Vector-Based Semantic Search using Amazon OpenSearch Service

This is a Vector-based Semantic Text Search project.

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

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

(.venv) $ cdk synth \
              --parameters SageMakerNotebookInstanceType="your-instance-type" \
              --parameters OpenSearchDomainName="your-opensearch-domain-name" \
              --parameters EC2KeyPairName="your-ec2-key-pair-name"

Use cdk deploy command to create the stack shown above.

(.venv) $ cdk deploy \
              --parameters SageMakerNotebookInstanceType="your-instance-type" \
              --parameters OpenSearchDomainName="your-opensearch-domain-name" \
              --parameters EC2KeyPairName="your-ec2-key-pair-name"

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.

Usage

After cdk deploy completed, open the Jupyter notebook on your Amazon SageMaker notebook instance. Then, upload the *.ipynb files into your SageMaker notebook that you use to complete the rest of the lab.

*.ipynb file	kernel spec
semantic-text-search-tf.ipynb	conda_tensorflow_p36
semantic-text-search-tf2.ipynb	conda_tensorflow2_p36

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

• Try semantic search with the Amazon OpenSearch Service vector engine (2023-08-21)
• Text similarity search with vector fields
• Vector-Based Semantic Search using Elasticsearch
• Universal Sentence Encoder (TF2.0 Version 4)
• OpenSearch k-NN