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bedrock demo
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‎.gitignore‎

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@@ -34,3 +34,6 @@ doc/labguide/Korean/~$S Korea AI ML Immersion Day Hands-on PilHoKim 23rd Novembe
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genai/aws-gen-ai-kr/30_fine_tune/reranker-kr/dataset/msmarco/
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genai/aws-gen-ai-kr/30_fine_tune/reranker-kr/dataset/translated/
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genai/aws-gen-ai-kr/30_fine_tune/reranker-kr/model/
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genai/aws-gen-ai-kr/11_bedrock-with-opensearch/data/hanwhalife/
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genai/aws-gen-ai-kr/11_bedrock-with-opensearch/data/kt/

‎genai/genai-app-demo/01-prompt-engineering-demo/README.md‎

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# Bedrock Demo
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This repository is a Gen AI demo using claude 3. It contains 5 pre-built examples which help customers getting started with the Amazon Bedrock.
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![Pic 1.](architecture.png)
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## Prerequisites
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- Make sure you have [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html) installed and configured with an aws account you want to use.
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- To build container image, [Docker Engine](https://docs.docker.com/engine/install/) must be installed.
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### install cdk
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```shell
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npm install -g aws-cdk
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cdk --version
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```
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### setting AWS_PROFILE
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```shell
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export AWS_PROFILE=[The configuration profile for aws-cli]
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```
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## How to deploy
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### Step 1. Create virtualenv
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After cloning this git repository. You need to create virtualenv.
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```shell
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cd bedrock-demo
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python3 -m venv .venv
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source .venv/bin/activate
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```
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### Step 2. Install requirements
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```shell
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pip install -r requirements.txt
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```
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### Step 3. Deploy CDK
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Before running the cdk deploy command, make sure the Docker Engine is running in the background.
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```shell
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cdk deploy BedrockDemo
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```
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### Step 4. Set Environment Variables
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After successfully deploying the cdk, there are two variables in the output. One is the DNS name of the ALB and the other is the name of the S3 bucket. Copy these and make them environment variables.
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![Pic 2.](cdk_output.png)
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```shell
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export ALB_URL=[The DNS name of the ALB]
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export BUCKET_NAME=[The name of the S3 bucket]
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```
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### Step 5. Run streamlit
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```shell
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cd frontUI
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streamlit run app/Home.py
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```
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![Pic 3.](run_streamlit.png)
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The browser will open a demo page if streamlit is running successfully.
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## Clean up resources
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If you've created anything by yourself, you'll need to delete it.
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### Destroy Stack
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```shell
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cdk destroy BedrockDemo
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```

‎genai/genai-app-demo/01-prompt-engineering-demo/app.py‎

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#!/usr/bin/env python3
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import os
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import aws_cdk as cdk
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from bedrock_demo.config import app_config
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from bedrock_demo.deployment import BedrockDemo
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from cdk_nag import AwsSolutionsChecks, NagSuppressions
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app = cdk.App()
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conf = app_config['config']
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stack = BedrockDemo(
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app,
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'BedrockDemo',
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conf=conf,
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env=cdk.Environment(account=os.getenv('CDK_DEFAULT_ACCOUNT'), region=os.getenv('CDK_DEFAULT_REGION')),
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tags={'Project': 'bedrock-demo'}
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)
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cdk.Aspects.of(app).add(AwsSolutionsChecks())
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NagSuppressions.add_stack_suppressions(stack, [
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{'id': 'AwsSolutions-CB3', 'reason': 'allowed in this stack'},
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{'id': 'AwsSolutions-CB4', 'reason': 'allowed in this stack'},
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{'id': 'AwsSolutions-IAM4', 'reason': 'allowed in this stack'},
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{'id': 'AwsSolutions-IAM5', 'reason': 'allowed in this stack'},
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{'id': 'AwsSolutions-VPC7', 'reason': 'allowed in this stack'},
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{'id': 'AwsSolutions-ELB2', 'reason': 'allowed in this stack'},
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{'id': 'AwsSolutions-EC23', 'reason': 'allowed in this stack'},
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{'id': 'AwsSolutions-S1', 'reason': 'allowed in this stack'},
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{'id': 'AwsSolutions-S10', 'reason': 'allowed in this stack'},
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])
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app.synth()

‎genai/genai-app-demo/01-prompt-engineering-demo/architecture.png‎

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‎genai/genai-app-demo/01-prompt-engineering-demo/bedrock_demo/__init__.py‎

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‎genai/genai-app-demo/01-prompt-engineering-demo/bedrock_demo/applications/__init__.py‎

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from aws_cdk import (
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aws_iam as iam
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)
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"""
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"containers/prompt": "prompt-func",
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"containers/summarization": "summary-func",
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"containers/identification": "identify-func",
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"containers/comment": "comment-func",
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"containers/shortform": "shortform-func"
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"""
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ECR_LAMBDA_POLICY = iam.PolicyStatement(
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actions=[
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'ecr:BatchGetImage',
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'ecr:DeleteRepositoryPolicy',
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'ecr:GetDownloadUrlForLayer',
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'ecr:GetRepositoryPolicy',
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'ecr:SetRepositoryPolicy'
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],
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principals=[
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iam.ServicePrincipal("lambda.amazonaws.com")
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],
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effect=iam.Effect.ALLOW
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)

‎genai/genai-app-demo/01-prompt-engineering-demo/bedrock_demo/applications/comment_func.py‎

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from aws_cdk import (
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RemovalPolicy, Duration,
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aws_ec2 as ec2,
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aws_ecr as ecr,
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aws_lambda as lambda_,
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aws_ecr_assets as ecr_assets,
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aws_iam as iam,
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aws_elasticloadbalancingv2 as elbv2,
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aws_elasticloadbalancingv2_targets as targets,
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)
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from constructs import Construct
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from . import ECR_LAMBDA_POLICY
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from cdk_ecr_deployment import DockerImageName, ECRDeployment
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class CommentFunc(Construct):
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def __init__(self, scope: Construct, id_: str, vpc: ec2.Vpc, sg: ec2.SecurityGroup,
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role: iam.Role, listener: elbv2.ApplicationListener) -> None:
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super().__init__(scope, id_)
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# Comment Repo
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self.ecr_repo = ecr.Repository(
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self,
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'CommentRepo',
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repository_name='comment-func',
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removal_policy=RemovalPolicy.DESTROY,
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empty_on_delete=True
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)
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self.ecr_repo.add_to_resource_policy(
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statement=ECR_LAMBDA_POLICY
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)
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image_asset = ecr_assets.DockerImageAsset(
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self,
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'CommentFuncDockerImage',
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directory='sample_source/containers/comment/',
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platform=ecr_assets.Platform.LINUX_AMD64
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)
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image_asset.node.add_dependency(self.ecr_repo)
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ecr_deployment = ECRDeployment(
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self,
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'DeployCommentFuncImage',
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src=DockerImageName(image_asset.image_uri),
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dest=DockerImageName(
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f'{self.ecr_repo.repository_uri}:{image_asset.asset_hash}'
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)
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)
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ecr_deployment.node.add_dependency(image_asset)
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lambda_func = lambda_.DockerImageFunction(
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self,
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'CommentFunc',
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function_name='CommentFunc',
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code=lambda_.DockerImageCode.from_ecr(
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repository=self.ecr_repo,
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tag_or_digest=image_asset.asset_hash
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),
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memory_size=512,
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timeout=Duration.seconds(300),
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role=role,
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vpc=vpc,
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vpc_subnets=ec2.SubnetSelection(
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subnet_type=ec2.SubnetType.PRIVATE_WITH_EGRESS
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),
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security_groups=[sg],
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environment={
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'BEDROCK_REGION_NAME': 'us-west-2',
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}
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)
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lambda_func.node.add_dependency(ecr_deployment)
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target_group = listener.add_targets(
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'CommentFuncTarget',
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target_group_name='comment-func-tg',
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targets=[targets.LambdaTarget(lambda_func)],
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conditions=[
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elbv2.ListenerCondition.path_patterns(['/comment'])
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],
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priority=1
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)
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target_group.node.add_dependency(lambda_func)

‎genai/genai-app-demo/01-prompt-engineering-demo/bedrock_demo/applications/identify_func.py‎

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from aws_cdk import (
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RemovalPolicy, Duration,
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aws_ec2 as ec2,
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aws_ecr as ecr,
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aws_lambda as lambda_,
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aws_ecr_assets as ecr_assets,
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aws_iam as iam,
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aws_elasticloadbalancingv2 as elbv2,
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aws_elasticloadbalancingv2_targets as targets,
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)
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from constructs import Construct
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from . import ECR_LAMBDA_POLICY
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from cdk_ecr_deployment import DockerImageName, ECRDeployment
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class IdentifyFunc(Construct):
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def __init__(self, scope: Construct, id_: str, vpc: ec2.Vpc, sg: ec2.SecurityGroup,
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role: iam.Role, listener: elbv2.ApplicationListener, bucket_name: str) -> None:
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super().__init__(scope, id_)
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# Identify Repo
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self.ecr_repo = ecr.Repository(
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self,
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'IdentifyRepo',
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repository_name='identify-func',
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removal_policy=RemovalPolicy.DESTROY,
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empty_on_delete=True
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)
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self.ecr_repo.add_to_resource_policy(
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statement=ECR_LAMBDA_POLICY
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)
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image_asset = ecr_assets.DockerImageAsset(
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self,
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'IdentifyFuncDockerImage',
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directory='sample_source/containers/identification/',
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platform=ecr_assets.Platform.LINUX_AMD64
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)
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image_asset.node.add_dependency(self.ecr_repo)
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ecr_deployment = ECRDeployment(
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self,
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'DeployIdentifyFuncImage',
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src=DockerImageName(image_asset.image_uri),
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dest=DockerImageName(
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f'{self.ecr_repo.repository_uri}:{image_asset.asset_hash}'
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)
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)
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ecr_deployment.node.add_dependency(image_asset)
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lambda_func = lambda_.DockerImageFunction(
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self,
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'IdentifyFunc',
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function_name='IdentifyFunc',
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code=lambda_.DockerImageCode.from_ecr(
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repository=self.ecr_repo,
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tag_or_digest=image_asset.asset_hash
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),
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memory_size=512,
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timeout=Duration.seconds(300),
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role=role,
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vpc=vpc,
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vpc_subnets=ec2.SubnetSelection(
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subnet_type=ec2.SubnetType.PRIVATE_WITH_EGRESS
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),
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security_groups=[sg],
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environment={
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'BEDROCK_REGION_NAME': 'us-west-2',
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'BUCKET_NAME': bucket_name
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}
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)
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lambda_func.node.add_dependency(ecr_deployment)
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target_group = listener.add_targets(
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'IdentifyFuncTarget',
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target_group_name='identify-func-tg',
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targets=[targets.LambdaTarget(lambda_func)],
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conditions=[
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elbv2.ListenerCondition.path_patterns(['/identify'])
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],
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priority=2
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)
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target_group.node.add_dependency(lambda_func)

‎genai/genai-app-demo/01-prompt-engineering-demo/bedrock_demo/applications/prompt_func.py‎

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from aws_cdk import (
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RemovalPolicy, Duration,
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aws_ec2 as ec2,
4+
aws_ecr as ecr,
5+
aws_lambda as lambda_,
6+
aws_ecr_assets as ecr_assets,
7+
aws_iam as iam,
8+
aws_elasticloadbalancingv2 as elbv2,
9+
aws_elasticloadbalancingv2_targets as targets,
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)
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from constructs import Construct
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from . import ECR_LAMBDA_POLICY
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from cdk_ecr_deployment import DockerImageName, ECRDeployment
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class PromptFunc(Construct):
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def __init__(self, scope: Construct, id_: str, vpc: ec2.Vpc, sg: ec2.SecurityGroup,
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role: iam.Role, listener: elbv2.ApplicationListener) -> None:
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super().__init__(scope, id_)
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# Prompt Repo
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self.ecr_repo = ecr.Repository(
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self,
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'PromptRepo',
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repository_name='prompt-func',
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removal_policy=RemovalPolicy.DESTROY,
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empty_on_delete=True
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)
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self.ecr_repo.add_to_resource_policy(
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statement=ECR_LAMBDA_POLICY
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)
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image_asset = ecr_assets.DockerImageAsset(
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self,
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'PromptFuncDockerImage',
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directory='sample_source/containers/prompt/',
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platform=ecr_assets.Platform.LINUX_AMD64
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)
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image_asset.node.add_dependency(self.ecr_repo)
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ecr_deployment = ECRDeployment(
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self,
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'DeployPromptFuncImage',
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src=DockerImageName(image_asset.image_uri),
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dest=DockerImageName(
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f'{self.ecr_repo.repository_uri}:{image_asset.asset_hash}'
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)
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)
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ecr_deployment.node.add_dependency(image_asset)
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lambda_func = lambda_.DockerImageFunction(
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self,
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'PromptFunc',
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function_name='PromptFunc',
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code=lambda_.DockerImageCode.from_ecr(
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repository=self.ecr_repo,
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tag_or_digest=image_asset.asset_hash
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),
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memory_size=512,
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timeout=Duration.seconds(300),
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role=role,
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vpc=vpc,
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vpc_subnets=ec2.SubnetSelection(
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subnet_type=ec2.SubnetType.PRIVATE_WITH_EGRESS
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),
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security_groups=[sg],
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environment={
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'BEDROCK_REGION_NAME': 'us-west-2',
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}
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)
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lambda_func.node.add_dependency(ecr_deployment)
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target_group = listener.add_targets(
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'PromptFuncTarget',
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target_group_name='prompt-func-tg',
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targets=[targets.LambdaTarget(lambda_func)],
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conditions=[
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elbv2.ListenerCondition.path_patterns(['/prompt'])
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],
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priority=3
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)
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target_group.node.add_dependency(lambda_func)

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