Importar documentos de BigQuery
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Importar documentos de BigQuery
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Código de ejemplo
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[[["Es fácil de entender","easyToUnderstand","thumb-up"],["Me ofreció una solución al problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Es difícil de entender","hardToUnderstand","thumb-down"],["La información o el código de muestra no son correctos","incorrectInformationOrSampleCode","thumb-down"],["Me faltan las muestras o la información que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],[],[[["\u003cp\u003eThis content provides a Python code sample for importing documents into a data store from BigQuery using the Vertex AI Agent Builder.\u003c/p\u003e\n"],["\u003cp\u003eIt uses the \u003ccode\u003ediscoveryengine\u003c/code\u003e library to create a client and define a request for importing documents, specifying details such as the project ID, location, data store ID, and BigQuery dataset and table information.\u003c/p\u003e\n"],["\u003cp\u003eThe code demonstrates how to set up authentication with Application Default Credentials and handles the result of the operation after completion.\u003c/p\u003e\n"],["\u003cp\u003eThe code sample supports both full and incremental reconciliation modes, allowing for complete data replacement or data additions, respectively.\u003c/p\u003e\n"],["\u003cp\u003eAdditional documentation is available on creating a search data store, and refreshing structured and unstructured data, as well as a reference to the Vertex AI Agent Builder Python API and the Google Cloud sample browser.\u003c/p\u003e\n"]]],[],null,["# Import documents from BigQuery\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Create a custom recommendations data store](/generative-ai-app-builder/docs/create-data-store-recommendations)\n- [Create a search data store](/generative-ai-app-builder/docs/create-data-store-es)\n- [Refresh structured and unstructured data](/agentspace/docs/refresh-data)\n- [Refresh structured and unstructured data](/generative-ai-app-builder/docs/refresh-data)\n\nCode sample\n-----------\n\n### Python\n\n\nFor more information, see the\n[AI Applications Python API\nreference documentation](/python/docs/reference/discoveryengine/latest).\n\n\nTo authenticate to AI Applications, 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 google.api_core.client_options import ClientOptions\n from google.cloud import discoveryengine\n\n # TODO(developer): Uncomment these variables before running the sample.\n # project_id = \"YOUR_PROJECT_ID\"\n # location = \"YOUR_LOCATION\" # Values: \"global\"\n # data_store_id = \"YOUR_DATA_STORE_ID\"\n # bigquery_dataset = \"YOUR_BIGQUERY_DATASET\"\n # bigquery_table = \"YOUR_BIGQUERY_TABLE\"\n\n # For more information, refer to:\n # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store\n client_options = (\n ClientOptions(api_endpoint=f\"{location}-discoveryengine.googleapis.com\")\n if location != \"global\"\n else None\n )\n\n # Create a client\n client = discoveryengine.https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.services.document_service.DocumentServiceClient.html(client_options=client_options)\n\n # The full resource name of the search engine branch.\n # e.g. projects/{project}/locations/{location}/dataStores/{data_store_id}/branches/{branch}\n parent = client.https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.services.document_service.DocumentServiceClient.html#google_cloud_discoveryengine_v1_services_document_service_DocumentServiceClient_branch_path(\n project=project_id,\n location=location,\n data_store=data_store_id,\n branch=\"default_branch\",\n )\n\n request = discoveryengine.https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.types.ImportDocumentsRequest.html(\n parent=parent,\n bigquery_source=discoveryengine.https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.types.BigQuerySource.html(\n project_id=project_id,\n dataset_id=bigquery_dataset,\n table_id=bigquery_table,\n data_schema=\"custom\",\n ),\n # Options: `FULL`, `INCREMENTAL`\n reconciliation_mode=discoveryengine.https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.types.ImportDocumentsRequest.html.https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.types.ImportDocumentsRequest.ReconciliationMode.html.INCREMENTAL,\n )\n\n # Make the request\n operation = client.https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.services.document_service.DocumentServiceClient.html#google_cloud_discoveryengine_v1_services_document_service_DocumentServiceClient_import_documents(request=request)\n\n print(f\"Waiting for operation to complete: {operation.operation.name}\")\n response = operation.result()\n\n # After the operation is complete,\n # get information from operation metadata\n metadata = discoveryengine.https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.types.ImportDocumentsMetadata.html(operation.metadata)\n\n # Handle the response\n print(response)\n print(metadata)\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=genappbuilder)."]]