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Example API

This project was created for the purpose of an interview test. Its goal is to build an API endpoint that returns name entities given a sentence as an input.

Dependencies

  • FastAPI: Web framework to build APIs with Python
  • spaCY: Library for NLP in Python
  • pre-commit: It allows me to run some checks before a git commit is done
  • commitizen: It gives a standard way to commit code and also allows me to version the code
  • pytest: Used to run the tests
  • Azure pipelines: Used to run a pipeline everytime there is a commit o a new tag is added for a new release with automated release notes

Install

Install pipenv and then install dependencies:

pipenv install --dev
pipenv run pre-commit install
pipenv run spacy download en_core_web_sm

Run

There are two ways to run the project, via Docker or through python virtual environment.

With docker

If we want to run with Docker, we first need to build an image:

docker build . -t example-api

And then run an instance:

docker run -i -d -p 8080:5000 example-api

Without docker

However, if we want to run without Docker, we can run directly with uvicorn:

pipenv run uvicorn --host 0.0.0.0 --port 5000 app.main:app

Tests

Run this command to run tests with coverage:

pipenv run pytest --cov=app

Formatting

Run the following command to format all the project:

pipenv run pre-commit run --all-files

Deploying

By running cz bump it automatically detecs the version to bump - major, minor or patch. It follows SemVer standard. Then when pushing the code to GitHub, it needs to be done like this:

git push origin master --tags

It will then push the code and the Azure Pipeline will create the GitHub release if the pipeline passes.

API and documentation

The API currently has 2 endpoints:

  • /api/v1/sentence/process: It processess a sentence
  • /check: It checks the system is running

Furthermore, FastAPI automatically generates the documentation for you, so when running you can access to /docs and you will be able to see in detail all the endpoints with its I/O.

For the sentence processing, I hace used spacy, a free open-source library for NLP in Python. These are the named entity types that can be sent by the API.