Praxis CI
a7f7c4e1cc
TASK-08-01: server/operator/cohort.py — GET /api/operator/cohort practice
volume view (sessions_count, active_learners_count), k-anon display.
TASK-08-02: server/operator/mastery.py — GET /api/operator/mastery
(gate_open_rate, median_mastery_score, rubric_criterion_means).
TASK-08-03: server/operator/failure_patterns.py — GET /api/operator/failure-patterns
(failure_mode:*, branch:* metrics).
TASK-08-04: server/operator/credentials.py — GET /api/operator/credentials
+ POST /api/operator/credentials/{id}/revoke (VC management, D-057).
TASK-08-05: tests/test_operator_endpoints.py — 401/200/503, suppressed
cells value=null, last_updated=max, revoke, no per-learner data (R-DASH-02).
server/operator/_common.py — shared Cell/PathView/ViewResponse models +
all_recent_aggregates query + group_by_path helper.
All endpoints auth-gated via Depends(current_operator) (D-057). No PII
beyond aggregates (D-031, R-DASH-02).
---ci---
project: praxis
phase: 2
milestone: v0.4
status: execute
persona: backend-engineer
task: 08-01..08-05
requirements:
covered: [REQ-DASH-01, REQ-NFR-DASH-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.