Praxis CI
d32e4d487e
SLICE-05 (devops-engineer): firstboot-hook.sh — installs Docker inside CT (apt: docker.io, docker-compose-v2, git, curl), clones praxis repo from Gitea (GITEA_TOKEN baked into snippet per G-101 fix), runs install-service.sh SLICE-06 (devops-engineer): install-service.sh — creates praxis user, writes /etc/praxis/server.env from lxc.environment vars (G-103: all 16 env vars), installs praxis.service systemd unit (Type=simple, ExecStartPre=docker compose build, ExecStart=docker compose up, TimeoutStartSec=600 per RESEARCH Q8) SLICE-07 (devops-engineer): lxc-deploy.sh orchestrator — stage snippet → clone → config → start → health-check, idempotent (--recreate/--reconfigure), rollback on failure, auto VMID allocation (D-027) REQ-DEPLOY-06, 09, 10, 11 covered. ---ci--- project: praxis phase: 1 milestone: v0.2 status: execute slice: 05-07 wave: 3 ---/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.