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