brummiesteven / caddy-chatbot-az

Caddy is an AI powered co-pilot for customer service functions everywhere.

Caddy Chatbot

Caddy is an LLM powered co-pilot for customer service functions everywhere. This contains the logic for the chatbot and responses, and also relies on storage of the data using caddy-scraper.

How to Run

This version is intended for deployment on serverless infrastructure, and will ideally use Docker and AWS CLI.

Environment Management

We recommend using Poetry for managing dependencies.

To create your virtual environment, run

$ 	poetry install
$ 	poetry shell

AWS CLI

You will require AWS CLI, either installed directly or through pip.

To confirm install, run

$ aws --version

To configure, run

$ aws configure

Use vscode on github (codespace)

To develop in codespaces, ensure you define your environment variables through Github settings.

If you are using windows or do not want to install vscode on your machine, you can click on the badge above to spin up a codespace environment.

Developing with Local DynamoDB

To explore the connection to DynamoDB, I have attached the docker-compose file to spin up a local DynamoDB. This will have to be span up before using the relevant notebook.

$ docker compose up

Testing

Running tests on platform agnostic Caddy components with pytest. Tests are stored in tests/caddy_components, and can be invoked by running the below:

$ docker compose up

$ pytest

Deployment

We'll use AWS CLI and Docker to create and deploy all the relevant resources.

Build and push the container image to your elastic container registry

$ aws ecr get-login-password | docker login --username AWS --password-stdin "INSERT_ELASTIC_CONTAINER_REGISTRY_ENDPOINT"

$ docker build -f ./caddy_chatbot/Dockerfile -t "INSERT_CONTAINER_REPO_ID":"IMAGE_TAG" .

$ docker tag "INSERT_CONTAINER_REPO_ID":"IMAGE_TAG" "INSERT_ELASTIC_CONTAINER_REGISTRY_ENDPOINT"/"INSERT_CONTAINER_REPO_ID":"IMAGE_TAG"

$ docker push "INSERT_ELASTIC_CONTAINER_REGISTRY_ENDPOINT"/"INSERT_CONTAINER_REPO_ID":"IMAGE_TAG"

Once the build is complete, you can deploy the stack with

$ aws cloudformation deploy --template-file infra/template.yaml --stack-name "INSERT_CUSTOM_STACK_NAME" --capabilities CAPABILITY_NAMED_IAM --parameter-overrides StageName="INSERT_STAGE_NAME"  MessageTableName="INSERT_CADDY_MESSAGE_TABLE_NAME" ResponsesTableName="INSERT_CADDY_RESPONSES_TABLE_NAME" UserTableName="INSERT_CADDY_USERS_TABLE_NAME" OfficesTableName="INSERT_CADDY_OFFICES_TABLE_NAME" EvaluationTableName="INSERT_CADDY_EVALUATION_TABLE_NAME" OpensearchUrl="INSERT_OPENSEARCH_URL*" CaddyServiceAccountSecretArn="INSERT_CADDY_GOOGLE_CHAT_SERVICE_ACCOUNT_SECRET_ARN" CaddySupervisorServiceAccountSecretArn="INSERT_CADDY_SUPERVISOR_GOOGLE_CHAT_SERVICE_ACCOUNT_SECRET_ARN" CaddyImage="INSERT_ELASTIC_CONTAINER_REGISTRY_ENDPOINT"/"INSERT_CONTAINER_REPO_ID":"IMAGE_TAG" CaddySupervisorGoogleCloudProject="INSERT_CADDY_SUPERVISOR_GOOGLE_CHAT_PROJECT_ID" CaddyGoogleCloudProject="INSERT_CADDY_GOOGLE_CHAT_PROJECT_ID" LLM="INSERT_BEDROCK_LLM_MODEL" DomainName="INSERT_DOMAIN_NAME_FOR_SSL_CERT" #pragma: allowlist secret

• OpensearchUrl: Opensearch is not built into the template yet this must be configured seperately
• CaddyServiceAccountSecretArn: Creation of Caddy Google Chat service account in Secret Manager is not built into the template yet and must be created manually
• CaddySupervisorServiceAccountSecretArn: Creation of Caddy Supervisor Google Chat service account in Secret Manager is not built into the template yet and must be created manually

For ease of deletion, you can remove all the created resources with

$ aws cloudformation delete-stack --stack-name "INSERT_CUSTOM_STACK_NAME"