Praxis CI
37f5cd4587
server/debrief.py — generate_debrief() loads the session turns + branch outcome + scenario debrief.debrief_focus, calls deepseek-v4-flash:cloud in no_think mode (D-020, REQ-LLM-02) with the debrief prompt template (docs/debrief/default.yaml), produces a concise 3-bullet text summary (what you did well / what to improve / one next step) referencing the learner's actual turns + branch outcome. TASK-05-02: the debrief text is routed through the CustomerServiceGuardrail output filter (debrief role) — legal-action recommendations are blocked and replaced with a coaching redirect. TASK-05-03: the debrief is synthesized via the same TTSProvider interface as the role-play (D-006 one voice) — no separate TTS path (verified by structural test). 5 tests pass (debrief references turns + branch, no_think mode asserted, guardrail blocks 'tell the customer to sue', normal coaching passes, TTSProvider synthesis reuses role-play voice). ---ci--- phase: 1 milestone: v0.1 plan: 05 task: 05-01,05-02,05-03 status: execute persona: backend-engineer requirements: covered: [REQ-DEBRIEF-01, REQ-LLM-02, REQ-NFR-SAFE-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.