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
parent 2f2b295531
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+# Unit tests for VocabularyCache.infer_competences with mocked LLM client.
+#
+# **Validates: Requirements 2.1, 2.2, 2.3, 2.4, 2.5**
+
+from __future__ import annotations
+
+import json
+import logging
+
+import pytest
+
+from teamlandkarte_mcp.matching.vocabulary import VocabularyCache
+
+
+# ---------------------------------------------------------------------------
+# Fakes / Stubs
+# ---------------------------------------------------------------------------
+
+
+class FakeDB:
+    """Stub DBClient returning a configurable competence list."""
+
+    def __init__(self, competences: list[str] | None = None) -> None:
+        self._competences = competences if competences is not None else []
+
+    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 a pre-configured response."""
+
+    def __init__(self, response: str | Exception) -> None:
+        self._response = response
+
+    async def chat_completion(self, system: str, user: str) -> str:
+        if isinstance(self._response, Exception):
+            raise self._response
+        return self._response
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_valid_json_response_returns_correct_tuples() -> None:
+    """Valid JSON response from LLM → correct list of (name, confidence) tuples."""
+    competences = ["Python", "TypeScript", "React", "Docker"]
+    llm_response = json.dumps(
+        {
+            "competences": [
+                {"name": "Python", "confidence": 0.95},
+                {"name": "React", "confidence": 0.8},
+            ]
+        }
+    )
+    db = FakeDB(competences=competences)
+    client = FakeLLMClient(response=llm_response)
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    result = await cache.infer_competences(task_text="Build a Python backend")
+
+    assert result == [("Python", 0.95), ("React", 0.8)]
+
+
+@pytest.mark.asyncio
+async def test_empty_task_text_returns_empty_list() -> None:
+    """Empty task text → []."""
+    competences = ["Python", "TypeScript"]
+    llm_response = json.dumps({"competences": [{"name": "Python", "confidence": 0.9}]})
+    db = FakeDB(competences=competences)
+    client = FakeLLMClient(response=llm_response)
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    assert await cache.infer_competences(task_text="") == []
+    assert await cache.infer_competences(task_text="   ") == []
+
+
+@pytest.mark.asyncio
+async def test_empty_competence_list_in_db_returns_empty_list() -> None:
+    """Empty competence list in DB → []."""
+    llm_response = json.dumps({"competences": [{"name": "Python", "confidence": 0.9}]})
+    db = FakeDB(competences=[])
+    client = FakeLLMClient(response=llm_response)
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    result = await cache.infer_competences(task_text="Build a Python backend")
+
+    assert result == []
+
+
+@pytest.mark.asyncio
+async def test_llm_error_returns_empty_list_and_logs_warning(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    """LLM error (Exception) → [] + warning logged."""
+    competences = ["Python", "TypeScript"]
+    db = FakeDB(competences=competences)
+    client = FakeLLMClient(response=RuntimeError("API timeout"))
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    with caplog.at_level(logging.WARNING):
+        result = await cache.infer_competences(task_text="Build a Python backend")
+
+    assert result == []
+    assert any("LLM call failed" in record.message for record in caplog.records)
+
+
+@pytest.mark.asyncio
+async def test_invalid_json_response_returns_empty_list(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    """Invalid JSON response → [] (json.loads raises, caught by except)."""
+    competences = ["Python", "TypeScript"]
+    db = FakeDB(competences=competences)
+    client = FakeLLMClient(response="this is not valid json {{{")
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    with caplog.at_level(logging.WARNING):
+        result = await cache.infer_competences(task_text="Build a Python backend")
+
+    assert result == []
+    assert any("LLM call failed" in record.message for record in caplog.records)
+
+
+@pytest.mark.asyncio
+async def test_unknown_competence_names_are_filtered_out() -> None:
+    """Competence names not in DB → filtered out."""
+    competences = ["Python", "TypeScript", "React"]
+    llm_response = json.dumps(
+        {
+            "competences": [
+                {"name": "Python", "confidence": 0.9},
+                {"name": "Rust", "confidence": 0.85},  # not in DB
+                {"name": "Go", "confidence": 0.7},  # not in DB
+                {"name": "React", "confidence": 0.6},
+            ]
+        }
+    )
+    db = FakeDB(competences=competences)
+    client = FakeLLMClient(response=llm_response)
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    result = await cache.infer_competences(task_text="Build a backend service")
+
+    assert result == [("Python", 0.9), ("React", 0.6)]
+
+
+@pytest.mark.asyncio
+async def test_max_competences_limited_to_10() -> None:
+    """Result is limited to max_competences (default 10)."""
+    # Create 15 competences and have the LLM return all of them
+    competences = [f"Competence_{i}" for i in range(15)]
+    llm_response = json.dumps(
+        {
+            "competences": [
+                {"name": name, "confidence": round(0.5 + i * 0.03, 2)}
+                for i, name in enumerate(competences)
+            ]
+        }
+    )
+    db = FakeDB(competences=competences)
+    client = FakeLLMClient(response=llm_response)
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    result = await cache.infer_competences(task_text="A complex task")
+
+    assert len(result) == 10
+    # All returned names must be in the DB list
+    for name, confidence in result:
+        assert name in competences
+        assert 0.0 <= confidence <= 1.0
+
+
+@pytest.mark.asyncio
+async def test_confidence_clamped_to_valid_range() -> None:
+    """Confidence values outside [0.0, 1.0] are clamped."""
+    competences = ["Python", "TypeScript"]
+    llm_response = json.dumps(
+        {
+            "competences": [
+                {"name": "Python", "confidence": 1.5},  # above 1.0
+                {"name": "TypeScript", "confidence": -0.3},  # below 0.0
+            ]
+        }
+    )
+    db = FakeDB(competences=competences)
+    client = FakeLLMClient(response=llm_response)
+    cache = VocabularyCache(db=db, client=client)  # type: ignore[arg-type]
+
+    result = await cache.infer_competences(task_text="Build something")
+
+    assert result == [("Python", 1.0), ("TypeScript", 0.0)]