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https://github.com/awslabs/amazon-bedrock-agentcore-samples.git
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1c2fb802a0
* Data Analyst Assistant Solution with Amazon Bedrock AgentCore * Data Analyst Assistant Solution with Amazon Bedrock AgentCore * Data Analyst Assistant Solution with Amazon Bedrock AgentCore * Data Analyst Assistant Solution with Amazon Bedrock AgentCore * Data Analyst Assistant Solution with Amazon Bedrock AgentCore * Data Analyst Assistant Solution with Amazon Bedrock AgentCore * Data Analyst Assistant Solution with Amazon Bedrock AgentCore --------- Co-authored-by: Uriel Ramirez <beralfon@amazon.com>
124 lines
3.6 KiB
Python
124 lines
3.6 KiB
Python
import boto3
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import os
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session = boto3.session.Session()
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region = session.region_name
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# Environment variables
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data_source_bucket_name = os.environ["DATA_SOURCE_BUCKET_NAME"]
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aurora_serverless_db_cluster_arn = os.environ["AURORA_SERVERLESS_DB_CLUSTER_ARN"]
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secret_arn = os.environ["SECRET_ARN"]
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database_name = "video_games_sales"
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# File path variables
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local_file_path = "resources/database/video_games_sales_no_headers.csv"
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s3_file_name = "video_games_sales_no_headers.csv"
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try:
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# Upload file to S3
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s3_client = boto3.client("s3")
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s3_client.upload_file(
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local_file_path,
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data_source_bucket_name,
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s3_file_name,
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)
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print(f"File '{s3_file_name}' uploaded to bucket '{data_source_bucket_name}'")
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# RDS data client
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client = boto3.client("rds-data")
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# Create table
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query1 = """ CREATE TABLE video_games_sales_units (
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title TEXT,
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console TEXT,
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genre TEXT,
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publisher TEXT,
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developer TEXT,
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critic_score NUMERIC(3,1),
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total_sales NUMERIC(4,2),
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na_sales NUMERIC(4,2),
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jp_sales NUMERIC(4,2),
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pal_sales NUMERIC(4,2),
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other_sales NUMERIC(4,2),
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release_date DATE
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); """
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response = client.execute_statement(
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resourceArn=aurora_serverless_db_cluster_arn,
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secretArn=secret_arn,
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sql=query1,
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database=database_name,
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)
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print("Query: " + query1)
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print("Query response: " + str(response))
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# Create AWS S3 extension
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query2 = "CREATE EXTENSION aws_s3 CASCADE;"
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response = client.execute_statement(
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resourceArn=aurora_serverless_db_cluster_arn,
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secretArn=secret_arn,
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sql=query2,
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database=database_name,
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)
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print("-----------------------------------------")
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print("Query: " + query2)
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print("Query response: " + str(response))
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# Import data from S3
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query3 = f"""
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SELECT aws_s3.table_import_from_s3(
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'video_games_sales_units',
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'title,console,genre,publisher,developer,critic_score,total_sales,na_sales,jp_sales,pal_sales,other_sales,release_date',
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'DELIMITER ''|''',
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aws_commons.create_s3_uri('{data_source_bucket_name}', '{s3_file_name}', '{region}')
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); """
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response = client.execute_statement(
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resourceArn=aurora_serverless_db_cluster_arn,
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secretArn=secret_arn,
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sql=query3,
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database=database_name,
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)
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print("-----------------------------------------")
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print("Query: " + query3)
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print("Query response: " + str(response))
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# Delete the file from S3
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versions_response = s3_client.list_object_versions(
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Bucket=data_source_bucket_name, Prefix=s3_file_name
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)
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# Delete all versions of the object
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delete_markers = []
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if "Versions" in versions_response:
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for version in versions_response["Versions"]:
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delete_markers.append(
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{"Key": s3_file_name, "VersionId": version["VersionId"]}
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)
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# Delete all delete markers
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if "DeleteMarkers" in versions_response:
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for marker in versions_response["DeleteMarkers"]:
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delete_markers.append(
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{"Key": s3_file_name, "VersionId": marker["VersionId"]}
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)
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# Execute the delete operation if there are versions to delete
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if delete_markers:
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s3_client.delete_objects(
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Bucket=data_source_bucket_name,
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Delete={"Objects": delete_markers, "Quiet": False},
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)
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print("-----------------------------------------")
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print("Database created successfully!")
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except Exception as e:
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print(f"Error: {e}")
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