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Author SHA1 Message Date
Praxis CI 30051fdfd6 feat(P01-03-03,P01-03-07): Pipecat Flows wiring + scenario-driven prompt 
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---
 2026-08-01 13:11:30 +00:00
Praxis CI 7b1b296430 feat(P01-02-05,P01-02-06): React client + latency readout 
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---
 2026-08-01 13:08:42 +00:00
Praxis CI bba99418df feat(P01-02-04): Pipecat server pipeline — Silero VAD→Deepgram→Ollama→TTS→WebRTC 
server/pipeline.py assembles the Pipecat pipeline (D-017): WebRTC audio in →
Deepgram Nova-3 STT → LLMContextAggregator(user) → OLLamaLLMService
(gemma4:cloud via https://ollama.com/v1 + bearer, R6) → Cartesia/Piper TTS
(selected via PRAXIS_TTS) → WebRTC audio out. Interruptibility via
allow_interruptions=True (D-008 abort-and-yield). Hardcoded single-turn
system prompt (SLICE-03 replaces with scenario YAML). All keys from env;
missing keys log a warning and the pipeline still starts (code structure is
the deliverable). server/__main__.py exposes a FastAPI app with /health
(reports key-provisioning status) and POST /pipecat/webrtc (accepts an SDP
offer, starts a pipeline task, returns the answer). Verified: imports
succeed, /health returns 200, routes wired.

---ci---
phase: 1
milestone: v0.1
plan: 02
task: 02-04
status: execute
persona: backend-engineer
requirements:
  covered: [REQ-VOICE-01, REQ-VOICE-02, REQ-VOICE-04, REQ-ORCH-01]
---/ci---
 2026-08-01 13:04:17 +00:00