# Praxis — v0.1 Foundation

Voice-first AI apprenticeship platform. v0.1 is a **tech-validation harness** (per G-008) for the minimal viable voice loop: a single learner speaks to an AI tutor playing a Customer Service role-play scenario, hears a <600ms-latency response, receives an end-of-session coaching debrief, and has the session logged to SQLite.

## Status

Phase 1 (minimal viable voice loop) — code-complete, pending live API keys for runtime verification.

## Stack

- **Orchestration:** Pipecat (D-017) with Silero VAD + interruptibility
- **ASR:** Deepgram Nova-3 streaming (D-013)
- **LLM:** Ollama Cloud direct API (D-020) — `gemma4:cloud` (role-play) + `deepseek-v4-flash:cloud` no-think (debrief)
- **TTS:** Cartesia Sonic (primary, D-014) / Piper (self-hosted, R4 mitigation) — behind an interface
- **Client:** React + Vite + WebRTC (Pipecat client SDK, D-015)
- **State:** SQLite `praxis.db` (D-007, single hardcoded learner, no auth)

## Layout

```
server/      Pipecat pipeline, services (TTS/LLM/Guardrail interfaces), scenario runtime, adapters
client/      React + Vite + WebRTC learner surface
scenarios/   YAML scenario definitions (D-018)
db/          SQLite schema, migrations, async store
scripts/     Latency probes (R1-R4), e2e smoke
tests/       Unit + e2e
docs/        Latency report, debrief templates
```

## Quickstart

1. Copy `.env.example` → `.env`, fill in `DEEPGRAM_API_KEY`, `CARTESIA_API_KEY`, `OLLAMA_API_KEY`.
2. Install server deps: `pip install -e ".[dev]"`
3. Install client deps: `cd client && npm install`
4. Run probes: `python scripts/probe_deepgram.py` (etc.)
5. Run server: `python -m server`
6. Run client: `cd client && npm run dev`

See `docs/latency-report.md` for the R1-R4 spike status and TTS decision.