Getting started (AWS SDK for Python (Boto3))
The following program is an example of using Amazon Kendra in a Python program. The program performs the following actions:
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Creates a new index using the CreateIndex operation.
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Waits for index creation to complete. It uses the DescribeIndex operation to monitor the status of the index.
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Once the index is active, it creates a data source using the CreateDataSource operation.
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Waits for data source creation to complete. It uses the DescribeDataSource operation to monitor the status of the data source.
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When the data source is active, it synchronizes the index with the contents of the data source using the StartDataSourceSyncJob operation.
import boto3 from botocore.exceptions import ClientError import pprint import time kendra = boto3.client("kendra") print("Create an index.") # Provide a name for the index index_name = "python-getting-started-index" # Provide an optional decription for the index description = "Getting started index" # Provide the IAM role ARN required for indexes index_role_arn = "arn:aws:iam::${accountId}:role/KendraRoleForGettingStartedIndex" try: index_response = kendra.create_index( Description = description, Name = index_name, RoleArn = index_role_arn ) pprint.pprint(index_response) index_id = index_response["Id"] print("Wait for Amazon Kendra to create the index.") while True: # Get the details of the index, such as the status index_description = kendra.describe_index( Id = index_id ) # When status is not CREATING quit. status = index_description["Status"] print(" Creating index. Status: "+status) time.sleep(60) if status != "CREATING": break print("Create an S3 data source.") # Provide a name for the data source data_source_name = "python-getting-started-data-source" # Provide an optional description for the data source data_source_description = "Getting started data source." # Provide the IAM role ARN required for data sources data_source_role_arn = "arn:aws:iam::${accountId}:role/KendraRoleForGettingStartedDataSource" # Provide the data source connection information S3_bucket_name = "S3-bucket-name" data_source_type = "S3" # Configure the data source configuration = {"S3Configuration": { "BucketName": S3_bucket_name } } """ If you connect to your data source using a template schema, configure the template schema configuration = {"TemplateConfiguration": { "Template": {JSON schema} } } """ data_source_response = kendra.create_data_source( Name = data_source_name, Description = data_source_name, RoleArn = data_source_role_arn, Type = data_source_type, Configuration = configuration, IndexId = index_id ) pprint.pprint(data_source_response) data_source_id = data_source_response["Id"] print("Wait for Amazon Kendra to create the data source.") while True: # Get the details of the data source, such as the status data_source_description = kendra.describe_data_source( Id = data_source_id, IndexId = index_id ) # If status is not CREATING, then quit status = data_source_description["Status"] print(" Creating data source. Status: "+status) time.sleep(60) if status != "CREATING": break print("Synchronize the data source.") sync_response = kendra.start_data_source_sync_job( Id = data_source_id, IndexId = index_id ) pprint.pprint(sync_response) print("Wait for the data source to sync with the index.") while True: jobs = kendra.list_data_source_sync_jobs( Id = data_source_id, IndexId = index_id ) # For this example, there should be one job status = jobs["History"][0]["Status"] print(" Syncing data source. Status: "+status) if status != "SYNCING": break time.sleep(60) except ClientError as e: print("%s" % e) print("Program ends.")