg',\n mimeType = 'image/jpeg'\n ) {\n // Initialize Vertex with your Cloud project and location\n const vertexAI = new https://cloud.google.com/nodejs/docs/reference/vertexai/latest/vertexai/vertexai.html({project: projectId, location: location});\n\n // Instantiate the model\n const generativeVisionModel = vertexAI.https://cloud.google.com/nodejs/docs/reference/vertexai/latest/vertexai/vertexai.html({\n model: model,\n });\n\n // For images, the SDK supports both Google Cloud Storage URI and base64 strings\n const filePart = {\n fileData: {\n fileUri: image,\n mimeType: mimeType,\n },\n };\n\n const textPart = {\n text: 'what is shown in this image?',\n };\n\n const request = {\n contents: [{role: 'user', parts: [filePart, textPart]}],\n };\n\n console.log('Prompt Text:');\n console.log(request.contents[0].https://cloud.google.com/nodejs/docs/reference/vertexai/latest/vertexai/content.html[1].text);\n\n console.log('Non-Streaming Response Text:');\n\n // Generate a response\n const response = await generativeVisionModel.generateContent(request);\n\n // Select the text from the response\n const fullTextResponse =\n response.response.https://cloud.google.com/nodejs/docs/reference/vertexai/latest/vertexai/generatecontentresponse.html[0].https://cloud.google.com/nodejs/docs/reference/vertexai/latest/vertexai/generatecontentcandidate.html.https://cloud.google.com/nodejs/docs/reference/vertexai/latest/vertexai/content.html[0].text;\n\n console.log(fullTextResponse);\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)."]]