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.
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
Then, you should set approperly the cdk context configuration file, cdk.context.json
.
For example,
{ "knowledge_base_data_source_name": "kb-data-source" }
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.
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
Delete the CloudFormation stack by running the below command.
(.venv) $ cdk destroy
cdk ls
list all stacks in the appcdk synth
emits the synthesized CloudFormation templatecdk deploy
deploy this stack to your default AWS account/regioncdk diff
compare deployed stack with current statecdk docs
open CDK documentation
Enjoy!
- 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).