시작하기(AWS SDK for Python (Boto3))

다음 프로그램은 Python Amazon Kendra 프로그램에서 사용하는 예입니다. 프로그램이 실행하는 작업은 다음과 같습니다.

1. CreateIndex작업을 사용하여 새 인덱스를 만듭니다.

2. 인덱스 생성이 완료될 때까지 기다립니다. DescribeIndex작업을 사용하여 인덱스 상태를 모니터링합니다.

3. 인덱스가 활성화되면 CreateDataSource작업을 사용하여 데이터 소스를 만듭니다.

4. 데이터 소스 생성이 완료될 때까지 기다립니다. DescribeDataSource작업을 사용하여 데이터 원본의 상태를 모니터링합니다.

5. 데이터 원본이 활성 상태이면 StartDataSourceSyncJob작업을 사용하여 색인을 데이터 원본의 내용과 동기화합니다.

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.")