📮 Redbox

Important

Incubation Project: This project is an incubation project; as such, we DON’T recommend using it in any critical use case. This project is in active development and a work in progress. This project may one day Graduate, in which case this disclaimer will be removed.

Note

The original streamlit-app has moved to its own repository https://github.com/i-dot-ai/redbox-copilot-streamlit.

Redbox is a retrieval augmented generation (RAG) app that uses GenAI to chat with and summarise civil service documents. It's designed to handle a variety of administrative sources, such as letters, briefings, minutes, and speech transcripts.

• Better retrieval. Redbox increases organisational memory by indexing documents
• Faster, accurate summarisation. Redbox can summarise reports read months ago, supplement them with current work, and produce a first draft that lets civil servants focus on what they do best

intro.mp4

Setup

Please refer to the DEVELOPER_SETUP.md for detailed instructions on setting up the project.

Codespace

For a quick start, you can use GitHub Codespaces to run the project in a cloud-based development environment. Click the button below to open the project in a new Codespace.

Development

Download and install pre-commit to benefit from pre-commit hooks

• pip install pre-commit
• pre-commit install

Testing

• Unit tests and QA run in CI
• At this time integration test(s) take 10+ mins to run so are triggered manually in CI
• Run make help to see all the available build activities.

Dependencies

This project is in two parts:

• A https://langchain-ai.github.io/langgraph/ based AI class library called redbox-core
• A django app to expose redbox-core to users over the web

The project is structured approximately like this:

redbox/
├── django_app
│  ├── redbox_app/
│  ├── static/
│  ├── tests/
│  ├── manage.py
│  ├── pyproject.toml
│  └── Dockerfile
├── redbox-core/
│  ├── redbox
│  │  ├── api/
│  │  ├── chains/
│  │  ├── graph/
│  │  ├── loader/
│  │  ├── models/
│  │  ├── retriever/
│  │  └── storage/
│  ├── tests/
│  ├── pyproject.toml
│  └── Dockerfile
├── docker-compose.yaml
├── pyproject.toml
├── Makefile
└── README.md

Configuration

System-wide, static, settings are defined Settings.py, these are set via environment file .env

Dynamic, per-request, settings are defined in AISettings.py, these are set within the django-app, and can be changed by an administrator. This includes the LLM to use which by default will be GPT-4o.

Contributing

We welcome contributions to this project. Please see the CONTRIBUTING.md file for more information.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Security

If you discover a security vulnerability within this project, please follow our Security Policy.

Troubleshooting

Error: Elasticsearch 137

ERROR: Elasticsearch exited unexpectedly, with exit code 137

This is caused by Elasticsearch not having enough memory.

Increase total memory available to 8gb.

colima down
colima start --memory 8

Error: Docker... no space left on device

docker: /var/lib/... no space left on device

This is caused by your own laptop being too full to create a new image.

Clear out old docker artefacts:

docker system prune --all --force

Frontend

To build the frontend assets, from the django_app/frontend/ folder run:

npm install

Then, for a one-off build run:

npx parcel build

Or, to watch for changes (e.g. if making CSS and JS changes):

npx parcel watch

On initial app setup you will need to run poetry run python manage.py collectstatic to copy them to the frontend folder from where runserver can serve them. Or you can run make build-django-static which combines the parcel build and collectstatic commands.

Testing

To run the web-component tests, from the frontend folder run:

npm run test

How to deploy

checkout the main branch of the following repos:

• https://github.com/i-dot-ai/redbox
• https://github.com/i-dot-ai/redbox-copilot-infra-config

Replace var.image_tag in infrastructure/aws/ecs.tf with the hash of the build you want deployed. Make sure that the hash corresponds to an image that exists in ECR, if in doubt build it via the build-action.

Login to aws via aws-vault exec admin-role and run the commands below from the redbox repo root

make tf_init env=<ENVIRONMENT>
make tf_apply env=<ENVIRONMENT>

where ENVIRONMENT is one of dev, preprod or prod

How to set up scheduled tasks

The django-app uses django-q to schedule task, this includes management tasks. Follow the instructions here https://django-q2.readthedocs.io/en/master/schedules.html#management-commands, i.e.

1. navigate the admin / Scheduled Tasks / Add Scheduled Task
2. name = delete old files
3. func = django.core.management.call_command
4. args = "delete_expired_data"
5. save

Vector databases

We are currently using ElasticSearch as our vector database.

We have also successfully deployed Redbox to OpenSearch Serverless but this support should be considered experimental at this stage.