Remove corpus counts from chat chrome

apps/ai-service/tests/test_understanding.py

@@ -51,6 +51,7 @@ class _FakeResolver:
     def __init__(self, known: dict[str, str], suggestions: dict[str, str] | None = None) -> None:
         self._known = known
         self._suggestions = suggestions or {}
+        self.suggest_calls = 0
 
     def resolve(self, query: str) -> _Resolution:
         low = query.lower()
@@ -60,6 +61,7 @@ class _FakeResolver:
         return _Resolution()
 
     def suggest(self, query: str, k: int = 3, min_score: float = 0.5):
+        self.suggest_calls += 1
         low = query.lower()
         return [
             (drug_id, 0.9) for needle, drug_id in self._suggestions.items() if needle in low
@@ -84,6 +86,21 @@ def test_drug_id_in_exact_underscore_form_resolves():
     assert frame.unknown_drugs == ()
 
 
+def test_exact_candidate_does_not_repeat_the_catalog_wide_fuzzy_scan():
+    resolver = _FakeResolver({"metformin": "metformin"})
+    understander = LlmQueryUnderstander(_FixedLlm({
+        "turn_type": "drug_attribute", "drugs": ["metformin"],
+        "unknown_drugs": [], "attribute": "chong_chi_dinh",
+        "population": None, "weight_kg": None, "age_text": None,
+        "indication": None, "needs_clarify": False, "clarify_reason": None,
+    }), CATALOG, resolver)
+
+    frame = understander.understand("chống chỉ định metformin")
+
+    assert frame.drugs == ("metformin",)
+    assert resolver.suggest_calls == 0
+
+
 def test_drug_id_echoed_with_spaces_instead_of_underscores_still_resolves():
     """Reproduces the live 2026-08-06 bug on a genuine multi-turn shape: the
     drug is named in an earlier turn (in history), the current turn is just
@@ -202,6 +219,40 @@ def test_quick_replies_are_parsed_when_the_model_offers_them():
     assert frame.quick_replies == ("Người lớn", "Trẻ em")
 
 
+def test_quick_replies_are_dynamic_but_bounded_before_becoming_ui_chips():
+    understander = LlmQueryUnderstander(_FixedLlm({
+        "turn_type": "dosing_calc", "drugs": ["paracetamol_acetaminophen"],
+        "unknown_drugs": [], "attribute": None, "population": None,
+        "weight_kg": None, "age_text": None, "indication": None,
+        "needs_clarify": True, "clarify_reason": "Chọn nhóm phù hợp?",
+        "quick_replies": [
+            "  Người   lớn  ", "người lớn", "Trẻ em", 12,
+            "Phụ nữ có thai", "Người cao tuổi", "Lựa chọn thứ năm",
+        ],
+    }), CATALOG, RESOLVER)
+
+    frame = understander.understand("liều paracetamol")
+
+    assert frame.quick_replies == (
+        "Người lớn", "Trẻ em", "Phụ nữ có thai", "Người cao tuổi"
+    )
+
+
+def test_string_false_does_not_turn_into_a_clarification():
+    understander = LlmQueryUnderstander(_FixedLlm({
+        "turn_type": "drug_attribute", "drugs": ["paracetamol_acetaminophen"],
+        "unknown_drugs": [], "attribute": "chong_chi_dinh", "population": None,
+        "weight_kg": None, "age_text": None, "indication": None,
+        "needs_clarify": "false", "clarify_reason": "Không được hiển thị",
+        "quick_replies": ["Có", "Không"],
+    }), CATALOG, RESOLVER)
+
+    frame = understander.understand("chống chỉ định paracetamol")
+
+    assert frame.needs_clarify is False
+    assert frame.quick_replies == ()
+
+
 def test_missing_quick_replies_key_defaults_to_empty_not_a_crash():
     """The model is asked for `quick_replies` but structured-output providers
     aren't guaranteed to include every optional key — a clarify without it
@@ -240,6 +291,26 @@ def test_route_is_parsed_when_the_model_resolves_it():
     assert frame.needs_clarify is False
 
 
+def test_follow_up_exposes_a_first_class_standalone_query():
+    understander = LlmQueryUnderstander(_FixedLlm({
+        "turn_type": "drug_attribute", "drugs": ["metformin"],
+        "unknown_drugs": [], "attribute": "chong_chi_dinh",
+        "population": None, "weight_kg": None, "age_text": None,
+        "indication": None, "route": None,
+        "standalone_query": "Chống chỉ định của metformin",
+        "depends_on_previous_turn": True,
+        "needs_clarify": False, "clarify_reason": None,
+    }), CATALOG, RESOLVER)
+
+    frame = understander.understand(
+        "thế còn chống chỉ định?",
+        history=("Người dùng: Metformin dùng để làm gì?",),
+    )
+
+    assert frame.standalone_query == "Chống chỉ định của metformin"
+    assert frame.depends_on_previous_turn is True
+
+
 def test_missing_route_key_defaults_to_none_not_a_crash():
     understander = LlmQueryUnderstander(_FixedLlm({
         "turn_type": "dosing_calc", "drugs": [],