server/debrief.py — generate_debrief() loads the session turns + branch
outcome + scenario debrief.debrief_focus, calls deepseek-v4-flash:cloud in
no_think mode (D-020, REQ-LLM-02) with the debrief prompt template
(docs/debrief/default.yaml), produces a concise 3-bullet text summary
(what you did well / what to improve / one next step) referencing the
learner's actual turns + branch outcome.
TASK-05-02: the debrief text is routed through the CustomerServiceGuardrail
output filter (debrief role) — legal-action recommendations are blocked and
replaced with a coaching redirect.
TASK-05-03: the debrief is synthesized via the same TTSProvider interface
as the role-play (D-006 one voice) — no separate TTS path (verified by
structural test).
5 tests pass (debrief references turns + branch, no_think mode asserted,
guardrail blocks 'tell the customer to sue', normal coaching passes,
TTSProvider synthesis reuses role-play voice).
---ci---
phase: 1
milestone: v0.1
plan: 05
task: 05-01,05-02,05-03
status: execute
persona: backend-engineer
requirements:
covered: [REQ-DEBRIEF-01, REQ-LLM-02, REQ-NFR-SAFE-01]
---/ci---
docs/latency-report.md documents the SLICE-01 spike status: probe
infrastructure is built and ready; live measurements are pending API key
provisioning (only GITEA_TOKEN exists at EXECUTE time). Includes the latency
budget, the TTS decision matrix (Cartesia vs Piper, pending live R4 numbers),
the SLICE-01 go/no-go gate no-go actions per G-003, Piper pre-staging
instructions, and the R6 resolution (Pipecat OLLamaLLMService accepts custom
base_url + bearer). TTS decision recorded as 'pending live measurement —
Piper pre-staged as R4 mitigation per ARCHITECTURE.md'.
---ci---
phase: 1
milestone: v0.1
plan: 01
task: 01-06
status: execute
persona: lead-developer
requirements:
covered: [REQ-VOICE-03, REQ-NFR-LAT-01, REQ-LLM-01, REQ-LLM-02]
---/ci---
Praxis CI