local-rag-omscs

# Bootstrap python service [](https://github.com/febus982/bootstrap-python-fastapi/actions/workflows/ci-pipeline.yml) [](https://github.com/febus982/bootstrap-python-fastapi/actions/workflows/python-tests.yml) [](https://codeclimate.com/github/febus982/bootstrap-python-fastapi/test_coverage) [](https://codeclimate.com/github/febus982/bootstrap-python-fastapi/maintainability) [](https://mypy-lang.org/) [](https://github.com/charliermarsh/ruff) [](https://github.com/psf/black) [](https://github.com/PyCQA/bandit) This is an example implementation of a python application applying concepts from [Clean Architecture](https://blog.cleancoder.com/uncle-bob/2012/08/13/the-clean-architecture.html) and [SOLID principles](https://en.wikipedia.org/wiki/SOLID). * The repository classes are isolated behind interfaces, enforcing the [Interface Segregation principle](https://en.wikipedia.org/wiki/Interface_segregation_principle) and the [Inversion of Control](https://en.wikipedia.org/wiki/Inversion_of_control) design pattern * The application frameworks are decoupled from the domain logic * The storage layer is decoupled from the domain logic This template provides out of the box some commonly used functionalities: * Sync and Async API Documentation using [FastAPI](https://fastapi.tiangolo.com/) and [AsyncAPI](https://www.asyncapi.com/en) * Async tasks execution using [Dramatiq](https://dramatiq.io/index.html) * Websocket application using [Socket.io](https://python-socketio.readthedocs.io/en/stable/index.html) * Repository pattern for databases using [SQLAlchemy](https://www.sqlalchemy.org/) and [SQLAlchemy bind manager](https://febus982.github.io/sqlalchemy-bind-manager/stable/) * Database migrations using [Alembic](https://alembic.sqlalchemy.org/en/latest/) (configured supporting both sync and async SQLAlchemy engines) * Database fixtures support using customized [Alembic](https://alembic.sqlalchemy.org/en/latest/) configuration * Authentication and Identity Provider using [ORY Zero Trust architecture](https://www.ory.sh/docs/kratos/guides/zero-trust-iap-proxy-identity-access-proxy) * Full observability setup using [OpenTelemetry](https://opentelemetry.io/) (Metrics, Traces and Logs) * Example CI/CD deployment pipeline for GitLab (The focus for this repository is still GitHub but, in case you want to use GitLab 🤷) * [TODO] Producer and consumer to emit and consume events using [CloudEvents](https://cloudevents.io/) format using HTTP, to be used with [Knative Eventing](https://knative.dev/docs/eventing/) ## Documentation The detailed documentation is available: * Online on [GitHub pages](https://febus982.github.io/bootstrap-python-fastapi/) * Offline by running `make docs` after installing dependencies with `make dev-dependencies` ## How to use Create your GitHub repository using this template (The big green `Use this template` button). Optionally tweak name and authors in the `pyproject.toml` file, however the metadata are not used when building the application, nor are referenced anywhere in the code. Before running any commands, install `uv` and `Docker`: - You can install `uv` on Mac using `brew`: `brew install uv` - Download and install Docker: https://www.docker.com/products/docker-desktop/ Using Docker: * `make containers`: Build containers * `docker compose up dev-http`: Run HTTP application with hot reload * `docker compose up dev-socketio`: Run HTTP application with hot reload * `docker compose up dramatiq-worker`: Run the dramatiq worker * `docker compose run --rm test`: Run test suite * `docker compose run --rm migrate`: Run database migrations * `docker compose run --rm autogenerate-migration`: Generate a new migration file Using Make (you still need Docker for most of them): * `make install-dependencies`: Install requirements * `make dev-dependencies`: Install dev requirements * `make update-dependencies`: Updates requirements * `make dev-http`: Run HTTP application with hot reload * `make dev-socketio`: Run HTTP application with hot reload * `make test`: Run test suite * `make migrate`: Run database migrations * `make autogenerate-migration`: Generate a new migration file ## Other commands for development * `make check`: Run tests, code style and lint checks * `make fix`: Run tests, code style and lint checks with automatic fixes (where possible) ## Multistage dockerfile configuration Python docker image tend to become large after installing the application requirements (the slim base is ~150 MB uncompressed), therefore it's important to spend efforts to minimise the image size, even if it produces a slightly more complex multistage Dockerfile. The following setup makes sure the production image will keep to a minimal size ("only" 360MB): * 150MB base image * 210MB python installed dependencies Using the following pipeline the "test" image is instead ~850MB, more than 400MB that would end up as a cost in traffic on each image pull.