client/ — React + Vite + TypeScript scaffolded with the Pipecat client SDK
(@pipecat-ai/client-js) and SmallWebRTCTransport
(@pipecat-ai/small-webrtc-transport). useVoiceSession.ts hook manages mic
permission, WebRTC connect, audio playback, live transcript, and a latency
readout (captures the e2e_latency_ms metric the server emits). App.tsx is a
minimal one-page session UI: disclaimer, Start/End buttons, status badge,
latency readout (within/over 600ms budget), live transcript. vite.config.ts
proxies /pipecat + /health to the Python server (port 8789). npm run
typecheck + npm run build pass.
server/latency.py — LatencyObserver (a Pipecat FrameProcessor) timestamps
transcript-ready, LLM-first-token, TTS-first-audio, and playback-start per
turn, computes ASR→TTS-first-audio (the v0.1 latency target), and logs it
to console with a within/over-budget verdict. Wired into the pipeline
between STT/LLM/TTS so it observes without altering the frame stream. 5
unit tests pass (LatencyRecord e2e math + observer construction +
reset_turn). Full server suite: 18 passed.
---ci---
phase: 1
milestone: v0.1
plan: 02
task: 02-05,02-06
status: execute
persona: frontend-engineer,backend-engineer
requirements:
covered: [REQ-VOICE-01, REQ-VOICE-02, REQ-VOICE-03, REQ-NFR-LAT-01]
---/ci---
server/guardrails/noop.py implements the Guardrail interface with an
always-allow stub so the Pipecat pipeline has the pluggable hook from
SLICE-02. Swapping to CustomerServiceGuardrail (SLICE-03 TASK-03-04)
requires no pipeline change (D-019). The session-start disclaimer text
(RESEARCH.md safety baseline) is defined here so the pipeline can play
it as the first AI utterance even before the real ruleset lands.
---ci---
phase: 1
milestone: v0.1
plan: 02
task: 02-07
status: execute
persona: backend-engineer
requirements:
covered: [REQ-ORCH-02]
---/ci---
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---
server/tts/cartesia_tts.py wraps the raw Cartesia Sonic WebSocket API
(wss://api.cartesia.ai/tts/websocket) — streaming PCM s16le, one voice
(CARTESIA_VOICE_ID, D-006). server/tts/piper_tts.py wraps piper-tts
self-hosted synthesis (R4 mitigation, open-weights). Both implement
TTSProvider (synthesize streaming + synthesize_all). Both degrade
gracefully (no audio, no crash) when the API key / voice model is absent.
PRAXIS_TTS=cartesia|piper selects the adapter via the registry with no
pipeline change (D-014). 7 unit tests pass (mock streams + env selection
+ graceful missing-key/model handling).
---ci---
phase: 1
milestone: v0.1
plan: 02
task: 02-02
status: execute
persona: backend-engineer
requirements:
covered: [REQ-VOICE-02]
---/ci---
Praxis CI