Migrate all repos into monorepo context folders

Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp, Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit, Projekt-KIQ-HP, teamlandkarte-mcp Dhive: Jury-Voting Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation) Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more Shared/references: symphony (read-only) Bahn repos remain available as independent remotes - this monorepo pulls them in via subtree, the originals are untouched.
2026-06-30 20:39:52 +02:00
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+# Bugfix: llm-competence-inference, Property 1: Fault Condition
+# Kompetenz-Inferenz liefert stets leere Liste (Bug-Bestätigung)
+
+"""Property-based exploration test for the competence inference bug.
+
+This test encodes the EXPECTED (correct) behaviour: given a non-empty task
+text and a non-empty competence list in the DB, `infer_competences` should
+return a non-empty list of up to 10 (name, confidence) tuples where each
+name is in the DB competence list and confidence is in [0.0, 1.0].
+
+On UNFIXED code this test is EXPECTED TO FAIL — the failure confirms the bug
+exists (infer_competences either doesn't exist or always returns []).
+
+**Validates: Requirements 1.1, 1.2, 1.3, 2.1, 2.2, 2.3**
+"""
+from __future__ import annotations
+
+import json
+
+import pytest
+from hypothesis import given, settings
+from hypothesis import strategies as st
+
+from teamlandkarte_mcp.matching.vocabulary import VocabularyCache
+
+
+# ---------------------------------------------------------------------------
+# Strategies
+# ---------------------------------------------------------------------------
+
+# Non-empty task text (printable characters, min 1 char)
+_task_text_strategy = st.text(min_size=1, max_size=200).filter(lambda s: s.strip())
+
+# Non-empty competence list (each competence is a non-empty string)
+_competence_list_strategy = st.lists(
+    st.text(min_size=1, max_size=50).filter(lambda s: s.strip()),
+    min_size=1,
+    max_size=50,
+    unique=True,
+)
+
+
+# ---------------------------------------------------------------------------
+# Fakes
+# ---------------------------------------------------------------------------
+
+
+class _FakeDB:
+    """Stub DBClient that returns a configurable competence list."""
+
+    def __init__(self, competences: list[str]) -> None:
+        self._competences = competences
+
+    def get_all_competence_names(self) -> list[str]:
+        return self._competences
+
+    def get_all_role_names(self) -> list[str]:
+        return ["Software Engineer"]
+
+
+class _FakeLLMClient:
+    """Stub AzureOpenAIClient that returns valid JSON with competences.
+
+    Simulates a working LLM that picks up to 5 competences from the
+    provided list (via the user prompt) and assigns confidence scores.
+    """
+
+    def __init__(self, competences: list[str]) -> None:
+        self._competences = competences
+
+    async def chat_completion(self, system: str, user: str) -> str:
+        # Pick up to 5 competences from the list and return valid JSON
+        selected = self._competences[:5]
+        result = {
+            "competences": [
+                {"name": name, "confidence": round(0.6 + i * 0.05, 2)}
+                for i, name in enumerate(selected)
+            ]
+        }
+        return json.dumps(result)
+
+
+# ---------------------------------------------------------------------------
+# Property Test
+# ---------------------------------------------------------------------------
+
+
+@settings(max_examples=100)
+@given(
+    task_text=_task_text_strategy,
+    competences=_competence_list_strategy,
+)
+@pytest.mark.asyncio
+async def test_infer_competences_returns_nonempty_for_valid_input(
+    task_text: str,
+    competences: list[str],
+) -> None:
+    """Property 1 (Fault Condition): For any non-empty task text and non-empty
+    competence list in the DB, infer_competences MUST return a non-empty list
+    of up to 10 tuples (name, confidence) where each name is in the DB
+    competence list and confidence is in [0.0, 1.0].
+
+    On unfixed code this WILL FAIL because:
+    - The method `infer_competences` does not exist on VocabularyCache, OR
+    - It always returns []
+    """
+    db = _FakeDB(competences=competences)
+    client = _FakeLLMClient(competences=competences)
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    # The method must exist and be callable
+    assert hasattr(cache, "infer_competences"), (
+        "VocabularyCache has no 'infer_competences' method — "
+        "the competence inference is not implemented"
+    )
+
+    result = await cache.infer_competences(task_text=task_text)
+
+    # Must return a non-empty list
+    assert isinstance(result, list), (
+        f"Expected list, got {type(result).__name__}"
+    )
+    assert len(result) > 0, (
+        f"infer_competences('{task_text[:50]}...') returned empty list [] "
+        f"instead of competences — bug confirmed: no LLM inference happening"
+    )
+
+    # Must return at most 10 tuples
+    assert len(result) <= 10, (
+        f"Expected at most 10 competences, got {len(result)}"
+    )
+
+    # Each element must be a tuple (name, confidence)
+    for item in result:
+        assert isinstance(item, tuple) and len(item) == 2, (
+            f"Expected tuple (name, confidence), got {item!r}"
+        )
+        name, confidence = item
+        assert name in competences, (
+            f"Returned competence '{name}' not in DB competence list"
+        )
+        assert isinstance(confidence, float), (
+            f"Expected float confidence, got {type(confidence).__name__}"
+        )
+        assert 0.0 <= confidence <= 1.0, (
+            f"Confidence {confidence} out of range [0.0, 1.0]"
+        )