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Executable
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"""Unit tests for shared cognitive agent infrastructure."""
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Executable
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from __future__ import annotations
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import unittest
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from cognitive_agent.auth import (
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AuthConfig,
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SessionCodec,
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SlidingWindowLimiter,
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hash_password,
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verify_password,
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)
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def auth_config() -> AuthConfig:
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return AuthConfig(
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enabled=True,
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username="demo-user",
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password_hash=hash_password(
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"correct horse battery staple",
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salt=b"0123456789abcdef",
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iterations=100_000,
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),
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session_secret=b"s" * 64,
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session_ttl_seconds=3600,
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login_attempts=3,
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login_window_seconds=60,
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requests_per_minute=10,
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trust_proxy_headers=False,
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)
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class AuthenticationUnitTests(unittest.TestCase):
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def test_password_hash_verification(self) -> None:
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encoded = auth_config().password_hash
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self.assertTrue(verify_password("correct horse battery staple", encoded))
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self.assertFalse(verify_password("wrong password", encoded))
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self.assertNotIn("correct horse battery staple", encoded)
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def test_signed_session_rejects_tampering_and_expiry(self) -> None:
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codec = SessionCodec(auth_config())
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token = codec.issue("demo-user", now=1000)
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self.assertEqual(codec.verify(token, now=1001), "demo-user")
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self.assertIsNone(codec.verify(token + "x", now=1001))
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self.assertIsNone(codec.verify(token, now=4600))
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def test_sliding_window_limiter_returns_retry_after(self) -> None:
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limiter = SlidingWindowLimiter(limit=2, window_seconds=60)
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limiter.record("client", now=100)
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limiter.record("client", now=110)
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self.assertEqual(limiter.retry_after("client", now=120), 40)
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self.assertEqual(limiter.retry_after("client", now=161), 0)
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if __name__ == "__main__":
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unittest.main()
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Executable
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from __future__ import annotations
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import unittest
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from cognitive_agent.application.capability_registry import CapabilityRegistry
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from cognitive_agent.application.seat_service import REPOSITORY_ROOT
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from cognitive_agent.infrastructure.llm.openai_responses import build_intent_schema
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class CapabilityRegistryTests(unittest.TestCase):
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def test_loads_domain_owned_capabilities_and_execution_availability(self) -> None:
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snapshot = CapabilityRegistry(REPOSITORY_ROOT, {"seat"}).snapshot()
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domains = {item["domain"]: item for item in snapshot["domains"]}
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self.assertEqual(domains["seat"]["availability"], "executable")
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self.assertEqual(domains["climate"]["availability"], "catalog_only")
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seat_capabilities = {item["name"] for item in domains["seat"]["capabilities"]}
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self.assertIn("seat.set_backrest_recline", seat_capabilities)
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self.assertIn("seat.set_legrest_extension", seat_capabilities)
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self.assertIn("seat.save_posture_preset", seat_capabilities)
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self.assertIn("seat.apply_posture_preset", seat_capabilities)
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self.assertNotIn("seat.set_height", seat_capabilities)
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self.assertIn(
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"prefer_executable_when_equally_suitable",
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snapshot["execution_policy"]["capability_selection"],
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)
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self.assertFalse(snapshot["execution_policy"]["named_preset_required"])
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self.assertEqual(
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snapshot["execution_policy"]["planning_environment"],
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"dry_run_candidate_synthesis",
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)
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self.assertIn(
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"vehicle",
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next(
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item
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for item in domains["climate"]["capabilities"]
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if item["name"] == "climate.set_fan_level"
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)["target_scopes"],
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)
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def test_structured_output_schema_is_derived_from_registry(self) -> None:
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snapshot = CapabilityRegistry(REPOSITORY_ROOT, {"seat"}).snapshot()
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schema = build_intent_schema(snapshot)
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properties = schema["properties"]
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self.assertIn("climate", properties["detected_domains"]["items"]["enum"])
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request = properties["requests"]["items"]["properties"]
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self.assertIn("climate.set_fan_level", request["capability"]["enum"])
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self.assertIn("seat.set_cushion_tilt", request["capability"]["enum"])
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self.assertIn("seat.save_posture_preset", request["capability"]["enum"])
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self.assertIn("save", request["operation"]["enum"])
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self.assertIn("apply", request["operation"]["enum"])
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self.assertIn("vehicle", request["target_scope"]["enum"])
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if __name__ == "__main__":
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unittest.main()
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Executable
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from __future__ import annotations
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from pathlib import Path
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from cognitive_agent.application.gesture_bridge import GestureCapabilityBridge
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def _write_bundled_assets(repository: Path) -> Path:
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config = repository / "config" / "gesture" / "default.yaml"
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config.parent.mkdir(parents=True)
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config.write_text(
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"recognition:\n action_hold_ms: 720\n armed_idle_timeout_ms: 3100\n",
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encoding="utf-8",
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)
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asset_root = repository / "apps" / "web-console" / "assets" / "gesture"
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asset_root.mkdir(parents=True)
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(asset_root / "gesture_recognizer.task").write_bytes(b"model")
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return asset_root
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def test_gesture_bridge_uses_bundled_assets(tmp_path: Path):
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asset_root = _write_bundled_assets(tmp_path)
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bridge = GestureCapabilityBridge(tmp_path, asset_root)
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info = bridge.runtime_info()
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assert bridge.available
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assert bridge.model_path == asset_root / "gesture_recognizer.task"
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assert info["asset_strategy"] == "bundled"
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assert info["source"] == "cognitive-agent-bundled-assets"
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assert info["safety"]["action_hold_ms"] == 720
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assert info["safety"]["armed_idle_timeout_ms"] == 3100
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assert info["actions"]["GUN_UP"]["action"] == "LEGREST_EXTEND"
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def test_gesture_bridge_fails_closed_when_bundled_model_is_missing(tmp_path: Path):
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config = tmp_path / "config" / "gesture" / "default.yaml"
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config.parent.mkdir(parents=True)
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config.write_text("recognition: {}\n", encoding="utf-8")
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bridge = GestureCapabilityBridge(tmp_path)
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info = bridge.runtime_info()
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assert not bridge.available
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assert not info["model_available"]
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assert info["safety"]["min_action_frames"] == 8
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Executable
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from __future__ import annotations
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import json
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import os
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import unittest
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import urllib.error
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from unittest.mock import patch
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from cognitive_agent.infrastructure.llm.openai_responses import OpenAIIntentInterpreter
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INTENT = {
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"detected_domains": ["seat"],
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"requests": [
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{
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"domain": "seat",
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"capability": "seat.set_ventilation",
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"target_scope": "driver",
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"operation": "activate",
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"parameters": [
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{"name": "state", "value": "on"},
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{"name": "level", "value": 1},
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],
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"reason": "Driver requested seat ventilation.",
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}
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],
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"ambiguities": [],
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"decision_summary": ["打开主驾座椅通风"],
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"confidence": 0.99,
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}
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class FakeResponse:
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def __init__(self, body: str) -> None:
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self.body = body.encode("utf-8")
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def __enter__(self) -> FakeResponse:
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return self
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def __exit__(self, *_args: object) -> None:
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return None
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def read(self) -> bytes:
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return self.body
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class OpenAIResponsesTests(unittest.TestCase):
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def test_non_streaming_structured_output(self) -> None:
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response_body = json.dumps({"output_text": json.dumps(INTENT)})
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environment = {
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"OPENAI_API_KEY": "test-key",
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"OPENAI_STREAM": "false",
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}
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with patch.dict(os.environ, environment, clear=False):
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interpreter = OpenAIIntentInterpreter()
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with patch(
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"cognitive_agent.infrastructure.llm.openai_responses.urllib.request.urlopen",
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return_value=FakeResponse(response_body),
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) as urlopen:
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result = interpreter.interpret(
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"主驾还有点闷",
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{
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"last_target": "driver",
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"devices": {
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"driver": {
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"ventilation": {"state": "on", "level": 1}
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}
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},
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},
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)
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request_payload = json.loads(urlopen.call_args.args[0].data.decode("utf-8"))
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self.assertEqual(result, INTENT)
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self.assertNotIn("stream", request_payload)
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self.assertIn("capability registry", request_payload["input"][1]["content"][0]["text"])
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self.assertIn('"level": 1', request_payload["input"][2]["content"][0]["text"])
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def test_streaming_structured_output(self) -> None:
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intent_text = json.dumps(INTENT, ensure_ascii=False)
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stream_body = "\n".join(
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[
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"event: response.output_text.delta",
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f"data: {json.dumps({'type': 'response.output_text.delta', 'delta': intent_text[:30]}, ensure_ascii=False)}",
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"",
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"event: response.output_text.done",
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f"data: {json.dumps({'type': 'response.output_text.done', 'text': intent_text}, ensure_ascii=False)}",
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"",
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"data: [DONE]",
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]
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)
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environment = {
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"OPENAI_API_KEY": "test-key",
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"OPENAI_STREAM": "true",
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}
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with patch.dict(os.environ, environment, clear=False):
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interpreter = OpenAIIntentInterpreter()
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with patch(
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"cognitive_agent.infrastructure.llm.openai_responses.urllib.request.urlopen",
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return_value=FakeResponse(stream_body),
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) as urlopen:
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result = interpreter.interpret("打开主驾座椅通风")
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request_payload = json.loads(urlopen.call_args.args[0].data.decode("utf-8"))
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self.assertEqual(result, INTENT)
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self.assertTrue(request_payload["stream"])
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def test_retries_transient_network_failure(self) -> None:
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response_body = json.dumps({"output_text": json.dumps(INTENT)})
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environment = {
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"OPENAI_API_KEY": "test-key",
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"OPENAI_STREAM": "false",
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"OPENAI_MAX_RETRIES": "1",
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}
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with patch.dict(os.environ, environment, clear=False):
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interpreter = OpenAIIntentInterpreter()
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with patch(
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"cognitive_agent.infrastructure.llm.openai_responses.urllib.request.urlopen",
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side_effect=[urllib.error.URLError("temporary TLS EOF"), FakeResponse(response_body)],
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) as urlopen, patch(
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"cognitive_agent.infrastructure.llm.openai_responses.time.sleep"
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) as sleep:
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result = interpreter.interpret("打开主驾座椅通风")
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self.assertEqual(result, INTENT)
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self.assertEqual(urlopen.call_count, 2)
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sleep.assert_called_once_with(1)
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def test_retries_transient_error_inside_stream(self) -> None:
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error_stream = "data: " + json.dumps(
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{
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"type": "error",
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"error": {
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"type": "service_unavailable_error",
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"code": "server_is_overloaded",
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"message": "try again",
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},
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}
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)
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intent_text = json.dumps(INTENT, ensure_ascii=False)
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success_stream = "data: " + json.dumps(
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{"type": "response.output_text.done", "text": intent_text},
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ensure_ascii=False,
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)
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environment = {
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"OPENAI_API_KEY": "test-key",
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"OPENAI_STREAM": "true",
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"OPENAI_MAX_RETRIES": "1",
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}
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with patch.dict(os.environ, environment, clear=False):
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interpreter = OpenAIIntentInterpreter()
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with patch(
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"cognitive_agent.infrastructure.llm.openai_responses.urllib.request.urlopen",
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side_effect=[FakeResponse(error_stream), FakeResponse(success_stream)],
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) as urlopen, patch(
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"cognitive_agent.infrastructure.llm.openai_responses.time.sleep"
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) as sleep:
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result = interpreter.interpret("打开主驾座椅通风")
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self.assertEqual(result, INTENT)
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self.assertEqual(urlopen.call_count, 2)
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sleep.assert_called_once_with(1)
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if __name__ == "__main__":
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unittest.main()
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