Praxis CI
2a9111c58c
server/llm/ollama_cloud.py wraps the Ollama Cloud direct API (https://ollama.com/api/chat + bearer, stream=True) behind LLMProvider. chat() streams LLMStreamChunk (is_first flag for TTFT measurement); chat_full() accumulates for the debrief / branch classifier (offline). Two models: gemma4:cloud (roleplay_model) + deepseek-v4-flash:cloud (debrief_model, no_think mode for latency, D-020). Resolves R6 — the adapter confirms the direct API + bearer path; a live first-token confirmation is pending the R3 probe with a real key. Graceful no-key degradation (no chunks, no crash). 6 unit tests pass (mocked httpx streaming response + env model selection + chat_full accumulation). ---ci--- phase: 1 milestone: v0.1 plan: 02 task: 02-03 status: execute persona: backend-engineer requirements: covered: [REQ-LLM-01, REQ-LLM-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.