Interview review workspace built with Python and FastAPI. Submit interview material, track assessment progress, and inspect structured technical and communication scorecards.
- Interview intake, artifact uploads, and a browser console.
- Separate technical review, communication assessment, and scorecard synthesis stages.
- Gemini integration with a heuristic fallback when a provider is unavailable.
- Async SQLAlchemy persistence with SQLite or PostgreSQL.
- Local artifact storage, optional S3 storage, and optional Celery processing.
Requires Python 3.11 or newer.
git clone https://github.com/Jemade/Vetta.git
cd Vetta
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000Open http://localhost:8000 for the console or http://localhost:8000/docs for the API.
Defaults use SQLite and local storage. Use .env.example as the configuration reference; replace or remove placeholder credentials before loading it. Set GOOGLE_API_KEY and a supported LLM_MODEL for Gemini-backed assessment. Enable USE_CELERY=true and configure Redis when using Celery.
docker compose up --buildReview the Compose environment and storage settings for your deployment.
pip install pytest pytest-asyncio
pytest -q tests| Directory | Purpose |
|---|---|
app/agents/ |
Review stages and pipeline |
app/api/ |
Interview and health endpoints |
app/db/ |
Persistence |
app/services/ |
Artifact storage |
app/static/ |
Web console |
Scorecards are review aids. Heuristic and model-generated scores are not validated hiring predictions. The default in-process task mode is intended for local development; durable distributed processing requires the Celery configuration.
Read the engineering notes for implementation boundaries and verification commands, the review checklist for evidence still required, and CONTRIBUTING.md to propose changes. Report vulnerabilities through SECURITY.md.
