Praxis CI
9108b07de1
server/interruptibility.py — programmatic check that the pipeline task is configured with allow_interruptions=True (D-008 abort-and-yield). The manual test (speaking during AI speech cuts it off) is documented in docs/latency-report.md; the automated test confirms the flag is set. server/scenarios/classifier.py — classify_branch() uses deepseek-v4-flash:cloud no-think (D-020) as LLM-as-judge to classify the learner's turn transcripts into accept_resolution or escalate at session end, OFFLINE from the voice loop (D-P1-05, G-002 post-hoc classification). Parses the LLM's JSON response leniently (code fences, unknown-id fallback, malformed JSON scan). classify_branch_sync_heuristic() is a rule-based fallback for tests/e2e when no API key is present. 11 tests pass (interruptibility flag on/off/missing; classifier accept/escalate scripted transcripts; JSON parse valid/code-fenced/unknown-id/malformed; fake-LLM async classify; offline structural assertion). ---ci--- phase: 1 milestone: v0.1 plan: 03 task: 03-05,03-06 status: execute persona: backend-engineer requirements: covered: [REQ-VOICE-04, REQ-SCEN-01] ---/ci---
125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
"""Tests for interruptibility (TASK-03-05) + branch classifier (TASK-03-06)."""
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from __future__ import annotations
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import asyncio
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from types import SimpleNamespace
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import pytest
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from server.interruptibility import pipeline_allows_interruptions
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from server.scenarios.classifier import (
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classify_branch,
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classify_branch_sync_heuristic,
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_parse_branch,
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)
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from server.scenarios.loader import load
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# ─── TASK-03-05: interruptibility ────────────────────────────────────────────
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def test_pipeline_allows_interruptions_flag():
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"""The pipeline task must be configured with allow_interruptions=True (D-008)."""
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task = SimpleNamespace(params=SimpleNamespace(allow_interruptions=True))
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assert pipeline_allows_interruptions(task) is True
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def test_pipeline_allows_interruptions_false_flag():
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task = SimpleNamespace(params=SimpleNamespace(allow_interruptions=False))
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assert pipeline_allows_interruptions(task) is False
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def test_pipeline_allows_interruptions_missing_params():
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task = SimpleNamespace()
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assert pipeline_allows_interruptions(task) is False
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# ─── TASK-03-06: branch classifier ───────────────────────────────────────────
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def test_heuristic_classifier_accept():
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"""A scripted empathetic transcript → accept_resolution."""
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scenario = load("customer_service_refund_ca_v01")
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turns = [
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"I'm really sorry you're frustrated. I can offer a full refund right now.",
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"Let me confirm the next steps for you.",
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]
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branch_id = classify_branch_sync_heuristic(scenario, turns)
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assert branch_id == "accept_resolution"
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def test_heuristic_classifier_escalate():
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"""A scripted defensive transcript → escalate."""
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scenario = load("customer_service_refund_ca_v01")
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turns = [
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"Well, our policy says we don't do refunds after 30 days.",
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"That's just how it works, I can't help you.",
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]
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branch_id = classify_branch_sync_heuristic(scenario, turns)
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assert branch_id == "escalate"
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def test_parse_branch_valid_json():
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scenario = load("customer_service_refund_ca_v01")
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text = '{"branch_id": "accept_resolution", "reason": "empathy shown"}'
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bid, reason = _parse_branch(text, scenario)
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assert bid == "accept_resolution"
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assert "empathy" in reason
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def test_parse_branch_code_fenced_json():
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scenario = load("customer_service_refund_ca_v01")
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text = '```json\n{"branch_id": "escalate", "reason": "defensive"}\n```'
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bid, reason = _parse_branch(text, scenario)
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assert bid == "escalate"
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def test_parse_branch_unknown_id_falls_back():
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scenario = load("customer_service_refund_ca_v01")
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text = '{"branch_id": "not_real", "reason": "x"}'
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bid, reason = _parse_branch(text, scenario)
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# Falls back to the first branch.
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assert bid in scenario.branch_ids()
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assert "fallback" in reason
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def test_parse_branch_malformed_json_scans_for_id():
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scenario = load("customer_service_refund_ca_v01")
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text = "The learner matches the escalate branch."
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bid, reason = _parse_branch(text, scenario)
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assert bid == "escalate"
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assert "fallback" in reason
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class _FakeLLM:
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"""A fake LLMProvider for the classifier test (no real API call)."""
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debrief_model = "deepseek-v4-flash:cloud"
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roleplay_model = "gemma4:cloud"
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async def chat_full(self, messages, *, model=None, no_think=False):
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return (
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'{"branch_id": "accept_resolution", "reason": "empathy + concrete_resolution"}',
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{"output_tokens": 10},
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)
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def test_classify_branch_with_fake_llm():
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"""The async classifier returns the LLM's branch verdict (R7, offline)."""
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scenario = load("customer_service_refund_ca_v01")
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turns = ["I'm sorry, I can offer a refund."]
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bid, reason = asyncio.run(classify_branch(_FakeLLM(), scenario, turns))
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assert bid == "accept_resolution"
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assert "empathy" in reason
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def test_classifier_runs_offline_from_voice_loop():
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"""D-P1-05: the classifier is a one-shot end-of-session call, not per-turn."""
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# This is a structural assertion: classify_branch takes the full turns list,
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# not a single turn — confirming it runs at session end, not on the
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# latency-critical voice path.
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scenario = load("customer_service_refund_ca_v01")
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turns = ["turn 1", "turn 2", "turn 3"]
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bid = classify_branch_sync_heuristic(scenario, turns)
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assert bid in scenario.branch_ids() |