Praxis CI
103 lines
3.5 KiB
Python
103 lines
3.5 KiB
Python
"""Scenario runtime — maps a Scenario to a Pipecat Flows state machine (TASK-03-03).
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The v0.1 branch point is a post-hoc outcome classification (G-002): the
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conversation is linear, and at session end an LLM-as-judge (TASK-03-06)
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classifies the learner's signals into accept_resolution or escalate. Pipecat
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Flows is wired so the branch field is part of the data model; Phase 2+ can
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activate true in-flight branching without a schema change.
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This module:
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- builds the system prompt from scenario.setup.system_prompt
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- provides the opening line (scenario.setup.opening_line) as the first TTS utterance
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- exposes the branch transition logic (driven by the classifier in TASK-03-06)
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TASK-03-07: the pipeline uses scenario-driven prompts instead of the
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SLICE-02 hardcoded walking-skeleton prompt.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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from server.scenarios.schema import Branch, Scenario
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@dataclass
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class ScenarioRuntime:
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"""Runtime state for one scenario session."""
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scenario: Scenario
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branch: Branch | None = None
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turn_count: int = 0
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@property
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def system_prompt(self) -> str:
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return self.scenario.setup.system_prompt
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@property
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def opening_line(self) -> str:
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return self.scenario.setup.opening_line
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@property
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def branch_id(self) -> str | None:
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return self.branch.id if self.branch else None
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@property
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def outcome(self) -> str | None:
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return self.branch.outcome if self.branch else None
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def set_branch(self, branch_id: str) -> Branch:
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"""Set the session's branch outcome (from the classifier, TASK-03-06)."""
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b = self.scenario.branch_by_id(branch_id)
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if b is None:
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raise ValueError(
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f"Unknown branch id {branch_id!r} for scenario {self.scenario.id!r}; "
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f"known: {self.scenario.branch_ids()}"
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)
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self.branch = b
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return b
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def debrief_focus(self) -> str:
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"""The debrief focus for the resolved branch (or a default)."""
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if self.branch:
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return self.branch.debrief_focus
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return "General coaching feedback for this session."
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def as_flow_spec(self) -> dict[str, Any]:
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"""Render the scenario as a Pipecat Flows state-machine spec.
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v0.1: a single 'conversation' state with the system prompt; branch
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transitions are post-hoc (G-002). The spec carries the branch metadata
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so Phase 2+ can fork in-flight.
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"""
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return {
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"initial_state": "conversation",
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"states": {
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"conversation": {
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"system_prompt": self.system_prompt,
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"opening_line": self.opening_line,
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"branches": [
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{"id": b.id, "outcome": b.outcome,
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"learner_signals": b.trigger.learner_signals}
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for b in self.scenario.branches
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],
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},
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},
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"transitions": [], # v0.1: no in-flight transitions (G-002)
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}
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def build_runtime(scenario: Scenario) -> ScenarioRuntime:
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"""Construct a ScenarioRuntime for the given scenario."""
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return ScenarioRuntime(scenario=scenario)
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def build_runtime_from_id(scenario_id: str) -> ScenarioRuntime:
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"""Load + build a runtime by scenario id (convenience for the pipeline)."""
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from server.scenarios.loader import load
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return build_runtime(load(scenario_id))
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__all__ = ["ScenarioRuntime", "build_runtime", "build_runtime_from_id"] |