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Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
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122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
# Feature: llm-batch-matching, Property 1: Vollständigkeit der Ergebnisse
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"""Property-based test for result completeness (partition) in batch matching.
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For any list of capacities and any mix of LLM successes/failures, the total
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number of items across all ``by_category`` buckets plus the number of entries
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in ``errors`` must equal the number of input candidates. No candidate may be
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lost or duplicated.
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The fake LLM is driven by a Hypothesis-generated boolean mask: position ``i``
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decides whether the call for capacity ``i+1`` raises ``AzureAPIError``
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(``True``) or returns a valid JSON payload (``False``).
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**Validates: Requirements 1.1, 1.4, 3.2, 4.2, 4.3**
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"""
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from __future__ import annotations
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import asyncio
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import json
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from typing import Any
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from hypothesis import given, settings
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from hypothesis import strategies as st
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from teamlandkarte_mcp.azure.openai_client import AzureAPIError
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from teamlandkarte_mcp.matching.llm_fulltext_matcher import LlmFulltextMatcher
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from teamlandkarte_mcp.matching.profiles import TaskProfile
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from teamlandkarte_mcp.models import Capacity
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class _MaskLlm:
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"""Returns valid JSON for mask[i] is False; raises AzureAPIError for True."""
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def __init__(self, mask: list[bool]) -> None:
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self._mask = mask
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self._call_index = 0
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async def chat_completion(self, system: str, user: str) -> str:
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idx = self._call_index
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self._call_index += 1
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if self._mask[idx]:
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raise AzureAPIError("simulated")
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return json.dumps({"category": "Top", "rationale": "ok"})
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class _StubDb:
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"""Minimal DBClient double returning empty extras for every id."""
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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 _make_capacities(n: int) -> list[Capacity]:
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return [
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Capacity(
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id=i + 1,
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owner_name=f"o{i + 1}",
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role_name="R",
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role_level=None,
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begin_date=None,
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end_date=None,
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competences=[],
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)
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for i in range(n)
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]
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@settings(max_examples=100, deadline=None)
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@given(mask=st.lists(st.booleans(), min_size=1, max_size=20))
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def test_batch_completeness_partition(mask: list[bool]) -> None:
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"""Property 1: all input candidates appear exactly once in output.
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The sum of items across all by_category buckets plus the number of
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errors must equal the number of input capacities. Additionally, all
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item_ids across by_category and errors must be unique (no duplicates).
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"""
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matcher = LlmFulltextMatcher(db=_StubDb(), client=_MaskLlm(mask))
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capacities = _make_capacities(len(mask))
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task_profile = TaskProfile(id="t", title="T", description="D", skills=[])
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result = asyncio.run(
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matcher.match_capacities(
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task_profile=task_profile,
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capacities=capacities,
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)
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)
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# Collect all item_ids from by_category
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category_ids: list[str] = []
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for items in result.by_category.values():
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for it in items:
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category_ids.append(it.item_id)
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# Collect all item_ids from errors
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error_ids: list[str] = [e.item_id for e in result.errors]
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# Property: completeness – no candidate lost
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total = len(category_ids) + len(error_ids)
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assert total == len(capacities), (
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f"Expected {len(capacities)} total items, got {total} "
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f"({len(category_ids)} categorized + {len(error_ids)} errors)"
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)
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# Property: no duplicates – all item_ids are unique
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all_ids = category_ids + error_ids
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assert len(all_ids) == len(set(all_ids)), (
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f"Duplicate item_ids detected: {len(all_ids)} total vs "
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f"{len(set(all_ids))} unique"
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)
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