Praxis CI
73b583342b
db/schema.sql + db/migrations/0001_init.sql — four tables: learner (single hardcoded 'learner-1'/'Alex' row, D-007 no auth), sessions (id, learner_id, scenario_id, started_at, ended_at, branch_path_json, outcome, cost_estimated_cents, debrief_text, cost_breakdown_json), turns (id, session_id, seq, role, asr_text, tts_text, latency_ms), progress (learner_id, scenario_id, attempts, last_outcome). db/migrate.py applies migrations idempotently via a _migrations tracking table. db/store.py — PraxisStore async access layer (aiosqlite): start_session, log_turn, end_session (branch_path + outcome + cost + debrief), update_progress (increment attempts + last_outcome), get_session, get_turns, get_learner. Type-annotated SessionRow/TurnRow dataclasses. 6 tests pass (migration creates all tables, hardcoded learner exists, idempotent migrations, start→log→end→query full session, update_progress, get_learner). ---ci--- phase: 1 milestone: v0.1 plan: 04 task: 04-01,04-02 status: execute persona: data-engineer requirements: covered: [REQ-STATE-01] ---/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.