入门 (AWS SDK for Python (Boto3)) - Amazon Kendra

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入门 (AWS SDK for Python (Boto3))

以下程序是在 Python 程序 Amazon Kendra 中使用的示例。此程序执行以下操作：

  1. 使用CreateIndex操作创建新索引。

  2. 等待索引创建完成。它使用该DescribeIndex操作来监视索引的状态。

  3. 一旦索引处于活动状态，它就会使用CreateDataSource操作创建数据源。

  4. 等待数据来源创建完成。它使用该DescribeDataSource操作来监视数据源的状态。

  5. 当数据源处于活动状态时，它会使用StartDataSourceSyncJob操作将索引与数据源的内容同步。

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.")