Praxis CI
7b1b296430
client/ — React + Vite + TypeScript scaffolded with the Pipecat client SDK (@pipecat-ai/client-js) and SmallWebRTCTransport (@pipecat-ai/small-webrtc-transport). useVoiceSession.ts hook manages mic permission, WebRTC connect, audio playback, live transcript, and a latency readout (captures the e2e_latency_ms metric the server emits). App.tsx is a minimal one-page session UI: disclaimer, Start/End buttons, status badge, latency readout (within/over 600ms budget), live transcript. vite.config.ts proxies /pipecat + /health to the Python server (port 8789). npm run typecheck + npm run build pass. server/latency.py — LatencyObserver (a Pipecat FrameProcessor) timestamps transcript-ready, LLM-first-token, TTS-first-audio, and playback-start per turn, computes ASR→TTS-first-audio (the v0.1 latency target), and logs it to console with a within/over-budget verdict. Wired into the pipeline between STT/LLM/TTS so it observes without altering the frame stream. 5 unit tests pass (LatencyRecord e2e math + observer construction + reset_turn). Full server suite: 18 passed. ---ci--- phase: 1 milestone: v0.1 plan: 02 task: 02-05,02-06 status: execute persona: frontend-engineer,backend-engineer requirements: covered: [REQ-VOICE-01, REQ-VOICE-02, REQ-VOICE-03, REQ-NFR-LAT-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.