mirror of
https://github.com/awslabs/amazon-bedrock-agentcore-samples.git
synced 2025-09-08 20:50:46 +00:00
05ada44c3f
* updated the cognito setup, gateway creation using agentcore sdk * added the config directory with test file * updated the automation scripts and simplified the approach to install and test it * updated the tool description in the target creation and updated readme file * added agentcore observability for agentcore gateway * updated architecture diagram * Update observability section in README.md with more concise information * removed old folder directory * updated the tool description in the targets
107 lines
4.8 KiB
Python
107 lines
4.8 KiB
Python
import boto3
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import os
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agentcore_client = boto3.client(
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'bedrock-agentcore-control',
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region_name=os.getenv('AWS_REGION', 'us-west-2'),
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endpoint_url=os.getenv('ENDPOINT_URL')
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)
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lambda_target_config = {
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"mcp": {
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"lambda": {
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"lambdaArn": os.getenv('LAMBDA_ARN'),
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"toolSchema": {
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"inlinePayload": [
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{
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"name": "explain_query",
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"description": "Analyzes and explains the execution plan for a SQL query to help optimize database performance. Provide the database environment (dev/prod) and the SQL query to analyze. Use action_type default value as explain_query.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"environment": {
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"type": "string"
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},
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"action_type": {
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"type": "string",
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"description": "The type of action to perform. Use 'explain_query' for this tool."
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},
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"query": {
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"type": "string"
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}
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},
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"required": ["environment","action_type","query"]
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}
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},
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{
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"name": "extract_ddl",
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"description": "Extracts the DDL (Data Definition Language) for a database object. Provide the environment (dev/prod), object_type (table, view, function, etc.), object_name, and object_schema to get the creation script. Use action_type default value as extract_ddl.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"environment": {
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"type": "string"
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},
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"action_type": {
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"type": "string",
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"description": "The type of action to perform. Use 'extract_ddl' for this tool."
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},
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"object_type": {
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"type": "string"
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},
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"object_name": {
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"type": "string"
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},
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"object_schema": {
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"type": "string"
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}
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},
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"required": ["environment","action_type","object_type","object_name","object_schema"]
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}
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},
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{
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"name": "execute_query",
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"description": "Executes a read-only SQL query safely and returns the results with performance metrics. Provide the environment (dev/prod) and the SQL query to execute. Use action_type default value as execute_query.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"environment": {
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"type": "string"
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},
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"action_type": {
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"type": "string",
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"description": "The type of action to perform. Use 'execute_query' for this tool."
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},
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"query": {
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"type": "string"
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}
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},
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"required": ["environment","action_type","query"]
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}
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}
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]
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}
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}
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}
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}
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credential_config = [
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{
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"credentialProviderType" : "GATEWAY_IAM_ROLE"
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}
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]
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response = agentcore_client.create_gateway_target(
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gatewayIdentifier=os.getenv('GATEWAY_IDENTIFIER'),
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name=os.getenv('TARGET_NAME', 'pg-analyze-db-performance'),
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description=os.getenv('TARGET_DESCRIPTION', 'PostgreSQL database performance analysis tool with query execution plan analysis, DDL extraction, and safe read-only query execution capabilities'),
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credentialProviderConfigurations=credential_config,
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targetConfiguration=lambda_target_config)
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target_id = response['targetId']
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print(f"Target ID: {target_id}")
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# Create target_config.env file
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with open('target_config.env', 'w') as f:
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f.write(f"TARGET_ID={target_id}\n")
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