 Praxis CI
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376fddf18e
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feat(P01-04-03,P01-04-04): wire store into pipeline + per-session cost logging
server/cost.py — derive_cost() counts LLM input/output tokens (gemma4 +
deepseek-v4-flash), Deepgram audio minutes, Cartesia/Piper characters;
derives estimated cents from scenarios/cost_rates.yaml. No enforced ceiling
(D-012 pilot). CostBreakdown dataclass carries the breakdown dict stored in
sessions.cost_breakdown_json. Per G-005: v0.1 logged costs are pilot-config
(Ollama tier + cloud), NOT at-scale /learner economics — that requires
self-hosted gemma4:e4b + Piper (post-pilot).
server/session_recorder.py — SessionRecorder wires the SQLite store into the
pipeline lifecycle: start() creates a session row, log_turn() writes turns
with ASR/TTS text + latency + accumulates cost inputs, set_branch_path(),
end() derives cost + writes outcome + debrief + updates progress. No auth —
learner_id is the hardcoded learner-1 (D-007).
7 tests pass (derive_cost basic/piper-zero/breakdown-dict, load_rates yaml,
no-enforced-ceiling, recorder full lifecycle with DB assertions, progress
updated).
---ci---
phase: 1
milestone: v0.1
plan: 04
task: 04-03,04-04
status: execute
persona: backend-engineer,data-engineer
requirements:
covered: [REQ-STATE-01, REQ-NFR-COST-01]
---/ci---
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2026-08-01 13:16:00 +00:00 |
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Praxis CI
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be3df525d8
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feat(P01-03-01,P01-03-02): Pydantic scenario schema + refund YAML
server/scenarios/schema.py defines the typed model: Scenario (id, path,
market, language, title, difficulty, failure_mode, persona, setup,
success_criteria, common_mistakes, branches[], debrief) + Branch (id,
trigger.learner_signals, outcome, failure_mode, debrief_focus) +
ScenarioDebrief (model=deepseek-v4-flash:cloud, mode=no_think, D-020).
failure_mode field present per D-009. server/scenarios/loader.py loads
YAML → Pydantic, validates at load time, raises typed ValidationError on
bad input.
scenarios/customer_service_refund_ca_v01.yaml — the v0.1 Canada Customer
Service scenario (D-010): 'Angry customer requesting refund on a damaged
product', one branch point (accept_resolution vs escalate),
failure_mode=escalates_unresolved, success criteria, common mistakes,
debrief config. Matches the RESEARCH.md example.
5 unit tests pass (valid parse, invalid raises typed error, branch
outcome Literal, branch_by_id, real YAML load). load() returns a valid
Scenario with both branches.
---ci---
phase: 1
milestone: v0.1
plan: 03
task: 03-01,03-02
status: execute
persona: data-engineer
requirements:
covered: [REQ-SCEN-01, REQ-SCEN-FMT-01]
---/ci---
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2026-08-01 13:10:00 +00:00 |
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