Praxis CI
30051fdfd6
server/scenarios/runtime.py — ScenarioRuntime maps a Scenario to a Pipecat Flows state-machine spec: initial 'conversation' state with the scenario's system prompt + opening line, branch metadata carried in the spec (v0.1 has no in-flight transitions per G-002 — branching is post-hoc; Phase 2+ can fork without schema change). set_branch() resolves the branch outcome from the classifier; debrief_focus() returns the per-branch focus. TASK-03-07: server/pipeline.py build_pipeline() now accepts a scenario_id, loads the runtime, and uses scenario.setup.system_prompt instead of the SLICE-02 hardcoded walking-skeleton prompt. Falls back gracefully if the scenario can't load. server/__main__.py passes PRAXIS_SCENARIO=customer_service_refund_ca_v01 by default. 7 runtime tests pass (system prompt, set_branch accept/escalate, unknown-branch error, debrief_focus per branch, flows spec branches, debrief model config). ---ci--- phase: 1 milestone: v0.1 plan: 03 task: 03-03,03-07 status: execute persona: backend-engineer requirements: covered: [REQ-SCEN-01, REQ-SCEN-FMT-01, REQ-ORCH-02] ---/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.