generative-ai-cdk-constructs

Knowledge Bases for Amazon Bedrock CDK Python project!

This is a Knowledge Base for Amazon Bedrock project for CDK development with Python.

The cdk.json file tells the CDK Toolkit how to execute your app.

This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .venv directory. To create the virtualenv it assumes that there is a python3 (or python for Windows) executable in your path with access to the venv package. If for any reason the automatic creation of the virtualenv fails, you can create the virtualenv manually.

To manually create a virtualenv on MacOS and Linux:

$ python3 -m venv .venv

After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.

$ source .venv/bin/activate

If you are a Windows platform, you would activate the virtualenv like this:

% .venv\Scripts\activate.bat

Once the virtualenv is activated, you can install the required dependencies.

(.venv) $ pip install -r requirements.txt

To add additional dependencies, for example other CDK libraries, just add them to your setup.py file and rerun the pip install -r requirements.txt command.

Prerequisites

Before deployment, you need to make sure docker daemon is running. Otherwise you will encounter the following errors:

ERROR: Cannot connect to the Docker daemon at unix://$HOME/.docker/run/docker.sock. Is the docker daemon running?
jsii.errors.JavaScriptError:
  Error: docker exited with status 1

Set up cdk.context.json

Then, you should set approperly the cdk context configuration file, cdk.context.json.

For example,

{
  "knowledge_base_data_source_name": "kb-data-source"
}

⚠️ It would be better NOT TO USE metadata for metadata_field in OpenSearch serverless field mapping. The popular LLM application frameworks like LangChain, LlamaIndex use metadata with data type other than text for OpenSearch field mapping. So to avoid conflicts when using the popular LLM frameworks, be careful to use metadata field name.

Deploy

At this point you can now synthesize the CloudFormation template for this code.

(.venv) $ export CDK_DEFAULT_ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
(.venv) $ export CDK_DEFAULT_REGION=$(aws configure get region)
(.venv) $ cdk synth

Use cdk deploy command to create the stack shown above.

(.venv) cdk deploy

Clean Up

Delete the CloudFormation stack by running the below command.

(.venv) $ cdk destroy

Useful commands

• cdk ls list all stacks in the app
• cdk synth emits the synthesized CloudFormation template
• cdk deploy deploy this stack to your default AWS account/region
• cdk diff compare deployed stack with current state
• cdk docs open CDK documentation

Enjoy!

References

• AWS Generative AI CDK Constructs
• Announcing Generative AI CDK Constructs (2024-01-31)
• (Video) AWS re:Invent 2023 - Use RAG to improve responses in generative AI applications (AIM336)
• Knowledge Bases now delivers fully managed RAG experience in Amazon Bedrock (2023-11-28)
• Knowledge base for Amazon Bedrock Developer Guide
• LangChain - AmazonKnowledgeBasesRetriever
• Building with Amazon Bedrock and LangChain - Hands-on labs using LangChain to build generative AI prototypes with Amazon Bedrock.
• Amazon Bedrock Workshop - Hands-on labs using Amazon Bedrock APIs, SDKs, and open-source software, such as LangChain and FAISS, to implement the most common Generative AI usage patterns (e.g., summarizing text, answering questions, building chatbots, creating images, and generating code).