README.md

MSSE AI Engineering Project

This project is a Retrieval-Augmented Generation (RAG) application that answers questions about a corpus of company policies.

Setup

1. Clone the repository:

   git clone https://github.com/sethmcknight/msse-ai-engineering.git
   cd msse-ai-engineering
   

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   

3. Install the dependencies:

   pip install -r requirements.txt
   

Running the Application (local)

To run the Flask application locally:

export FLASK_APP=app.py
flask run

The app will be available at http://127.0.0.1:5000/ and exposes /health and / endpoints.

Running Tests

To run the test suite:

pytest

Current tests cover the basic application endpoints (/health and /). As we implement more features (ingestion, embeddings, RAG, chat API), we will add tests for those components following TDD.

CI/CD and Deployment

This repository includes a GitHub Actions workflow that runs tests on push and pull requests. After merging to main, the workflow triggers a Render deploy and runs a post-deploy smoke test against /health.

If you are deploying to Render manually:

• Create a Web Service in Render (Environment: Docker).
• Dockerfile Path: Dockerfile
• Build Context: .
• Health Check Path: /health
• Auto-Deploy: Off (recommended if you want GitHub Actions to trigger deploys)

To enable automated deploys from GitHub Actions, set these repository secrets in GitHub:

• RENDER_API_KEY — Render API key
• RENDER_SERVICE_ID — Render service id
• RENDER_SERVICE_URL — Render public URL (used for smoke tests)

The workflow will create a small deploy-update-<ts> branch with an updated deployed.md after a successful deploy; that commit is marked with [skip-deploy] so merging it will not trigger another deploy.

Notes

• run.sh binds Gunicorn to the PORT environment variable so it works on Render.
• The Dockerfile copies only runtime files and uses .dockerignore to avoid including development artifacts.

Next steps

• Add ingestion, embedding, and RAG components (with tests). See project-plan.md for detailed milestones.

Developer tooling

To keep the codebase formatted and linted automatically, we use pre-commit hooks.

1. Create and activate your virtualenv (see Setup above).
2. Install developer dependencies:

pip install -r dev-requirements.txt

3. Install the hooks (runs once per clone):

pre-commit install

4. To run all hooks locally (for example before pushing):

pre-commit run --all-files

CI has a dedicated pre-commit-check job that runs on pull requests and will fail the PR if any hook fails. We also run formatters and tests in the main build job.