chore(doc): add python code snippets for cdk-lib folder (#369)

chore(doc): add python code snippets for cdk-lib

Author: krokoko

README.md

@@ -108,6 +108,7 @@ The official samples repository https://github.com/aws-samples/generative-ai-cdk
 | [amazon-bedrock-rag](https://github.com/aws-samples/amazon-bedrock-rag) | Fully managed RAG solution using Knowledge Bases for Amazon Bedrock. |
 | [Amazon Bedrock Knowledge Bases with Private Data](https://blog.serverlessadvocate.com/amazon-bedrock-knowledge-bases-with-private-data-7685d04ef396) | Blog post and associated code sample demonstrating how to integrate Knowledge Bases into Amazon Bedrock to provide foundational models with contextual data from private data sources. |
 | [Automating tasks using Amazon Bedrock Agents and AI](https://blog.serverlessadvocate.com/automating-tasks-using-amazon-bedrock-agents-and-ai-4b6fb8856589) | Blog post and associated code sample demonstrating how to deploy an Amazon Bedrock Agent and a Knowledge Base through a hotel and spa use case. |
+| [Agents for Amazon Bedrock - Powertools for AWS Lambda (Python)](https://docs.powertools.aws.dev/lambda/python/latest/core/event_handler/bedrock_agents/#using-aws-cloud-developer-kit-cdk) | Create Agents for Amazon Bedrock using event handlers and auto generation of OpenAPI schemas. |
 
 # Contributors
 

apidocs/interfaces/amazonaurora.AmazonAuroraDefaultVectorStoreProps.md

@@ -16,8 +16,8 @@
 
 • `Readonly` **embeddingsModelVectorDimension**: `number`
 
-The embeddings model for the knowledge base.
-Must be identical as in the KnowledgeBaseV2 construct.
+The embeddings model vector dimension for the knowledge base.
+Must be identical as in the KnowledgeBase construct.
 This is due to the factor that the embeddings models
 have different vector dimensions and this construct
 needs to know the vector dimensions to create the vector

src/cdk-lib/amazonaurora/README.md

@@ -16,6 +16,7 @@
 | **Language**     | **Package**        |
 |:-------------|-----------------|
 |![Typescript Logo](https://docs.aws.amazon.com/cdk/api/latest/img/typescript32.png) TypeScript|`@cdklabs/generative-ai-cdk-constructs`|
+|![Python Logo](https://docs.aws.amazon.com/cdk/api/latest/img/python32.png) Python|`cdklabs.generative_ai_cdk_constructs`|
 
 This construct library provides a class that defines a `AmazonAuroraVectorStore` class for an existing Amazon Aurora to be used for a vector store for a Knowledge Base. Additionally, it provides an `AmazonAuroraDefaultVectorStore` L3 resource that creates a VPC with 3 subnets (public private with NAT Gateway, private without NAT Gateway), with the Amazon Aurora Serverless V2 Cluster. The cluster has 1 writer/reader instance with PostgreSQL 15.5 version (min capacity 0.5, max capacity 4). Lambda custom resource executes required pgvector and Amazon Bedrock Knowledge Base SQL queries (see more [here](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL.VectorDB.html)) against Aurora cluster during deployment. The secret containing databases credentials is being deployed and securely stored in AWS Secrets Manager. You must specify the same embeddings model that you are going to use in KnowledgeBase construct.
 
@@ -31,6 +32,8 @@ See the [API documentation](../../../apidocs/modules/amazonaurora.md).
 
 ## Amazon Aurora Vector Store
 
+TypeScript
+
 ```ts
 import { amazonaurora } from '@cdklabs/generative-ai-cdk-constructs';
 
@@ -47,12 +50,48 @@ new amazonaurora.AmazonAuroraVectoStore(
   });
 ```
 
+Python
+
+```python
+
+from cdklabs.generative_ai_cdk_constructs import (
+    amazonaurora
+)
+
+aurora = amazonaurora.AmazonAuroraVectorStore(
+            credentials_secret_arn='arn:aws:secretsmanager:your-region:123456789876:secret:your-key-name',
+            database_name='bedrock_vector_db',
+            metadata_field='metadata',
+            primary_key_field='id',
+            resource_arn='arn:aws:rds:your-region:123456789876:cluster:aurora-cluster-manual',
+            table_name='bedrock_integration.bedrock_kb',
+            text_field='chunks',
+            vector_field='embedding',
+        )
+```
+
 ## Amazon Aurora Default Vector Store
 
+TypeScript
+
 ```ts
 import { amazonaurora } from '@cdklabs/generative-ai-cdk-constructs';
 
 new amazonaurora.AmazonAuroraDefaultVectorStore(stack, 'AuroraDefaultVectorStore', {
   embeddingsModel: BedrockFoundationModel.COHERE_EMBED_ENGLISH_V3.vectorDimensions!,
 });
 ```
+
+Python
+```python
+from cdklabs.generative_ai_cdk_constructs import (
+    amazonaurora,
+    bedrock
+)
+
+dimension = bedrock.BedrockFoundationModel.COHERE_EMBED_ENGLISH_V3.vector_dimensions
+
+aurora = amazonaurora.AmazonAuroraDefaultVectorStore(self, 'AuroraDefaultVectorStore',
+    embeddings_model_vector_dimension=dimension
+)
+```

src/cdk-lib/amazonaurora/aurora-default-vector-store.ts

@@ -25,8 +25,8 @@ import { buildVpc, DefaultVpcProps } from '../../common/helpers/vpc-helper';
 
 export interface AmazonAuroraDefaultVectorStoreProps {
   /**
-   * The embeddings model for the knowledge base.
-   * Must be identical as in the KnowledgeBaseV2 construct.
+   * The embeddings model vector dimension for the knowledge base.
+   * Must be identical as in the KnowledgeBase construct.
    * This is due to the factor that the embeddings models
    * have different vector dimensions and this construct
    * needs to know the vector dimensions to create the vector