Praxis CI
ea1b77535e
server/, client/, scenarios/, db/, scripts/, tests/, docs/ dirs match PERSONAS.md territory. pyproject.toml declares pipecat-ai[deepgram,cartesia,piper,webrtc] + openai + pydantic + pyyaml + aiosqlite + httpx + websockets. .env.example documents DEEPGRAM_API_KEY, CARTESIA_API_KEY, OLLAMA_API_KEY and the TTS selection (PRAXIS_TTS=cartesia|piper). Verified: python -c 'import pipecat' succeeds (pipecat-ai 1.6.0 installed). ---ci--- phase: 1 milestone: v0.1 plan: 01 task: 01-01 status: execute persona: lead-developer requirements: covered: [REQ-ORCH-01] ---/ci---
39 lines
1.7 KiB
Markdown
39 lines
1.7 KiB
Markdown
# Praxis — v0.1 Foundation
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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.
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## Status
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Phase 1 (minimal viable voice loop) — code-complete, pending live API keys for runtime verification.
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## Stack
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- **Orchestration:** Pipecat (D-017) with Silero VAD + interruptibility
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- **ASR:** Deepgram Nova-3 streaming (D-013)
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- **LLM:** Ollama Cloud direct API (D-020) — `gemma4:cloud` (role-play) + `deepseek-v4-flash:cloud` no-think (debrief)
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- **TTS:** Cartesia Sonic (primary, D-014) / Piper (self-hosted, R4 mitigation) — behind an interface
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- **Client:** React + Vite + WebRTC (Pipecat client SDK, D-015)
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- **State:** SQLite `praxis.db` (D-007, single hardcoded learner, no auth)
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## Layout
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```
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server/ Pipecat pipeline, services (TTS/LLM/Guardrail interfaces), scenario runtime, adapters
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client/ React + Vite + WebRTC learner surface
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scenarios/ YAML scenario definitions (D-018)
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db/ SQLite schema, migrations, async store
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scripts/ Latency probes (R1-R4), e2e smoke
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tests/ Unit + e2e
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docs/ Latency report, debrief templates
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```
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## Quickstart
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1. Copy `.env.example` → `.env`, fill in `DEEPGRAM_API_KEY`, `CARTESIA_API_KEY`, `OLLAMA_API_KEY`.
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2. Install server deps: `pip install -e ".[dev]"`
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3. Install client deps: `cd client && npm install`
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4. Run probes: `python scripts/probe_deepgram.py` (etc.)
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5. Run server: `python -m server`
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6. Run client: `cd client && npm run dev`
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See `docs/latency-report.md` for the R1-R4 spike status and TTS decision. |