DA Caselaw Document Processing

AWS Lambda function for document processing (privacy, metadata stripping, etc.).

Local Development and Testing

1. Run Tests in Docker container (matches CI/CD)

# From project root
./run-tests.sh

This builds the test Docker image with all required system dependencies (including pdfcpu for PDF processing) and runs the complete test suite in the same environment used in CI/CD, ensuring consistency between local development and deployment.

2. Test Lambda Locally

You can test the Lambda locally using Docker:

script/server

will start the server and

script/upload document_cleanser_lambda/test-event.json

will submit a valid Lambda event payload to your handler (see AWS docs for examples).

Output will be at the same filename with .output.json appended.

3. Linting and Pre-commit

Install and run pre-commit hooks to ensure code quality:

pip install pre-commit
pre-commit install
pre-commit run --all-files

4. Updating dependencies with poetry

Dependencies should be managed by renovate (see renovate.json). If you need to update dependencies, run poetry update; see pyproject.toml

5. Development Guidelines

Ensure all tests pass locally before opening a PR via ./run-tests.sh

Follow repo and code style guidelines.

Document new environment variables or requirements in the README.

Deployment

Terraform configuration

Detailed CI documentation

• Changes to the main branch are deployed to staging.
• Creating a new Github version deploys to production.