Remove corpus counts from chat chrome

apps/ai-service/tests/test_retrieval_service.py

@@ -197,6 +197,34 @@ def test_retrieve_framed_pools_lexically_strong_neighbour_section():
     ]
 
 
+def test_explicit_dosage_section_does_not_pool_a_lexical_interaction_match():
+    documents = [
+        RetrievalDocument(
+            doc_id=f"paracetamol::{section}::0", drug_id="paracetamol",
+            kind="prose", section_key=section,
+            text=f"Nội dung mục {section}.", source_refs=(SOURCE,),
+        )
+        for section in ("lieu_luong_va_cach_dung", "tuong_tac_thuoc")
+    ]
+    interaction = next(d for d in documents if d.section_key == "tuong_tac_thuoc")
+    retriever = _OverviewRetriever(
+        documents, lexical_hits=[SearchHit(interaction, score=10.0)]
+    )
+    service = RetrievalService(retriever, InMemoryParentStore([]), EvidencePolicy())
+
+    result = service.retrieve_framed(
+        "paracetamol", "lieu_luong_va_cach_dung",
+        "Liều uống paracetamol cho người lớn",
+    )
+
+    assert result.decision == EvidenceDecision.ANSWERABLE
+    returned_sections = {
+        evidence.matched_doc_id.split("::")[1] for evidence in result.evidence
+    }
+    assert returned_sections == {"lieu_luong_va_cach_dung"}
+    assert retriever.lexical_calls == []
+
+
 def test_row_hit_hydrates_complete_parent_and_keeps_citation():
     result = table_service().retrieve("acetylcystein 45 kg bao nhiêu ml", "acetylcystein")
     assert result.decision == EvidenceDecision.ANSWERABLE
@@ -318,6 +346,18 @@ def test_verified_aliases_reach_common_parenthesized_drug_names():
     )
 
 
+def test_autocomplete_prioritizes_canonical_name_over_an_unrelated_trade_alias():
+    resolver = CatalogDrugResolver({
+        "paracetamol_acetaminophen": {"paracetamol", "acetaminophen"},
+        "galantamin": {"paragal"},
+        "metformin": {"metformin"},
+        "alpha_tocopherol_vitamin_e": {"met-alpha"},
+    })
+
+    assert resolver.complete("para", k=2)[0] == "paracetamol_acetaminophen"
+    assert resolver.complete("met", k=2)[0] == "metformin"
+
+
 def test_verified_catalog_protects_canonical_substring_traps():
     resolver = CatalogDrugResolver(load_aliases(VERIFIED_ENTITIES))
     traps = {