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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+# Feature: llm-fulltext-matching, Property 11: META enthält das verwendete Verfahren
+"""Property test verifying that META-JSON includes ``matching_method``.
+
+The MCP server emits a ``META=...`` line in the response of
+``find_matching_capacities`` and ``find_matching_tasks`` containing a
+JSON object with ``search_id``, ``filter_id``, ``default_category`` and
+``matching_method``. For any successful tool call, the value of
+``matching_method`` in this JSON must be exactly the method that was
+used (``"score"`` or ``"llm_fulltext"``).
+
+Hypothesis would only enumerate two distinct values here, so the
+property is encoded as a parametrized pytest test which still rebuilds
+the server per case for full isolation. The header retains the
+"Property 11" tag for traceability with the spec.
+
+**Validates: Requirements 1.6, 9.5**
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import date
+from typing import Any
+from unittest.mock import AsyncMock
+
+import pytest
+
+import teamlandkarte_mcp.mcp_server as mcp_mod
+from teamlandkarte_mcp.mcp_server import build_server
+from teamlandkarte_mcp.models import Capacity
+
+
+_CONFIG_TEMPLATE = """
+[database]
+host='x'
+port=1
+username='u'
+password='p'
+backend='trino'
+
+[matching]
+competence_weight=0.8
+role_weight=0.2
+require_confirmation=false
+
+[cache]
+db_ttl_hours=1
+search_ttl_minutes=60
+max_size=100
+
+[azure_openai]
+endpoint='https://example.openai.azure.com'
+api_version='2024-02-15-preview'
+chat_deployment='gpt-4'
+""".strip()
+
+
+class _FakeDB:
+    """Minimal fake DB that returns one matching capacity."""
+
+    def test_connection(self) -> None:
+        return
+
+    def get_table_columns(self, table: str) -> list[str]:
+        if table == "teamlandkarte_v_capacity_roles_latest":
+            return ["name", "active", "staffing_board_relevant"]
+        if table == "teamlandkarte_v_capacities_latest":
+            return ["creation_date"]
+        if table == "teamlandkarte_v_teams_latest":
+            return [
+                "team_id",
+                "ouid",
+                "about_us",
+                "offerings",
+                "interests",
+                "focus_name",
+            ]
+        if table == "teamlandkarte_v_teammeter_organizational_units_latest":
+            return ["id", "name"]
+        if table == "teamlandkarte_v_teammeter_team_competences_latest":
+            return ["ouid", "competence_id", "top_competency"]
+        if table == "teamlandkarte_v_team_references_latest":
+            return ["ouid", "partner_id", "projects"]
+        return []
+
+    def get_all_role_names(self) -> list[str]:
+        return ["X"]
+
+    def get_all_competence_names(self) -> list[str]:
+        return ["A"]
+
+    def get_open_tasks(self, limit: int = 20):
+        return []
+
+    def get_all_capacities_with_competences(self):
+        return [
+            Capacity(
+                id=1,
+                owner_name="A",
+                role_name="X",
+                role_level=None,
+                begin_date=date(2025, 1, 1),
+                end_date=date(2025, 12, 31),
+                competences=["A"],
+            )
+        ]
+
+    def get_recent_free_capacities(self, limit: int = 20):
+        return []
+
+    # LLM-fulltext path needs the batch description/cert/ref methods.
+    def batch_get_capacity_descriptions(
+        self, capacity_ids: list[Any]
+    ) -> dict[str, str | None]:
+        return {str(i): None for i in capacity_ids}
+
+    def batch_get_capacity_certificates(
+        self, capacity_ids: list[Any]
+    ) -> dict[str, list[str]]:
+        return {str(i): [] for i in capacity_ids}
+
+    def batch_get_capacity_references(
+        self, capacity_ids: list[Any]
+    ) -> dict[str, list[dict]]:
+        return {str(i): [] for i in capacity_ids}
+
+
+def _result_to_text(result: Any) -> str:
+    if isinstance(result, tuple) and len(result) == 2:
+        content, structured = result
+        if isinstance(structured, dict) and isinstance(
+            structured.get("result"), str
+        ):
+            return structured["result"]
+        if isinstance(content, list) and content:
+            text = getattr(content[0], "text", None)
+            if isinstance(text, str):
+                return text
+    return str(result)
+
+
+async def _call_tool(srv: Any, name: str, args: dict[str, Any]) -> str:
+    return _result_to_text(await srv.call_tool(name, args))
+
+
+def _extract_meta(output: str) -> dict[str, Any]:
+    for line in output.splitlines():
+        if line.startswith("META="):
+            return json.loads(line[len("META=") :])
+    raise AssertionError(f"no META= line in tool output:\n{output}")
+
+
+@pytest.fixture
+def _env(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("DATA_LAKE_USERNAME", "u")
+    monkeypatch.setenv("DATA_LAKE_PASSWORD", "p")
+    monkeypatch.setenv("AZURE_OPENAI_LLM_API_KEY", "k")
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("method", ["score", "llm_fulltext"])
+async def test_meta_includes_matching_method(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
+    _env,
+    method: str,
+) -> None:
+    """Property 11: META carries the matching_method actually used."""
+    cfg = tmp_path / "config.toml"
+    cfg.write_text(_CONFIG_TEMPLATE, encoding="utf-8")
+
+    monkeypatch.setattr(
+        mcp_mod, "create_db_client", lambda *_a, **_k: _FakeDB()
+    )
+
+    # A single payload that satisfies both consumers:
+    #   - score path reads ``similarity``
+    #   - llm_fulltext path reads ``category``/``rationale``
+    monkeypatch.setattr(
+        "teamlandkarte_mcp.azure.openai_client.AzureOpenAIClient.chat_completion",
+        AsyncMock(
+            return_value=(
+                '{"similarity": 1.0, "category": "Top", "rationale": "ok"}'
+            )
+        ),
+    )
+
+    srv = build_server(str(cfg))
+
+    res = await _call_tool(
+        srv,
+        "find_matching_capacities",
+        {
+            "role_name": "X",
+            "competences": ["A"],
+            "matching_method": method,
+        },
+    )
+
+    meta = _extract_meta(res)
+    assert meta.get("matching_method") == method, (
+        f"expected matching_method={method!r}, got META={meta!r}"
+    )