Updated readme

Author: m-messer

readme.md

@@ -1,44 +1,12 @@
-# Evaluation Function Template Repository
+# IsSimilar
 
-.
-This template repository contains the boilerplate code needed in order to create an AWS Lambda function that can be written by any tutor to grade a response area in any way they like.
+This function checks whether a student's numeric response is within an acceptable tolerance of the correct answer, using absolute (`atol`) and relative (`rtol`) tolerance parameters. The comparison follows the formula: `|response - answer| ≤ atol + rtol × |answer|`. By default both tolerances are 0, requiring an exact match (within floating-point precision).
 
-This version is specifically for python, however the ultimate goal is to make similar boilerplate repositories in any language, allowing tutors the freedom to code in what they feel most comfortable with.
+For more information, look at the docs in `app/docs/`.
 
-## Table of Contents
+## Deployment
+[![Create Release Request](https://img.shields.io/badge/Create%20Release%20Request-blue?style=for-the-badge)](https://github.com/lambda-feedback/IsSimilar/issues/new?template=release-request.yml)
 
-- [Evaluation Function Template Repository](#evaluation-function-template-repository)
-  - [Table of Contents](#table-of-contents)
-  - [Repository Structure](#repository-structure)
-  - [Usage](#usage)
-    - [Getting Started](#getting-started)
-  - [How it works](#how-it-works)
-    - [Docker & Amazon Web Services (AWS)](#docker--amazon-web-services-aws)
-    - [Middleware Functions](#middleware-functions)
-    - [GitHub Actions](#github-actions)
-  - [Pre-requisites](#pre-requisites)
-  - [Contact](#contact)
-
-## Repository Structure
-
-```bash
-app/
-    __init__.py
-    evaluation.py # Script containing the main evaluation_function
-    docs.md # Documentation page for this function (required)
-    evaluation_tests.py # Unittests for the main evaluation_function
-    requirements.txt # list of packages needed for algorithm.py
-    Dockerfile # for building whole image to deploy to AWS
-
-.github/
-    workflows/
-        test-and-deploy.yml # Testing and deployment pipeline
-
-config.json # Specify the name of the evaluation function in this file
-.gitignore
-```
-
-## Usage
 
 ### Getting Started
 
@@ -50,16 +18,15 @@ config.json # Specify the name of the evaluation function in this file
    - [Production API Gateway Integrations](https://eu-west-2.console.aws.amazon.com/apigateway/main/develop/integrations/attach?api=cttolq2oph&integration=qpbgva8&region=eu-west-2&routes=0xsoy4q)
 
 4. Merge commits into the default branch
-
-   - This will trigger the `test-and-deploy.yml` workflow, which will build the docker image, push it to a shared ECR repository, then call the backend `grading-function/ensure` route to build the necessary infrastructure to make the function available from the client app.
+   - This will trigger the `staging-deploy.yml` workflow, which will build the docker image, push it to a shared ECR repository, then call the backend `grading-function/ensure` route to build the necessary infrastructure to make the function available from the client app.
 
 5. You are now ready to start developing your function:
-
+   
    - Edit the `app/evaluation.py` file, which ultimately gets called when the function is given the `eval` command
    - Edit the `app/evaluation_tests.py` file to add tests which get run:
-     - Every time you commit to this repo, before the image is built and deployed
-     - Whenever the `healthcheck` command is supplied to the deployed function
-   - Edit the `app/docs.md` file to reflect your changes. This file is baked into the function's image, and is made available using the `docs` command. This feature is used to display this function's documentation on our [Documentation](https://lambda-feedback.github.io/Documentation/) website once it's been hooked up!
+       - On every pull request, via the `test-lint.yml` workflow, before the image is built and deployed
+       - Whenever the `healthcheck` command is supplied to the deployed function
+   - Edit the `app/docs/` files to reflect your changes. These files are baked into the function's image, and are made available using the `docs` command. This feature is used to display this function's documentation on our [Documentation](https://lambda-feedback.github.io/Documentation/) website once it's been hooked up!
 
 ---
 
@@ -74,31 +41,22 @@ The grading scripts are hosted AWS Lambda, using containers to run a docker imag
 Images are run within **containers** on AWS, which give us a lot of flexibility over what programming language and packages/libraries can be used. For more information on Docker, read this [introduction to containerisation](https://www.freecodecamp.org/news/a-beginner-friendly-introduction-to-containers-vms-and-docker-79a9e3e119b/). To learn more about AWS Lambda, click [here](https://geekflare.com/aws-lambda-for-beginners/).
 
 ### Middleware Functions
-
 In order to run the algorithm and schema on AWS Lambda, some middleware functions have been provided to handle, validate and return the data so all you need to worry about is the evaluation script and testing.
 
 The code needed to build the image using all the middleware functions are available in the [BaseEvaluationFunctionLayer](https://github.com/lambda-feedback/BaseEvalutionFunctionLayer) repository.
 
 ### GitHub Actions
-
-Whenever a commit is made to the GitHub repository, the new code will go through a pipeline, where it will be tested for syntax errors and code coverage. The pipeline used is called **GitHub Actions** and the scripts for these can be found in `.github/workflows/`.
+Whenever a pull request is opened or updated, the new code will go through a pipeline, where it will be tested for syntax errors and code coverage. The pipeline used is called **GitHub Actions** and the scripts for these can be found in `.github/workflows/`.
 
 On top of that, when starting a new evaluation function, you will have to complete a set of unit test scripts, which not only make sure your code is reliable, but also helps you to build a _specification_ for how the code should function before you start programming.
 
 Once the code passes all these tests, it will then be uploaded to AWS and will be deployed and ready to go in only a few minutes.
 
 ## Pre-requisites
-
 Although all programming can be done through the GitHub interface, it is recommended you do this locally on your machine. To do this, you must have installed:
 
 - Python 3.8 or higher.
 
 - GitHub Desktop or the `git` CLI.
 
-- A code editor such as Atom, VS Code, or Sublime.
-
-Copy this template over by clicking **Use this template** button found in the repository on GitHub. Save it to the `lambda-feedback` Organisation.
-
-## Contact
-
-# TBC
+- A code editor such as Atom, VS Code, or Sublime.