Praxis CI
f7cd162019
---ci--- phase: 2 milestone: v0.4 status: verify requirements: covered: REQ-DASH-01, REQ-NFR-DASH-01, REQ-NFR-DASH-02, REQ-MT-02 partial: [] lessons: - All 4 layers pass (structural, behavioral, security, quality); 4/4 P2 REQ-IDs covered. - Grill MUSTs honored: G-038 (differencing-attack test at unit + API layers), G-041 (SPA fallback via custom SpaStaticFiles subclass, not catch-all route). - 317 pytest passed, 36 skipped (Postgres-requiring), 0 failed; 17/17 vitest passed; build + typecheck clean. - Voice UI at / unchanged (R-DASH-03/05) — confirmed via server SpaStaticFiles mount + client App.tsx route / -> VoiceSession + 0 test regressions. - No P0 issues found; 4 P1+ flagged for P3 (credential revocation audit log, nightly DST, in-memory cache persistence, f-string SQL code smell). ---/ci---
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:cloudno-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
- Copy
.env.example→.env, fill inDEEPGRAM_API_KEY,CARTESIA_API_KEY,OLLAMA_API_KEY. - Install server deps:
pip install -e ".[dev]" - Install client deps:
cd client && npm install - Run probes:
python scripts/probe_deepgram.py(etc.) - Run server:
python -m server - Run client:
cd client && npm run dev
See docs/latency-report.md for the R1-R4 spike status and TTS decision.