Praxis CI
be3df525d8
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---
24 lines
482 B
Python
24 lines
482 B
Python
"""Scenario runtime package — YAML → Pydantic → Pipecat Flows (D-018)."""
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from server.scenarios.schema import (
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Branch,
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BranchTrigger,
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Scenario,
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ScenarioDebrief,
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ScenarioPersona,
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ScenarioSetup,
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ValidationError,
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)
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from server.scenarios.loader import load, load_all
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__all__ = [
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"Scenario",
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"ScenarioPersona",
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"ScenarioSetup",
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"Branch",
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"BranchTrigger",
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"ScenarioDebrief",
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"ValidationError",
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"load",
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"load_all",
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] |