go-guess

Recruitment workspace for interviewers to define job profiles and a reusable question library, manage participants, extract known traits from uploaded CVs, invite candidates who match at least 60% of a posting's labels and skills, and run timed assessments followed by collaborative interviews.

Stack

• Go 1.27.1 HTTP API using Chi, GORM, PostgreSQL, Goose, bcrypt, and signed JWTs
• React 19.3, TypeScript, and Vite
• PostgreSQL 18
• RabbitMQ 4 for live interview event fanout

Run locally

The complete stack runs with:

make dev

Open http://localhost:5173 and sign in with:

• Email: interviewer@go-guess.local
• Password: admin123

To run the applications outside containers:

make db-up
make migrate
make seed
make backend
make frontend

The API listens on http://localhost:8080. Set a different secure JWT_SECRET outside local development.

Quality commands

make test                 # full suite, including disposable infrastructure integration tests
make test-integration     # Go integration suite with PostgreSQL and RabbitMQ containers
make lint
make build

Run one backend test:

cd backend && go test ./internal/service -run '^TestCandidatesRequireSixtyPercent$'

Run one frontend test:

cd frontend && npm test -- src/app/App.test.tsx -t "filters jobs by title"

Run the browser-level BDD user stories against an isolated production image:

make test-bdd

The human-readable scenarios live in bdd/features/recruitment-workspace.feature. Add, remove, or edit scenarios in that single file; reusable Playwright-backed steps live under bdd/steps. Every run creates a fresh PostgreSQL database, loads deterministic test fixtures, starts the complete production container with RabbitMQ, and writes an HTML report to bdd/reports/cucumber.html. Use make test-bdd-down to remove a manually started BDD stack.

Docker must be available for make test. The integration suite starts PostgreSQL 18 and RabbitMQ 4 in disposable Testcontainers, applies every Goose migration, loads deterministic test fixtures, exercises login and the complete assessment and interview workflow, verifies live note and document fanout, and removes both containers afterward.

Start an isolated application stack populated with test data for manual UI testing:

make test-env
# Open http://localhost:15173 and use integration@example.com / integration-password
make test-env-down

Production image

Build the complete application as a single image:

docker build -t go-guess .

The image serves the frontend and API on port 8080. It applies database migrations and shared production fixtures before startup. PostgreSQL, RabbitMQ, and these environment variables must be provided:

docker run --rm -p 8080:8080 \
  -e DATABASE_URL='postgres://user:password@postgres:5432/go_guess?sslmode=disable' \
  -e RABBITMQ_URL='amqp://user:password@rabbitmq:5672/' \
  -e JWT_SECRET='replace-with-at-least-32-characters' \
  -e FRONTEND_URL='https://go-guess.example.com' \
  go-guess

Pushes to master publish go-guess:latest and go-guess:<commit-sha> to the private registry configured in .github/workflows/publish-image.yml. The repository must provide REGISTRY_USERNAME and REGISTRY_PASSWORD secrets.

Rancher and Helm

The chart under deploy/helm/go-guess deploys the application, PostgreSQL, and RabbitMQ. It works with any Kubernetes cluster managed by Rancher; Rancher's free edition does not restrict Helm or GitHub Actions deployments.

Validate or deploy one of the environment profiles:

make helm-lint
make helm-deploy-development
make helm-deploy-production

Development uses demo fixtures, ephemeral database and message-broker storage, and NodePort 30080. Production uses production fixtures, persistent volumes, generated secrets that are preserved across upgrades, and larger resource limits. It exposes the application at NodePort 31374 for the dedicated-server Nginx proxy while PostgreSQL and RabbitMQ remain cluster-internal. The public URL is https://goguess.urpi.be; one.com DNS, Nginx, Certbot, firewall, and verification instructions are in deploy/nginx/README.md.

To use external managed services, disable the bundled StatefulSets and provide full connection URLs:

helm upgrade --install go-guess deploy/helm/go-guess \
  --namespace go-guess-production --create-namespace \
  -f deploy/helm/go-guess/values-production.yaml \
  --set postgresql.enabled=false \
  --set rabbitmq.enabled=false \
  --set-string secrets.databaseURL="$DATABASE_URL" \
  --set-string secrets.rabbitmqURL="$RABBITMQ_URL"

.github/workflows/deploy-rancher.yml supports manual development or production deployment. It also deploys production automatically after the image publishing workflow succeeds. Create GitHub environments named development and production, then configure:

• Secret RANCHER_KUBE_CONFIG_BASE64: base64-encoded kubeconfig downloaded from Rancher.
• Secrets REGISTRY_USERNAME, REGISTRY_PASSWORD, and GO_GUESS_JWT_SECRET.
• Variable RANCHER_NAMESPACE for the target namespace.
• Optional variable GO_GUESS_FRONTEND_URL when overriding an environment's configured public URL.

Because the configured image registry uses HTTP, every Rancher cluster node must trust batty1039.startdedicated.net:5000 as an insecure registry. Use HTTPS for the registry in production when possible.

Architecture

Requests enter the Chi router and web handlers under backend/internal/web. Web request/response models are translated to service models before business logic runs. Services own validation, CV trait extraction, and candidate scoring. The repository maps service models to GORM entities and persists them in PostgreSQL.

The React application uses a typed API client and routes for authentication, job postings, the searchable question library, participants, candidate matches, and invitations. Public /participant/:invitationId routes provide an acceptance-gated welcome screen, timed question navigation, autosaved answers, progress, and submission without requiring an interviewer login. Interviewers can review every saved answer from the job's invitation list. Vite proxies /api to the Go service during local development.

Completed assessments can be marked passed or failed. A passed candidate can be scheduled for an interview with a date, location or meeting link, shared document, and selected co-interviewers. Each interviewer has an inbox for invitations and a calendar for accepted interviews. During a started interview, attendees can add private notes. Notes and shared-document changes are published through a durable RabbitMQ fanout exchange and delivered over server-sent-event streams. Authenticated interviewer sessions receive both event types; the candidate's opaque meeting link uses a token-authorized stream that receives shared-document updates but never private notes or assessment reference answers.

Development fixtures include co-interviewer@go-guess.local with password admin123. Additional co-interviewers can be created from the Users screen.

Goose migrations in backend/migrations are schema-only and run in every environment. Embedded SQL under backend/internal/database/seed separates shared reference traits from idempotent development and test fixtures. Docker Compose loads development fixtures by default; production loads only shared data. Uploaded CVs are stored as bytes; plain text, PDF, and DOCX content is inspected for normalized traits already defined by job postings.

Jobs move through draft, published, and deprecated states. Questions are editable, permanent library records: jobs attach and detach links rather than owning or deleting questions. Deprecated questions remain available for historical jobs but cannot be newly attached. Invitations snapshot the interview duration and can only be generated for matching participants on published jobs.

Open and code-review questions store an interviewer-only reference answer. Multiple-choice and radio questions instead store a list of possible responses. Reference answers are deliberately omitted from the public participant interview payload. Code-review questions also store a participant-visible code snippet that is presented with line numbers in a pull-request-style review panel.