fix(ontology): guard against empty query string in SPARQL generator

Closes #870.  query.split()[0] in _generate_with_llm raised IndexError
when the LLM response 'query' field passed the keyword-startswith guard
but split to an empty list (e.g. if the guard is ever weakened during
refactoring).  Compute parts once, return None when empty, and reuse
parts[0] for the query_type read.

Signed-off-by: SAY-5 <saiasish.cnp@gmail.com>

tests/unit/test_query/test_sparql_generator.py

@@ -0,0 +1,115 @@
+"""
+Tests for the ontology-driven SPARQL generator.
+
+Covers regression for issue #870: an empty / whitespace-only LLM response
+must not raise IndexError when the keyword-startswith guard is bypassed.
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock
+
+# The trustgraph.query.ontology package __init__ chains imports that can
+# fail outside the deployed FlowProcessor environment, so the suite is
+# skipped at collection if the module cannot be imported.
+sparql_generator = pytest.importorskip(
+    "trustgraph.query.ontology.sparql_generator",
+)
+question_analyzer = pytest.importorskip(
+    "trustgraph.query.ontology.question_analyzer",
+)
+ontology_matcher = pytest.importorskip(
+    "trustgraph.query.ontology.ontology_matcher",
+)
+
+SPARQLGenerator = sparql_generator.SPARQLGenerator
+SPARQLQuery = sparql_generator.SPARQLQuery
+QuestionComponents = question_analyzer.QuestionComponents
+QuestionType = question_analyzer.QuestionType
+QueryOntologySubset = ontology_matcher.QueryOntologySubset
+
+
+def _make_question_components():
+    return QuestionComponents(
+        original_question="dummy",
+        question_type=QuestionType.RETRIEVAL,
+        entities=[],
+        relationships=[],
+        constraints=[],
+        aggregations=[],
+        expected_answer_type="entity",
+        keywords=[],
+    )
+
+
+def _make_ontology_subset():
+    return QueryOntologySubset(
+        ontology_id="test",
+        classes={},
+        object_properties={},
+        datatype_properties={},
+        metadata={"namespace": "http://example.org/"},
+    )
+
+
+class TestGenerateWithLlm:
+    """Regression tests for _generate_with_llm parsing safety."""
+
+    @pytest.mark.asyncio
+    async def test_valid_select_response_returns_query(self):
+        """Sanity: a well-formed LLM response yields a SPARQLQuery."""
+        prompt_service = MagicMock()
+        prompt_service.generate_sparql = AsyncMock(return_value={
+            "query": "SELECT ?s WHERE { ?s ?p ?o }",
+            "explanation": "test",
+        })
+        generator = SPARQLGenerator(prompt_service=prompt_service)
+
+        result = await generator._generate_with_llm(
+            _make_question_components(), _make_ontology_subset(),
+        )
+
+        assert isinstance(result, SPARQLQuery)
+        assert result.query_type == "SELECT"
+
+    @pytest.mark.asyncio
+    async def test_empty_query_does_not_raise_index_error(self):
+        """An empty 'query' string in the LLM response must not crash.
+
+        Regression for issue #870: query.split()[0] raised IndexError
+        when the keyword-startswith guard was bypassed. Parsing now
+        returns None instead of crashing.
+        """
+        prompt_service = MagicMock()
+        prompt_service.generate_sparql = AsyncMock(return_value={
+            "query": "",
+            "explanation": "empty",
+        })
+        generator = SPARQLGenerator(prompt_service=prompt_service)
+
+        result = await generator._generate_with_llm(
+            _make_question_components(), _make_ontology_subset(),
+        )
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_whitespace_only_query_does_not_raise_index_error(self):
+        """A whitespace-only 'query' string must not crash.
+
+        After .strip() the value becomes empty; the keyword-startswith
+        guard rejects it, but the parsing code stays safe even if the
+        guard is ever weakened. Verifies the empty-parts defence added
+        for issue #870.
+        """
+        prompt_service = MagicMock()
+        prompt_service.generate_sparql = AsyncMock(return_value={
+            "query": "   \n\t  ",
+            "explanation": "whitespace",
+        })
+        generator = SPARQLGenerator(prompt_service=prompt_service)
+
+        result = await generator._generate_with_llm(
+            _make_question_components(), _make_ontology_subset(),
+        )
+
+        assert result is None