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#1	"""[issue #45 followup] Tests for hermes_plugin.tools.mnemosyne_recall.
#2
#3	Adversarial review of issue #45's PR caught that the Hermes plugin's recall
#4	handler ALSO drops vec_weight / fts_weight / importance_weight. The
#5	RECALL_SCHEMA at hermes_plugin/tools.py:66-110 doesn't advertise the
#6	scoring weights, and the handler at lines 375-393 doesn't forward them
#7	to mem.recall.
#8
#9	Same bug class as the MCP-side fix in PR #46 and the
#10	hermes_memory_provider fix on the C12.b branch — schema/handler mismatch
#11	with what BeamMemory.recall actually accepts.
#12	"""
#13
#14	from __future__ import annotations
#15
#16	import json
#17	from unittest.mock import MagicMock, patch
#18
#19	import pytest
#20
#21
#22	def test_recall_schema_advertises_scoring_weights():
#23	"""Hermes plugin's RECALL_SCHEMA must advertise vec_weight / fts_weight /
#24	importance_weight as type=number properties so Hermes' tool-arg validator
#25	accepts them instead of stripping as unknown fields."""
#26	from hermes_plugin.tools import RECALL_SCHEMA
#27
#28	props = RECALL_SCHEMA["parameters"]["properties"]
#29	for key in ("vec_weight", "fts_weight", "importance_weight"):
#30	assert key in props, (
#31	f"hermes_plugin RECALL_SCHEMA missing {key!r} — schema "
#32	f"advertises 'hybrid vector + full-text search' but doesn't "
#33	f"let clients tune the weights"
#34	)
#35	assert props[key]["type"] == "number", (
#36	f"{key} should be type=number, got {props[key].get('type')!r}"
#37	)
#38
#39
#40	def test_mnemosyne_recall_forwards_scoring_weights_to_mem(monkeypatch):
#41	"""hermes_plugin.tools.mnemosyne_recall handler must forward vec_weight /
#42	fts_weight / importance_weight to mem.recall when caller supplies them."""
#43	from hermes_plugin import tools as plugin_tools
#44
#45	captured = {}
#46
#47	class _StubMem:
#48	def recall(self, query, **kwargs):
#49	captured["query"] = query
#50	captured.update(kwargs)
#51	return []
#52
#53	monkeypatch.setattr(plugin_tools, "_get_memory", lambda: _StubMem())
#54
#55	response = plugin_tools.mnemosyne_recall({
#56	"query": "anything",
#57	"top_k": 3,
#58	"vec_weight": 0.55,
#59	"fts_weight": 0.25,
#60	"importance_weight": 0.20,
#61	})
#62
#63	parsed = json.loads(response)
#64	assert "error" not in parsed, parsed
#65	assert captured.get("vec_weight") == 0.55, (
#66	f"mnemosyne_recall did not forward vec_weight; captured={captured!r}"
#67	)
#68	assert captured.get("fts_weight") == 0.25
#69	assert captured.get("importance_weight") == 0.20
#70
#71
#72	def test_mnemosyne_recall_omits_weights_when_caller_does_not_supply(monkeypatch):
#73	"""When caller omits the scoring weights, the handler must NOT pass
#74	spurious values to mem.recall — beam treats None as 'fall back to env
#75	var or default' via _normalize_weights, and forcing 0.0 / 0.5 / etc.
#76	would override that resolution and break MNEMOSYNE_*_WEIGHT env-var
#77	deployments."""
#78	from hermes_plugin import tools as plugin_tools
#79
#80	captured = {}
#81
#82	class _StubMem:
#83	def recall(self, query, **kwargs):
#84	captured["query"] = query
#85	captured.update(kwargs)
#86	return []
#87
#88	monkeypatch.setattr(plugin_tools, "_get_memory", lambda: _StubMem())
#89
#90	plugin_tools.mnemosyne_recall({"query": "anything", "top_k": 3})
#91
#92	# Acceptable: kwarg not in mem.recall's call OR explicitly None.
#93	# Failing path: a numeric default (0.5 / 0.0) leaked through.
#94	for key in ("vec_weight", "fts_weight", "importance_weight"):
#95	val = captured.get(key, "OMITTED")
#96	assert val in (None, "OMITTED"), (
#97	f"mnemosyne_recall forwarded {key}={val!r} when caller "
#98	f"omitted it; this overrides beam's env/default resolution. "
#99	f"Either pass None or omit the kwarg entirely."
#100	)
#101

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