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Create an index
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This sample demonstrates how to create an index to import or upload documents.
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For detailed documentation that includes this code sample, see the following:
Code sample
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[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],[],[],[],null,["# Create an index\n\nThis sample demonstrates how to create an index to import or upload documents.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [RAG Engine API](/vertex-ai/generative-ai/docs/model-reference/rag-api-v1)\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\n from vertexai import rag\n import https://cloud.google.com/python/docs/reference/vertexai/latest/\n\n # TODO(developer): Update and un-comment below lines\n # PROJECT_ID = \"your-project-id\"\n # display_name = \"test_corpus\"\n # description = \"Corpus Description\"\n\n # Initialize Vertex AI API once per session\n https://cloud.google.com/python/docs/reference/vertexai/latest/.init(project=PROJECT_ID, location=\"us-central1\")\n\n # Configure backend_config\n backend_config = rag.RagVectorDbConfig(\n rag_embedding_model_config=rag.RagEmbeddingModelConfig(\n vertex_prediction_endpoint=rag.VertexPredictionEndpoint(\n publisher_model=\"publishers/google/models/text-embedding-005\"\n )\n )\n )\n\n corpus = rag.create_corpus(\n display_name=display_name,\n description=description,\n backend_config=backend_config,\n )\n print(corpus)\n # Example response:\n # RagCorpus(name='projects/1234567890/locations/us-central1/ragCorpora/1234567890',\n # display_name='test_corpus', description='Corpus Description', embedding_model_config=...\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)."]]