Consult is a machine learning and LLM-powered tool to automate the processing of public consultations.
Important
Incubation Project: This project is an incubation project; as such, we don't recommend using this for critical use cases yet. We are currently in a research stage, trialling the tool for case studies across the Civil Service. If you are a civil servant and wish to take part in our research stage, please contact us at [email protected].
- PostgreSQL (
brew install postgresql) - redis (
brew install redis) - GraphViz (
brew install graphviz), for generating diagrams - precommit (
brew install pre-commit)
Installation instructions assume using a Mac with Homebrew.
git clone [email protected]:i-dot-ai/consult.git
In the new repo install pre-commit:
cd consult
pre-commit install
Pre-commit identifies some potential secrets on commit (but will not catch all potential sensitive information).
Populate .env by copying .env.example and filling in required values.
Ensure you have python 3.12.3, poetry and npm installed. Then run poetry install, and npm install.
brew services start postgresql
This will run the postgresql locally.
make dev_environment
This will set up dev and test databases with dummy data. See the definition of that make task for the various steps.
make dev_admin_user
This will set up the admin account to dev environment.
You will have an staff user (i.e. one that can access the admin) created with the username [email protected] and the password admin.
Confirm everything is working with
make check_db
(You can see all the available make commands by running bare make or make help).
Make sure redis is running:
brew services start redis
The database should also be running as described above.
Then run:
make serve
Go to http://localhost:8000 in the browser.
make test
Only run this in development. Will create a consultation with 10 complete
responses in a variety of question formats. This runs as part of make dev_environment, but you can run it more than once.
make dummy_data
Or go to /support/consultations/ and generate a dummy consultation from there.
If you use the make migrate command to run migrations, the diagram below will
be regenerated automatically. If you need to generate it outside that process,
you can run manage.py generate_erd. (You will need graphviz installed: see
pydot docs).
You can sign into the application using a magic link, requested via /sign-in/.
You need to have a user set-up first - add new users in /support/users/
(only be done by staff members).
When running locally, you can create your first admin user using make dev_admin_user,
on dev/pre-prod/prod ask one of the existing members of the team.
For convenience, in local dev environments the value of the magic link will be logged along with the rest of the server logs.
We depend on govuk-frontend for GOV.UK Design System styles.
django-compressor should work automatically to compile the govuk-frontend
SCSS on the first request and any subsequent request after the SCSS has
changed. In the meantime it will read from frontend/CACHE, which is
.gitignored.
In production, we prepopulate frontend/CACHE using manage.py compress
which will mean that every request is served from the cache.
django-compressor also takes care of fingerprinting and setting cache headers
for our CSS.
The govuk assets are versioned in the npm package. make dev_environment
includes a step to copy them to the frontend folder from where runserver
can serve them; you can rerun this with make govuk_frontend.
The data schema for consultations supplied to the tool is defined in consultation_analyser/consultations/public_schema/public_schema.yaml.
To build the JSON schemas and examples from this file, run make schema_docs.
The json-schema-faker-options.js file configures (JSON Schema Faker)[https://github.com/json-schema-faker] to make the JSON examples.