A project to learn more about generative AI
This particular project demonstrates the ability to define and deploy an Amazon Bedrock Agent that can use a single Lambda Function as a Tool to check the danger-level of routes between starsystems in the MMORPG EVE Online's in-game universe. To answer these questions, the Tool uses the EVE Swagger Interface (ESI) for REST calls against the game universe. You can either invoke the Lambda directly by running the code locally (as explained below), or you can deploy using Terraform and chat with the Agent in the AWS Console (also explained below).
To run the code locally, use a Python virtual environment:
# Start in the repo root
python3 -m venv venv
source venv/bin/activate
cd ai_agents
pipenv sync --dev
python3 -m handlers.check_route_handler
The package uses Terraform to manage its cloud deployments. You'll need valid AWS Credentials in your keyring (check using aws sts get-caller-identity).
cd ai_agents
./package.sh
terraform init
terraform plan
terraform apply
You can then run the Lambda manually in the AWS console using test events.
pipenv is used to managed dependencies within the project. The Pipefile and Pipefile.lock handle the local environment. You can add dependencies like so:
pipenv install boto3
This updates the Pipfile/Pipfile.lock with the new dependency. To create a local copy of the dependencies, such as for bundling a distribution, you can use pip like so:
pipenv requirements > requirements.txt
python3 -m pip install -r requirements.txt -t ./package --upgrade
zip -r9 ai_agents.zip tools/ package/