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Expand Up @@ -30,6 +30,8 @@ This construct library facilitates the deployment of Knowledge Bases, Bedrock Ag

- [API](#api)
- [Knowledge Bases](#knowledge-bases)
- [Vector Knowledge Base](#vector-knowledge-base)
- [Kendra Knowledge Base](#kendra-knowledge-base)
- [Agents](#agents)
- [Guardrails](#bedrock-guardrails)
- [Prompt management](#prompt-management)
Expand All @@ -43,15 +45,15 @@ See the [API documentation](../../../apidocs/namespaces/bedrock/README.md).

Amazon Bedrock Knowledge Bases enable you to provide foundation models and agents with contextual information from your company’s private data sources. This enhances the relevance, accuracy, and customization of their responses.

### Create a Knowledge Base
### Vector Knowledge Base

A vector index on a vector store is required to create a Knowledge Base. This construct currently supports [Amazon OpenSearch Serverless](../opensearchserverless), [Amazon RDS Aurora PostgreSQL](../amazonaurora/), [Pinecone](../pinecone/) . By default, this resource will create an OpenSearch Serverless vector collection and index for each Knowledge Base you create, but you can provide an existing collection and/or index to have more control. For other resources you need to have the vector stores already created and credentials stored in AWS Secrets Manager. For Aurora, the construct provides an option to create a default `AmazonAuroraDefaultVectorStore` construct that will provision the vector store backed by Amazon Aurora for you. To learn more you can read [here](../amazonaurora/README.md).
#### Create a vector Knowledge Base

The resource accepts an `instruction` prop that is provided to any Bedrock Agent it is associated with so the agent can decide when to query the Knowledge Base.
A vector index on a vector store is required to create a vector Knowledge Base. This construct currently supports [Amazon OpenSearch Serverless](../opensearchserverless), [Amazon RDS Aurora PostgreSQL](../amazonaurora/), [Pinecone](../pinecone/) . By default, this resource will create an OpenSearch Serverless vector collection and index for each Knowledge Base you create, but you can provide an existing collection and/or index to have more control. For other resources you need to have the vector stores already created and credentials stored in AWS Secrets Manager. For Aurora, the construct provides an option to create a default `AmazonAuroraDefaultVectorStore` construct that will provision the vector store backed by Amazon Aurora for you. To learn more you can read [here](../amazonaurora/README.md).

Amazon Bedrock Knowledge Bases currently only supports S3 as a data source. The `S3DataSource` resource is used to configure how the Knowledge Base handles the data source.
The resource accepts an `instruction` prop that is provided to any Bedrock Agent it is associated with so the agent can decide when to query the Knowledge Base.

## Knowledge Base Properties
#### Vector Knowledge Base Properties


| Name | Type | Required | Description |
Expand All @@ -68,7 +70,7 @@ Amazon Bedrock Knowledge Bases currently only supports S3 as a data source. The
| knowledgeBaseState | string | No | Specifies whether to use the knowledge base or not when sending an InvokeAgent request |
| tags | Record<string, string> | No | Tag (KEY-VALUE) bedrock agent resource |

## Initializer
### Initializer

Example of `OpenSearch Serverless`:

Expand All @@ -78,7 +80,7 @@ TypeScript
import * as s3 from 'aws-cdk-lib/aws-s3';
import { bedrock } from '@cdklabs/generative-ai-cdk-constructs';

const kb = new bedrock.KnowledgeBase(this, 'KnowledgeBase', {
const kb = new bedrock.VectorKnowledgeBase(this, 'KnowledgeBase', {
embeddingsModel: bedrock.BedrockFoundationModel.TITAN_EMBED_TEXT_V1,
instruction: 'Use this knowledge base to answer questions about books. ' + 'It contains the full text of novels.',
});
Expand Down Expand Up @@ -107,7 +109,7 @@ from cdklabs.generative_ai_cdk_constructs import (
bedrock
)

kb = bedrock.KnowledgeBase(self, 'KnowledgeBase',
kb = bedrock.VectorKnowledgeBase(self, 'KnowledgeBase',
embeddings_model= bedrock.BedrockFoundationModel.TITAN_EMBED_TEXT_V1,
instruction= 'Use this knowledge base to answer questions about books. ' +
'It contains the full text of novels.'
Expand Down Expand Up @@ -138,7 +140,7 @@ const auroraDb = new amazonaurora.AmazonAuroraVectorStore(stack, 'AuroraDefaultV
embeddingsModelVectorDimension: embeddingsModelVectorDimension,
});

const kb = new bedrock.KnowledgeBase(this, 'KnowledgeBase', {
const kb = new bedrock.VectorKnowledgeBase(this, 'KnowledgeBase', {
vectorStore: auroraDb,
embeddingsModelVectorDimension: embeddingsModelVectorDimension,
instruction: 'Use this knowledge base to answer questions about books. ' + 'It contains the full text of novels.',
Expand Down Expand Up @@ -176,7 +178,7 @@ aurora_db = amazonaurora.AmazonAuroraVectorStore(self, 'AuroraDefaultVectorStore
embeddings_model_vector_dimension=embeddings_model_vector_dimension
)

kb = bedrock.KnowledgeBase(self, 'KnowledgeBase',
kb = bedrock.VectorKnowledgeBase(self, 'KnowledgeBase',
vector_store= aurora_db,
embeddings_model_vector_dimension=embeddings_model_vector_dimension,
instruction= 'Use this knowledge base to answer questions about books. ' +
Expand Down Expand Up @@ -231,7 +233,7 @@ const auroraDb = aurora.AmazonAuroraVectorStore.fromExistingAuroraVectorStore(st
),
});

const kb = new bedrock.KnowledgeBase(this, "KnowledgeBase", {
const kb = new bedrock.VectorKnowledgeBase(this, "KnowledgeBase", {
vectorStore: auroraDb,
embeddingsModel: bedrock.BedrockFoundationModel.COHERE_EMBED_ENGLISH_V3,
instruction:
Expand Down Expand Up @@ -292,7 +294,7 @@ aurora_db = amazonaurora.AmazonAuroraVectorStore.from_existing_aurora_vector_sto
)
)

kb = bedrock.KnowledgeBase(self, 'KnowledgeBase',
kb = bedrock.VectorKnowledgeBase(self, 'KnowledgeBase',
vector_store= aurora_db,
embeddings_model= bedrock.BedrockFoundationModel.COHERE_EMBED_ENGLISH_V3,
instruction= 'Use this knowledge base to answer questions about books. ' +
Expand Down Expand Up @@ -324,7 +326,7 @@ const pineconeds = new pinecone.PineconeVectorStore({
metadataField: 'metadata',
});

const kb = new bedrock.KnowledgeBase(this, 'KnowledgeBase', {
const kb = new bedrock.VectorKnowledgeBase(this, 'KnowledgeBase', {
vectorStore: pineconeds,
embeddingsModel: bedrock.BedrockFoundationModel.TITAN_EMBED_TEXT_V1,
instruction: 'Use this knowledge base to answer questions about books. ' + 'It contains the full text of novels.',
Expand Down Expand Up @@ -359,7 +361,7 @@ pineconevs = pinecone.PineconeVectorStore(
metadata_field='metadata'
)

kb = bedrock.KnowledgeBase(self, 'KnowledgeBase',
kb = bedrock.VectorKnowledgeBase(self, 'KnowledgeBase',
vector_store= pineconevs,
embeddings_model= bedrock.BedrockFoundationModel.COHERE_EMBED_ENGLISH_V3,
instruction= 'Use this knowledge base to answer questions about books. ' +
Expand All @@ -376,7 +378,7 @@ bedrock.S3DataSource(self, 'DataSource',
)
```

#### Knowledge Base - Data Sources
#### Vector Knowledge Base - Data Sources

Data sources are the various repositories or systems from which information is extracted and ingested into the
knowledge base. These sources provide the raw content that will be processed, indexed, and made available for
Expand All @@ -401,7 +403,7 @@ Typescript
const app = new cdk.App();
const stack = new cdk.Stack(app, 'aws-cdk-bedrock-data-sources-integ-test');

const kb = new KnowledgeBase(stack, 'MyKnowledgeBase', {
const kb = new VectorKnowledgeBase(stack, 'MyKnowledgeBase', {
name: 'MyKnowledgeBase',
embeddingsModel: BedrockFoundationModel.COHERE_EMBED_MULTILINGUAL_V3,
});
Expand Down Expand Up @@ -512,7 +514,7 @@ class PythonTestStack(Stack):
def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:
super().__init__(scope, construct_id, **kwargs)

kb = bedrock.KnowledgeBase(self, 'MyKnowledgeBase',
kb = bedrock.VectorKnowledgeBase(self, 'MyKnowledgeBase',
embeddings_model= bedrock.BedrockFoundationModel.COHERE_EMBED_MULTILINGUAL_V3,
)

Expand Down Expand Up @@ -604,7 +606,7 @@ class PythonTestStack(Stack):

```

#### Knowledge Base - Chunking Strategies
#### Vector Knowledge Base - Chunking Strategies

- **Default Chunking**: Applies Fixed Chunking with the default chunk size of 300 tokens and 20% overlap.

Expand Down Expand Up @@ -731,7 +733,7 @@ class PythonTestStack(Stack):
ChunkingStrategy.NONE
```

#### Knowledge Base - Parsing Strategy
#### Vector Knowledge Base - Parsing Strategy

A parsing strategy in Amazon Bedrock is a configuration that determines how the service
processes and interprets the contents of a document. It involves converting the document's
Expand Down Expand Up @@ -789,6 +791,88 @@ CustomTransformation.lambda_(
)
```

### Kendra Knowledge Base

#### Create a Kendra Knowledge Base

With Amazon Bedrock Knowledge Bases, you can build a knowledge base from an Amazon Kendra GenAI index to create more sophisticated and accurate Retrieval Augmented Generation (RAG)-powered digital assistants. By combining an Amazon Kendra GenAI index with Amazon Bedrock Knowledge Bases, you can:

- Reuse your indexed content across multiple Amazon Bedrock applications without rebuilding indexes or re-ingesting data.
- Leverage the advanced GenAI capabilities of Amazon Bedrock while benefiting from the high-accuracy information retrieval of Amazon Kendra.
- Customize your digital assistant's behavior using the tools of Amazon Bedrock while maintaining the semantic accuracy of an Amazon Kendra GenAI index.

#### Kendra Knowledge Base properties

| Name | Type | Required | Description |
|------|------|----------|-------------|
| kendraIndex | IKendraGenAiIndex | Yes | The Kendra Index to use for the knowledge base. |
| name | string | No | The name of the knowledge base. If not provided, a name will be auto-generated. |
| description | string | No | Description of the knowledge base. |
| instruction | string | No | Instructions for the knowledge base. |
| existingRole | iam.IRole | No | An existing IAM role to use for the knowledge base. If not provided, a new role will be created. |

#### Initializer


TypeScript

```ts
import * as s3 from 'aws-cdk-lib/aws-s3';
import { bedrock, kendra } from '@cdklabs/generative-ai-cdk-constructs';

const cmk = new kms.Key(stack, 'cmk', {});

// you can create a new index using the api below
const index = new kendra.KendraGenAiIndex(this, 'index', {
name: 'kendra-index-cdk',
kmsKey: cmk,
documentCapacityUnits: 1, // 40K documents
queryCapacityUnits: 1, // 0.2 QPS
});

// or import an existing one

const index = kendra.KendraGenAiIndex.fromAttrs(
indexId: 'myindex',
role: myRole
);

new bedrock.KendraKnowledgeBase(this, 'kb', {
name: 'kendra-kb-cdk',
kendraIndex: index,
});
```

Python

```py
from aws_cdk import aws_kms as kms
from cdklabs.generative_ai_cdk_constructs import bedrock, kendra

# Create a KMS key
cmk = kms.Key(stack, 'cmk')

# Create a new Kendra index
index = kendra.KendraGenAiIndex(self, 'index',
name='kendra-index-cdk',
kms_key=cmk,
document_capacity_units=1, # 40K documents
query_capacity_units=1 # 0.2 QPS
)

# Or import an existing index
index = kendra.KendraGenAiIndex.from_attrs(
index_id='myindex',
role=my_role
)

# Create a Kendra Knowledge Base
kb = bedrock.KendraKnowledgeBase(self, 'kb',
name='kendra-kb-cdk',
kendra_index=index
)
```

## Agents

Amazon Bedrock Agents allow generative AI applications to automate complex, multistep tasks by seamlessly integrating with your company’s systems, APIs, and data sources.
Expand Down

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